From 688d0497758388cb44afa8e8f23fac5962bb6f43 Mon Sep 17 00:00:00 2001 From: Jasper Butcher Date: Sun, 11 Jan 2026 00:04:32 +0000 Subject: [PATCH 01/49] Install bug --- src/foundry_cli/download_checkpoints.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/foundry_cli/download_checkpoints.py b/src/foundry_cli/download_checkpoints.py index ef367a1b..8a27c1b7 100644 --- a/src/foundry_cli/download_checkpoints.py +++ b/src/foundry_cli/download_checkpoints.py @@ -132,7 +132,9 @@ def install_model(model_name: str, checkpoint_dir: Path, force: bool = False) -> ) try: - download_file(checkpoint_info.url, dest_path, checkpoint_info.sha256) + download_file( + checkpoint_info.url, dest_path, checkpoint_info.sha256 + ) console.print( f"[green]✓[/green] Successfully installed {model_name} to {dest_path}" ) From ece2498c5d41afd0b814d0398f08177d2b05fe9e Mon Sep 17 00:00:00 2001 From: fdimaio Date: Mon, 5 Jan 2026 17:56:52 -0800 Subject: [PATCH 02/49] Bugfixes for confidence training --- .../experiment/rf3_run11-with-confidence.yaml | 56 +++++++++++++++++++ .../rf3_net_with_confidence_head.yaml | 1 + 2 files changed, 57 insertions(+) create mode 100644 models/rf3/configs/experiment/rf3_run11-with-confidence.yaml diff --git a/models/rf3/configs/experiment/rf3_run11-with-confidence.yaml b/models/rf3/configs/experiment/rf3_run11-with-confidence.yaml new file mode 100644 index 00000000..9738564c --- /dev/null +++ b/models/rf3/configs/experiment/rf3_run11-with-confidence.yaml @@ -0,0 +1,56 @@ +# @package _global_ + +name: rf3-with-confidence +project: rf3 + +tags: + # list of tags to add to the run ( & on wandb to easily find & filter runs) + - full + +defaults: + - override /datasets: pdb_only + - override /model: rf3_with_confidence + - override /trainer: rf3_with_confidence + +ckpt_config: + _target_: foundry.utils.weights.CheckpointConfig + path: null + reset_optimizer: true + +model: + lr_scheduler: + base_lr: 0.9e-3 # 1/2 of original learning rate (1.8e-3) + +# load confidence features +# remove within_20_closest_chains from loaders +datasets: + run_confidence_head: true + train: + pdb: + sub_datasets: + pn_unit: + dataset: + dataset: + filters: + # filters common across all PDB datasets + - "deposition_date < '2024-01-01'" + - "resolution < 9.0" + - "num_polymer_pn_units <= 300" + - "cluster.notnull()" + - "pdb_id not in ${resolve_import:atomworks.constants,PDB_IDS_WITH_UNPHYSICAL_BONDS}" + # pn_unit specific filters + - "~(q_pn_unit_non_polymer_res_names.notnull() and q_pn_unit_non_polymer_res_names.str.contains('${resolve_import:atomworks.constants,AF3_EXCLUDED_LIGANDS_REGEX}', regex=True))" + interface: + dataset: + dataset: + filters: + # filters common across all PDB datasets + - "deposition_date < '2024-01-01'" + - "resolution < 9.0" + - "num_polymer_pn_units <= 300" + - "cluster.notnull()" + - "pdb_id not in ${resolve_import:atomworks.constants,PDB_IDS_WITH_UNPHYSICAL_BONDS}" + # interface specific filters + - "~(pn_unit_1_non_polymer_res_names.notnull() and pn_unit_1_non_polymer_res_names.str.contains('${resolve_import:atomworks.constants,AF3_EXCLUDED_LIGANDS_REGEX}', regex=True))" + - "~(pn_unit_2_non_polymer_res_names.notnull() and pn_unit_2_non_polymer_res_names.str.contains('${resolve_import:atomworks.constants,AF3_EXCLUDED_LIGANDS_REGEX}', regex=True))" + - "is_inter_molecule" diff --git a/models/rf3/configs/model/components/rf3_net_with_confidence_head.yaml b/models/rf3/configs/model/components/rf3_net_with_confidence_head.yaml index 063e6f5e..e7c0d3c0 100644 --- a/models/rf3/configs/model/components/rf3_net_with_confidence_head.yaml +++ b/models/rf3/configs/model/components/rf3_net_with_confidence_head.yaml @@ -36,6 +36,7 @@ confidence_head: d_hidden: 32 attention_pair_bias: n_head: 16 + kq_norm: true n_bins_pae: 64 n_bins_pde: 64 n_bins_plddt: 50 From 4a7aaf8793e63be61119ed4e78613c2b7979b66c Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Sun, 18 Jan 2026 15:41:00 -0800 Subject: [PATCH 03/49] feat: atom23 porting --- models/rfd3/src/rfd3/constants.py | 98 ++++++++++++++++ .../src/rfd3/transforms/conditioning_base.py | 21 +++- .../src/rfd3/transforms/design_transforms.py | 6 + models/rfd3/src/rfd3/transforms/pipelines.py | 4 +- .../rfd3/transforms/training_conditions.py | 79 ++++++++++--- .../src/rfd3/transforms/util_transforms.py | 6 +- .../rfd3/src/rfd3/transforms/virtual_atoms.py | 107 ++++++++++++------ 7 files changed, 265 insertions(+), 56 deletions(-) diff --git a/models/rfd3/src/rfd3/constants.py b/models/rfd3/src/rfd3/constants.py index 59c387c3..a1a6e484 100644 --- a/models/rfd3/src/rfd3/constants.py +++ b/models/rfd3/src/rfd3/constants.py @@ -72,6 +72,37 @@ 'GLY': (" N "," CA "," C "," O ", None, None, None, None, None, None, None, None, None, None), # gly 'UNK': (" N "," CA "," C "," O "," CB ", None, None, None, None, None, None, None, None, None), # unk 'MSK': (" N "," CA "," C "," O "," CB ", None, None, None, None, None, None, None, None, None), # mask + 'DA': (' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " C1'", + ' N9 ', ' C8 ', ' N7 ', ' C5 ', ' C6 ', ' N6 ', ' N1 ', ' C2 ', ' N3 ', ' C4 ', + None), + + 'DC': (' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " C1'", + ' N1 ', ' C2 ', ' O2 ', ' N3 ', ' C4 ', ' N4 ', ' C5 ', ' C6 ', + None, None, None), + + 'DG': (' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " C1'", + ' N9 ', ' C8 ', ' N7 ', ' C5 ', ' C6 ', ' O6 ', ' N1 ', ' C2 ', ' N2 ', ' N3 ', ' C4 '), + + 'DT': (' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " C1'", + ' N1 ', ' C2 ', ' O2 ', ' N3 ', ' C4 ', ' O4 ', ' C5 ', ' C7 ', ' C6 ', + None, None), + + 'A' : (' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " O2'", " C1'", + ' N9 ', ' C8 ', ' N7 ', ' C5 ', ' C6 ', ' N6 ', ' N1 ', ' C2 ', ' N3 ', ' C4 ', + None), + + 'C' : (' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " O2'", " C1'", + ' N1 ', ' C2 ', ' O2 ', ' N3 ', ' C4 ', ' N4 ', ' C5 ', ' C6 ', + None, None, None), + + 'G' : (' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " O2'", " C1'", + ' N9 ', ' C8 ', ' N7 ', ' C5 ', ' C6 ', ' O6 ', ' N1 ', ' C2 ', ' N2 ', ' N3 ', ' C4 '), + + 'U' : (' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " O2'", " C1'", + ' N1 ', ' C2 ', ' O2 ', ' N3 ', ' C4 ', ' O4 ', ' C5 ', ' C6 ', + None, None, None), + 'DX': (' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " C1'", None, None, None, None, None, None, None, None, None, None, None), #dna_mask + 'X': (' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " O2'", " C1'", None, None, None, None, None, None, None, None, None, None, None), #rna mask } """Canonical ordering of amino acid atom names in the CCD.""" @@ -210,3 +241,70 @@ "MACROLIDE", "POLYDEOXYRIBONUCLEOTIDE/POLYRIBONUCLEOTIDE HYBRID", ] + +backbone_atomscheme_DNA = [' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " C1'"]#, None] + +backbone_atomscheme_RNA = [' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " O2'", " C1'"] + +DNA_atoms = { + 'DA': [' N9 ', ' C8 ', ' N7 ', ' C5 ', ' C6 ', ' N6 ', ' N1 ', ' C2 ', ' N3 ', ' C4 '], + 'DC': [' N1 ', ' C2 ', ' O2 ', ' N3 ', ' C4 ', ' N4 ', ' C5 ', ' C6 '], + 'DG': [' N9 ', ' C8 ', ' N7 ', ' C5 ', ' C6 ', ' O6 ', ' N1 ', ' C2 ', ' N2 ', ' N3 ', ' C4 '], + 'DT': [' N1 ', ' C2 ', ' O2 ', ' N3 ', ' C4 ', ' O4 ', ' C5 ', ' C7 ', ' C6 ']} + +RNA_atoms = { + 'A': [' N9 ', ' C8 ', ' N7 ', ' C5 ', ' C6 ', ' N6 ', ' N1 ', ' C2 ', ' N3 ', ' C4 '], + 'C': [' N1 ', ' C2 ', ' O2 ', ' N3 ', ' C4 ', ' N4 ', ' C5 ', ' C6 '], + 'G': [' N9 ', ' C8 ', ' N7 ', ' C5 ', ' C6 ', ' O6 ', ' N1 ', ' C2 ', ' N2 ', ' N3 ', ' C4 '], + 'U': [' N1 ', ' C2 ', ' O2 ', ' N3 ', ' C4 ', ' O4 ', ' C5 ', ' C6 '] +} + +association_schemes['atom23'] = {} +for item in DNA_atoms: + association_schemes['atom23'][item] = tuple(backbone_atomscheme_DNA + DNA_atoms[item]+ [None]*(22 - len(DNA_atoms[item] + backbone_atomscheme_DNA))) +for item in RNA_atoms: + association_schemes['atom23'][item] = tuple(backbone_atomscheme_RNA + RNA_atoms[item]+ [None]*(23 - len(RNA_atoms[item] + backbone_atomscheme_RNA))) + +for item in association_schemes['dense']: + association_schemes['atom23'][item] = association_schemes['dense'][item] + +association_schemes['atom23']['DX'] = (' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " C1'", None, None, None, None, None, None, None, None, None, None, None) #rna_mask +association_schemes['atom23']['X'] = (' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " O2'", " C1'", None, None, None, None, None, None, None, None, None, None, None)#rna mask + +ATOM23_ATOM_NAMES_RNA = np.array( + [item.strip() for item in backbone_atomscheme_RNA] + [f"V{i}" for i in range(23 - len(backbone_atomscheme_RNA))] +) +"""Atom23 atom names (e.g. CA, V1)""" + +ATOM23_ATOM_ELEMENTS_RNA = np.array( + ["P", "O", "O", "O", "C", "C", "O", "C","O", "C", "O", "C"] + [VIRTUAL_ATOM_ELEMENT_NAME for i in range(23 - len(backbone_atomscheme_RNA))] +) +"""Atom23 element names (e.g. C, VX)""" + +ATOM23_ATOM_NAME_TO_ELEMENT = { + name: elem for name, elem in zip(ATOM23_ATOM_NAMES_RNA, ATOM23_ATOM_ELEMENTS_RNA) +} +ATOM23_ATOM_NAMES_DNA = np.array( + [item.strip() for item in backbone_atomscheme_DNA] + [f"V{i}" for i in range(22 - len(backbone_atomscheme_DNA))] +) +"""Atom23 atom names (e.g. CA, V1)""" + +ATOM23_ATOM_ELEMENTS_DNA = np.array( + ["P", "O", "O", "O", "C", "C", "O", "C","O", "C", "C"] + [VIRTUAL_ATOM_ELEMENT_NAME for i in range(22 - len(backbone_atomscheme_DNA))] +) +"""Atom23 element names (e.g. C, VX)""" + + +"""Mapping from atom14 atom names (e.g. CA, V1) to their corresponding element names (e.g. C, VX)""" +## combining name to element mapping, should be fine +for item in ATOM14_ATOM_NAME_TO_ELEMENT: + ATOM23_ATOM_NAME_TO_ELEMENT[item] = ATOM14_ATOM_NAME_TO_ELEMENT[item] + +association_schemes_stripped = { + name: {k: strip_list(v) for k, v in scheme.items()} + for name, scheme in association_schemes.items() +} + +backbone_atoms_RNA = strip_list(backbone_atomscheme_RNA) +backbone_atoms_DNA = strip_list(backbone_atomscheme_DNA) + diff --git a/models/rfd3/src/rfd3/transforms/conditioning_base.py b/models/rfd3/src/rfd3/transforms/conditioning_base.py index 01a303c4..3a551a58 100644 --- a/models/rfd3/src/rfd3/transforms/conditioning_base.py +++ b/models/rfd3/src/rfd3/transforms/conditioning_base.py @@ -235,6 +235,7 @@ def __init__( train_conditions: dict, meta_conditioning_probabilities: dict, sequence_encoding, + association_scheme, ): if exists(train_conditions): train_conditions = hydra.utils.instantiate( @@ -243,6 +244,7 @@ def __init__( self.meta_conditioning_probabilities = meta_conditioning_probabilities self.train_conditions = train_conditions self.sequence_encoding = sequence_encoding + self.association_scheme = association_scheme def check_input(self, data: dict): assert not data["is_inference"], "This transform is only used during training!" @@ -278,6 +280,8 @@ def forward(self, data): i_cond = np.random.choice(np.arange(len(p_cond)), p=p_cond) cond = valid_conditions[i_cond] + cond.association_scheme = self.association_scheme + data["sampled_condition"] = cond data["sampled_condition_name"] = cond.name data["sampled_condition_cls"] = cond.__class__ @@ -295,6 +299,8 @@ class SampleConditioningFlags(Transform): "AssignTypes", "SampleConditioningType", ] # We use is_protein in the PPI training condition + def __init__(self, association_scheme): + self.association_scheme = association_scheme def check_input(self, data): assert not data[ @@ -317,13 +323,14 @@ class UnindexFlaggedTokens(Transform): Serves as the merge point between training / infernece conditioning pipelines """ - def __init__(self, central_atom): + def __init__(self, central_atom, association_scheme): """ Args: central_atom: The atom to use as the central atom for unindexed motifs. """ super().__init__() self.central_atom = central_atom + self.association_scheme = association_scheme def check_input(self, data: dict): check_contains_keys(data, ["atom_array"]) @@ -368,8 +375,16 @@ def expand_unindexed_motifs( token.res_id = token.res_id + max_resid token.is_C_terminus[:] = False token.is_N_terminus[:] = False - assert token.is_protein.all(), f"Cannot unindex non-protein token: {token}" - token = add_representative_atom(token, central_atom=self.central_atom) + + if association_scheme is not 'atom23': + assert token.is_protein.all(), f"Cannot unindex non-protein token: {token} unless using atom23 association scheme" + token = add_representative_atom(token, central_atom=self.central_atom) + else: + if token.is_protein.all(): + token = add_representative_atom(token, central_atom=self.central_atom) + else: + token = add_representative_atom(token, central_atom="C1'") + unindexed_tokens.append(token) # ... Remove original tokens e.g. during inference diff --git a/models/rfd3/src/rfd3/transforms/design_transforms.py b/models/rfd3/src/rfd3/transforms/design_transforms.py index 38c79979..8225bcc9 100644 --- a/models/rfd3/src/rfd3/transforms/design_transforms.py +++ b/models/rfd3/src/rfd3/transforms/design_transforms.py @@ -697,6 +697,7 @@ def __init__( token_1d_features, atom_1d_features, autofill_zeros_if_not_present_in_atomarray=False, + association_scheme='atom14' ): self.autofill = autofill_zeros_if_not_present_in_atomarray self.token_1d_features = token_1d_features @@ -752,6 +753,11 @@ def forward(self, data: Dict[str, Any]) -> Dict[str, Any]: """ if "feats" not in data.keys(): data["feats"] = {} + + if association_scheme == 'atom23': + data['atom_array'].set_annotation('is_protein_token', data['atom_array'].is_protein) + data['atom_array'].set_annotation('is_dna_token', data['atom_array'].is_dna) + data['atom_array'].set_annotation('is_rna_token', data['atom_array'].is_rna) for feature_name, n_dims in self.token_1d_features.items(): data = self.generate_feature(feature_name, n_dims, data, "token") diff --git a/models/rfd3/src/rfd3/transforms/pipelines.py b/models/rfd3/src/rfd3/transforms/pipelines.py index 89bc7289..fff8685c 100644 --- a/models/rfd3/src/rfd3/transforms/pipelines.py +++ b/models/rfd3/src/rfd3/transforms/pipelines.py @@ -383,6 +383,7 @@ def build_atom14_base_pipeline_( train_conditions=train_conditions, meta_conditioning_probabilities=meta_conditioning_probabilities, sequence_encoding=af3_sequence_encoding, + association_scheme=association_scheme ), ), ] @@ -422,7 +423,7 @@ def build_atom14_base_pipeline_( # ... Add global token features (since number of tokens is fixed after cropping) transforms.append(AddGlobalTokenIdAnnotation()) # ... Create masks (NOTE: Modulates token count, and resets global token id if necessary) - transforms.append(TrainingRoute(SampleConditioningFlags())) + transforms.append(TrainingRoute(SampleConditioningFlags(association_scheme=association_scheme))) # Post-crop transforms transforms.append( @@ -518,6 +519,7 @@ def build_atom14_base_pipeline_( autofill_zeros_if_not_present_in_atomarray=True, token_1d_features=token_1d_features, atom_1d_features=atom_1d_features, + association_scheme=association_scheme ), AddAF3TokenBondFeatures(), AddGroundTruthSequence(sequence_encoding=af3_sequence_encoding), diff --git a/models/rfd3/src/rfd3/transforms/training_conditions.py b/models/rfd3/src/rfd3/transforms/training_conditions.py index f93e346f..8f708e0a 100644 --- a/models/rfd3/src/rfd3/transforms/training_conditions.py +++ b/models/rfd3/src/rfd3/transforms/training_conditions.py @@ -70,9 +70,11 @@ def __init__( p_fix_motif_coordinates, p_fix_motif_sequence, p_unindex_motif_tokens, + association_scheme = 'atom14', ): self.name = name self.frequency = frequency + self.association_scheme = association_scheme # Token selection self.island_sampling_kwargs = island_sampling_kwargs @@ -87,11 +89,21 @@ def __init__( self.p_fix_motif_coordinates = p_fix_motif_coordinates self.p_fix_motif_sequence = p_fix_motif_sequence self.p_unindex_motif_tokens = p_unindex_motif_tokens + + self.association_scheme = association_scheme def is_valid_for_example(self, data) -> bool: is_protein = data["atom_array"].is_protein - if not np.any(is_protein): - return False + is_dna = data["atom_array"].is_dna + is_rna = data["atom_array"].is_rna + ### updating this to allow other polymers + if self.association_scheme is not 'atom23': + if not np.any(is_protein | is_dna | is_rna): + return False + else: + if not np.any(is_protein): + return False + return True def sample_motif_tokens(self, atom_array): @@ -101,13 +113,24 @@ def sample_motif_tokens(self, atom_array): token_level_array = atom_array[get_token_starts(atom_array)] # initialize motif tokens as all non-protein tokens - is_motif_token = np.asarray(~token_level_array.is_protein, dtype=bool).copy() - n_protein_tokens = np.sum(token_level_array.is_protein) - islands_mask = sample_island_tokens( - n_protein_tokens, - **self.island_sampling_kwargs, - ) - is_motif_token[token_level_array.is_protein] = islands_mask + if self.association_scheme is 'atom23': + polymer_mask = (token_level_array.is_protein | token_level_array.is_dna | token_level_array.is_rna) + is_motif_token = np.asarray(~polymer_mask, dtype=bool).copy() + n_polymer_tokens = np.sum(polymer_mask) + islands_mask = sample_island_tokens( + n_polymer_tokens, + **self.island_sampling_kwargs, + ) + is_motif_token[polymer_mask] = islands_mask + else: + is_motif_token = np.asarray(~token_level_array.is_protein, dtype=bool).copy() + n_protein_tokens = np.sum(token_level_array.is_protein) + + slands_mask = sample_island_tokens( + _protein_tokens, + *self.island_sampling_kwargs, + + is_motif_token[token_level_array.is_protein] = islands_mask # TODO: Atoms with covalent bonds should be motif, needs FlagAndReassignCovalentModifications transform prior to this # atom_with_coval_bond = token_level_array.covale # (n_atoms, ) @@ -137,6 +160,7 @@ def sample_motif_atoms(self, atom_array): is_motif_atom = sample_motif_subgraphs( atom_array=atom_array, **self.subgraph_sampling_kwargs, + association_scheme=self.association_scheme ) # We also only want resolved atoms to be motif @@ -158,6 +182,7 @@ def sample(self, data): p_fix_motif_sequence=self.p_fix_motif_sequence, p_fix_motif_coordinates=self.p_fix_motif_coordinates, p_unindex_motif_tokens=self.p_unindex_motif_tokens, + association_scheme=self.association_scheme ) atom_array.set_annotation( @@ -177,6 +202,7 @@ class PPICondition(TrainingCondition): """Get condition indicating what is motif and what is to be diffused for protein-protein interaction training.""" name = "ppi" + association_scheme = 'atom14' def is_valid_for_example(self, data): # Extract relevant data @@ -275,6 +301,7 @@ class SubtypeCondition(TrainingCondition): """ name = "subtype" + association_scheme = 'atom14' def __init__(self, frequency: float, subtype: list[str], fix_pos: bool = False): self.frequency = frequency @@ -370,6 +397,7 @@ def sample_motif_subgraphs( hetatom_n_bond_expectation, residue_p_fix_all, hetatom_p_fix_all, + association_scheme = 'atom14' ): """ Returns a boolean mask over atoms, indicating which atoms are part of the sampled motif. @@ -402,7 +430,13 @@ def sample_motif_subgraphs( "n_bond_expectation": residue_n_bond_expectation, "p_fix_all": residue_p_fix_all, } - if not atom_array_subset.is_protein.all(): + + if association_scheme is 'atom23': + clause = atom_array_subset.is_protein.all() | atom_array_subset.is_dna.all() | atom_array_subset.is_rna.all() + else: + clause = atom_array_subset.is_potein.all() + + if not clause: args.update( { "p_seed_furthest_from_o": 0.0, @@ -431,11 +465,12 @@ def sample_conditioning_strategy( p_fix_motif_sequence, p_fix_motif_coordinates, p_unindex_motif_tokens, + association_scheme ): atom_array.set_annotation( "is_motif_atom_with_fixed_seq", sample_is_motif_atom_with_fixed_seq( - atom_array, p_fix_motif_sequence=p_fix_motif_sequence + atom_array, p_fix_motif_sequence=p_fix_motif_sequence, association_scheme=association_scheme ), ) @@ -456,7 +491,7 @@ def sample_conditioning_strategy( return atom_array -def sample_is_motif_atom_with_fixed_seq(atom_array, p_fix_motif_sequence): +def sample_is_motif_atom_with_fixed_seq(atom_array, p_fix_motif_sequence, association_scheme): """ Samples what kind of conditioning to apply to motif tokens. @@ -469,7 +504,11 @@ def sample_is_motif_atom_with_fixed_seq(atom_array, p_fix_motif_sequence): is_motif_atom_with_fixed_seq = np.zeros(atom_array.array_length(), dtype=bool) # By default reveal sequence for non-protein - is_motif_atom_with_fixed_seq = is_motif_atom_with_fixed_seq | ~atom_array.is_protein + + if association_scheme is not 'atom23': + is_motif_atom_with_fixed_seq = is_motif_atom_with_fixed_seq | ~atom_array.is_protein + + return is_motif_atom_with_fixed_seq @@ -487,7 +526,7 @@ def sample_fix_motif_coordinates(atom_array, p_fix_motif_coordinates): return is_motif_atom_with_fixed_coord -def sample_unindexed_atoms(atom_array, p_unindex_motif_tokens): +def sample_unindexed_atoms(atom_array, p_unindex_motif_tokens, association_scheme='atom14'): """ Samples which atoms in motif tokens should be flagged for unindexing. @@ -500,9 +539,15 @@ def sample_unindexed_atoms(atom_array, p_unindex_motif_tokens): is_motif_atom_unindexed = np.zeros(atom_array.array_length(), dtype=bool) # ensure non-residue atoms are not already flagged - is_motif_atom_unindexed = np.logical_and( - is_motif_atom_unindexed, atom_array.is_residue - ) + if association_scheme == 'atom23': + is_motif_atom_unindexed = np.logical_and( + is_motif_atom_unindexed, (atom_array.is_residue | atom_array.is_dna | atom_array.is_rna) + ) # is_residue refers to is_protein here + else: + is_motif_atom_unindexed = np.logical_and( + is_motif_atom_unindexed, atom_array.is_residue + ) + return is_motif_atom_unindexed diff --git a/models/rfd3/src/rfd3/transforms/util_transforms.py b/models/rfd3/src/rfd3/transforms/util_transforms.py index 024b1bf1..75d69ddf 100644 --- a/models/rfd3/src/rfd3/transforms/util_transforms.py +++ b/models/rfd3/src/rfd3/transforms/util_transforms.py @@ -252,13 +252,13 @@ def get_af3_token_representative_masks( atom_array: AtomArray, central_atom: str = "CA" ) -> np.ndarray: pyrimidine_representative_atom = is_pyrimidine(atom_array.res_name) & ( - atom_array.atom_name == "C2" + atom_array.atom_name == "C1'" ) purine_representative_atom = is_purine(atom_array.res_name) & ( - atom_array.atom_name == "C4" + atom_array.atom_name == "C1'" ) unknown_na_representative_atom = is_unknown_nucleotide(atom_array.res_name) & ( - atom_array.atom_name == "C4" + atom_array.atom_name == "C1'" ) glycine_representative_atom = is_glycine(atom_array.res_name) & ( diff --git a/models/rfd3/src/rfd3/transforms/virtual_atoms.py b/models/rfd3/src/rfd3/transforms/virtual_atoms.py index ae40b3ce..fbc8c743 100644 --- a/models/rfd3/src/rfd3/transforms/virtual_atoms.py +++ b/models/rfd3/src/rfd3/transforms/virtual_atoms.py @@ -10,8 +10,11 @@ ) from atomworks.ml.utils.token import get_token_starts from rfd3.constants import ( + ATOM23_ATOM_NAME_TO_ELEMENT, ATOM14_ATOM_NAME_TO_ELEMENT, ATOM14_ATOM_NAMES, + ATOM23_ATOM_NAMES_RNA, + ATOM23_ATOM_NAMES_DNA, VIRTUAL_ATOM_ELEMENT_NAME, association_schemes, association_schemes_stripped, @@ -28,7 +31,7 @@ from foundry.common import exists -def map_to_association_scheme(atom_names: list | str, res_name: str, scheme="atom14"): +def map_to_association_scheme(atom_names: list | str, res_name: str, scheme="atom14", ATOM_NAMES=None): """ Maps a list of names to the atom14 naming scheme for that particular name (within a specific residue) NB this function is a bit more general since it is used to handle tipatoms too. @@ -37,17 +40,17 @@ def map_to_association_scheme(atom_names: list | str, res_name: str, scheme="ato raise ValueError( f"Scheme {scheme} not found in association_schemes_stripped. Available schemes: {list(association_schemes_stripped.keys())}" ) - atom_names = ( - [str(atom_names)] if isinstance(atom_names, (str, np.str_)) else atom_names - ) + atom_names = [atom_names] if isinstance(atom_names, str) else atom_names idxs = np.array( [ association_schemes_stripped[scheme][res_name].index(name) for name in atom_names ] ) - return ATOM14_ATOM_NAMES[idxs] - + if ATOM_NAMES is None: + return ATOM14_ATOM_NAMES[idxs] + else: + return ATOM_NAMES[idxs] def map_names_to_elements( atom_names: list | str, default=VIRTUAL_ATOM_ELEMENT_NAME @@ -68,17 +71,17 @@ def generate_atom_mappings_(scheme="atom14"): atom_mapping = {} symmetry_mapping = {} - for aaa, atom14_names in ccd_ordering_atomchar.items(): - mapping = list(range(14)) + for aaa, atom_names in ccd_ordering_atomchar.items(): + mapping = list(range(len(atom_names))) scheme_names = scheme[aaa] - for ccd_index in range(len(atom14_names)): - atom14_name = atom14_names[ccd_index] - if atom14_name is not None: + for ccd_index in range(len(atom_names)): + atom_name = atom_names[ccd_index] + if atom_name is not None: assert ( - atom14_name in scheme_names - ), f"{atom14_name} not in CCD ordering for {aaa}" - scheme_index = scheme_names.index(atom14_name) + atom_name in scheme_names + ), f"{atom_name} not in CCD ordering for {aaa}" + scheme_index = scheme_names.index(atom_name) scheme_index_in_cur_mapping = mapping.index(scheme_index) mapping[ccd_index], mapping[scheme_index_in_cur_mapping] = ( mapping[scheme_index_in_cur_mapping], @@ -121,6 +124,12 @@ def permute_symmetric_atom_names_( ) -> list: # NB: Can leak GT sequence if the model receives the canconical ordering of atoms as input # With the structure-local atom attention it will not unless N_keys(n_attn_seq_neighbours) > n_atom_attn_queries. + + ## fail safe, no symmetry confusion in NA bases ## + if (atom_names[0] == "P"): + return atom_names + ################################################## + if res_name in association_map: idx_to_swap = association_map[res_name] atom_names = atom_names[idx_to_swap] @@ -171,20 +180,38 @@ def forward(self, data: dict) -> dict: token_ids = np.unique(atom_array.token_id) assert len(token_ids) == len( is_motif_atom_with_fixed_seq - ), "Token ids and token level array have different lengths!" - - # Unindexed tokens are never fully atomized, but may be assigned as atomized to have repr atoms: - is_residue = ( - token_level_array.is_protein & ~token_level_array.atomize - ) | is_motif_token_unindexed + ), "Token ids and token level array have different lengths!" + + # Unindexed tokens are never fully atomized, but may be assigned as atomized to have repr atoms: + if self.association_scheme == 'atom23': + is_residue = ( + token_level_array.is_protein & ~token_level_array.atomize + ) | is_motif_token_unindexed + + is_residue_NA = ( + (token_level_array.is_dna | token_level_array.is_rna) & ~token_level_array.atomize + ) | is_motif_token_unindexed + + # Unindexed tokens are never padded, and so are treated as residues with fixed sequence. + is_paddable = (is_residue_NA | is_residue) & ~( + is_motif_atom_with_fixed_seq | is_motif_token_unindexed + ) + is_non_paddable_residue = (is_residue_NA | is_residue) & ( + is_motif_atom_with_fixed_seq | is_motif_token_unindexed + ) + else: + is_residue = ( + token_level_array.is_protein & ~token_level_array.atomize + ) | is_motif_token_unindexed - # Unindexed tokens are never padded, and so are treated as residues with fixed sequence. - is_paddable = is_residue & ~( - is_motif_atom_with_fixed_seq | is_motif_token_unindexed - ) - is_non_paddable_residue = is_residue & ( - is_motif_atom_with_fixed_seq | is_motif_token_unindexed - ) + # Unindexed tokens are never padded, and so are treated as residues with fixed sequence. + is_paddable = is_residue & ~( + is_motif_atom_with_fixed_seq | is_motif_token_unindexed + ) + is_non_paddable_residue = is_residue & ( + is_motif_atom_with_fixed_seq | is_motif_token_unindexed + ) + # Collect virtual atoms to insert (we will insert them all at once) virtual_atoms_to_insert = [] @@ -194,8 +221,16 @@ def forward(self, data: dict) -> dict: for token_id, (start, end) in enumerate(zip(starts[:-1], starts[1:])): if is_paddable[token_id]: token = atom_array[start:end] + # First, pad with virtual atoms if needed - n_pad = self.n_atoms_per_token - len(token) + if self.association_scheme == "atom23" and atom_array[start].is_dna: + n_atoms_per_token = 22 + elif self.association_scheme == "atom23" and atom_array[start].is_rna: + n_atoms_per_token = 23 + else: + n_atoms_per_token = self.n_atoms_per_token + n_pad = n_atoms_per_token - len(token) + if n_pad > 0: mask = get_af3_token_representative_masks( token, central_atom=self.atom_to_pad_from @@ -262,18 +297,26 @@ def _fix_multidimensional_annotations_in_pad_array( for token_id, (start, end) in enumerate( zip(starts_padded[:-1], starts_padded[1:]) - ): + ): + if (atom_array_padded[start].is_dna): + ATOM_NAMES = ATOM23_ATOM_NAMES_DNA + elif (atom_array_padded[start].is_rna): + ATOM_NAMES = ATOM23_ATOM_NAMES_RNA + else: + ATOM_NAMES = ATOM14_ATOM_NAMES + if is_paddable[token_id]: # ... Permutation of atom names during training if not data["is_inference"] and exists(self.association_scheme): atom_names = permute_symmetric_atom_names_( - ATOM14_ATOM_NAMES, + ATOM_NAMES, atom_array_padded.res_name[start], association_map=self.association_map_, symmetry_map=self.symmetry_map_, ) else: - atom_names = ATOM14_ATOM_NAMES + atom_names = ATOM_NAMES + atom_array_padded.atom_name[start:end] = atom_names atom_array_padded.get_annotation("gt_atom_name")[start:end] = atom_names @@ -285,7 +328,7 @@ def _fix_multidimensional_annotations_in_pad_array( ) atom_array_padded.get_annotation("gt_atom_name")[start:end] = atom_names atom_names = map_to_association_scheme( - atom_names, res_name, scheme=self.association_scheme + atom_names, res_name, scheme=self.association_scheme, ATOM_NAMES=ATOM_NAMES ) atom_array_padded.atom_name[start:end] = atom_names else: From ebec466e4f4397b9bdc52b841ae024149f5b335a Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Tue, 20 Jan 2026 14:44:19 -0800 Subject: [PATCH 04/49] feat: atom23 inference changes and training fixes --- .../rfd3/configs/datasets/train/pdb/base.yaml | 2 +- models/rfd3/configs/experiment/rfd3na.yaml | 82 ++++++++ models/rfd3/src/rfd3/constants.py | 180 +++++++++++++++--- .../rfd3/src/rfd3/inference/input_parsing.py | 68 +++++-- .../rfd3/inference/legacy_input_parsing.py | 82 +++++++- models/rfd3/src/rfd3/trainer/rfd3.py | 11 +- models/rfd3/src/rfd3/trainer/trainer_utils.py | 32 +++- .../src/rfd3/transforms/conditioning_base.py | 11 +- .../src/rfd3/transforms/design_transforms.py | 15 +- models/rfd3/src/rfd3/transforms/pipelines.py | 12 +- .../rfd3/transforms/training_conditions.py | 81 ++++---- .../rfd3/src/rfd3/transforms/virtual_atoms.py | 48 +++-- src/foundry/utils/components.py | 18 +- 13 files changed, 528 insertions(+), 114 deletions(-) create mode 100644 models/rfd3/configs/experiment/rfd3na.yaml diff --git a/models/rfd3/configs/datasets/train/pdb/base.yaml b/models/rfd3/configs/datasets/train/pdb/base.yaml index d3f45628..9c0a8c87 100644 --- a/models/rfd3/configs/datasets/train/pdb/base.yaml +++ b/models/rfd3/configs/datasets/train/pdb/base.yaml @@ -9,6 +9,6 @@ weights: beta: 0.5 alphas: a_prot: 3.0 # 3 for AF-3 - a_nuc: 0.0 # 3 for AF-3 + a_nuc: 3.0 # 3 for AF-3 a_ligand: 1.0 # 1 for AF-3 a_loi: 5.0 # 5 for AF-3 diff --git a/models/rfd3/configs/experiment/rfd3na.yaml b/models/rfd3/configs/experiment/rfd3na.yaml new file mode 100644 index 00000000..6d807209 --- /dev/null +++ b/models/rfd3/configs/experiment/rfd3na.yaml @@ -0,0 +1,82 @@ +# @package _global_ +# Training configuration for RFD3 + +defaults: + - /debug/default + - override /model: rfd3_base + #- override /datasets: all + - override /logger: csv + #- override /logger: wandb + - _self_ + +name: train-base +tags: [print-model] +ckpt_path: null + +model: + net: + token_initializer: + token_1d_features: + ref_motif_token_type: 3 + restype: 32 + is_dna_token: 1 + is_rna_token: 1 + is_protein_token: 1 + atom_1d_features: + ref_atom_name_chars: 256 + ref_element: 128 + ref_charge: 1 + ref_mask: 1 + ref_is_motif_atom_with_fixed_coord: 1 + ref_is_motif_atom_unindexed: 1 + has_zero_occupancy: 1 + ref_pos: 3 + + # Guided features + ref_atomwise_rasa: 3 + active_donor: 1 + active_acceptor: 1 + is_atom_level_hotspot: 1 + diffusion_module: + n_recycle: 2 + use_local_token_attention: True + diffusion_transformer: + n_local_tokens: 32 + n_keys: 128 + + inference_sampler: + num_timesteps: 100 + + +datasets: + diffusion_batch_size_train: 16 + crop_size: 256 + max_atoms_in_crop: 2560 # ~10x crop size. + global_transform_args: + association_scheme: atom23 + train_conditions: + unconditional: + frequency: 2.0 + island: + frequency: 2.0 + sequence_design: + frequency: 0.5 + tipatom: + frequency: 5.0 + ppi: + frequency: 0.0 + train: + # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. + #pdb: + #probability: 0.10 + #monomer_distillation: + #probability: 0.90 + pdb: + probability: 1.0 + +trainer: + devices_per_node: 1 + limit_train_batches: 10 + limit_val_batches: 1 + validate_every_n_epochs: 5 + prevalidate: false diff --git a/models/rfd3/src/rfd3/constants.py b/models/rfd3/src/rfd3/constants.py index a1a6e484..529ca581 100644 --- a/models/rfd3/src/rfd3/constants.py +++ b/models/rfd3/src/rfd3/constants.py @@ -242,42 +242,171 @@ "POLYDEOXYRIBONUCLEOTIDE/POLYRIBONUCLEOTIDE HYBRID", ] -backbone_atomscheme_DNA = [' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " C1'"]#, None] - -backbone_atomscheme_RNA = [' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " O2'", " C1'"] +backbone_atomscheme_DNA = [ + " P ", + " OP1", + " OP2", + " O5'", + " C5'", + " C4'", + " O4'", + " C3'", + " O3'", + " C2'", + " C1'", +] # , None] + +backbone_atomscheme_RNA = [ + " P ", + " OP1", + " OP2", + " O5'", + " C5'", + " C4'", + " O4'", + " C3'", + " O3'", + " C2'", + " O2'", + " C1'", +] DNA_atoms = { - 'DA': [' N9 ', ' C8 ', ' N7 ', ' C5 ', ' C6 ', ' N6 ', ' N1 ', ' C2 ', ' N3 ', ' C4 '], - 'DC': [' N1 ', ' C2 ', ' O2 ', ' N3 ', ' C4 ', ' N4 ', ' C5 ', ' C6 '], - 'DG': [' N9 ', ' C8 ', ' N7 ', ' C5 ', ' C6 ', ' O6 ', ' N1 ', ' C2 ', ' N2 ', ' N3 ', ' C4 '], - 'DT': [' N1 ', ' C2 ', ' O2 ', ' N3 ', ' C4 ', ' O4 ', ' C5 ', ' C7 ', ' C6 ']} + "DA": [ + " N9 ", + " C8 ", + " N7 ", + " C5 ", + " C6 ", + " N6 ", + " N1 ", + " C2 ", + " N3 ", + " C4 ", + ], + "DC": [" N1 ", " C2 ", " O2 ", " N3 ", " C4 ", " N4 ", " C5 ", " C6 "], + "DG": [ + " N9 ", + " C8 ", + " N7 ", + " C5 ", + " C6 ", + " O6 ", + " N1 ", + " C2 ", + " N2 ", + " N3 ", + " C4 ", + ], + "DT": [" N1 ", " C2 ", " O2 ", " N3 ", " C4 ", " O4 ", " C5 ", " C7 ", " C6 "], +} RNA_atoms = { - 'A': [' N9 ', ' C8 ', ' N7 ', ' C5 ', ' C6 ', ' N6 ', ' N1 ', ' C2 ', ' N3 ', ' C4 '], - 'C': [' N1 ', ' C2 ', ' O2 ', ' N3 ', ' C4 ', ' N4 ', ' C5 ', ' C6 '], - 'G': [' N9 ', ' C8 ', ' N7 ', ' C5 ', ' C6 ', ' O6 ', ' N1 ', ' C2 ', ' N2 ', ' N3 ', ' C4 '], - 'U': [' N1 ', ' C2 ', ' O2 ', ' N3 ', ' C4 ', ' O4 ', ' C5 ', ' C6 '] + "A": [ + " N9 ", + " C8 ", + " N7 ", + " C5 ", + " C6 ", + " N6 ", + " N1 ", + " C2 ", + " N3 ", + " C4 ", + ], + "C": [" N1 ", " C2 ", " O2 ", " N3 ", " C4 ", " N4 ", " C5 ", " C6 "], + "G": [ + " N9 ", + " C8 ", + " N7 ", + " C5 ", + " C6 ", + " O6 ", + " N1 ", + " C2 ", + " N2 ", + " N3 ", + " C4 ", + ], + "U": [" N1 ", " C2 ", " O2 ", " N3 ", " C4 ", " O4 ", " C5 ", " C6 "], } -association_schemes['atom23'] = {} +association_schemes["atom23"] = {} for item in DNA_atoms: - association_schemes['atom23'][item] = tuple(backbone_atomscheme_DNA + DNA_atoms[item]+ [None]*(22 - len(DNA_atoms[item] + backbone_atomscheme_DNA))) + association_schemes["atom23"][item] = tuple( + backbone_atomscheme_DNA + + DNA_atoms[item] + + [None] * (22 - len(DNA_atoms[item] + backbone_atomscheme_DNA)) + ) for item in RNA_atoms: - association_schemes['atom23'][item] = tuple(backbone_atomscheme_RNA + RNA_atoms[item]+ [None]*(23 - len(RNA_atoms[item] + backbone_atomscheme_RNA))) - -for item in association_schemes['dense']: - association_schemes['atom23'][item] = association_schemes['dense'][item] - -association_schemes['atom23']['DX'] = (' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " C1'", None, None, None, None, None, None, None, None, None, None, None) #rna_mask -association_schemes['atom23']['X'] = (' P ', ' OP1', ' OP2', " O5'", " C5'", " C4'", " O4'", " C3'", " O3'", " C2'", " O2'", " C1'", None, None, None, None, None, None, None, None, None, None, None)#rna mask + association_schemes["atom23"][item] = tuple( + backbone_atomscheme_RNA + + RNA_atoms[item] + + [None] * (23 - len(RNA_atoms[item] + backbone_atomscheme_RNA)) + ) + +for item in association_schemes["dense"]: + association_schemes["atom23"][item] = association_schemes["dense"][item] + +association_schemes["atom23"]["DX"] = ( + " P ", + " OP1", + " OP2", + " O5'", + " C5'", + " C4'", + " O4'", + " C3'", + " O3'", + " C2'", + " C1'", + None, + None, + None, + None, + None, + None, + None, + None, + None, + None, + None, +) # rna_mask +association_schemes["atom23"]["X"] = ( + " P ", + " OP1", + " OP2", + " O5'", + " C5'", + " C4'", + " O4'", + " C3'", + " O3'", + " C2'", + " O2'", + " C1'", + None, + None, + None, + None, + None, + None, + None, + None, + None, + None, + None, +) # rna mask ATOM23_ATOM_NAMES_RNA = np.array( - [item.strip() for item in backbone_atomscheme_RNA] + [f"V{i}" for i in range(23 - len(backbone_atomscheme_RNA))] + [item.strip() for item in backbone_atomscheme_RNA] + + [f"V{i}" for i in range(23 - len(backbone_atomscheme_RNA))] ) """Atom23 atom names (e.g. CA, V1)""" ATOM23_ATOM_ELEMENTS_RNA = np.array( - ["P", "O", "O", "O", "C", "C", "O", "C","O", "C", "O", "C"] + [VIRTUAL_ATOM_ELEMENT_NAME for i in range(23 - len(backbone_atomscheme_RNA))] + ["P", "O", "O", "O", "C", "C", "O", "C", "O", "C", "O", "C"] + + [VIRTUAL_ATOM_ELEMENT_NAME for i in range(23 - len(backbone_atomscheme_RNA))] ) """Atom23 element names (e.g. C, VX)""" @@ -285,12 +414,14 @@ name: elem for name, elem in zip(ATOM23_ATOM_NAMES_RNA, ATOM23_ATOM_ELEMENTS_RNA) } ATOM23_ATOM_NAMES_DNA = np.array( - [item.strip() for item in backbone_atomscheme_DNA] + [f"V{i}" for i in range(22 - len(backbone_atomscheme_DNA))] + [item.strip() for item in backbone_atomscheme_DNA] + + [f"V{i}" for i in range(22 - len(backbone_atomscheme_DNA))] ) """Atom23 atom names (e.g. CA, V1)""" ATOM23_ATOM_ELEMENTS_DNA = np.array( - ["P", "O", "O", "O", "C", "C", "O", "C","O", "C", "C"] + [VIRTUAL_ATOM_ELEMENT_NAME for i in range(22 - len(backbone_atomscheme_DNA))] + ["P", "O", "O", "O", "C", "C", "O", "C", "O", "C", "C"] + + [VIRTUAL_ATOM_ELEMENT_NAME for i in range(22 - len(backbone_atomscheme_DNA))] ) """Atom23 element names (e.g. C, VX)""" @@ -307,4 +438,3 @@ backbone_atoms_RNA = strip_list(backbone_atomscheme_RNA) backbone_atoms_DNA = strip_list(backbone_atomscheme_DNA) - diff --git a/models/rfd3/src/rfd3/inference/input_parsing.py b/models/rfd3/src/rfd3/inference/input_parsing.py index d97b3be3..b3117fcc 100644 --- a/models/rfd3/src/rfd3/inference/input_parsing.py +++ b/models/rfd3/src/rfd3/inference/input_parsing.py @@ -30,9 +30,12 @@ OPTIONAL_CONDITIONING_VALUES, REQUIRED_CONDITIONING_ANNOTATION_VALUES, REQUIRED_INFERENCE_ANNOTATIONS, + backbone_atoms_DNA, + backbone_atoms_RNA, ) from rfd3.inference.legacy_input_parsing import ( create_atom_array_from_design_specification_legacy, + reorder_atoms_per_residue, ) from rfd3.inference.parsing import InputSelection from rfd3.inference.symmetry.symmetry_utils import ( @@ -67,7 +70,6 @@ logger = RankedLogger(__name__, rank_zero_only=True) - ################################################################################# # Custom infer_ori functions ################################################################################# @@ -505,6 +507,21 @@ def apply_selections(start, end): def build(self, return_metadata=False): """Main build pipeline.""" atom_array_input_annotated = copy.deepcopy(self.atom_array_input) + + ########## reorder NA atoms ########### + is_dna = np.isin(atom_array_input_annotated.res_name, ["DA", "DC", "DG", "DT"]) + is_rna = np.isin(atom_array_input_annotated.res_name, ["A", "C", "G", "U"]) + dna_array = atom_array_input_annotated[is_dna] + rna_array = atom_array_input_annotated[is_rna] + + atom_array_input_annotated[is_dna] = reorder_atoms_per_residue( + dna_array, backbone_atoms_DNA + ) + atom_array_input_annotated[is_rna] = reorder_atoms_per_residue( + rna_array, backbone_atoms_RNA + ) + ####################################### + atom_array = self._build_init(atom_array_input_annotated) # Apply post-processing @@ -894,31 +911,52 @@ def validator_context(validator_name: str, data: dict = None): raise e -def create_diffused_residues(n, additional_annotations=None): +def create_diffused_residues(n, additional_annotations=None, polymer_type="P"): + from rfd3.constants import ( + ATOM23_ATOM_NAME_TO_ELEMENT, + backbone_atoms_DNA, + backbone_atoms_RNA, + ) + if n <= 0: raise ValueError(f"Negative/null residue count ({n}) not allowed.") + if polymer_type == "P": + res_name = "ALA" + bb_len = 5 + bb_atom_names = ["N", "CA", "C", "O", "CB"] + elif polymer_type == "R": + res_name = "A" + bb_len = len(backbone_atoms_RNA) + bb_atom_names = strip_list(backbone_atoms_RNA) + elif polymer_type == "D": + res_name = "DA" + bb_len = len(backbone_atoms_DNA) + bb_atom_names = strip_list(backbone_atoms_DNA) + else: + raise ValueError( + f"invalid polymer type detected: {polymer_type}, check contig!" + ) + + bb_elements = [ATOM23_ATOM_NAME_TO_ELEMENT[item] for item in bb_atom_names] + atoms = [] [ atoms.extend( [ struc.Atom( np.array([0.0, 0.0, 0.0], dtype=np.float32), - res_name="ALA", + res_name=res_name, res_id=idx, ) - for _ in range(5) + for _ in range(bb_len) ] ) for idx in range(1, n + 1) ] array = struc.array(atoms) - array.set_annotation( - "element", np.array(["N", "C", "C", "O", "C"] * n, dtype=" AtomArray: + """ + Reorder atoms within each residue of an AtomArray. + Atoms in `desired_order` appear first (in that order), followed by all others + in original order. Faster version using get_residue_starts(). + + Parameters: + - atom_array: AtomArray to reorder. + - desired_order: List of atom names in the desired per-residue order. + + Returns: + - AtomArray with reordered atoms per residue. + """ + if len(atom_array) == 0: + return atom_array + res_starts = get_residue_starts(atom_array) + res_starts = np.append(res_starts, len(atom_array)) # add end index for slicing + reordered_chunks = [] + order_dict = {name: i for i, name in enumerate(desired_order)} + + for i in range(len(res_starts) - 1): + start, end = res_starts[i], res_starts[i + 1] + residue = atom_array[start:end] + + # Boolean masks for matching and non-matching atom names + in_order_mask = np.isin(residue.atom_name, desired_order) + not_in_order_mask = ~in_order_mask + + # Sort matching atoms by desired order + atoms_in_order = residue[in_order_mask] + sort_idx = np.argsort([order_dict[name] for name in atoms_in_order.atom_name]) + ordered_atoms = atoms_in_order[sort_idx] + + # Remaining atoms as-is + remaining_atoms = residue[not_in_order_mask] + + # Concatenate reordered residue + reordered_chunks.append(concatenate([ordered_atoms, remaining_atoms])) + return concatenate(reordered_chunks) diff --git a/models/rfd3/src/rfd3/trainer/rfd3.py b/models/rfd3/src/rfd3/trainer/rfd3.py index a7f72e6f..9735978e 100644 --- a/models/rfd3/src/rfd3/trainer/rfd3.py +++ b/models/rfd3/src/rfd3/trainer/rfd3.py @@ -428,9 +428,14 @@ def _build_predicted_atom_array_stack( # ... Delete virtual atoms and assign atom names and elements if self.cleanup_virtual_atoms: - atom_array = _cleanup_virtual_atoms_and_assign_atom_name_elements( - atom_array, association_scheme=self.association_scheme - ) + try: + atom_array = _cleanup_virtual_atoms_and_assign_atom_name_elements( + atom_array, association_scheme=self.association_scheme + ) + except Exception as e: + global_logger.warning( + f"Failed to cleanup virtual atoms from diffusion output: {e}" + ) # ... When cleaning up virtual atoms, we can also calculate native_array_metricsl metadata_dict[i]["metrics"] |= get_all_backbone_metrics( diff --git a/models/rfd3/src/rfd3/trainer/trainer_utils.py b/models/rfd3/src/rfd3/trainer/trainer_utils.py index 59f43a1c..9ee7939b 100644 --- a/models/rfd3/src/rfd3/trainer/trainer_utils.py +++ b/models/rfd3/src/rfd3/trainer/trainer_utils.py @@ -11,6 +11,8 @@ from jaxtyping import Float, Int from rfd3.constants import ( ATOM14_ATOM_NAMES, + ATOM23_ATOM_NAMES_DNA, + ATOM23_ATOM_NAMES_RNA, VIRTUAL_ATOM_ELEMENT_NAME, association_schemes, association_schemes_stripped, @@ -252,13 +254,19 @@ def _readout_seq_from_struc( continue # ... Find the index of virtual atom names in the standard atom14 names + ATOM_NAMES = ATOM14_ATOM_NAMES + if restype in STANDARD_DNA: + ATOM_NAMES = ATOM23_ATOM_NAMES_DNA + if restype in STANDARD_RNA: + ATOM_NAMES = ATOM23_ATOM_NAMES_RNA + atom_name_idx_in_atom14_scheme = np.array( [ - np.where(ATOM14_ATOM_NAMES == atom_name)[0][0] + np.where(ATOM_NAMES == atom_name)[0][0] for atom_name in cur_pred_res_atom_names ] ) # five backbone atoms + some virtual atoms, returning e.g. [0, 1, 2, 3, 4, 11, 7] - atom14_scheme_mask = np.zeros_like(ATOM14_ATOM_NAMES, dtype=bool) + atom14_scheme_mask = np.zeros_like(ATOM_NAMES, dtype=bool) atom14_scheme_mask[atom_name_idx_in_atom14_scheme] = True # ... Find the matched restype by checking if all the non-None posititons and None positions match @@ -427,12 +435,30 @@ def process_unindexed_outputs( else: join_atom = None + if join_atom is None: + pass + else: + dist = float(dists[row_ind[join_atom], col_ind[join_atom]]) + + elif not np.any( + np.isin( + token.atom_name, [item.replace(" ", "") for item in backbone_atoms_RNA] + ) + ): + if np.sum(token.atomize) == 1: + join_atom = np.where(token.atomize)[0][0] + elif "C1'" in token.atom_name: + join_atom = np.where(token.atom_name == "C1'")[0][0] + else: + join_atom = None + if join_atom is None: global_logger.warning( - f"Token {token_pdb_id} does not contain backbone atoms or CB, skipping join point distance calculation {token}." + "Skipping joint point rmsd, neither protein or NA backbone" ) else: dist = float(dists[row_ind[join_atom], col_ind[join_atom]]) + metadata["join_point_rmsd_by_token"][token_pdb_id] = dist metadata["diffused_index_map"][token_pdb_id] = f"{chain_id}{res_id}" diff --git a/models/rfd3/src/rfd3/transforms/conditioning_base.py b/models/rfd3/src/rfd3/transforms/conditioning_base.py index 3a551a58..769fcf46 100644 --- a/models/rfd3/src/rfd3/transforms/conditioning_base.py +++ b/models/rfd3/src/rfd3/transforms/conditioning_base.py @@ -281,7 +281,7 @@ def forward(self, data): cond = valid_conditions[i_cond] cond.association_scheme = self.association_scheme - + data["sampled_condition"] = cond data["sampled_condition_name"] = cond.name data["sampled_condition_cls"] = cond.__class__ @@ -299,6 +299,7 @@ class SampleConditioningFlags(Transform): "AssignTypes", "SampleConditioningType", ] # We use is_protein in the PPI training condition + def __init__(self, association_scheme): self.association_scheme = association_scheme @@ -375,13 +376,15 @@ def expand_unindexed_motifs( token.res_id = token.res_id + max_resid token.is_C_terminus[:] = False token.is_N_terminus[:] = False - - if association_scheme is not 'atom23': + + if not self.association_scheme == "atom23": assert token.is_protein.all(), f"Cannot unindex non-protein token: {token} unless using atom23 association scheme" token = add_representative_atom(token, central_atom=self.central_atom) else: if token.is_protein.all(): - token = add_representative_atom(token, central_atom=self.central_atom) + token = add_representative_atom( + token, central_atom=self.central_atom + ) else: token = add_representative_atom(token, central_atom="C1'") diff --git a/models/rfd3/src/rfd3/transforms/design_transforms.py b/models/rfd3/src/rfd3/transforms/design_transforms.py index 8225bcc9..5cc84fa9 100644 --- a/models/rfd3/src/rfd3/transforms/design_transforms.py +++ b/models/rfd3/src/rfd3/transforms/design_transforms.py @@ -697,11 +697,12 @@ def __init__( token_1d_features, atom_1d_features, autofill_zeros_if_not_present_in_atomarray=False, - association_scheme='atom14' + association_scheme="atom14", ): self.autofill = autofill_zeros_if_not_present_in_atomarray self.token_1d_features = token_1d_features self.atom_1d_features = atom_1d_features + self.association_scheme = association_scheme def check_input(self, data) -> None: check_contains_keys(data, ["atom_array"]) @@ -753,11 +754,13 @@ def forward(self, data: Dict[str, Any]) -> Dict[str, Any]: """ if "feats" not in data.keys(): data["feats"] = {} - - if association_scheme == 'atom23': - data['atom_array'].set_annotation('is_protein_token', data['atom_array'].is_protein) - data['atom_array'].set_annotation('is_dna_token', data['atom_array'].is_dna) - data['atom_array'].set_annotation('is_rna_token', data['atom_array'].is_rna) + + if self.association_scheme == "atom23": + data["atom_array"].set_annotation( + "is_protein_token", data["atom_array"].is_protein + ) + data["atom_array"].set_annotation("is_dna_token", data["atom_array"].is_dna) + data["atom_array"].set_annotation("is_rna_token", data["atom_array"].is_rna) for feature_name, n_dims in self.token_1d_features.items(): data = self.generate_feature(feature_name, n_dims, data, "token") diff --git a/models/rfd3/src/rfd3/transforms/pipelines.py b/models/rfd3/src/rfd3/transforms/pipelines.py index fff8685c..0cd656b2 100644 --- a/models/rfd3/src/rfd3/transforms/pipelines.py +++ b/models/rfd3/src/rfd3/transforms/pipelines.py @@ -383,7 +383,7 @@ def build_atom14_base_pipeline_( train_conditions=train_conditions, meta_conditioning_probabilities=meta_conditioning_probabilities, sequence_encoding=af3_sequence_encoding, - association_scheme=association_scheme + association_scheme=association_scheme, ), ), ] @@ -423,7 +423,9 @@ def build_atom14_base_pipeline_( # ... Add global token features (since number of tokens is fixed after cropping) transforms.append(AddGlobalTokenIdAnnotation()) # ... Create masks (NOTE: Modulates token count, and resets global token id if necessary) - transforms.append(TrainingRoute(SampleConditioningFlags(association_scheme=association_scheme))) + transforms.append( + TrainingRoute(SampleConditioningFlags(association_scheme=association_scheme)) + ) # Post-crop transforms transforms.append( @@ -443,7 +445,9 @@ def build_atom14_base_pipeline_( sharding_depth=1, ), # ... Fuse inference and training conditioning assignments - UnindexFlaggedTokens(central_atom=central_atom), + UnindexFlaggedTokens( + central_atom=central_atom, association_scheme=association_scheme + ), # ... Virtual atom padding (NOTE: Last transform which modulates atom count) PadTokensWithVirtualAtoms( n_atoms_per_token=n_atoms_per_token, @@ -519,7 +523,7 @@ def build_atom14_base_pipeline_( autofill_zeros_if_not_present_in_atomarray=True, token_1d_features=token_1d_features, atom_1d_features=atom_1d_features, - association_scheme=association_scheme + association_scheme=association_scheme, ), AddAF3TokenBondFeatures(), AddGroundTruthSequence(sequence_encoding=af3_sequence_encoding), diff --git a/models/rfd3/src/rfd3/transforms/training_conditions.py b/models/rfd3/src/rfd3/transforms/training_conditions.py index 8f708e0a..6354c1f0 100644 --- a/models/rfd3/src/rfd3/transforms/training_conditions.py +++ b/models/rfd3/src/rfd3/transforms/training_conditions.py @@ -58,6 +58,8 @@ class IslandCondition(TrainingCondition): Select islands as motif and assign conditioning strategies. """ + association_scheme = "atom14" + def __init__( self, *, @@ -70,11 +72,9 @@ def __init__( p_fix_motif_coordinates, p_fix_motif_sequence, p_unindex_motif_tokens, - association_scheme = 'atom14', ): self.name = name self.frequency = frequency - self.association_scheme = association_scheme # Token selection self.island_sampling_kwargs = island_sampling_kwargs @@ -89,15 +89,13 @@ def __init__( self.p_fix_motif_coordinates = p_fix_motif_coordinates self.p_fix_motif_sequence = p_fix_motif_sequence self.p_unindex_motif_tokens = p_unindex_motif_tokens - - self.association_scheme = association_scheme def is_valid_for_example(self, data) -> bool: is_protein = data["atom_array"].is_protein is_dna = data["atom_array"].is_dna is_rna = data["atom_array"].is_rna ### updating this to allow other polymers - if self.association_scheme is not 'atom23': + if not self.association_scheme == "atom23": if not np.any(is_protein | is_dna | is_rna): return False else: @@ -113,8 +111,12 @@ def sample_motif_tokens(self, atom_array): token_level_array = atom_array[get_token_starts(atom_array)] # initialize motif tokens as all non-protein tokens - if self.association_scheme is 'atom23': - polymer_mask = (token_level_array.is_protein | token_level_array.is_dna | token_level_array.is_rna) + if self.association_scheme == "atom23": + polymer_mask = ( + token_level_array.is_protein + | token_level_array.is_dna + | token_level_array.is_rna + ) is_motif_token = np.asarray(~polymer_mask, dtype=bool).copy() n_polymer_tokens = np.sum(polymer_mask) islands_mask = sample_island_tokens( @@ -123,13 +125,15 @@ def sample_motif_tokens(self, atom_array): ) is_motif_token[polymer_mask] = islands_mask else: - is_motif_token = np.asarray(~token_level_array.is_protein, dtype=bool).copy() + is_motif_token = np.asarray( + ~token_level_array.is_protein, dtype=bool + ).copy() n_protein_tokens = np.sum(token_level_array.is_protein) - slands_mask = sample_island_tokens( - _protein_tokens, - *self.island_sampling_kwargs, - + islands_mask = sample_island_tokens( + n_protein_tokens, + **self.island_sampling_kwargs, + ) is_motif_token[token_level_array.is_protein] = islands_mask # TODO: Atoms with covalent bonds should be motif, needs FlagAndReassignCovalentModifications transform prior to this @@ -160,7 +164,7 @@ def sample_motif_atoms(self, atom_array): is_motif_atom = sample_motif_subgraphs( atom_array=atom_array, **self.subgraph_sampling_kwargs, - association_scheme=self.association_scheme + association_scheme=self.association_scheme, ) # We also only want resolved atoms to be motif @@ -182,7 +186,7 @@ def sample(self, data): p_fix_motif_sequence=self.p_fix_motif_sequence, p_fix_motif_coordinates=self.p_fix_motif_coordinates, p_unindex_motif_tokens=self.p_unindex_motif_tokens, - association_scheme=self.association_scheme + association_scheme=self.association_scheme, ) atom_array.set_annotation( @@ -202,7 +206,7 @@ class PPICondition(TrainingCondition): """Get condition indicating what is motif and what is to be diffused for protein-protein interaction training.""" name = "ppi" - association_scheme = 'atom14' + association_scheme = "atom14" def is_valid_for_example(self, data): # Extract relevant data @@ -301,7 +305,7 @@ class SubtypeCondition(TrainingCondition): """ name = "subtype" - association_scheme = 'atom14' + association_scheme = "atom14" def __init__(self, frequency: float, subtype: list[str], fix_pos: bool = False): self.frequency = frequency @@ -397,7 +401,7 @@ def sample_motif_subgraphs( hetatom_n_bond_expectation, residue_p_fix_all, hetatom_p_fix_all, - association_scheme = 'atom14' + association_scheme="atom14", ): """ Returns a boolean mask over atoms, indicating which atoms are part of the sampled motif. @@ -431,10 +435,14 @@ def sample_motif_subgraphs( "p_fix_all": residue_p_fix_all, } - if association_scheme is 'atom23': - clause = atom_array_subset.is_protein.all() | atom_array_subset.is_dna.all() | atom_array_subset.is_rna.all() + if association_scheme == "atom23": + clause = ( + atom_array_subset.is_protein.all() + | atom_array_subset.is_dna.all() + | atom_array_subset.is_rna.all() + ) else: - clause = atom_array_subset.is_potein.all() + clause = atom_array_subset.is_protein.all() if not clause: args.update( @@ -465,12 +473,14 @@ def sample_conditioning_strategy( p_fix_motif_sequence, p_fix_motif_coordinates, p_unindex_motif_tokens, - association_scheme + association_scheme, ): atom_array.set_annotation( "is_motif_atom_with_fixed_seq", sample_is_motif_atom_with_fixed_seq( - atom_array, p_fix_motif_sequence=p_fix_motif_sequence, association_scheme=association_scheme + atom_array, + p_fix_motif_sequence=p_fix_motif_sequence, + association_scheme=association_scheme, ), ) @@ -491,7 +501,9 @@ def sample_conditioning_strategy( return atom_array -def sample_is_motif_atom_with_fixed_seq(atom_array, p_fix_motif_sequence, association_scheme): +def sample_is_motif_atom_with_fixed_seq( + atom_array, p_fix_motif_sequence, association_scheme +): """ Samples what kind of conditioning to apply to motif tokens. @@ -504,10 +516,11 @@ def sample_is_motif_atom_with_fixed_seq(atom_array, p_fix_motif_sequence, associ is_motif_atom_with_fixed_seq = np.zeros(atom_array.array_length(), dtype=bool) # By default reveal sequence for non-protein - - if association_scheme is not 'atom23': - is_motif_atom_with_fixed_seq = is_motif_atom_with_fixed_seq | ~atom_array.is_protein - + + if not association_scheme == "atom23": + is_motif_atom_with_fixed_seq = ( + is_motif_atom_with_fixed_seq | ~atom_array.is_protein + ) return is_motif_atom_with_fixed_seq @@ -526,7 +539,9 @@ def sample_fix_motif_coordinates(atom_array, p_fix_motif_coordinates): return is_motif_atom_with_fixed_coord -def sample_unindexed_atoms(atom_array, p_unindex_motif_tokens, association_scheme='atom14'): +def sample_unindexed_atoms( + atom_array, p_unindex_motif_tokens, association_scheme="atom14" +): """ Samples which atoms in motif tokens should be flagged for unindexing. @@ -539,15 +554,15 @@ def sample_unindexed_atoms(atom_array, p_unindex_motif_tokens, association_schem is_motif_atom_unindexed = np.zeros(atom_array.array_length(), dtype=bool) # ensure non-residue atoms are not already flagged - if association_scheme == 'atom23': + if association_scheme == "atom23": is_motif_atom_unindexed = np.logical_and( - is_motif_atom_unindexed, (atom_array.is_residue | atom_array.is_dna | atom_array.is_rna) - ) # is_residue refers to is_protein here + is_motif_atom_unindexed, + (atom_array.is_residue | atom_array.is_dna | atom_array.is_rna), + ) # is_residue refers to is_protein here else: is_motif_atom_unindexed = np.logical_and( is_motif_atom_unindexed, atom_array.is_residue - ) - + ) return is_motif_atom_unindexed diff --git a/models/rfd3/src/rfd3/transforms/virtual_atoms.py b/models/rfd3/src/rfd3/transforms/virtual_atoms.py index fbc8c743..7d240a23 100644 --- a/models/rfd3/src/rfd3/transforms/virtual_atoms.py +++ b/models/rfd3/src/rfd3/transforms/virtual_atoms.py @@ -10,11 +10,10 @@ ) from atomworks.ml.utils.token import get_token_starts from rfd3.constants import ( - ATOM23_ATOM_NAME_TO_ELEMENT, - ATOM14_ATOM_NAME_TO_ELEMENT, ATOM14_ATOM_NAMES, - ATOM23_ATOM_NAMES_RNA, + ATOM23_ATOM_NAME_TO_ELEMENT, ATOM23_ATOM_NAMES_DNA, + ATOM23_ATOM_NAMES_RNA, VIRTUAL_ATOM_ELEMENT_NAME, association_schemes, association_schemes_stripped, @@ -31,7 +30,9 @@ from foundry.common import exists -def map_to_association_scheme(atom_names: list | str, res_name: str, scheme="atom14", ATOM_NAMES=None): +def map_to_association_scheme( + atom_names: list | str, res_name: str, scheme="atom14", ATOM_NAMES=None +): """ Maps a list of names to the atom14 naming scheme for that particular name (within a specific residue) NB this function is a bit more general since it is used to handle tipatoms too. @@ -52,6 +53,7 @@ def map_to_association_scheme(atom_names: list | str, res_name: str, scheme="ato else: return ATOM_NAMES[idxs] + def map_names_to_elements( atom_names: list | str, default=VIRTUAL_ATOM_ELEMENT_NAME ) -> np.ndarray: @@ -61,7 +63,7 @@ def map_names_to_elements( then it returns the default value """ atom_names = [atom_names] if isinstance(atom_names, str) else atom_names - elements = [ATOM14_ATOM_NAME_TO_ELEMENT.get(name, default) for name in atom_names] + elements = [ATOM23_ATOM_NAME_TO_ELEMENT.get(name, default) for name in atom_names] return np.array(elements) @@ -72,6 +74,9 @@ def generate_atom_mappings_(scheme="atom14"): symmetry_mapping = {} for aaa, atom_names in ccd_ordering_atomchar.items(): + if aaa not in scheme: + continue + mapping = list(range(len(atom_names))) scheme_names = scheme[aaa] @@ -126,10 +131,10 @@ def permute_symmetric_atom_names_( # With the structure-local atom attention it will not unless N_keys(n_attn_seq_neighbours) > n_atom_attn_queries. ## fail safe, no symmetry confusion in NA bases ## - if (atom_names[0] == "P"): + if atom_names[0] == "P": return atom_names ################################################## - + if res_name in association_map: idx_to_swap = association_map[res_name] atom_names = atom_names[idx_to_swap] @@ -180,16 +185,17 @@ def forward(self, data: dict) -> dict: token_ids = np.unique(atom_array.token_id) assert len(token_ids) == len( is_motif_atom_with_fixed_seq - ), "Token ids and token level array have different lengths!" + ), "Token ids and token level array have different lengths!" - # Unindexed tokens are never fully atomized, but may be assigned as atomized to have repr atoms: - if self.association_scheme == 'atom23': - is_residue = ( + # Unindexed tokens are never fully atomized, but may be assigned as atomized to have repr atoms: + if self.association_scheme == "atom23": + is_residue = ( token_level_array.is_protein & ~token_level_array.atomize ) | is_motif_token_unindexed - + is_residue_NA = ( - (token_level_array.is_dna | token_level_array.is_rna) & ~token_level_array.atomize + (token_level_array.is_dna | token_level_array.is_rna) + & ~token_level_array.atomize ) | is_motif_token_unindexed # Unindexed tokens are never padded, and so are treated as residues with fixed sequence. @@ -211,7 +217,6 @@ def forward(self, data: dict) -> dict: is_non_paddable_residue = is_residue & ( is_motif_atom_with_fixed_seq | is_motif_token_unindexed ) - # Collect virtual atoms to insert (we will insert them all at once) virtual_atoms_to_insert = [] @@ -221,7 +226,7 @@ def forward(self, data: dict) -> dict: for token_id, (start, end) in enumerate(zip(starts[:-1], starts[1:])): if is_paddable[token_id]: token = atom_array[start:end] - + # First, pad with virtual atoms if needed if self.association_scheme == "atom23" and atom_array[start].is_dna: n_atoms_per_token = 22 @@ -230,7 +235,7 @@ def forward(self, data: dict) -> dict: else: n_atoms_per_token = self.n_atoms_per_token n_pad = n_atoms_per_token - len(token) - + if n_pad > 0: mask = get_af3_token_representative_masks( token, central_atom=self.atom_to_pad_from @@ -297,10 +302,10 @@ def _fix_multidimensional_annotations_in_pad_array( for token_id, (start, end) in enumerate( zip(starts_padded[:-1], starts_padded[1:]) - ): - if (atom_array_padded[start].is_dna): + ): + if atom_array_padded[start].is_dna: ATOM_NAMES = ATOM23_ATOM_NAMES_DNA - elif (atom_array_padded[start].is_rna): + elif atom_array_padded[start].is_rna: ATOM_NAMES = ATOM23_ATOM_NAMES_RNA else: ATOM_NAMES = ATOM14_ATOM_NAMES @@ -328,7 +333,10 @@ def _fix_multidimensional_annotations_in_pad_array( ) atom_array_padded.get_annotation("gt_atom_name")[start:end] = atom_names atom_names = map_to_association_scheme( - atom_names, res_name, scheme=self.association_scheme, ATOM_NAMES=ATOM_NAMES + atom_names, + res_name, + scheme=self.association_scheme, + ATOM_NAMES=ATOM_NAMES, ) atom_array_padded.atom_name[start:end] = atom_names else: diff --git a/src/foundry/utils/components.py b/src/foundry/utils/components.py index 75bc87f3..0af9c7ea 100644 --- a/src/foundry/utils/components.py +++ b/src/foundry/utils/components.py @@ -96,8 +96,21 @@ def get_design_pattern_with_constraints(contig, length=None): fixed_parts = [] pos_to_put_motif = [] + suff = [] # suffixes for diffused regions P(optional),R,D + for part in contig_parts: - if any(c.isalpha() for c in part): # Detect parts containing letters as fixed + ## updating to include DNA and RNA generation + if part[-1] in ["R", "D"]: ##Detect non-fixed RNA and DNA contig part + suff.append(part[-1]) + part = part[:-1] + if "-" in part: + start, end = map(int, part.split("-")) + else: + start = end = int(part) + variable_ranges.append([start, end]) + pos_to_put_motif.append(0) + + elif any(c.isalpha() for c in part): # Detect parts containing letters as fixed pn_unit_id, pn_unit_start, pn_unit_end = extract_pn_unit_info(part) fixed_parts.append([pn_unit_id, pn_unit_start, pn_unit_end]) pos_to_put_motif.append(1) @@ -110,6 +123,7 @@ def get_design_pattern_with_constraints(contig, length=None): start = end = int(part) variable_ranges.append([start, end]) pos_to_put_motif.append(0) + suff.append("P") # adjust the total length to solely for free residues num_motif_residues = sum([i[2] - i[1] + 1 for i in fixed_parts]) @@ -167,7 +181,7 @@ def get_design_pattern_with_constraints(contig, length=None): atoms_with_motif.append(f"{pn_unit_id}{index}") elif pos_to_put_motif[idx] == 0: free_atom = num_free_atoms.pop(0) - atoms_with_motif.append(free_atom) + atoms_with_motif.append(str(free_atom) + suff.pop(0)) elif pos_to_put_motif[idx] == 2: atoms_with_motif.append("/0") From 94d9d635cd694b8e6184934d19144ef5aa8877ae Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Sun, 18 Jan 2026 15:41:00 -0800 Subject: [PATCH 05/49] feat: atom23 porting --- models/rfd3/src/rfd3/constants.py | 150 ++++++++++++++---- .../src/rfd3/transforms/conditioning_base.py | 4 +- .../src/rfd3/transforms/design_transforms.py | 6 + models/rfd3/src/rfd3/transforms/pipelines.py | 2 +- .../rfd3/transforms/training_conditions.py | 12 +- .../rfd3/src/rfd3/transforms/virtual_atoms.py | 12 +- 6 files changed, 144 insertions(+), 42 deletions(-) diff --git a/models/rfd3/src/rfd3/constants.py b/models/rfd3/src/rfd3/constants.py index 529ca581..78d48925 100644 --- a/models/rfd3/src/rfd3/constants.py +++ b/models/rfd3/src/rfd3/constants.py @@ -242,35 +242,6 @@ "POLYDEOXYRIBONUCLEOTIDE/POLYRIBONUCLEOTIDE HYBRID", ] -backbone_atomscheme_DNA = [ - " P ", - " OP1", - " OP2", - " O5'", - " C5'", - " C4'", - " O4'", - " C3'", - " O3'", - " C2'", - " C1'", -] # , None] - -backbone_atomscheme_RNA = [ - " P ", - " OP1", - " OP2", - " O5'", - " C5'", - " C4'", - " O4'", - " C3'", - " O3'", - " C2'", - " O2'", - " C1'", -] - DNA_atoms = { "DA": [ " N9 ", @@ -438,3 +409,124 @@ backbone_atoms_RNA = strip_list(backbone_atomscheme_RNA) backbone_atoms_DNA = strip_list(backbone_atomscheme_DNA) + + +# Mapping from residue type to its backbone and sidechain atoms (for convenience) +ATOM_REGION_BY_RESI = { + 'ALA': {'bb':('N','CA','C','O'), + 'sc':('CB')}, + 'ARG': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD','NE','CZ','NH1','NH2')}, + 'ASN': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','OD1','ND2')}, + 'ASP': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','OD1','OD2')}, + 'CYS': {'bb':('N','CA','C','O'), + 'sc':('CB','SG')}, + 'GLN': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD','OE1','NE2')}, + 'GLU': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD','OE1','OE2')}, + 'GLY': {'bb':('N','CA','C','O'), + 'sc':()}, + 'HIS': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','ND1','CD2','CE1','NE2')}, + 'ILE': {'bb':('N','CA','C','O'), + 'sc':('CB','CG1','CG2','CD1')}, + 'LEU': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD1','CD2')}, + 'LYS': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD','CE','NZ')}, + 'MET': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','SD','CE')}, + 'PHE': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD1','CD2','CE1','CE2','CZ')}, + 'PRO': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD')}, + 'SER': {'bb':('N','CA','C','O'), + 'sc':('CB','OG')}, + 'THR': {'bb':('N','CA','C','O'), + 'sc':('CB','OG1','CG2')}, + 'TRP': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD1','CD2','CE2','CE3','NE1','CZ2','CZ3','CH2')}, + 'TYR': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD1','CD2','CE1','CE2','CZ','OH')}, + 'VAL': {'bb':('N','CA','C','O'), + 'sc':('CB','CG1','CG2')}, + 'UNK': {'bb':('N','CA','C','O'), + 'sc':('CB')}, + 'MAS': {'bb':('N','CA','C','O'), + 'sc':('CB')}, + 'DA': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), + 'sc':('N9','C4','N3','C2','N1','C6','C5','N7','C8','N6')}, + 'DC': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), + 'sc':('N1','C2','O2','N3','C4','N4','C5','C6')}, + 'DG': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), + 'sc':('N9','C4','N3','C2','N1','C6','C5','N7','C8','N2','O6')}, + 'DT': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), + 'sc':('N1','C2','O2','N3','C4','O4','C5','C7','C6')}, + 'DX': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), + 'sc':()}, + 'A': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), + 'sc':('N1','C2','N3','C4','C5','C6','N6','N7','C8','N9')}, + 'C': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), + 'sc':('N1','C2','O2','N3','C4','N4','C5','C6')}, + 'G': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), + 'sc':('N1','C2','N2','N3','C4','C5','C6','O6','N7','C8','N9')}, + 'U': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), + 'sc':('N1','C2','O2','N3','C4','O4','C5','C6')}, + 'X': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), + 'sc':()}, + 'HIS_D': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','NE2','CD2','CE1','ND1')}, +} + +# Known planar sidechain atoms for each canonical residue type: +PLANAR_ATOMS_BY_RESI = { + 'ALA': [], + 'ARG': ['NH1', 'NH2', 'CZ', 'NE', 'CD'], + 'ASN': ['OD1', 'ND2', 'CG', 'CB'], + 'ASP': ['OD1', 'OD2', 'CG', 'CB'], + 'CYS': [], + 'GLN': ['OE1', 'NE2', 'CD', 'CG'], + 'GLU': ['OE1', 'OE2', 'CD', 'CG'], + 'GLY': [], + 'HIS': ['ND1', 'CE1', 'NE2', 'CD2', 'CG', 'CB'], + 'ILE': [], + 'LEU': [], + 'LYS': [], + 'MET': [], + 'PHE': ['CZ', 'CE1', 'CE2', 'CD1', 'CD2', 'CG', 'CB'], + 'PRO': [], + 'SER': [], + 'THR': [], + 'TRP': ['CH2', 'CZ3', 'CZ2', 'CE3', 'CE2', 'CD2', 'NE1', 'CD1', 'CG', 'CB'], + 'TYR': ['OH', 'CZ', 'CE1', 'CE2', 'CD1', 'CD2', 'CG', 'CB'], + 'VAL': [], + 'UNK': [], + 'MAS': [], + 'DA': ['N6', 'C6', 'N1', 'C2', 'N3', 'C4', 'C5', 'N7', 'C8', 'N9'], + 'DC': ['N4', 'C4', 'N3', 'O2', 'C2', 'C5', 'C6', 'N1'], + 'DG': ['O6', 'C6', 'N1', 'N2', 'C2', 'N3', 'C4', 'C5', 'N7', 'C8', 'N9'], + 'DT': ['O4', 'O2', 'N3', 'C4', 'C2', 'C5', 'C6', 'N1', 'C7'], + 'DX': [], + 'A': ['N6', 'C6', 'N1', 'C2', 'N3', 'C4', 'C5', 'N7', 'C8', 'N9'], + 'C': ['N4', 'C4', 'N3', 'O2', 'C2', 'C5', 'C6', 'N1'], + 'G': ['O6', 'C6', 'N1', 'N2', 'C2', 'N3', 'C4', 'C5', 'N7', 'C8', 'N9'], + 'U': ['O4', 'O2', 'N3', 'C4', 'C2', 'C5', 'C6', 'N1'], + 'X': [], + 'HIS_D': ['ND1', 'CD2', 'CE1', 'NE2', 'CG', 'CB'], + } + +# fix C/U symmetry +temp = list(association_schemes['atom23']['U']) +temp[19], temp[20] = temp[20], temp[19] +association_schemes['atom23']['U'] = tuple(temp) + +association_schemes_stripped = { + name: {k: strip_list(v) for k, v in scheme.items()} + for name, scheme in association_schemes.items() +} + +if __name__ == "__main__": + import pdb; pdb.set_trace() diff --git a/models/rfd3/src/rfd3/transforms/conditioning_base.py b/models/rfd3/src/rfd3/transforms/conditioning_base.py index 769fcf46..aec455c1 100644 --- a/models/rfd3/src/rfd3/transforms/conditioning_base.py +++ b/models/rfd3/src/rfd3/transforms/conditioning_base.py @@ -281,7 +281,7 @@ def forward(self, data): cond = valid_conditions[i_cond] cond.association_scheme = self.association_scheme - + data["sampled_condition"] = cond data["sampled_condition_name"] = cond.name data["sampled_condition_cls"] = cond.__class__ @@ -299,6 +299,8 @@ class SampleConditioningFlags(Transform): "AssignTypes", "SampleConditioningType", ] # We use is_protein in the PPI training condition + def __init__(self, association_scheme): + self.association_scheme = association_scheme def __init__(self, association_scheme): self.association_scheme = association_scheme diff --git a/models/rfd3/src/rfd3/transforms/design_transforms.py b/models/rfd3/src/rfd3/transforms/design_transforms.py index 5cc84fa9..e853322d 100644 --- a/models/rfd3/src/rfd3/transforms/design_transforms.py +++ b/models/rfd3/src/rfd3/transforms/design_transforms.py @@ -698,6 +698,7 @@ def __init__( atom_1d_features, autofill_zeros_if_not_present_in_atomarray=False, association_scheme="atom14", + association_scheme='atom14' ): self.autofill = autofill_zeros_if_not_present_in_atomarray self.token_1d_features = token_1d_features @@ -754,6 +755,11 @@ def forward(self, data: Dict[str, Any]) -> Dict[str, Any]: """ if "feats" not in data.keys(): data["feats"] = {} + + if association_scheme == 'atom23': + data['atom_array'].set_annotation('is_protein_token', data['atom_array'].is_protein) + data['atom_array'].set_annotation('is_dna_token', data['atom_array'].is_dna) + data['atom_array'].set_annotation('is_rna_token', data['atom_array'].is_rna) if self.association_scheme == "atom23": data["atom_array"].set_annotation( diff --git a/models/rfd3/src/rfd3/transforms/pipelines.py b/models/rfd3/src/rfd3/transforms/pipelines.py index 0cd656b2..378251f6 100644 --- a/models/rfd3/src/rfd3/transforms/pipelines.py +++ b/models/rfd3/src/rfd3/transforms/pipelines.py @@ -523,7 +523,7 @@ def build_atom14_base_pipeline_( autofill_zeros_if_not_present_in_atomarray=True, token_1d_features=token_1d_features, atom_1d_features=atom_1d_features, - association_scheme=association_scheme, + association_scheme=association_scheme ), AddAF3TokenBondFeatures(), AddGroundTruthSequence(sequence_encoding=af3_sequence_encoding), diff --git a/models/rfd3/src/rfd3/transforms/training_conditions.py b/models/rfd3/src/rfd3/transforms/training_conditions.py index 6354c1f0..bfd52570 100644 --- a/models/rfd3/src/rfd3/transforms/training_conditions.py +++ b/models/rfd3/src/rfd3/transforms/training_conditions.py @@ -72,9 +72,11 @@ def __init__( p_fix_motif_coordinates, p_fix_motif_sequence, p_unindex_motif_tokens, + association_scheme = 'atom14', ): self.name = name self.frequency = frequency + self.association_scheme = association_scheme # Token selection self.island_sampling_kwargs = island_sampling_kwargs @@ -89,13 +91,15 @@ def __init__( self.p_fix_motif_coordinates = p_fix_motif_coordinates self.p_fix_motif_sequence = p_fix_motif_sequence self.p_unindex_motif_tokens = p_unindex_motif_tokens + + self.association_scheme = association_scheme def is_valid_for_example(self, data) -> bool: is_protein = data["atom_array"].is_protein is_dna = data["atom_array"].is_dna is_rna = data["atom_array"].is_rna ### updating this to allow other polymers - if not self.association_scheme == "atom23": + if self.association_scheme == "atom23": if not np.any(is_protein | is_dna | is_rna): return False else: @@ -134,6 +138,7 @@ def sample_motif_tokens(self, atom_array): n_protein_tokens, **self.island_sampling_kwargs, ) + is_motif_token[token_level_array.is_protein] = islands_mask # TODO: Atoms with covalent bonds should be motif, needs FlagAndReassignCovalentModifications transform prior to this @@ -305,7 +310,7 @@ class SubtypeCondition(TrainingCondition): """ name = "subtype" - association_scheme = "atom14" + association_scheme = 'atom14' def __init__(self, frequency: float, subtype: list[str], fix_pos: bool = False): self.frequency = frequency @@ -521,6 +526,8 @@ def sample_is_motif_atom_with_fixed_seq( is_motif_atom_with_fixed_seq = ( is_motif_atom_with_fixed_seq | ~atom_array.is_protein ) + + return is_motif_atom_with_fixed_seq @@ -564,6 +571,7 @@ def sample_unindexed_atoms( is_motif_atom_unindexed, atom_array.is_residue ) + return is_motif_atom_unindexed diff --git a/models/rfd3/src/rfd3/transforms/virtual_atoms.py b/models/rfd3/src/rfd3/transforms/virtual_atoms.py index 7d240a23..a5744129 100644 --- a/models/rfd3/src/rfd3/transforms/virtual_atoms.py +++ b/models/rfd3/src/rfd3/transforms/virtual_atoms.py @@ -53,7 +53,6 @@ def map_to_association_scheme( else: return ATOM_NAMES[idxs] - def map_names_to_elements( atom_names: list | str, default=VIRTUAL_ATOM_ELEMENT_NAME ) -> np.ndarray: @@ -130,11 +129,6 @@ def permute_symmetric_atom_names_( # NB: Can leak GT sequence if the model receives the canconical ordering of atoms as input # With the structure-local atom attention it will not unless N_keys(n_attn_seq_neighbours) > n_atom_attn_queries. - ## fail safe, no symmetry confusion in NA bases ## - if atom_names[0] == "P": - return atom_names - ################################################## - if res_name in association_map: idx_to_swap = association_map[res_name] atom_names = atom_names[idx_to_swap] @@ -185,7 +179,7 @@ def forward(self, data: dict) -> dict: token_ids = np.unique(atom_array.token_id) assert len(token_ids) == len( is_motif_atom_with_fixed_seq - ), "Token ids and token level array have different lengths!" + ), "Token ids and token level array have different lengths!" # Unindexed tokens are never fully atomized, but may be assigned as atomized to have repr atoms: if self.association_scheme == "atom23": @@ -226,7 +220,7 @@ def forward(self, data: dict) -> dict: for token_id, (start, end) in enumerate(zip(starts[:-1], starts[1:])): if is_paddable[token_id]: token = atom_array[start:end] - + # First, pad with virtual atoms if needed if self.association_scheme == "atom23" and atom_array[start].is_dna: n_atoms_per_token = 22 @@ -235,7 +229,7 @@ def forward(self, data: dict) -> dict: else: n_atoms_per_token = self.n_atoms_per_token n_pad = n_atoms_per_token - len(token) - + if n_pad > 0: mask = get_af3_token_representative_masks( token, central_atom=self.atom_to_pad_from From c2ec30274579695b3c92c888eca8c409ad91bba3 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Tue, 20 Jan 2026 14:44:19 -0800 Subject: [PATCH 06/49] feat: atom23 inference changes and training fixes --- models/rf3/src/rf3/loss/af3_losses.py | 10 +++++-- models/rfd3/src/rfd3/constants.py | 30 ++++++++++++++++++- .../rfd3/inference/legacy_input_parsing.py | 1 + .../src/rfd3/transforms/conditioning_base.py | 3 +- .../src/rfd3/transforms/design_transforms.py | 13 ++++---- models/rfd3/src/rfd3/transforms/pipelines.py | 2 +- .../rfd3/transforms/training_conditions.py | 10 +------ .../rfd3/src/rfd3/transforms/virtual_atoms.py | 7 +++-- 8 files changed, 52 insertions(+), 24 deletions(-) diff --git a/models/rf3/src/rf3/loss/af3_losses.py b/models/rf3/src/rf3/loss/af3_losses.py index 974db267..1bb0cd27 100644 --- a/models/rf3/src/rf3/loss/af3_losses.py +++ b/models/rf3/src/rf3/loss/af3_losses.py @@ -349,9 +349,13 @@ def forward(self, network_output, loss_input, symm_input): x_native = symm_input["coord_atom_lvl"].to(x_pred.device) mask_native = symm_input["mask_atom_lvl"].to(x_pred.device) - x_native_aln, x_native_mask = self._resolve_subunits( - mol_entities, mol_iid, crop_mask, x_native, mask_native, x_pred - ) + try: + x_native_aln, x_native_mask = self._resolve_subunits( + mol_entities, mol_iid, crop_mask, x_native, mask_native, x_pred + ) + except Exception: + # fd ... TO DO: DEBUG! + return loss_input loss_input["X_gt_L"] = x_native_aln loss_input["crd_mask_L"] = x_native_mask diff --git a/models/rfd3/src/rfd3/constants.py b/models/rfd3/src/rfd3/constants.py index 78d48925..4d762621 100644 --- a/models/rfd3/src/rfd3/constants.py +++ b/models/rfd3/src/rfd3/constants.py @@ -242,6 +242,35 @@ "POLYDEOXYRIBONUCLEOTIDE/POLYRIBONUCLEOTIDE HYBRID", ] +backbone_atomscheme_DNA = [ + " P ", + " OP1", + " OP2", + " O5'", + " C5'", + " C4'", + " O4'", + " C3'", + " O3'", + " C2'", + " C1'", +] # , None] + +backbone_atomscheme_RNA = [ + " P ", + " OP1", + " OP2", + " O5'", + " C5'", + " C4'", + " O4'", + " C3'", + " O3'", + " C2'", + " O2'", + " C1'", +] + DNA_atoms = { "DA": [ " N9 ", @@ -410,7 +439,6 @@ backbone_atoms_RNA = strip_list(backbone_atomscheme_RNA) backbone_atoms_DNA = strip_list(backbone_atomscheme_DNA) - # Mapping from residue type to its backbone and sidechain atoms (for convenience) ATOM_REGION_BY_RESI = { 'ALA': {'bb':('N','CA','C','O'), diff --git a/models/rfd3/src/rfd3/inference/legacy_input_parsing.py b/models/rfd3/src/rfd3/inference/legacy_input_parsing.py index 401cd2a8..5f77bdff 100644 --- a/models/rfd3/src/rfd3/inference/legacy_input_parsing.py +++ b/models/rfd3/src/rfd3/inference/legacy_input_parsing.py @@ -186,6 +186,7 @@ def fetch_motif_residue_( subarray.set_annotation( "is_motif_atom_with_fixed_seq", np.zeros(subarray.shape[0], dtype=int) ) + elif redesign_motif_sidechains and res_name in (STANDARD_DNA + STANDARD_RNA): is_backbone = np.isin(subarray.atom_name, backbone_atoms_RNA) subarray.set_annotation("is_motif_atom", is_backbone) diff --git a/models/rfd3/src/rfd3/transforms/conditioning_base.py b/models/rfd3/src/rfd3/transforms/conditioning_base.py index aec455c1..25b51a70 100644 --- a/models/rfd3/src/rfd3/transforms/conditioning_base.py +++ b/models/rfd3/src/rfd3/transforms/conditioning_base.py @@ -281,7 +281,7 @@ def forward(self, data): cond = valid_conditions[i_cond] cond.association_scheme = self.association_scheme - + data["sampled_condition"] = cond data["sampled_condition_name"] = cond.name data["sampled_condition_cls"] = cond.__class__ @@ -299,6 +299,7 @@ class SampleConditioningFlags(Transform): "AssignTypes", "SampleConditioningType", ] # We use is_protein in the PPI training condition + def __init__(self, association_scheme): self.association_scheme = association_scheme diff --git a/models/rfd3/src/rfd3/transforms/design_transforms.py b/models/rfd3/src/rfd3/transforms/design_transforms.py index e853322d..ac17ec29 100644 --- a/models/rfd3/src/rfd3/transforms/design_transforms.py +++ b/models/rfd3/src/rfd3/transforms/design_transforms.py @@ -698,7 +698,6 @@ def __init__( atom_1d_features, autofill_zeros_if_not_present_in_atomarray=False, association_scheme="atom14", - association_scheme='atom14' ): self.autofill = autofill_zeros_if_not_present_in_atomarray self.token_1d_features = token_1d_features @@ -755,11 +754,13 @@ def forward(self, data: Dict[str, Any]) -> Dict[str, Any]: """ if "feats" not in data.keys(): data["feats"] = {} - - if association_scheme == 'atom23': - data['atom_array'].set_annotation('is_protein_token', data['atom_array'].is_protein) - data['atom_array'].set_annotation('is_dna_token', data['atom_array'].is_dna) - data['atom_array'].set_annotation('is_rna_token', data['atom_array'].is_rna) + + if self.association_scheme == "atom23": + data["atom_array"].set_annotation( + "is_protein_token", data["atom_array"].is_protein + ) + data["atom_array"].set_annotation("is_dna_token", data["atom_array"].is_dna) + data["atom_array"].set_annotation("is_rna_token", data["atom_array"].is_rna) if self.association_scheme == "atom23": data["atom_array"].set_annotation( diff --git a/models/rfd3/src/rfd3/transforms/pipelines.py b/models/rfd3/src/rfd3/transforms/pipelines.py index 378251f6..0cd656b2 100644 --- a/models/rfd3/src/rfd3/transforms/pipelines.py +++ b/models/rfd3/src/rfd3/transforms/pipelines.py @@ -523,7 +523,7 @@ def build_atom14_base_pipeline_( autofill_zeros_if_not_present_in_atomarray=True, token_1d_features=token_1d_features, atom_1d_features=atom_1d_features, - association_scheme=association_scheme + association_scheme=association_scheme, ), AddAF3TokenBondFeatures(), AddGroundTruthSequence(sequence_encoding=af3_sequence_encoding), diff --git a/models/rfd3/src/rfd3/transforms/training_conditions.py b/models/rfd3/src/rfd3/transforms/training_conditions.py index bfd52570..dbb07b76 100644 --- a/models/rfd3/src/rfd3/transforms/training_conditions.py +++ b/models/rfd3/src/rfd3/transforms/training_conditions.py @@ -72,11 +72,9 @@ def __init__( p_fix_motif_coordinates, p_fix_motif_sequence, p_unindex_motif_tokens, - association_scheme = 'atom14', ): self.name = name self.frequency = frequency - self.association_scheme = association_scheme # Token selection self.island_sampling_kwargs = island_sampling_kwargs @@ -91,8 +89,6 @@ def __init__( self.p_fix_motif_coordinates = p_fix_motif_coordinates self.p_fix_motif_sequence = p_fix_motif_sequence self.p_unindex_motif_tokens = p_unindex_motif_tokens - - self.association_scheme = association_scheme def is_valid_for_example(self, data) -> bool: is_protein = data["atom_array"].is_protein @@ -138,7 +134,6 @@ def sample_motif_tokens(self, atom_array): n_protein_tokens, **self.island_sampling_kwargs, ) - is_motif_token[token_level_array.is_protein] = islands_mask # TODO: Atoms with covalent bonds should be motif, needs FlagAndReassignCovalentModifications transform prior to this @@ -310,7 +305,7 @@ class SubtypeCondition(TrainingCondition): """ name = "subtype" - association_scheme = 'atom14' + association_scheme = "atom14" def __init__(self, frequency: float, subtype: list[str], fix_pos: bool = False): self.frequency = frequency @@ -526,8 +521,6 @@ def sample_is_motif_atom_with_fixed_seq( is_motif_atom_with_fixed_seq = ( is_motif_atom_with_fixed_seq | ~atom_array.is_protein ) - - return is_motif_atom_with_fixed_seq @@ -571,7 +564,6 @@ def sample_unindexed_atoms( is_motif_atom_unindexed, atom_array.is_residue ) - return is_motif_atom_unindexed diff --git a/models/rfd3/src/rfd3/transforms/virtual_atoms.py b/models/rfd3/src/rfd3/transforms/virtual_atoms.py index a5744129..6ce16759 100644 --- a/models/rfd3/src/rfd3/transforms/virtual_atoms.py +++ b/models/rfd3/src/rfd3/transforms/virtual_atoms.py @@ -53,6 +53,7 @@ def map_to_association_scheme( else: return ATOM_NAMES[idxs] + def map_names_to_elements( atom_names: list | str, default=VIRTUAL_ATOM_ELEMENT_NAME ) -> np.ndarray: @@ -179,7 +180,7 @@ def forward(self, data: dict) -> dict: token_ids = np.unique(atom_array.token_id) assert len(token_ids) == len( is_motif_atom_with_fixed_seq - ), "Token ids and token level array have different lengths!" + ), "Token ids and token level array have different lengths!" # Unindexed tokens are never fully atomized, but may be assigned as atomized to have repr atoms: if self.association_scheme == "atom23": @@ -220,7 +221,7 @@ def forward(self, data: dict) -> dict: for token_id, (start, end) in enumerate(zip(starts[:-1], starts[1:])): if is_paddable[token_id]: token = atom_array[start:end] - + # First, pad with virtual atoms if needed if self.association_scheme == "atom23" and atom_array[start].is_dna: n_atoms_per_token = 22 @@ -229,7 +230,7 @@ def forward(self, data: dict) -> dict: else: n_atoms_per_token = self.n_atoms_per_token n_pad = n_atoms_per_token - len(token) - + if n_pad > 0: mask = get_af3_token_representative_masks( token, central_atom=self.atom_to_pad_from From 3952c976a3711951a3b7f211b6207eeb7f8ca99b Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Tue, 20 Jan 2026 14:50:40 -0800 Subject: [PATCH 07/49] add .env, can remove paths later --- .env | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.env b/.env index 74786fe4..88ac0bb3 100644 --- a/.env +++ b/.env @@ -10,7 +10,7 @@ # expected that you use the same saving conventions as the RCSB PDB, which means: # `1a2b` --> /path/to/pdb_mirror/a2/1a2b.cif.gz # To set up a mirror, you can use tha atomworks commandline: `atomworks pdb sync /path/to/mirror` -PDB_MIRROR_PATH= +PDB_MIRROR_PATH=/projects/ml/frozen_pdb_copies/2024_12_01_pdb # The `CCD_MIRROR_PATH` is a path to a local mirror of the CCD database. # It's expected that you use the same saving conventions as the RCSB CCD, which means: @@ -19,7 +19,7 @@ PDB_MIRROR_PATH= # If no mirror is provided, the internal biotite CCD will be used as a fallback. To provide a # custom CCD for a ligand, you can place it in the in the CCD mirror path following the CCDs pattern. # Example: /path/to/ccd_mirror/M/MYLIGAND1/MYLIGAND1.cif -CCD_MIRROR_PATH= +CCD_MIRROR_PATH=/projects/ml/frozen_pdb_copies/2024_12_11_ccd # --- Local MSA directories --- LOCAL_MSA_DIRS= @@ -29,14 +29,14 @@ LOCAL_MSA_DIRS= # The HBPLUS_PATH is a path to the hbplus tool, which is used for hydrogen bond calculation # during training and during metrics computation. # Example: /path/to/hbplus -HBPLUS_PATH= +HBPLUS_PATH=/projects/ml/hbplus # The `X3DNA_PATH` is a path to the x3dna tool, which is used for DNA structure analysis. # Example: /path/to/x3dna-v2.4 -X3DNA_PATH= +X3DNA_PATH=/projects/ml/prot_dna/x3dna-v2.4 # For secondary structure prediction (not currently used) -DSSP_PATH= +DSSP_PATH=/projects/ml/dssp/install/bin/mkdssp # The `HHFILTER_PATH` is a path to the hhfilter tool from the HH-suite, which is used for # filtering MSAs to reduce redundancy. From 716c16a7ec38d0aacfff435c0ba0d0347a73d543 Mon Sep 17 00:00:00 2001 From: afavor Date: Fri, 30 Jan 2026 13:32:46 -0800 Subject: [PATCH 08/49] Add nucleic SS metrics + conditioning + pseudoknot dataset config --- models/rfd3/configs/datasets/design_base.yaml | 3 + .../train/pdb/base_transform_args.yaml | 6 +- .../datasets/val/design_validation_base.yaml | 3 +- .../rfd3/configs/datasets/val/pseudoknot.yaml | 9 + models/rfd3/configs/experiment/rfd3na-ss.yaml | 87 + .../configs/model/components/rfd3_net.yaml | 3 + .../trainer/metrics/design_metrics.yaml | 9 + .../src/rfd3/metrics/nucleic_ss_metrics.py | 314 +++ models/rfd3/src/rfd3/model/layers/blocks.py | 32 + models/rfd3/src/rfd3/model/layers/encoders.py | 5 + .../src/rfd3/transforms/design_transforms.py | 85 + models/rfd3/src/rfd3/transforms/na_geom.py | 285 +++ .../rfd3/src/rfd3/transforms/na_geom_utils.py | 1708 +++++++++++++++++ models/rfd3/src/rfd3/transforms/pipelines.py | 20 + 14 files changed, 2567 insertions(+), 2 deletions(-) create mode 100644 models/rfd3/configs/datasets/val/pseudoknot.yaml create mode 100644 models/rfd3/configs/experiment/rfd3na-ss.yaml create mode 100644 models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py create mode 100644 models/rfd3/src/rfd3/transforms/na_geom.py create mode 100644 models/rfd3/src/rfd3/transforms/na_geom_utils.py diff --git a/models/rfd3/configs/datasets/design_base.yaml b/models/rfd3/configs/datasets/design_base.yaml index a6995893..02ee92a6 100644 --- a/models/rfd3/configs/datasets/design_base.yaml +++ b/models/rfd3/configs/datasets/design_base.yaml @@ -62,6 +62,9 @@ global_transform_args: min_ss_island_len: 1 max_ss_island_len: 10 + # Nucleic acid features + add_na_pair_features: false + train_conditions: unconditional: frequency: 5.0 diff --git a/models/rfd3/configs/datasets/train/pdb/base_transform_args.yaml b/models/rfd3/configs/datasets/train/pdb/base_transform_args.yaml index 08c735ca..28e2384d 100644 --- a/models/rfd3/configs/datasets/train/pdb/base_transform_args.yaml +++ b/models/rfd3/configs/datasets/train/pdb/base_transform_args.yaml @@ -43,6 +43,9 @@ dataset: min_ss_island_len: ${datasets.global_transform_args.min_ss_island_len} max_ss_island_len: ${datasets.global_transform_args.max_ss_island_len} + # Nucleic acid features + add_na_pair_features: ${datasets.global_transform_args.add_na_pair_features} + # Cropping crop_size: ${datasets.crop_size} max_atoms_in_crop: ${datasets.max_atoms_in_crop} @@ -56,4 +59,5 @@ dataset: # Other dataset-specific parameters atom_1d_features: ${model.net.token_initializer.atom_1d_features} - token_1d_features: ${model.net.token_initializer.token_1d_features} \ No newline at end of file + token_1d_features: ${model.net.token_initializer.token_1d_features} + token_2d_features: ${model.net.token_initializer.token_2d_features} diff --git a/models/rfd3/configs/datasets/val/design_validation_base.yaml b/models/rfd3/configs/datasets/val/design_validation_base.yaml index 5aabcc07..90931e09 100644 --- a/models/rfd3/configs/datasets/val/design_validation_base.yaml +++ b/models/rfd3/configs/datasets/val/design_validation_base.yaml @@ -37,4 +37,5 @@ dataset: # Other dataset-specific parameters atom_1d_features: ${model.net.token_initializer.atom_1d_features} - token_1d_features: ${model.net.token_initializer.token_1d_features} \ No newline at end of file + token_1d_features: ${model.net.token_initializer.token_1d_features} + token_2d_features: ${model.net.token_initializer.token_2d_features} \ No newline at end of file diff --git a/models/rfd3/configs/datasets/val/pseudoknot.yaml b/models/rfd3/configs/datasets/val/pseudoknot.yaml new file mode 100644 index 00000000..27b801c5 --- /dev/null +++ b/models/rfd3/configs/datasets/val/pseudoknot.yaml @@ -0,0 +1,9 @@ + +defaults: + - unconditional + - _self_ + +dataset: + name: pseudoknot + eval_every_n: 1 + data: /home/afavor/git/RFD3/modelhub/projects/aa_design/tests/test_data/pseudoknot.json diff --git a/models/rfd3/configs/experiment/rfd3na-ss.yaml b/models/rfd3/configs/experiment/rfd3na-ss.yaml new file mode 100644 index 00000000..614f20fc --- /dev/null +++ b/models/rfd3/configs/experiment/rfd3na-ss.yaml @@ -0,0 +1,87 @@ +# @package _global_ +# Training configuration for RFD3 + +defaults: + - /debug/default + - override /model: rfd3_base + - override /logger: null + - _self_ + +name: rfd3na-SScond +tags: [print-model] +ckpt_path: null + +model: + net: + token_initializer: + token_1d_features: + ref_motif_token_type: 3 + restype: 32 + is_dna_token: 1 + is_rna_token: 1 + is_protein_token: 1 + token_2d_features: + bp_partners: 3 # Unspecified, pair, loop + atom_1d_features: + ref_atom_name_chars: 256 + ref_element: 128 + ref_charge: 1 + ref_mask: 1 + ref_is_motif_atom_with_fixed_coord: 1 + ref_is_motif_atom_unindexed: 1 + has_zero_occupancy: 1 + ref_pos: 3 + + # Guided features + ref_atomwise_rasa: 3 + active_donor: 1 + active_acceptor: 1 + is_atom_level_hotspot: 1 + diffusion_module: + n_recycle: 2 + use_local_token_attention: True + diffusion_transformer: + n_local_tokens: 32 + n_keys: 128 + + inference_sampler: + num_timesteps: 100 + + +datasets: + diffusion_batch_size_train: 16 + crop_size: 256 + max_atoms_in_crop: 2560 # ~10x crop size. + global_transform_args: + association_scheme: atom23 + add_na_pair_features: true + train_conditions: + unconditional: + frequency: 2.0 + island: + frequency: 2.0 + sequence_design: + frequency: 0.5 + tipatom: + frequency: 5.0 + ppi: + frequency: 0.0 + train: + # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. + pdb: + probability: 1.0 + # rna_monomer_distillation: + # probability: 1.0 + + # val: + # pseudoknot: + # dataset: + # # eval_every_n: 10 + # eval_every_n: 2 + +trainer: + devices_per_node: 1 + limit_train_batches: 10 + limit_val_batches: 1 + validate_every_n_epochs: 5 + prevalidate: false diff --git a/models/rfd3/configs/model/components/rfd3_net.yaml b/models/rfd3/configs/model/components/rfd3_net.yaml index 40334833..83cddc4b 100644 --- a/models/rfd3/configs/model/components/rfd3_net.yaml +++ b/models/rfd3/configs/model/components/rfd3_net.yaml @@ -25,6 +25,9 @@ token_initializer: # formerly known as the trunk ref_plddt: 1 is_non_loopy: 1 + # Optional 2D token feature definitions (empty by default) + token_2d_features: {} + downcast: ${model.net.diffusion_module.downcast} atom_1d_features: ref_atom_name_chars: 256 diff --git a/models/rfd3/configs/trainer/metrics/design_metrics.yaml b/models/rfd3/configs/trainer/metrics/design_metrics.yaml index 2a456051..b73bd840 100644 --- a/models/rfd3/configs/trainer/metrics/design_metrics.yaml +++ b/models/rfd3/configs/trainer/metrics/design_metrics.yaml @@ -20,3 +20,12 @@ hbond_metrics: _target_: rfd3.metrics.hbonds_hbplus_metrics.HbondMetrics cutoff_HA_dist: 3 cutoff_DA_distance: 3.5 + +nucleic_ss_similarity: + _target_: rfd3.metrics.nucleic_ss_metrics.NucleicSSSimilarityMetrics + restrict_to_nucleic: True + compute_for_diffused_region_only: False + annotate_predicted_fresh: True + annotation_NA_only: False + annotation_planar_only: True + diff --git a/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py b/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py new file mode 100644 index 00000000..6a0c8d44 --- /dev/null +++ b/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py @@ -0,0 +1,314 @@ +import logging + +import bdb +import numpy as np +from biotite.structure import AtomArray +from atomworks.ml.utils.token import ( + get_token_starts, +) + +from rfd3.transforms.na_geom_utils import annotate_na_ss + +from foundry.metrics.metric import Metric +from foundry.utils.ddp import RankedLogger + +logging.basicConfig(level=logging.INFO) +global_logger = RankedLogger(__name__, rank_zero_only=False) + + +def _safe_f1_from_sizes(intersection_n: int, pred_n: int, gt_n: int) -> float: + """Return F1 with sensible empty-set handling.""" + if pred_n == 0 and gt_n == 0: + return 1.0 + + precision = float(intersection_n / pred_n) if pred_n > 0 else 0.0 + recall = float(intersection_n / gt_n) if gt_n > 0 else 0.0 + + if precision + recall == 0.0: + return 0.0 + + return float(2.0 * precision * recall / (precision + recall)) + + +def _get_token_ids(atom_array: AtomArray) -> np.ndarray: + token_starts = get_token_starts(atom_array) + token_level_array = atom_array[token_starts] + return np.asarray(token_level_array.token_id, dtype=int) + + +def _get_candidate_token_ids( + atom_array: AtomArray, + *, + restrict_to_nucleic: bool, + compute_for_diffused_region_only: bool, +) -> set[int]: + """Return a set of token_ids to include for scoring.""" + token_starts = get_token_starts(atom_array) + token_level_array = atom_array[token_starts] + token_ids = np.asarray(token_level_array.token_id, dtype=int) + + token_mask = np.ones(len(token_ids), dtype=bool) + + if restrict_to_nucleic: + is_rna = ( + np.asarray(getattr(token_level_array, "is_rna"), dtype=bool) + if hasattr(token_level_array, "is_rna") + else np.zeros(len(token_ids), dtype=bool) + ) + is_dna = ( + np.asarray(getattr(token_level_array, "is_dna"), dtype=bool) + if hasattr(token_level_array, "is_dna") + else np.zeros(len(token_ids), dtype=bool) + ) + token_mask &= (is_rna | is_dna) if (is_rna.any() or is_dna.any()) else token_mask + + if compute_for_diffused_region_only: + if hasattr(token_level_array, "is_motif_atom"): + token_mask &= ~np.asarray(token_level_array.is_motif_atom, dtype=bool) + elif hasattr(token_level_array, "is_motif_token"): + token_mask &= ~np.asarray(token_level_array.is_motif_token, dtype=bool) + + return set(int(t) for t in token_ids[token_mask].tolist()) + + +def _extract_bp_pairs( + atom_array: AtomArray, + *, + allowed_token_ids: set[int], +) -> set[tuple[int, int]]: + """Extract unordered base-pair edges from bp_partner annotations. + + Pairs are represented as (min_token_id, max_token_id). + """ + if "bp_partner" not in atom_array.get_annotation_categories(): + raise ValueError("atom_array missing bp_partner annotation") + + token_starts = get_token_starts(atom_array) + token_level_array = atom_array[token_starts] + token_ids = np.asarray(token_level_array.token_id, dtype=int) + token_id_to_pos = {int(tid): i for i, tid in enumerate(token_ids.tolist())} + + bp_partner_ann = atom_array.bp_partner + pairs: set[tuple[int, int]] = set() + + for pos, start_idx in enumerate(token_starts.tolist()): + i_tid = int(token_ids[pos]) + if i_tid not in allowed_token_ids: + continue + + partners = bp_partner_ann[int(start_idx)] + if partners is None: + continue + if not isinstance(partners, (list, tuple, np.ndarray)): + continue + + for partner_token_id in partners: + try: + j_tid = int(partner_token_id) + except Exception: + continue + + if j_tid == i_tid or j_tid not in allowed_token_ids: + continue + + if j_tid not in token_id_to_pos: + continue + + a, b = (i_tid, j_tid) if i_tid < j_tid else (j_tid, i_tid) + pairs.add((a, b)) + + return pairs + + +def _extract_loop_and_paired_token_ids( + atom_array: AtomArray, + *, + allowed_token_ids: set[int], +) -> tuple[set[int], set[int]]: + """Return (loop_token_ids, paired_token_ids) within the allowed token set.""" + if "bp_partner" not in atom_array.get_annotation_categories(): + raise ValueError("atom_array missing bp_partner annotation") + + token_starts = get_token_starts(atom_array) + token_level_array = atom_array[token_starts] + token_ids = np.asarray(token_level_array.token_id, dtype=int) + token_id_to_pos = {int(tid): i for i, tid in enumerate(token_ids.tolist())} + + bp_partner_ann = atom_array.bp_partner + + loop_token_ids: set[int] = set() + paired_token_ids: set[int] = set() + + for pos, start_idx in enumerate(token_starts.tolist()): + i_tid = int(token_ids[pos]) + if i_tid not in allowed_token_ids: + continue + + partners = bp_partner_ann[int(start_idx)] + # New semantics: + # - None => unannotated/masked (NOT a loop) + # - [] => explicitly unpaired loop + if partners is None: + continue + if not isinstance(partners, (list, tuple, np.ndarray)): + continue + if len(partners) == 0: + loop_token_ids.add(i_tid) + continue + + for partner_token_id in partners: + try: + j_tid = int(partner_token_id) + except Exception: + continue + + if j_tid == i_tid or j_tid not in allowed_token_ids: + continue + if j_tid not in token_id_to_pos: + continue + paired_token_ids.add(i_tid) + paired_token_ids.add(j_tid) + + return loop_token_ids, paired_token_ids + + +class NucleicSSSimilarityMetrics(Metric): + """Secondary-structure similarity for nucleic acids. + + Reports: + - `pair_f1`: F1 over the set of basepair edges implied by token-level `bp_partner`. + - `loop_f1`: F1 over explicitly-unpaired loop tokens (`bp_partner == []`). + Unannotated tokens (`bp_partner is None`) are masked. + - `weighted_f1`: GT-weighted average of `pair_f1` and `loop_f1`, weighted by + the prevalence of paired vs loop tokens in the GT. + """ + + def __init__( + self, + *, + restrict_to_nucleic: bool = True, + compute_for_diffused_region_only: bool = False, + annotate_predicted_fresh: bool = False, + annotation_NA_only: bool = False, + annotation_planar_only: bool = True, + ): + super().__init__() + self.restrict_to_nucleic = restrict_to_nucleic + self.compute_for_diffused_region_only = compute_for_diffused_region_only + self.annotate_predicted_fresh = annotate_predicted_fresh + self.annotation_NA_only = annotation_NA_only + self.annotation_planar_only = annotation_planar_only + + @property + def kwargs_to_compute_args(self): + return { + "ground_truth_atom_array_stack": ("ground_truth_atom_array_stack",), + "predicted_atom_array_stack": ("predicted_atom_array_stack",), + } + + def compute(self, *, ground_truth_atom_array_stack, predicted_atom_array_stack): + if ground_truth_atom_array_stack is None or predicted_atom_array_stack is None: + return {} + + pair_f1_list: list[float] = [] + loop_f1_list: list[float] = [] + weighted_f1_list: list[float] = [] + + n_valid = 0 + + for gt_arr, pred_arr in zip(ground_truth_atom_array_stack, predicted_atom_array_stack): + try: + if "bp_partner" not in gt_arr.get_annotation_categories(): + continue + + # Important: predicted AtomArrays are built from a template AtomArray. + # If that template already carries bp_partner (often GT-derived), the + # prediction can inherit it, yielding artificially perfect scores. + # Optionally recompute bp_partner from the *predicted coordinates*. + + if self.annotate_predicted_fresh: + annotate_na_ss( + pred_arr, + NA_only=self.annotation_NA_only, + planar_only=self.annotation_planar_only, + overwrite=True, + p_canonical_bp_filter=0.0, + ) + + if "bp_partner" not in pred_arr.get_annotation_categories(): + continue + + # Basic sanity check: token counts should match for aligned comparisons. + gt_token_ids = _get_token_ids(gt_arr) + pred_token_ids = _get_token_ids(pred_arr) + if len(gt_token_ids) != len(pred_token_ids): + continue + + # Restrict to token_ids that are valid in both arrays. + gt_allowed = _get_candidate_token_ids( + gt_arr, + restrict_to_nucleic=self.restrict_to_nucleic, + compute_for_diffused_region_only=self.compute_for_diffused_region_only, + ) + pred_allowed = _get_candidate_token_ids( + pred_arr, + restrict_to_nucleic=self.restrict_to_nucleic, + compute_for_diffused_region_only=self.compute_for_diffused_region_only, + ) + allowed = gt_allowed & pred_allowed + + if len(allowed) == 0: + continue + + gt_pairs = _extract_bp_pairs(gt_arr, allowed_token_ids=allowed) + pred_pairs = _extract_bp_pairs(pred_arr, allowed_token_ids=allowed) + + gt_loop, gt_paired_tokens = _extract_loop_and_paired_token_ids( + gt_arr, allowed_token_ids=allowed + ) + pred_loop, _pred_paired_tokens = _extract_loop_and_paired_token_ids( + pred_arr, allowed_token_ids=allowed + ) + + pair_tp = len(gt_pairs & pred_pairs) + pair_pred_n = len(pred_pairs) + pair_gt_n = len(gt_pairs) + + loop_tp = len(gt_loop & pred_loop) + loop_pred_n = len(pred_loop) + loop_gt_n = len(gt_loop) + + pair_f1 = _safe_f1_from_sizes(pair_tp, pair_pred_n, pair_gt_n) + loop_f1 = _safe_f1_from_sizes(loop_tp, loop_pred_n, loop_gt_n) + + pair_weight = len(gt_paired_tokens) + loop_weight = len(gt_loop) + total_weight = pair_weight + loop_weight + if total_weight == 0: + weighted_f1 = 1.0 + else: + weighted_f1 = float( + (pair_weight * pair_f1 + loop_weight * loop_f1) / total_weight + ) + + pair_f1_list.append(pair_f1) + loop_f1_list.append(loop_f1) + weighted_f1_list.append(weighted_f1) + n_valid += 1 + + except bdb.BdbQuit: + # Allow interactive debuggers (pdb) to cleanly abort without being swallowed. + raise + except Exception as e: + global_logger.error(f"Error computing nucleic-SS similarity: {e} | Skipping") + continue + + if n_valid == 0: + return {} + + return { + "pair_f1": float(np.mean(pair_f1_list)), + "loop_f1": float(np.mean(loop_f1_list)), + "weighted_f1": float(np.mean(weighted_f1_list)), + "n_valid_samples": int(n_valid), + } diff --git a/models/rfd3/src/rfd3/model/layers/blocks.py b/models/rfd3/src/rfd3/model/layers/blocks.py index eaf08093..f5cfe34f 100644 --- a/models/rfd3/src/rfd3/model/layers/blocks.py +++ b/models/rfd3/src/rfd3/model/layers/blocks.py @@ -143,6 +143,38 @@ def forward(self, f, collapse_length): ) ) +class TwoDFeatureEmbedder(nn.Module): + """ + Embeds 2D features into a single vector. + + Args: + features (dict): Dictionary of feature names and their number of channels. + output_channels (int): Output dimension of the projected embedding. + """ + + def __init__(self, features, output_channels): + super().__init__() + self.features = {k: v for k, v in features.items() if exists(v)} + total_embedding_input_features = sum(self.features.values()) + self.embedders = nn.ModuleDict( + { + feature: EmbeddingLayer( + n_channels, total_embedding_input_features, output_channels + ) + for feature, n_channels in self.features.items() + } + ) + def collapse2D(self, x, L): + return x.reshape((L, L, x.numel() // (L * L))) + + def forward(self, f, collapse_length): + return sum( + tuple( + self.embedders[feature](self.collapse2D(f[feature].float(), collapse_length)) + for feature, n_channels in self.features.items() + if exists(n_channels) + ) + ) class SinusoidalDistEmbed(nn.Module): """ diff --git a/models/rfd3/src/rfd3/model/layers/encoders.py b/models/rfd3/src/rfd3/model/layers/encoders.py index b0ed86fa..363fc4de 100644 --- a/models/rfd3/src/rfd3/model/layers/encoders.py +++ b/models/rfd3/src/rfd3/model/layers/encoders.py @@ -11,6 +11,7 @@ Downcast, LocalAtomTransformer, OneDFeatureEmbedder, + TwoDFeatureEmbedder, PositionPairDistEmbedder, RelativePositionEncodingWithIndexRemoval, SinusoidalDistEmbed, @@ -49,6 +50,7 @@ def __init__( pairformer_block, downcast, token_1d_features, + token_2d_features, atom_1d_features, atom_transformer, use_chunked_pll=False, # New parameter for memory optimization @@ -62,6 +64,7 @@ def __init__( self.atom_1d_embedder_1 = OneDFeatureEmbedder(atom_1d_features, c_s) self.atom_1d_embedder_2 = OneDFeatureEmbedder(atom_1d_features, c_atom) self.token_1d_embedder = OneDFeatureEmbedder(token_1d_features, c_s) + self.token_2d_embedder = TwoDFeatureEmbedder(token_2d_features, c_z) self.downcast_atom = Downcast(c_atom=c_s, c_token=c_s, c_s=None, **downcast) self.transition_post_token = Transition(c=c_s, n=2) @@ -202,6 +205,8 @@ def init_tokens(): Z_init_II = Z_init_II + self.ref_pos_embedder_tok( f["ref_pos"][f["is_ca"]], valid_mask ) + # Add extra token pair features + Z_init_II = Z_init_II + self.token_2d_embedder(f, I) # Run a small transformer to provide position encodings to single. for block in self.transformer_stack: diff --git a/models/rfd3/src/rfd3/transforms/design_transforms.py b/models/rfd3/src/rfd3/transforms/design_transforms.py index ac17ec29..36a8b43d 100644 --- a/models/rfd3/src/rfd3/transforms/design_transforms.py +++ b/models/rfd3/src/rfd3/transforms/design_transforms.py @@ -45,6 +45,7 @@ get_af3_token_representative_masks, ) from rfd3.transforms.virtual_atoms import PadTokensWithVirtualAtoms +from rfd3.transforms.na_geom import get_bp_feats_from_atom_array from foundry.utils.ddp import RankedLogger # noqa @@ -776,6 +777,90 @@ def forward(self, data: Dict[str, Any]) -> Dict[str, Any]: data = self.generate_feature(feature_name, n_dims, data, "atom") return data + + + +class AddAdditional2dFeaturesToFeats(Transform): + """ + Adds any net.token_initializer.token_2d_features and net.diffusion_module.diffusion_atom_encoder.atom_2d_features present in the atomarray but not in data['feats'] to data['feats'] + Args: + - autofill_zeros_if_not_present_in_atomarray: self explanatory + - token_2d_features: List of single-item dictionaries, corresponding to feature_name: n_feature_dims. Should be hydra interpolated from + net.token_initializer.token_2d_features + """ + + incompatible_previous_transforms = ["AddAdditional2dFeaturesToFeats"] + + def __init__( + self, + token_2d_features, + autofill_zeros_if_not_present_in_atomarray=False, + association_scheme="atom14", + ): + self.autofill = autofill_zeros_if_not_present_in_atomarray + self.token_2d_features = token_2d_features + self.association_scheme = association_scheme + + # Need to pre-define custom constructor functions + # to map from atomarray annotations to tensors. + self.constructor_functions = { + 'bp_partners': get_bp_feats_from_atom_array, + } + + def check_input(self, data) -> None: + check_contains_keys(data, ["atom_array"]) + check_is_instance(data, "atom_array", AtomArray) + + def generate_token_feature(self, feature_name, n_dims, data): + + # Don't do this if we already have the feature + if feature_name in data["feats"].keys(): + return data + + # For these, we need to use a constructor function mapping, + # since pair features may require custom logic/conventions. + if feature_name in self.constructor_functions.keys(): + feature_array = self.constructor_functions[feature_name](data["atom_array"]) + else: + raise ValueError( + f"No constructor function found for 2d feature `{feature_name}`" + ) + + # We can fix shape issues here: + if len(feature_array.shape) == 2 and n_dims == 1: + feature_array = feature_array.unsqueeze(1) + + # ensure that feature_array is a 3d array with third dim == n_dims: + if len(feature_array.shape) != 3: + raise ValueError( + f"token 2d_feature `{feature_name}` must be a 3d array, got {len(feature_array.shape)}d." + ) + if feature_array.shape[2] != n_dims: + raise ValueError( + f"token 2d_feature `{feature_name}` dimensions in atomarray ({feature_array.shape[-1]}) does not match dimension declared in config, ({n_dims})" + ) + # Ensure correct shape in first two dims (I,I,...) + if feature_array.shape[0] != feature_array.shape[1]: + raise ValueError( + f"token 2d_feature `{feature_name}` first two dimensions must be equal (square matrix), got {feature_array.shape[0]} and {feature_array.shape[1]}" + ) + + data["feats"][feature_name] = feature_array + + return data + + def forward(self, data: Dict[str, Any]) -> Dict[str, Any]: + """ + Checks if the 2d_features are present in data['feats']. If not present, adds them from the atomarray. + If annotation is not present in atomarray, either autofills the feature with 0s or throws an error + """ + if "feats" not in data.keys(): + data["feats"] = {} + # Only apply for features that the model is expecting: + for feature_name, n_dims in self.token_2d_features.items(): + data = self.generate_token_feature(feature_name, n_dims, data) + + return data class FeaturizepLDDT(Transform): diff --git a/models/rfd3/src/rfd3/transforms/na_geom.py b/models/rfd3/src/rfd3/transforms/na_geom.py new file mode 100644 index 00000000..ebb076d3 --- /dev/null +++ b/models/rfd3/src/rfd3/transforms/na_geom.py @@ -0,0 +1,285 @@ +from typing import Any +import numpy as np +from functools import partial +from biotite.structure import AtomArray +from atomworks.ml.transforms._checks import ( + check_atom_array_annotation, + check_contains_keys, + check_is_instance, +) +from atomworks.ml.transforms.base import Transform +from rfd3.transforms.conditioning_utils import sample_island_tokens +from rfd3.transforms.na_geom_utils import ( + annotate_na_ss, + annotate_na_ss_from_data_specification, + bp_partner_to_ss_matrix, +) + +from atomworks.ml.utils.token import spread_token_wise, get_token_starts + +def get_bp_feats_from_atom_array( + atom_array: AtomArray, +) -> np.ndarray: + """Build NA-SS features from atom_array annotations, assuming 'bp_partners' is present. + + This function reconstructs the SS matrix from the 'bp_partners' annotation on the atom_array, + then one-hot encodes it into a 3-class matrix (mask, pair, loop). + """ + # Fixed feature info (inferred from usage in other functions) + feature_info = { + 'NA_SS_MASK': 0, # Unspecified + 'NA_SS_PAIR': 1, # Paired + 'NA_SS_LOOP': 2, # Loop / unpaired + 'num_classes_nucleic_ss': 3, + } + + # Check for required annotation + if "bp_partners" not in atom_array.get_annotation_categories(): + raise ValueError("atom_array must have 'bp_partners' annotation for NA-SS feature building.") + + # Reconstruct SS matrix from annotations + na_ss_matrix = np.asarray( + bp_partner_to_ss_matrix( + atom_array, + feature_info=feature_info, + NA_only=False, # Include all residues (logic from other utils) + planar_only=True, # Use planar interactions (common default) + include_loops=True, # Include loop states + ), + dtype=np.int64, + ) + + # One-hot encode the matrix + na_ss_matrix_int = np.asarray(na_ss_matrix, dtype=np.int64) + eye = np.eye(int(feature_info['num_classes_nucleic_ss']), dtype=np.int64) + return eye[na_ss_matrix_int] + + +def _build_na_ss_features_from_annotations( + atom_array: AtomArray, + *, + feature_info: dict, + num_classes: int, + NA_only: bool, + planar_only: bool, + is_nucleic_ss_example: bool, + give_partial_feats: bool, + get_feature_mask_fn, +) -> np.ndarray: + """Reconstruct SS matrix from annotations, optionally mask, then one-hot.""" + na_ss_matrix = np.asarray( + bp_partner_to_ss_matrix( + atom_array, + feature_info=feature_info, + NA_only=NA_only, + planar_only=planar_only, + include_loops=True, + ), + dtype=np.int64, + ) + + n_tokens = int(na_ss_matrix.shape[0]) + + if give_partial_feats: + is_shown = ( + np.asarray(get_feature_mask_fn(n_tokens), dtype=bool) + if is_nucleic_ss_example + else np.zeros((n_tokens,), dtype=bool) + ) + na_ss_matrix[~is_shown, :] = feature_info["NA_SS_MASK"] + na_ss_matrix[:, ~is_shown] = feature_info["NA_SS_MASK"] + + na_ss_matrix_int = np.asarray(na_ss_matrix, dtype=np.int64) + eye = np.eye(int(num_classes), dtype=np.int64) + return eye[na_ss_matrix_int] + + +class CalculateNucleicAcidGeomFeats(Transform): + """ + Transform for constructing nucleic-acid conditioning features. + + This transform currently produces only nucleic-acid secondary-structure (NA-SS) + features as a 2D token-token matrix with 3 bins: + * 0: mask / unspecified + * 1: paired + * 2: loop / explicitly unpaired + + Training: + - Computes geometry/H-bond-based base pairs and writes them onto the AtomArray + via the ``bp_partner`` annotation (annotation-first), then reconstructs the + matrix (and optionally masks parts of it) before one-hot encoding. + + Inference: + - Interprets user-provided secondary-structure specifications, writes the same + ``bp_partner`` annotation, then follows the same matrix + one-hot path. + + Note: helical-parameter features are not implemented/used in this refactored path. + """ + + def __init__( + self, + is_inference, + add_nucleic_ss_feats: bool = True, + + p_is_nucleic_ss_example: float = 0.3, + p_show_partial_feats: float = 0.5, + nucleic_ss_min_shown: float = 0.0, + nucleic_ss_max_shown: float = 1.0, + n_islands_min: int = 1, + n_islands_max: int = 6, + p_canonical_bp_filter: float = 0.0, + + # USE_RF2AA_NAMES: bool = False, + NA_only: bool = False, + planar_only : bool = True, + + ): + # Critical, must always have to know how to handle + self.is_inference = is_inference + + # For sampling whether we add nucleic-ss features (extra t2d) + self.add_nucleic_ss_feats = add_nucleic_ss_feats + self.p_canonical_bp_filter = p_canonical_bp_filter # enforce that bp labels are only canonical + self.p_is_nucleic_ss_example = p_is_nucleic_ss_example + self.nucleic_ss_min_shown = nucleic_ss_min_shown + self.nucleic_ss_max_shown = nucleic_ss_max_shown + self.n_islands_min = n_islands_min + self.n_islands_max = n_islands_max + + self.p_show_partial_feats = p_show_partial_feats + + # Filters for what can be considered a planar contact interaction + self.NA_only = NA_only # only annotate base-like interactions for nucleic acid residues + self.planar_only = planar_only # only consider planar atoms in sidechains for geometry calculations, + self.p_canonical_bp_filter = p_canonical_bp_filter # probability of enforcing canonical base pair filter + + # Inds of annotation types in the nucleic-ss features (stack of 3 matrices): + self.feature_info = { + 'NA_SS_MASK' : 0, # Unspecified, or sm, or protein: + 'NA_SS_PAIR' : 1, + 'NA_SS_LOOP' : 2, + 'num_classes_nucleic_ss' : 3, + } + + + def check_input(self, data: dict[str, Any]) -> None: + check_contains_keys(data, ["atom_array"]) + check_is_instance(data, "atom_array", AtomArray) + check_atom_array_annotation(data, ["res_name"]) + # maybe do later: check_atom_array_has_hydrogen(data) + + def _sample_training_flags(self) -> tuple[bool, bool]: + """Sample booleans controlling whether/how features are shown in training.""" + is_nucleic_ss_example = bool( + self.add_nucleic_ss_feats + and (np.random.rand() < self.p_is_nucleic_ss_example) + ) + give_partial_feats = bool( + np.random.rand() < self.p_show_partial_feats + ) + return is_nucleic_ss_example, give_partial_feats + + def forward(self, data: dict) -> dict: + atom_array = data["atom_array"] + + # Calculate n_tokens (assuming one token per residue for simplicity) + token_starts = get_token_starts(atom_array) + token_level_array = atom_array[token_starts] + token_ids = [int(t) for t in token_level_array.token_id] + n_tokens = len(token_starts) + print(" DO I NEED TO CHANGE TO TOKEN_ID???") + # Handle the training case with ground truth and masking: + if not self.is_inference: + + # First, annotate as usual + # atom_array = annotate_na_ss(atom_array, **kwargs) + atom_array = annotate_na_ss(atom_array, + NA_only=self.NA_only, + planar_only=self.planar_only, + p_canonical_bp_filter=self.p_canonical_bp_filter, + ) + + # Sample mask on token level: + is_nucleic_ss_example, give_partial_feats = self._sample_training_flags() + is_ss_shown = self._sample_where_to_show_ss(n_tokens, + is_nucleic_ss_example=is_nucleic_ss_example, + give_partial_feats=give_partial_feats) # Mask vec for tokens where ss shown + # Spread mask to atom level + is_ss_shown = spread_token_wise(atom_array, is_ss_shown) + + + # Extract the base pair annotations + bp_partners_atom = atom_array.get_annotation("bp_partners") + + # Remove unshown positions from bp_partners annotation + bp_partners_atom[~is_ss_shown] = None + + # Reset the annotation with newly hidden positions + atom_array.set_annotation("bp_partners", bp_partners_atom) + + # Inference case: create from commandline args + else: + """ + Different cases handled: + - 1). Single dot-bracket string + - 2). multiple dot bracket strings with chain/ind ranges specified + - 3). Lists of paired indices + + """ + is_nucleic_ss_example=True + give_partial_feats=False + atom_array = annotate_na_ss_from_data_specification( + data, + overwrite=True, + ) + + # Check feats existence and update: + if "feats" not in data: + data["feats"] = {} + + # data["feats"].update(nucleic_features) + data.setdefault("log_dict", {}) + log_dict = data["log_dict"] + data["log_dict"] = log_dict + data["atom_array"] = atom_array + + return data + + + def _sample_where_to_show_ss(self, n_tokens: int, + is_nucleic_ss_example: bool = True, + give_partial_feats: bool = True, + ) -> np.ndarray: + """Sample token-level islands indicating which SS rows/cols to reveal.""" + # If NOT is_nucleic_ss_example, set is_shown to all False + if not is_nucleic_ss_example: + return np.zeros((n_tokens,), dtype=bool) + + # If NOT give_partial_feats, set is_shown to all True + if not give_partial_feats: + return np.ones((n_tokens,), dtype=bool) + else: + frac_shown = ( + self.nucleic_ss_min_shown + + (self.nucleic_ss_max_shown - self.nucleic_ss_min_shown) * np.random.rand() + ) + frac_shown = float(np.clip(frac_shown, 0.0, 1.0)) + max_length = int(np.ceil(frac_shown * n_tokens)) + if max_length <= 0: + return np.zeros((n_tokens,), dtype=bool) + + island_len_min = max(1, int(frac_shown * n_tokens // max(int(self.n_islands_max), 1))) + island_len_max = max(1, int(frac_shown * n_tokens // max(int(self.n_islands_min), 1))) + island_len_min = min(island_len_min, n_tokens) + island_len_max = min(island_len_max, n_tokens) + island_len_max = max(island_len_max, island_len_min) + + return sample_island_tokens( + n_tokens, + island_len_min=island_len_min, + island_len_max=island_len_max, + n_islands_min=self.n_islands_min, + n_islands_max=self.n_islands_max, + max_length=max_length, + ) + \ No newline at end of file diff --git a/models/rfd3/src/rfd3/transforms/na_geom_utils.py b/models/rfd3/src/rfd3/transforms/na_geom_utils.py new file mode 100644 index 00000000..61f80594 --- /dev/null +++ b/models/rfd3/src/rfd3/transforms/na_geom_utils.py @@ -0,0 +1,1708 @@ +import os +import subprocess +from datetime import datetime +from typing import Dict, Optional +import math +import numpy as np +from biotite.structure import AtomArray + +from atomworks.constants import ( + STANDARD_DNA, + STANDARD_RNA, +) + +from atomworks.io.utils.sequence import ( + is_purine, + is_pyrimidine, +) +from atomworks.ml.utils.token import ( + get_token_starts, + is_glycine, + is_protein_unknown, + is_standard_aa_not_glycine, + is_unknown_nucleotide, +) +from rfd3.transforms.hbonds_hbplus import save_atomarray_to_pdb + +from atomworks.ml.encoding_definitions import AF3SequenceEncoding + + +DEFAULT_NA_SS_FEATURE_INFO: dict[str, int] = { + "NA_SS_MASK": 0, + "NA_SS_PAIR": 1, + "NA_SS_LOOP": 2, + "num_classes_nucleic_ss": 3, +} + + +# Move to function scope to avoid module-level memory retention +def _get_sequence_encoding_data(): + """Get sequence encoding data on demand to avoid persistent module-level variables.""" + sequence_encoding = AF3SequenceEncoding() + return { + 'aa_like_res_names': sequence_encoding.all_res_names[sequence_encoding.is_aa_like], + 'rna_like_res_names': sequence_encoding.all_res_names[sequence_encoding.is_rna_like], + 'dna_like_res_names': sequence_encoding.all_res_names[sequence_encoding.is_dna_like], + 'sequence_encoding': sequence_encoding + } + + + +class NucMolInfo: + """ + Initializes constants and parameters relevant for computing nucleic acid geometry and interactions. + """ + def __init__(self, + cutoff_HA_dist = 2.5, + cutoff_DA_dist = 3.9, + ): + """ + Args: + kwargs: Optional keyword arguments for customization. + """ + + + # Optional parameters with default values + # self.incl_protein = True + self.eps = 1e-8 + # self.clamp_pairwise_params = True + # self.use_eigennormals = kwargs.get('use_eigennormals', True) + # self.use_all_base_atoms_for_MBD = kwargs.get('use_all_base_atoms_for_MBD', False) + self.edges_to_compute = ['S'] # list base edges to compute, if we want to analyze WC/Hoog/etc + self.perp_base_edge = 'S' # edge orthogonal to x- and z-directions in base frames (which is generally the sugar edge) + + self.cutoff_HA_dist = cutoff_HA_dist + self.cutoff_DA_dist = cutoff_DA_dist + self.seq_cutoff = 2 + self.gap_length = 200 + + + + + # Hbond interaction type inds when counting: + self.BB_BB = 0 + self.BB_SC = 1 + self.SC_SC = 2 + + self.bp_weight_BB_BB = 0.0 + self.bp_weight_BB_SC = 0.5 + self.bp_weight_SC_SC = 1.0 + + self.bp_summation_weights = [self.bp_weight_BB_BB, + self.bp_weight_BB_SC, + self.bp_weight_SC_SC] + + self.min_hbonds_for_bp = 2.0 + self.bp_hbond_coeff = 9.8 # determined heuristically + self.bp_val_cutoff = 0.5 # minimum basepairing score for binarizing basepairs when needed + + self.base_geometry_limits = {} + self.base_geometry_limits['D_ij'] = 20.0 + self.base_geometry_limits['H_ij'] = 1.5 + self.base_geometry_limits['P_ij'] = math.pi/5 + self.base_geometry_limits['B_ij'] = math.pi/5 + + # For interaction-edge classification (Watson-Crick, Hoogstein, Sugar, Base-other): + # self.edge_to_ind = {'W':0 , 'H':1 , 'S':2 ,'B':3} + self.rep_atom_dict={"protein": "CA", "rna": "C1'", "dna": "C1'"} + + self.has_planar_sc = { + 'ALA': False, + 'ARG': True, + 'ASN': True, + 'ASP': True, + 'CYS': False, + 'GLN': True, + 'GLU': True, + 'GLY': False, + 'HIS': True, + 'ILE': False, + 'LEU': False, + 'LYS': False, + 'MET': False, + 'PHE': True, + 'PRO': False, + 'SER': False, + 'THR': False, + 'TRP': True, + 'TYR': True, + 'VAL': False, + 'UNK': False, + 'MAS': False, + 'DA': True, + 'DC': True, + 'DG': True, + 'DT': True, + 'DX': False, + 'A': True, + 'C': True, + 'G': True, + 'U': True, + 'X': False, + 'HIS_D': True, + } + + + + # Make self.planar_atom_list_dict based on known planar atoms for each residue type: + self.planar_atom_list_dict = { + 'ALA': [], + 'ARG': ['NH1', 'NH2', 'CZ', 'NE', 'CD'], + 'ASN': ['OD1', 'ND2', 'CG', 'CB'], + 'ASP': ['OD1', 'OD2', 'CG', 'CB'], + 'CYS': [], + 'GLN': ['OE1', 'NE2', 'CD', 'CG'], + 'GLU': ['OE1', 'OE2', 'CD', 'CG'], + 'GLY': [], + 'HIS': ['ND1', 'CE1', 'NE2', 'CD2', 'CG', 'CB'], + 'ILE': [], + 'LEU': [], + 'LYS': [], + 'MET': [], + 'PHE': ['CZ', 'CE1', 'CE2', 'CD1', 'CD2', 'CG', 'CB'], + 'PRO': [], + 'SER': [], + 'THR': [], + 'TRP': ['CH2', 'CZ3', 'CZ2', 'CE3', 'CE2', 'CD2', 'NE1', 'CD1', 'CG', 'CB'], + 'TYR': ['OH', 'CZ', 'CE1', 'CE2', 'CD1', 'CD2', 'CG', 'CB'], + 'VAL': [], + 'UNK': [], + 'MAS': [], + 'DA': ['N6', 'C6', 'N1', 'C2', 'N3', 'C4', 'C5', 'N7', 'C8', 'N9'], + 'DC': ['N4', 'C4', 'N3', 'O2', 'C2', 'C5', 'C6', 'N1'], + 'DG': ['O6', 'C6', 'N1', 'N2', 'C2', 'N3', 'C4', 'C5', 'N7', 'C8', 'N9'], + 'DT': ['O4', 'O2', 'N3', 'C4', 'C2', 'C5', 'C6', 'N1', 'C7'], + 'DX': [], + 'A': ['N6', 'C6', 'N1', 'C2', 'N3', 'C4', 'C5', 'N7', 'C8', 'N9'], + 'C': ['N4', 'C4', 'N3', 'O2', 'C2', 'C5', 'C6', 'N1'], + 'G': ['O6', 'C6', 'N1', 'N2', 'C2', 'N3', 'C4', 'C5', 'N7', 'C8', 'N9'], + 'U': ['O4', 'O2', 'N3', 'C4', 'C2', 'C5', 'C6', 'N1'], + 'X': [], + 'HIS_D': ['ND1', 'CD2', 'CE1', 'NE2', 'CG', 'CB'], + } + + + # from pdb import set_trace; set_trace() + + self.nuc_resi_3letter = ["DA","DG","DC","DT","A","G","C","U"] + self.ring_atom_list = ["N1","C2","N3","C4","C6","C5"] + + # go through self.vec_atom_dict and remove spaces from atom names (values in inner dicts), and remove spaces from keys + replace 'R' with '' in outer dict keys + self.vec_atom_dict = { + "DA": {"W_start":"N1", "W_stop":"N6", "H_start":"N7", "H_stop":"N6", "S_start":"C1'", "S_stop":"N3", "B_start":"C1'", "B_stop":"N9" }, + "DG": {"W_start":"N1", "W_stop":"O6", "H_start":"N7", "H_stop":"O6", "S_start":"C1'", "S_stop":"N3", "B_start":"C1'", "B_stop":"N9" }, + "DC": {"W_start":"N3", "W_stop":"N4", "H_start":"C5", "H_stop":"N4", "S_start":"C1'", "S_stop":"O2", "B_start":"C1'", "B_stop":"N1" }, + "DT": {"W_start":"N3", "W_stop":"O4", "H_start":"C5", "H_stop":"O4", "S_start":"C1'", "S_stop":"O2", "B_start":"C1'", "B_stop":"N1" }, + "A": {"W_start":"N1", "W_stop":"N6", "H_start":"N7", "H_stop":"N6", "S_start":"C1'", "S_stop":"N3", "B_start":"C1'", "B_stop":"N9" }, + "G": {"W_start":"N1", "W_stop":"O6", "H_start":"N7", "H_stop":"O6", "S_start":"C1'", "S_stop":"N3", "B_start":"C1'", "B_stop":"N9" }, + "C": {"W_start":"N3", "W_stop":"N4", "H_start":"C5", "H_stop":"N4", "S_start":"C1'", "S_stop":"O2", "B_start":"C1'", "B_stop":"N1" }, + "U": {"W_start":"N3", "W_stop":"O4", "H_start":"C5", "H_stop":"O4", "S_start":"C1'", "S_stop":"O2", "B_start":"C1'", "B_stop":"N1" }, + } + + + + self.atom_region_dict = { + 'ALA': {'bb':('N','CA','C','O'), + 'sc':('CB')}, + 'ARG': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD','NE','CZ','NH1','NH2')}, + 'ASN': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','OD1','ND2')}, + 'ASP': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','OD1','OD2')}, + 'CYS': {'bb':('N','CA','C','O'), + 'sc':('CB','SG')}, + 'GLN': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD','OE1','NE2')}, + 'GLU': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD','OE1','OE2')}, + 'GLY': {'bb':('N','CA','C','O'), + 'sc':()}, + 'HIS': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','ND1','CD2','CE1','NE2')}, + 'ILE': {'bb':('N','CA','C','O'), + 'sc':('CB','CG1','CG2','CD1')}, + 'LEU': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD1','CD2')}, + 'LYS': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD','CE','NZ')}, + 'MET': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','SD','CE')}, + 'PHE': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD1','CD2','CE1','CE2','CZ')}, + 'PRO': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD')}, + 'SER': {'bb':('N','CA','C','O'), + 'sc':('CB','OG')}, + 'THR': {'bb':('N','CA','C','O'), + 'sc':('CB','OG1','CG2')}, + 'TRP': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD1','CD2','CE2','CE3','NE1','CZ2','CZ3','CH2')}, + 'TYR': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','CD1','CD2','CE1','CE2','CZ','OH')}, + 'VAL': {'bb':('N','CA','C','O'), + 'sc':('CB','CG1','CG2')}, + 'UNK': {'bb':('N','CA','C','O'), + 'sc':('CB')}, + 'MAS': {'bb':('N','CA','C','O'), + 'sc':('CB')}, + 'DA': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), + 'sc':('N9','C4','N3','C2','N1','C6','C5','N7','C8','N6')}, + 'DC': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), + 'sc':('N1','C2','O2','N3','C4','N4','C5','C6')}, + 'DG': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), + 'sc':('N9','C4','N3','C2','N1','C6','C5','N7','C8','N2','O6')}, + 'DT': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), + 'sc':('N1','C2','O2','N3','C4','O4','C5','C7','C6')}, + 'DX': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), + 'sc':()}, + 'A': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), + 'sc':('N1','C2','N3','C4','C5','C6','N6','N7','C8','N9')}, + 'C': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), + 'sc':('N1','C2','O2','N3','C4','N4','C5','C6')}, + 'G': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), + 'sc':('N1','C2','N2','N3','C4','C5','C6','O6','N7','C8','N9')}, + 'U': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), + 'sc':('N1','C2','O2','N3','C4','O4','C5','C6')}, + 'X': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), + 'sc':()}, + 'HIS_D': {'bb':('N','CA','C','O'), + 'sc':('CB','CG','NE2','CD2','CE1','ND1')}, + } + + + self.aa_planar_atoms = ['NH1', 'NH2', 'CZ', 'NE', 'OD1', 'ND2', + 'OD2', 'OE1', 'NE2', 'CD', 'OE2', 'ND1', + 'CD2', 'CE1', 'CD1', 'CE2', 'NE1', 'CZ2', + 'CZ3', 'CH2', 'CE3', 'OH', 'CG', 'CB',] + + self.na_planar_atoms = ['C4', 'N3', 'C2', 'C6', 'C5', 'N7', 'C8', + 'N6', 'O2', 'N4', 'N2', 'O6', 'O4', 'C7', + 'N9', 'N1'] + +def find_planar_positions( + atom_array: AtomArray, + mol_info: NucMolInfo, + tol: float = 1e-2, + ) -> Dict: + """ + Finds residues with planar sidechains based on four tip-most atoms, + but also checks for valid atoms to use for this type of calculation. + + Returns: + dict of planar atom lists + """ + unique_positions_list = [] + for atm in atom_array: + pos_id = (atm.chain_iid, atm.res_id, atm.res_name) + if pos_id not in unique_positions_list: + unique_positions_list.append(pos_id) + + # Get candidate planar atoms: + planar_atom_list_dict = {} + + # for chain_iid, res_id in unique_positions_list: + for chain_iid, res_id, res_name in unique_positions_list: + + mask = ( + (atom_array.chain_iid == chain_iid) & + (atom_array.res_id == res_id) & + (atom_array.res_name == res_name) + ) + res_atoms = atom_array[mask] + + # If possible, speed up by using known planar atoms for this residue type: + if res_name in mol_info.planar_atom_list_dict.keys(): + # Shared atoms between residue and known planar atoms for that residue type: + planar_atom_list = list( + set([atm.atom_name for atm in res_atoms]) & + set(mol_info.planar_atom_list_dict[res_name]) + ) + planar_atom_list_dict[(chain_iid, res_id)] = planar_atom_list + + # If unknown or noncanonical residue, compute planar atoms geometrically: + else: + candidate_planar_atm_names = [] + candidate_planar_atm_coords = [] + + for atm in res_atoms: + # Can pre-filter protein planar atoms: + if atm.is_protein and (atm.atom_name in mol_info.aa_planar_atoms): + candidate_planar_atm_names.append(atm.atom_name) + candidate_planar_atm_coords.append(atm.coord) + # Can pre-filter nucleic acid planar atoms: + elif (atm.is_rna or atm.is_dna) and (atm.atom_name in mol_info.na_planar_atoms): + candidate_planar_atm_names.append(atm.atom_name) + candidate_planar_atm_coords.append(atm.coord) + # Otherwise, consider all atoms for plane fitting: + else: + candidate_planar_atm_names.append(atm.atom_name) + candidate_planar_atm_coords.append(atm.coord) + + # reverse order to prioritize atoms further away from bb: + candidate_planar_atm_names = list(reversed(candidate_planar_atm_names)) + candidate_planar_atm_coords = list(reversed(candidate_planar_atm_coords)) + + # Use first four candidate atoms only to define the plane: + if len(candidate_planar_atm_coords) >= 4: + coords = np.asarray(candidate_planar_atm_coords, dtype=np.float32) + + # compute 4-atom based plane: + quad_coords = coords[:4, :] + + # fit plane via PCA (use smallest‑variance eigenvector as normal) + quad_center = quad_coords.mean(axis=0, keepdims=True) + all_quad_centered = coords - quad_center + quad_centered = quad_coords - quad_center + # covariance matrix + quad_cov = (quad_centered.T @ quad_centered) / max(quad_coords.shape[0] - 1, 1) + # eigen decomposition + _, quad_eigvecs = np.linalg.eigh(quad_cov) + quad_normal = quad_eigvecs[:, 0] # eigenvector with smallest eigenvalue + quad_normal = quad_normal / (np.linalg.norm(quad_normal) + 1e-8) + # compute distances from plane for all candidate atoms + quad_dists = np.abs(all_quad_centered @ quad_normal) + # keep only atoms within tolerance + quad_valid_mask = quad_dists <= tol + + # Filter for if we have a valid plane in the first place: + valid_plane_filter = (np.nanmax(quad_dists[:4]) < tol) + # Filter for if we have enough atoms in the plane: + plane_atom_filter = (int(np.sum(quad_valid_mask)) >= 4) + if valid_plane_filter and plane_atom_filter: + # Set the planar atom list for this position to those that are within tol of the plane: + # using quad_valid_mask and candidate_planar_atm_names: + planar_atom_list = [n for n, keep in zip(candidate_planar_atm_names, quad_valid_mask.tolist()) if keep] + + # not enough atoms close to a common plane + else: + planar_atom_list = [] + + else: + + # need at least 4 atoms to define a robust plane + planar_atom_list = [] + + planar_atom_list_dict[(chain_iid, res_id)] = planar_atom_list + + + return planar_atom_list_dict + + + + +def calculate_hb_counts( + atom_array: AtomArray, + token_level_data: dict, + mol_info: NucMolInfo, + cutoff_HA_dist: float = 2.5, + cutoff_DA_dist: float = 3.9, + ): + """ + Compute hbond counts between residues and return an (L, L, 3) + numpy array where the last dimension encodes: + 0 -> both backbone (BB-BB) + 1 -> one backbone, one sidechain (BB-SC) + 2 -> both sidechain (SC-SC) + """ + dtstr = datetime.now().strftime("%Y%m%d%H%M%S") + pdb_path = f"{dtstr}_{np.random.randint(10000)}.pdb" + + atom_array, nan_mask, chain_map = save_atomarray_to_pdb(atom_array, pdb_path) + subprocess.call( + [ + "/projects/ml/hbplus", + "-h", + str(cutoff_HA_dist), + "-d", + str(cutoff_DA_dist), + pdb_path, + pdb_path, + ], + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) + + + num_resis_total = len(token_level_data["token_id_list"]) + + hbond_count = np.zeros((num_resis_total, num_resis_total, 3), dtype=np.int32) + + hb2_path = pdb_path.replace("pdb", "hb2") + with open(hb2_path, "r") as hb2_f: + for i, line in enumerate(hb2_f): + if i < 8: + continue + if len(line) < 28: + continue + + # Initialize donor/acceptor sidechain/backbone flags: + # then replace with True if valid for summation + d_is_sc = False + d_is_bb = False + a_is_sc = False + a_is_bb = False + + d_chain_iid = chain_map[line[0]] + d_resi = int(line[1:5].strip()) + d_resn = line[6:9].strip() + d_atom_name = line[9:13].strip() + + d_mask = ( + (atom_array.atom_name == d_atom_name) + & (atom_array.res_name == d_resn) + & (atom_array.res_id == d_resi) + & (atom_array.chain_iid == d_chain_iid) + ) + d_atm = atom_array[d_mask] + d_idx = d_atm.token_id + + # Handle standard polymer residues for donor atom: + if d_resn in mol_info.atom_region_dict.keys(): + d_is_sc = (d_atom_name in mol_info.atom_region_dict[d_resn]['sc']) + d_is_bb = (d_atom_name in mol_info.atom_region_dict[d_resn]['bb']) + else: + # If non-polymer, define any ligand HBonding atom as backbone: + if d_mask.sum() > 0: + d_is_bb = atom_array[d_mask][0].is_ligand + + a_chain_iid = chain_map[line[14]] + a_resi = int(line[15:19].strip()) + a_resn = line[20:23].strip() + a_atom_name = line[23:27].strip() + + a_mask = ( + (atom_array.atom_name == a_atom_name) + & (atom_array.res_name == a_resn) + & (atom_array.res_id == a_resi) + & (atom_array.chain_iid == a_chain_iid) + ) + a_atm = atom_array[a_mask] + a_idx = a_atm.token_id + + # Handle standard polymer residues for acceptor atom: + if a_resn in mol_info.atom_region_dict.keys(): + a_is_sc = (a_atom_name in mol_info.atom_region_dict[a_resn]['sc']) + a_is_bb = (a_atom_name in mol_info.atom_region_dict[a_resn]['bb']) + else: + # If non-polymer, define any ligand HBonding atom as backbone: + if a_mask.sum() > 0: + a_is_bb = atom_array[a_mask][0].is_ligand + + # 0 -> both backbone (BB-BB) + hbond_count[a_idx, d_idx, 0] += (a_is_bb * d_is_bb) + hbond_count[d_idx, a_idx, 0] += (d_is_bb * a_is_bb) + + # 1 -> one backbone, one sidechain (BB-SC) + hbond_count[a_idx, d_idx, 1] += (a_is_bb * d_is_sc) | (a_is_sc * d_is_bb) + hbond_count[d_idx, a_idx, 1] += (d_is_bb * a_is_sc) | (d_is_sc * a_is_bb) + + # 2 -> both sidechain (SC-SC) + hbond_count[a_idx, d_idx, 2] += (a_is_sc * d_is_sc) + hbond_count[d_idx, a_idx, 2] += (d_is_sc * a_is_sc) + + os.remove(pdb_path) + os.remove(hb2_path) + + return hbond_count + + + + +def make_coord_list(atom_array: AtomArray, + residue_list: list[str], + chain_list: list[str], + atom_list: list[str], + ) -> list[list[str]]: + """ + Given an atom array, and lists of residues, chains, and atom names, + return a list of coordinates for the specified atoms in the specified residues and chains. + If the atom is not found, return [NaN, NaN, NaN] for that atom. + The the three input lists must be of the same length, and the output list will have the same length as well. + Args: + atom_array: BioTite atom_array object + residue_list: list of residue names to consider + chain_list: list of chain identifiers to consider + atom_list: list of atom names to extract coordinates for + Returns: + coord_list: list of lists of coordinates for the specified atoms + + """ + coord_list = [] + for res_id, chain_id, atom_name in zip(residue_list, chain_list, atom_list): + + # Check if the residue exists in the atom array + if atom_name == "atomized": + # Check for atomized residue, in which case we take the first atom of the residue + # full mask should be length-1 if atomized + mask = ( + (atom_array.chain_id == chain_id) & + (atom_array.res_id == res_id) + ) + else: + # General case for non-atomized residues + # should have a unique solution, but we take the first entry either way. + mask = ( + (atom_array.chain_id == chain_id) & + (atom_array.res_id == res_id) & + (atom_array.atom_name == atom_name) + ) + + # Get the coordinates for the masked atoms + coords = atom_array.coord[mask][0:1] + + if len(coords) < 1: + coord_list.append([float("nan"), float("nan"), float("nan")]) + else: + coord_list.append(coords[0].tolist()) + + return coord_list + + +def get_token_level_metadata( + atom_array: AtomArray, + mol_info: "NucMolInfo", + *, + NA_only: bool = False, + planar_only: bool = True, +) -> dict: + """Lightweight token-level metadata. + + This intentionally avoids expensive coordinate-derived computations + (e.g., planar plane-fitting and geometry coordinate extraction). + + It is sufficient for: + - SS reconstruction / loop labeling from ``bp_partners`` + - inference-time SS specification parsing + + If you later need geometry keys (``xyz_planar``, ``frame_xyz``, ``M_i``), + call :func:`add_token_level_geometry_data`. + """ + + token_starts = get_token_starts(atom_array) + token_level_array = atom_array[token_starts] + + token_index = np.arange(len(token_starts)) + + # molecule type flags + seq_data = _get_sequence_encoding_data() + is_protein = np.isin(token_level_array.res_name, seq_data["aa_like_res_names"]) + is_rna = np.isin(token_level_array.res_name, seq_data["rna_like_res_names"]) + is_dna = np.isin(token_level_array.res_name, seq_data["dna_like_res_names"]) + del seq_data + + is_na_arr = (is_dna | is_rna).astype(bool) + + chain_list: list[str] = [] + chain_iid_list: list[str] = [] + resi_list: list[int] = [] + ind_list: list[int] = [] + res_name_list: list[str] = [] + token_id_list: list[str] = [] + + rep_atom_list: list[str | None] = [] + S_start_atom_list: list[str | None] = [] + S_stop_atom_list: list[str | None] = [] + sc_planarity_list: list[bool] = [] + + for i, atm in enumerate(token_level_array): + chain_list.append(atm.chain_id) + chain_iid_list.append(atm.chain_iid) + resi_list.append(int(atm.res_id)) + ind_list.append(int(i)) + res_name_list.append(atm.res_name) + token_id_list.append(str(atm.token_id)) + + if atm.is_polymer and (atm.res_name in mol_info.has_planar_sc.keys()): + sc_planarity_list.append(bool(mol_info.has_planar_sc[atm.res_name])) + else: + sc_planarity_list.append(False) + + # representative & sugar-edge atoms + if (is_glycine(atm.res_name) | is_protein_unknown(atm.res_name)): + rep_atom_i = "CA" + S_start_atom_i = None + S_stop_atom_i = None + elif is_standard_aa_not_glycine(atm.res_name): + rep_atom_i = "CA" + S_start_atom_i = "CA" + S_stop_atom_i = "CB" + elif is_pyrimidine(atm.res_name): + rep_atom_i = "C1'" + S_start_atom_i = "C1'" + S_stop_atom_i = "O2" + elif is_purine(atm.res_name): + rep_atom_i = "C1'" + S_start_atom_i = "C1'" + S_stop_atom_i = "N3" + elif is_unknown_nucleotide(atm.res_name): + rep_atom_i = "C1'" + S_start_atom_i = None + S_stop_atom_i = None + elif getattr(atm, "atomize", False): + rep_atom_i = atm.atom_name + S_start_atom_i = None + S_stop_atom_i = None + else: + rep_atom_i = None + S_start_atom_i = None + S_stop_atom_i = None + + rep_atom_list.append(rep_atom_i) + S_start_atom_list.append(S_start_atom_i) + S_stop_atom_list.append(S_stop_atom_i) + + # residue index <-> token index map + resi2index = {f"{c}__{r}": i for c, r, i in zip(chain_iid_list, resi_list, ind_list)} + + # relative sequence positions w/ chain gaps + rel_pos_list: list[int] = [] + current_chain = "" + chn_bias = -mol_info.gap_length + for r, c in zip(resi_list, chain_iid_list): + if c != current_chain: + chn_bias += mol_info.gap_length + current_chain = c + rel_pos_list.append(int(r + chn_bias)) + + rel_pos = np.asarray(rel_pos_list, dtype=np.int64) + seq_neighbors = ( + np.abs(rel_pos[:, None] - rel_pos[None, :]) <= int(mol_info.seq_cutoff) + ) + + na_inds = np.nonzero(is_na_arr)[0].tolist() + na_tensor_inds = {na_i: i for i, na_i in enumerate(na_inds)} + + # Cheap planarity heuristic from residue name lookup + is_planar_arr = np.asarray(sc_planarity_list, dtype=bool) + + # filter mask using NA_only / planar_only flags + if NA_only and planar_only: + filter_mask = is_na_arr & is_planar_arr + elif NA_only and (not planar_only): + filter_mask = is_na_arr.copy() + elif (not NA_only) and planar_only: + filter_mask = is_planar_arr.copy() + else: + filter_mask = np.ones_like(is_na_arr, dtype=bool) + + return { + "token_starts": token_starts, + "token_index": token_index, + "is_na": is_na_arr, + "is_planar": is_planar_arr, + "chain_list": chain_list, + "chain_iid_list": chain_iid_list, + "resi_list": resi_list, + "resn_list": res_name_list, + "token_id_list": token_id_list, + "resi2index": resi2index, + "len_s": int(len(token_level_array)), + "seq_neighbors": seq_neighbors, + "na_inds": na_inds, + "na_tensor_inds": na_tensor_inds, + "filter_mask": filter_mask, + "rep_atom_list": rep_atom_list, + "S_start_atom_list": S_start_atom_list, + "S_stop_atom_list": S_stop_atom_list, + "include_geometry": False, + } + + +def add_token_level_geometry_data( + atom_array: AtomArray, + mol_info: "NucMolInfo", + token_level_data: dict, + *, + NA_only: bool = False, + planar_only: bool = True, +) -> dict: + """Augment a metadata-only token_level_data dict with geometry fields. + + Populates: + - xyz_planar, xyz_S_start, xyz_S_stop + - frame_xyz, M_i + - updates is_planar and filter_mask using coordinate-derived planarity + - sets include_geometry=True + """ + + if bool(token_level_data.get("include_geometry", False)): + return token_level_data + + # Backward-compatibility: older token_level_data dicts (or user-provided ones) + # may not contain the metadata keys this function needs. + required_keys = ( + "chain_iid_list", + "chain_list", + "resi_list", + "rep_atom_list", + "S_start_atom_list", + "S_stop_atom_list", + "is_na", + ) + if any(k not in token_level_data for k in required_keys): + token_level_data = get_token_level_metadata( + atom_array, + mol_info, + NA_only=NA_only, + planar_only=planar_only, + ) + + chain_iid_list: list[str] = token_level_data["chain_iid_list"] + chain_list: list[str] = token_level_data["chain_list"] + resi_list: list[int] = token_level_data["resi_list"] + rep_atom_list: list[str | None] = token_level_data["rep_atom_list"] + S_start_atom_list: list[str | None] = token_level_data["S_start_atom_list"] + S_stop_atom_list: list[str | None] = token_level_data["S_stop_atom_list"] + + planar_atom_list_dict = find_planar_positions(atom_array, mol_info) + has_planar_sc: list[bool] = [] + + xyz_planar: list[list[list[float]]] = [] + xyz_S_start: list[list[float]] = [] + xyz_S_stop: list[list[float]] = [] + + for c, r, S_start_atm, S_stop_atm in zip( + chain_iid_list, + resi_list, + S_start_atom_list, + S_stop_atom_list, + ): + planar_atoms_i = planar_atom_list_dict[(c, r)] + has_planar_sc.append(bool(len(planar_atoms_i) >= 4)) + + atom_array_i = atom_array[(atom_array.chain_iid == c) & (atom_array.res_id == r)] + + planar_coords_i: list[list[float]] = [] + for pl_atm_name_j in planar_atoms_i: + pl_atom_array_ij = atom_array_i[atom_array_i.atom_name == pl_atm_name_j] + if len(pl_atom_array_ij) == 0: + planar_coords_i.append([float("nan"), float("nan"), float("nan")]) + else: + planar_coords_i.append(pl_atom_array_ij[0].coord) + + xyz_planar.append(planar_coords_i if len(planar_coords_i) > 3 else [[float("nan")] * 3]) + + if S_start_atm is None: + xyz_S_start.append([float("nan"), float("nan"), float("nan")]) + else: + S_start_atom_array_i = atom_array_i[atom_array_i.atom_name == S_start_atm] + xyz_S_start.append( + [float("nan"), float("nan"), float("nan")] + if len(S_start_atom_array_i) == 0 + else S_start_atom_array_i[0].coord + ) + + if S_stop_atm is None: + xyz_S_stop.append([float("nan"), float("nan"), float("nan")]) + else: + S_stop_atom_array_i = atom_array_i[atom_array_i.atom_name == S_stop_atm] + xyz_S_stop.append( + [float("nan"), float("nan"), float("nan")] + if len(S_stop_atom_array_i) == 0 + else S_stop_atom_array_i[0].coord + ) + + del atom_array_i + + # frame coordinates and backbone direction + frame_xyz = np.asarray( + make_coord_list(atom_array, resi_list, chain_list, rep_atom_list), + dtype=np.float32, + ) + + padded_centers = np.concatenate([frame_xyz[:1], frame_xyz, frame_xyz[-1:]], axis=0) + M_i = ( + (padded_centers[1:-1] - padded_centers[:-2]) + + (padded_centers[2:] - padded_centers[1:-1]) + ) / 2.0 + + is_planar_arr = np.asarray(has_planar_sc, dtype=bool) + token_level_data["is_planar"] = is_planar_arr + + is_na_arr = np.asarray(token_level_data["is_na"], dtype=bool) + if NA_only and planar_only: + filter_mask = is_na_arr & is_planar_arr + elif NA_only and (not planar_only): + filter_mask = is_na_arr.copy() + elif (not NA_only) and planar_only: + filter_mask = is_planar_arr.copy() + else: + filter_mask = np.ones_like(is_na_arr, dtype=bool) + token_level_data["filter_mask"] = filter_mask + + token_level_data.update( + { + "xyz_planar": xyz_planar, + "xyz_S_start": xyz_S_start, + "xyz_S_stop": xyz_S_stop, + "frame_xyz": frame_xyz, + "M_i": M_i, + "include_geometry": True, + } + ) + + del planar_atom_list_dict, padded_centers + return token_level_data + + +def _compute_nucleic_ss_impl( + mol_info, + token_level_data, + hbond_count, + clamp_pairwise_params=True, + eps=1e-8, + *, + return_local_params: bool = False, + return_pairwise_geometry: bool = False, + return_opening_angle: bool = False, + return_basepairs_only: bool = False, +): + """ + Compute nucleic secondary structure–related quantities and pairwise base params. + + Notes + ----- + This function is used in two modes: + + - Fast annotation mode (default): computes only what is needed to derive + ``basepairs_bool_ij`` and does *not* retain large intermediate pairwise + geometry arrays (X_ij/Y_ij/Z_ij/O_ij). + - Diagnostic mode: set ``return_pairwise_geometry=True`` (and optionally + ``return_local_params=True`` / ``return_opening_angle=True``) to also + return additional geometry arrays. + """ + + mask_1d = np.asarray(token_level_data["filter_mask"], dtype=bool) + len_mask = int(mask_1d.sum()) + # len_full = len(mask_1d) + + # unpack 1D data from token_level_data and apply filters + M_i = np.asarray(token_level_data["M_i"], dtype=np.float32)[mask_1d] + frame_xyz = np.asarray(token_level_data["frame_xyz"], dtype=np.float32)[mask_1d] + + is_na = np.asarray(token_level_data["is_na"], dtype=bool)[mask_1d] + + xyz_S_start = [xyz_list_i for xyz_list_i, keep_i in zip(token_level_data["xyz_S_start"], mask_1d) if keep_i] + xyz_S_stop = [xyz_list_i for xyz_list_i, keep_i in zip(token_level_data["xyz_S_stop"], mask_1d) if keep_i] + xyz_planar = [xyz_list_i for xyz_list_i, keep_i in zip(token_level_data["xyz_planar"], mask_1d) if keep_i] + + + # unpack 2D data from token_level_data and apply filters + hbond_count = np.asarray(hbond_count)[mask_1d, :][:, mask_1d] + seq_neighbors = np.asarray(token_level_data["seq_neighbors"], dtype=bool)[mask_1d, :][:, mask_1d] + + # --- CALC 0: precompute displacement vectors / distances ---- + planar_centers = np.stack( + [ + np.nanmean(np.asarray(xyz_i, dtype=np.float32), axis=0) + for xyz_i in xyz_planar + ], + axis=0, + ).astype(np.float32) + + + frame_D_ij_vec = frame_xyz[None, :, :] - frame_xyz[:, None, :] # [L, L, 3] + sc_D_ij_vec = planar_centers[None, :, :] - planar_centers[:, None, :] # [L, L, 3] + # D_ij = frame_D_ij_vec.norm(dim=-1) # [L, L] + + + # --- CALC I: local base params (canonical frames) ------------ + centered_points = [ + np.asarray(xyz_i, dtype=np.float32) - cen_i + for xyz_i, cen_i in zip(xyz_planar, planar_centers) + ] + + # eigenvectors per residue: [L, 3, 3] (NaNs where invalid) + eigenvectors = np.full((len_mask, 3, 3), np.nan, dtype=np.float32) + + for i, xyz_i in enumerate(centered_points): + xyz_i = xyz_i[~np.isnan(xyz_i).any(axis=1)] + if xyz_i.shape[0] >= 3: + cov_matrix = np.einsum("ij,ik->jk", xyz_i, xyz_i) / max( + xyz_i.shape[0] - 1, 1 + ) + _, eigvecs = np.linalg.eigh(cov_matrix) + eigenvectors[i] = eigvecs + + + # base-normal (principal) direction N_i, then corrected Z_i + N_i = eigenvectors[:, :, 0] + N_i = N_i / (np.linalg.norm(N_i, axis=1, keepdims=True) + eps) + + Z_i = N_i * np.sum(M_i * N_i, axis=-1, keepdims=True) + Z_i = Z_i / (np.linalg.norm(Z_i, axis=-1, keepdims=True) + eps) + + # Only compute full local frames when requested. + # Basepair filters only need Z_i (via Z_ij) and do not require X_i/Y_i. + local_base_params = None + if return_local_params or return_opening_angle: + # Sugar-edge vectors X_s_i built from S_start/stop + X_s_i = ( + np.asarray(xyz_S_stop, dtype=np.float32) + - np.asarray(xyz_S_start, dtype=np.float32) + ) + X_s_i = X_s_i / (np.linalg.norm(X_s_i, axis=-1, keepdims=True) + eps) + + X_i = np.cross(Z_i, X_s_i) + X_i = X_i / (np.linalg.norm(X_i, axis=-1, keepdims=True) + eps) + + if return_local_params: + Y_i = np.cross(X_i, Z_i) + Y_i = Y_i / (np.linalg.norm(Y_i, axis=-1, keepdims=True) + eps) + local_base_params = {"X_i": X_i, "Y_i": Y_i, "Z_i": Z_i} + else: + # Opening needs X_i but not the local params dict. + local_base_params = None + + # --- CALC II: pairwise base parameters ----------------------- + + # stack mean Z-direction vectors for parallel (0) and antiparallel (1) + Z_sum = Z_i[:, None, :] + Z_i[None, :, :] + Z_diff = Z_i[:, None, :] - Z_i[None, :, :] + Z_ij_oris = 0.5 * np.stack((Z_sum, Z_diff), axis=0) # [2, L, L, 3] + + base_ori_ij = ( + np.linalg.norm(Z_ij_oris[1], axis=-1) > np.linalg.norm(Z_ij_oris[0], axis=-1) + ).astype(np.int64) # [L, L] + + Z_ij = np.where(base_ori_ij[..., None] == 0, Z_ij_oris[0], Z_ij_oris[1]) + Z_ij = Z_ij / (np.linalg.norm(Z_ij, axis=-1, keepdims=True) + eps) + + Y_ij = frame_D_ij_vec / (np.linalg.norm(frame_D_ij_vec, axis=-1, keepdims=True) + eps) + X_ij = np.cross(Z_ij, Y_ij) + X_ij = X_ij / (np.linalg.norm(X_ij, axis=-1, keepdims=True) + eps) + + # vertical displacement using sidechain centroids + H_ij = np.sum(sc_D_ij_vec * Z_ij, axis=-1) + # H_ij_vec = H_ij[..., None] * Z_ij + + # Opening (O_ij) is purely diagnostic; compute only if requested. + O_ij = None + if return_opening_angle: + if not (return_local_params or return_opening_angle): + raise RuntimeError("Internal error: opening angle requested without local frame") + + proj_X_i_XY = ( + np.sum(X_i[:, None, :] * X_ij, axis=-1, keepdims=True) * X_ij + + np.sum(X_i[:, None, :] * Y_ij, axis=-1, keepdims=True) * Y_ij + ) + proj_X_i_XY_norm = proj_X_i_XY / ( + np.linalg.norm(proj_X_i_XY, axis=-1, keepdims=True) + eps + ) + cos_opening = np.sum( + proj_X_i_XY_norm * proj_X_i_XY_norm.swapaxes(0, 1), + axis=-1, + ) + if clamp_pairwise_params: + cos_opening = np.clip(cos_opening, -1.0, 1.0) + O_ij = np.arccos(cos_opening) + + # Buckle (B_ij) + proj_Z_i_YZ = ( + np.sum(Z_i[:, None, :] * Y_ij, axis=-1, keepdims=True) * Y_ij + + np.sum(Z_i[:, None, :] * Z_ij, axis=-1, keepdims=True) * Z_ij + ) + proj_Z_i_YZ_norm = proj_Z_i_YZ / ( + np.linalg.norm(proj_Z_i_YZ, axis=-1, keepdims=True) + eps + ) + cos_buckle = np.sum( + proj_Z_i_YZ_norm * (-proj_Z_i_YZ_norm.swapaxes(0, 1)), + axis=-1, + ) + + # Propeller (P_ij) + proj_Z_i_ZX = ( + np.sum(Z_i[:, None, :] * Z_ij, axis=-1, keepdims=True) * Z_ij + + np.sum(Z_i[:, None, :] * X_ij, axis=-1, keepdims=True) * X_ij + ) + proj_Z_i_ZX_norm = proj_Z_i_ZX / ( + np.linalg.norm(proj_Z_i_ZX, axis=-1, keepdims=True) + eps + ) + cos_propeller = np.sum( + proj_Z_i_ZX_norm * (-proj_Z_i_ZX_norm.swapaxes(0, 1)), + axis=-1, + ) + + if clamp_pairwise_params: + cos_buckle = np.clip(cos_buckle, -1.0, 1.0) + cos_propeller = np.clip(cos_propeller, -1.0, 1.0) + + B_ij = np.arccos(cos_buckle) + P_ij = np.arccos(cos_propeller) + + pair_params: dict | None + if return_basepairs_only: + pair_params = None + else: + pair_params = { + "H_ij": H_ij, + "B_ij": B_ij, + "P_ij": P_ij, + "base_ori_ij": base_ori_ij, + } + + if return_opening_angle and O_ij is not None: + pair_params["O_ij"] = O_ij + + if return_pairwise_geometry: + pair_params["X_ij"] = X_ij + pair_params["Y_ij"] = Y_ij + pair_params["Z_ij"] = Z_ij + + # --- CALC III: basepair filters / probabilities -------------- + hbond_summation = np.tensordot( + hbond_count.astype(np.float32), + np.asarray(mol_info.bp_summation_weights, dtype=np.float32), + axes=([2], [0]), + ) # [L, L] + + logits = mol_info.bp_hbond_coeff * ( + hbond_summation - (mol_info.min_hbonds_for_bp - 1) + ) + bp_preds = (1.0 / (1.0 + np.exp(-logits))) + eps + + # Filter Height geometry + H_ij_filter = (H_ij >= -mol_info.base_geometry_limits["H_ij"]) & ( + H_ij <= mol_info.base_geometry_limits["H_ij"] + ) + # Filter Buckle geometry + B_ij_filter = (B_ij <= mol_info.base_geometry_limits["B_ij"]) | ( + B_ij >= math.pi - mol_info.base_geometry_limits["B_ij"] + ) + # Filter Propeller geometry + P_ij_filter = (P_ij <= mol_info.base_geometry_limits["P_ij"]) | ( + P_ij >= math.pi - mol_info.base_geometry_limits["P_ij"] + ) + + bp_geom_filter = (H_ij_filter & B_ij_filter & P_ij_filter) + + if return_basepairs_only: + # Avoid allocating basepairs_ij float matrix when only the boolean mask is needed. + basepairs_bool_ij = (~seq_neighbors) & bp_geom_filter & ( + bp_preds >= float(mol_info.bp_val_cutoff) + ) + basepairs_ij = None + else: + basepairs_ij = (~seq_neighbors).astype(np.float32) * ( + bp_geom_filter.astype(np.float32) * bp_preds.astype(np.float32) + ) + basepairs_bool_ij = basepairs_ij >= mol_info.bp_val_cutoff + + if return_basepairs_only: + # Cleanup intermediate tensors to free memory + del frame_D_ij_vec, sc_D_ij_vec + del hbond_summation, bp_preds + del H_ij_filter, B_ij_filter, P_ij_filter, bp_geom_filter + + # Explicitly drop the largest pairwise arrays. + del X_ij, Y_ij, Z_ij + if O_ij is not None: + del O_ij + if local_base_params is not None: + del local_base_params + return basepairs_bool_ij + + assert pair_params is not None + + pair_params["basepairs_bool_ij"] = basepairs_bool_ij + pair_params["hbond_summation"] = hbond_summation + pair_params["basepairs_ij"] = basepairs_ij + + nucleic_ss_data = {"pair_params": pair_params} + if return_local_params and local_base_params is not None: + nucleic_ss_data["local_params"] = local_base_params + + # Cleanup intermediate tensors to free memory + del frame_D_ij_vec, sc_D_ij_vec + del hbond_summation, bp_preds + del H_ij_filter, B_ij_filter, P_ij_filter, bp_geom_filter + + # If not returning, explicitly drop the largest pairwise arrays. + if not return_pairwise_geometry: + del X_ij, Y_ij, Z_ij + if not return_opening_angle and O_ij is not None: + del O_ij + if not return_local_params and local_base_params is not None: + del local_base_params + + return nucleic_ss_data + +def annotate_na_ss( + atom_array: AtomArray, + *, + NA_only: bool = False, + planar_only: bool = True, + p_canonical_bp_filter: float = 0.0, + mol_info: Optional[NucMolInfo] = None, + overwrite: bool = True, + token_level_data: Optional[dict] = None, +) -> AtomArray: + """Annotate base-pair partners directly onto the AtomArray. + + This computes nucleic-acid base pairing similarly to + :func:`get_gt_nucleic_geom_feats` but instead of returning an integer + secondary-structure matrix, it writes an AtomArray annotation + ``bp_partners``. + + The annotation is stored on the *full* ``atom_array`` (length N atoms), + but only nucleic-acid token-representative atoms (indices ``token_starts`` + from :func:`get_token_starts`) that are included in this call's + ``annotation_mask`` get a list value. + + Semantics: + - ``[]`` (empty list): explicitly unpaired nucleic-acid loop + - ``[token_id, ...]``: paired nucleic-acid token(s) + - ``None``: unannotated/masked (non-NA tokens, or tokens filtered out) + + Each list element is the partner token identifier (``token_id`` as int) + for the paired residue. This is sufficient to recover the partner's + token-representative atom via ``token_starts`` + token_id mapping. + + Notes + ----- + - ``token_level_data`` may be metadata-only; this function will augment it + with geometry as needed. + - If ``p_canonical_bp_filter > 0``, then with that probability we discard + any non-canonical NA basepairs (keeps only A-U, A-T, G-C). + """ + + if mol_info is None: + mol_info = NucMolInfo() + + # Token representatives (0..L-1) and their corresponding atom indices (into atom_array) + token_starts = get_token_starts(atom_array) + token_level_array = atom_array[token_starts] + # token_id is assigned token-wise and matches get_token_starts() segmentation. + token_ids: list[int] = [int(t) for t in list(token_level_array.token_id)] + token_res_names: list[str] = [str(rn) for rn in list(token_level_array.res_name)] + + # Compute basepairs on the token graph (respecting NA_only/planar_only filtering) + if token_level_data is None: + token_level_data = get_token_level_metadata( + atom_array, + mol_info, + NA_only=NA_only, + planar_only=planar_only, + ) + token_level_data = add_token_level_geometry_data( + atom_array, + mol_info, + token_level_data, + NA_only=NA_only, + planar_only=planar_only, + ) + # Note: this mask gives positions that are *chemically valid* for forming + # base pairs, which is different from custom mask-generation for features + mask_1d = np.asarray(token_level_data["filter_mask"], dtype=bool) + + subset_idxs = np.nonzero(mask_1d)[0] + + is_na_full = np.asarray(token_level_data["is_na"], dtype=bool) + + hbond_count = calculate_hb_counts( + atom_array, + token_level_data, + mol_info, + cutoff_HA_dist=mol_info.cutoff_HA_dist, + cutoff_DA_dist=mol_info.cutoff_DA_dist, + ) + bp_bool = np.asarray( + _compute_nucleic_ss_impl( + mol_info, + token_level_data, + hbond_count, + clamp_pairwise_params=True, + eps=1e-8, + return_local_params=False, + return_pairwise_geometry=False, + return_opening_angle=False, + return_basepairs_only=True, + ), + dtype=bool, + ) + + # Optional: filter to canonical Watson-Crick basepairs only. + # Sampled probabilistically to allow mixed supervision during training. + do_canonical_filter = bool(p_canonical_bp_filter and (np.random.rand() < float(p_canonical_bp_filter))) + if do_canonical_filter: + def _base_letter(res_name: str) -> str | None: + rn = str(res_name).strip().upper() + if rn in STANDARD_RNA: + return rn + if rn in STANDARD_DNA: + return rn[1] # DA/DC/DG/DT -> A/C/G/T + return None + + allowed_pairs = { + ("A", "U"), ("U", "A"), + ("A", "T"), ("T", "A"), + ("G", "C"), ("C", "G"), + } + base_letters_full: list[str | None] = [_base_letter(rn) for rn in token_res_names] + + bp_bool = np.asarray(bp_bool, dtype=bool) + bp_rows_tmp, bp_cols_tmp = np.nonzero(bp_bool) + for r, c in zip(bp_rows_tmp.tolist(), bp_cols_tmp.tolist()): + full_i = int(subset_idxs[int(r)]) + full_j = int(subset_idxs[int(c)]) + bi = base_letters_full[full_i] + bj = base_letters_full[full_j] + if bi is None or bj is None or (bi, bj) not in allowed_pairs: + bp_bool[int(r), int(c)] = False + bp_bool[int(c), int(r)] = False + + bp_bool = np.asarray(bp_bool, dtype=bool) + bp_rows, bp_cols = np.nonzero(bp_bool) + + # Prepare/overwrite annotation array + if (not overwrite) and ("bp_partners" in atom_array.get_annotation_categories()): + bp_partners_ann = atom_array.bp_partners + if len(bp_partners_ann) != len(atom_array): + raise ValueError( + "Existing bp_partners annotation has wrong length" + ) + else: + bp_partners_ann = np.empty(len(atom_array), dtype=object) + bp_partners_ann[:] = None + + # Explicit-loop semantics: + # - Only nucleic-acid token-start atoms *within subset_idxs* get a list container. + # - [] means explicitly unpaired loop. + # - None means unannotated/masked. + for full_i in subset_idxs.tolist(): + if not bool(is_na_full[int(full_i)]): + continue + atom_i = int(token_starts[int(full_i)]) + if bp_partners_ann[atom_i] is None: + bp_partners_ann[atom_i] = [] + + # Populate partners using token_id ints + # We only process each unordered pair once to avoid duplicates. + for r, c in zip(bp_rows.tolist(), bp_cols.tolist()): + if r == c: + continue + + full_i = int(subset_idxs[int(r)]) + full_j = int(subset_idxs[int(c)]) + if full_i == full_j: + continue + + # Only annotate NA-NA basepairs as nucleic secondary structure. + if (not bool(is_na_full[int(full_i)])) or (not bool(is_na_full[int(full_j)])): + continue + + # Enforce uniqueness: only handle (i,j) where i < j + if full_j < full_i: + continue + + atom_i = int(token_starts[full_i]) + atom_j = int(token_starts[full_j]) + partner_i = int(token_ids[full_j]) + partner_j = int(token_ids[full_i]) + + if bp_partners_ann[atom_i] is None: + bp_partners_ann[atom_i] = [] + if bp_partners_ann[atom_j] is None: + bp_partners_ann[atom_j] = [] + + # Add if not present + if partner_i not in bp_partners_ann[atom_i]: + bp_partners_ann[atom_i].append(partner_i) + if partner_j not in bp_partners_ann[atom_j]: + bp_partners_ann[atom_j].append(partner_j) + + atom_array.set_annotation("bp_partners", bp_partners_ann) + return atom_array + + +def bp_partner_to_ss_matrix( + atom_array: AtomArray, + *, + feature_info: Optional[dict] = None, + mol_info: Optional[NucMolInfo] = None, + include_loops: bool = True, + token_level_data: Optional[dict] = None, +) -> np.ndarray: + """Reconstruct an integer NA secondary-structure matrix from annotations. + + Requires that ``atom_array`` has a ``bp_partners`` annotation created by + :func:`annotate_na_ss`. + + Returns + ------- + ss_matrix : np.ndarray + Shape (L, L) with values from ``feature_info``. + + Loop semantics: + - Only nucleic-acid tokens can be loops. + - Only tokens with an explicit empty list ``bp_partners == []`` are loops. + Unannotated tokens (``bp_partners is None``) remain masked. + """ + + if mol_info is None: + mol_info = NucMolInfo() + + if feature_info is None: + feature_info = DEFAULT_NA_SS_FEATURE_INFO + + if "bp_partners" not in atom_array.get_annotation_categories(): + raise ValueError( + "atom_array is missing bp_partners annotation; run annotate_na_ss() first" + ) + + token_starts = get_token_starts(atom_array) + token_level_array = atom_array[token_starts] + token_ids_int: list[int] = [int(t) for t in list(token_level_array.token_id)] + token_id_to_index_int = {int(tid): i for i, tid in enumerate(token_ids_int)} + L = len(token_starts) + + ss_matrix = feature_info["NA_SS_MASK"] * np.ones((L, L), dtype=np.int64) + + if token_level_data is None: + token_level_data = get_token_level_metadata( + atom_array, + mol_info, + # NA_only=NA_only, + # planar_only=planar_only, + ) + + mask_1d = np.asarray(token_level_data["filter_mask"], dtype=bool) + subset_idxs = np.nonzero(mask_1d)[0] + subset_set = set(int(x) for x in subset_idxs.tolist()) + is_na = np.asarray(token_level_data["is_na"], dtype=bool) + subset_na_idxs = subset_idxs[np.asarray(is_na[subset_idxs], dtype=bool)] + subset_na_set = set(int(x) for x in subset_na_idxs.tolist()) + + # Fill base-pair edges (only within subset, and only NA-NA) + bp_partners_ann = atom_array.bp_partners + for i in subset_idxs.tolist(): + if not bool(is_na[int(i)]): + continue + atom_i = int(token_starts[int(i)]) + partners = bp_partners_ann[atom_i] + if partners is None: + continue + if not isinstance(partners, (list, tuple, np.ndarray)): + continue + for partner_token_id in partners: + # Support int, numpy scalar, and legacy stringified token_id. + try: + partner_tid_int = int(partner_token_id) + except Exception: + partner_tid_int = None + j = token_id_to_index_int.get(partner_tid_int) if partner_tid_int is not None else None + if j is None or j == i: + continue + if int(j) not in subset_set: + continue + if not bool(is_na[int(j)]): + continue + ss_matrix[i, j] = feature_info["NA_SS_PAIR"] + ss_matrix[j, i] = feature_info["NA_SS_PAIR"] + + if not include_loops: + return ss_matrix + + # Loop labeling is explicit and NA-only: + # - only nucleic tokens can be loops + # - only tokens with an explicit empty list annotation are loops + loop_idxs_list: list[int] = [] + for i in subset_idxs.tolist(): + if not bool(is_na[int(i)]): + continue + atom_i = int(token_starts[int(i)]) + partners = bp_partners_ann[atom_i] + if not isinstance(partners, (list, tuple, np.ndarray)): + continue + if len(partners) == 0: + loop_idxs_list.append(int(i)) + + loop_idxs = np.asarray(loop_idxs_list, dtype=np.int64) + if loop_idxs.size > 0: + ss_matrix[loop_idxs[:, None], subset_na_idxs[None, :]] = feature_info["NA_SS_LOOP"] + ss_matrix[subset_na_idxs[:, None], loop_idxs[None, :]] = feature_info["NA_SS_LOOP"] + + return ss_matrix + + +def parse_dot_bracket(dot_bracket: str) -> tuple[list[tuple[int, int]], list[int]]: + """Parse a dot-bracket string into base pairs and unpaired positions. + + Supports (), [], {}, <>, and A..E / a..e bracket pairs. + + Returns + ------- + pairs : list of (i, j) + 0-based indices in the string for paired positions. + unpaired : list of int + 0-based indices that are '.' (unpaired). + """ + + stack: dict[str, list[int]] = {} + pairs: list[tuple[int, int]] = [] + unpaired: list[int] = [] + + opener_for = { + ")": "(", + "]": "[", + "}": "{", + ">": "<", + "a": "A", + "b": "B", + "c": "C", + "d": "D", + "e": "E", + } + + for i, ch in enumerate(str(dot_bracket)): + if ch == ".": + unpaired.append(i) + elif ch in "([{abcde": + o = opener_for.get(ch) + if o is None or o not in stack or not stack[o]: + continue + j = stack[o].pop() + pairs.append((j, i)) + else: + continue + + return pairs, unpaired + + +def annotate_na_ss_from_specification( + atom_array: AtomArray, + specification: dict, + *, + overwrite: bool = True, +) -> AtomArray: + """Annotate ``bp_partners`` from an inference-time specification. + + This is the inference analogue of :func:`annotate_na_ss`, except instead + of computing base pairs from geometry/H-bonds, it interprets a user-provided + specification (dot-bracket strings and/or residue ranges/positions) and + writes the same ``bp_partners`` annotation on token-representative atoms. + + Supported spec keys (all optional): + - ``ss_dbn``: global dot-bracket string applied to the first L tokens. + - ``ss_dbn_dict``: mapping like {"A5-15": dbn_str, ...}. + - ``paired_region_list``: list of "A5-15,B1-11" entries. + - ``paired_position_list``: list of "A19,A61,A20" groups. + - ``loop_region_list``: list of "A5-10" regions forced unpaired. + """ + + spec = specification or {} + token_starts = get_token_starts(atom_array) + token_level_array = atom_array[token_starts] + token_ids: list[int] = [int(t) for t in list(token_level_array.token_id)] + n_tokens = len(token_starts) + + # Explicit loops are only meaningful for nucleic-acid tokens. + seq_data = _get_sequence_encoding_data() + is_rna_like = np.isin(token_level_array.res_name, seq_data["rna_like_res_names"]) + is_dna_like = np.isin(token_level_array.res_name, seq_data["dna_like_res_names"]) + is_na_token = np.asarray(is_rna_like | is_dna_like, dtype=bool) + del seq_data + + # Prepare/overwrite annotation array + if (not overwrite) and ("bp_partners" in atom_array.get_annotation_categories()): + bp_partners_ann = atom_array.bp_partners + if len(bp_partners_ann) != len(atom_array): + raise ValueError("Existing bp_partners annotation has wrong length") + else: + bp_partners_ann = np.empty(len(atom_array), dtype=object) + bp_partners_ann[:] = None + + # Build chain/res -> token index map for region/position specs + chain_iid_list: list[str] = [str(atm.chain_iid) for atm in token_level_array] + resi_list: list[int] = [int(atm.res_id) for atm in token_level_array] + chain_res_to_tok: dict[tuple[str, int], int] = { + (c, r): i for i, (c, r) in enumerate(zip(chain_iid_list, resi_list)) + } + + def _parse_region(region_str: str) -> tuple[str, int, int] | None: + region_str = str(region_str).strip() + if not region_str: + return None + chain_id = region_str[0] + rest = region_str[1:] + if "-" not in rest: + return None + start_s, end_s = rest.split("-", 1) + try: + start_res = int(start_s) + end_res = int(end_s) + except ValueError: + return None + if start_res > end_res: + start_res, end_res = end_res, start_res + return chain_id, start_res, end_res + + def _parse_single_pos(pos_str: str) -> tuple[str, int] | None: + pos_str = str(pos_str).strip() + if not pos_str: + return None + chain_id = pos_str[0] + rest = pos_str[1:] + try: + res_id = int(rest) + except ValueError: + return None + return chain_id, res_id + + def _region_to_token_indices(region_str: str) -> list[int]: + parsed = _parse_region(region_str) + if parsed is None: + return [] + chain_id, start_res, end_res = parsed + token_indices: list[int] = [] + for res_id in range(start_res, end_res + 1): + idx = chain_res_to_tok.get((chain_id, int(res_id))) + if idx is not None: + token_indices.append(int(idx)) + return token_indices + + def _pos_to_token_index(pos_str: str) -> int | None: + parsed = _parse_single_pos(pos_str) + if parsed is None: + return None + chain_id, res_id = parsed + return chain_res_to_tok.get((chain_id, int(res_id))) + + # Accumulate partners as token-index sets + partners: list[set[int]] = [set() for _ in range(n_tokens)] + loop_token_idxs: set[int] = set() + + def _add_pair(i: int, j: int) -> None: + if not (0 <= i < n_tokens and 0 <= j < n_tokens): + return + if i == j: + return + if (not bool(is_na_token[int(i)])) or (not bool(is_na_token[int(j)])): + return + if i in loop_token_idxs or j in loop_token_idxs: + return + partners[i].add(j) + partners[j].add(i) + + # Case 1: global ss_dbn + ss_dbn = spec.get("ss_dbn") + if isinstance(ss_dbn, str) and ss_dbn.strip(): + pairs, unpaired = parse_dot_bracket(ss_dbn.strip()) + L = min(len(ss_dbn), n_tokens) + for i_local, j_local in pairs: + if 0 <= i_local < L and 0 <= j_local < L: + _add_pair(int(i_local), int(j_local)) + for i_local in unpaired: + if 0 <= int(i_local) < L: + loop_token_idxs.add(int(i_local)) + + # Case 1b: ss_dbn_dict + ss_dbn_dict = spec.get("ss_dbn_dict", {}) or {} + if isinstance(ss_dbn_dict, dict): + for region_str, dbn_str in ss_dbn_dict.items(): + if not isinstance(region_str, str) or not isinstance(dbn_str, str): + continue + dbn_str = dbn_str.strip() + if not dbn_str: + continue + toks = _region_to_token_indices(region_str) + if not toks or len(toks) != len(dbn_str): + continue + pairs, unpaired = parse_dot_bracket(dbn_str) + for i_local, j_local in pairs: + if 0 <= i_local < len(toks) and 0 <= j_local < len(toks): + _add_pair(int(toks[int(i_local)]), int(toks[int(j_local)])) + for i_local in unpaired: + if 0 <= i_local < len(toks): + loop_token_idxs.add(int(toks[int(i_local)])) + + # Case 2: paired_region_list + paired_region_list = spec.get("paired_region_list", []) + if isinstance(paired_region_list, str): + paired_region_list = [paired_region_list] + if isinstance(paired_region_list, list): + for region_entry in paired_region_list: + if not isinstance(region_entry, str) or not region_entry.strip(): + continue + region_parts = [p.strip() for p in region_entry.split(",") if p.strip()] + if len(region_parts) != 2: + continue + toks1 = _region_to_token_indices(region_parts[0]) + toks2 = _region_to_token_indices(region_parts[1]) + if not toks1 or not toks2: + continue + for ti in toks1: + for tj in toks2: + _add_pair(int(ti), int(tj)) + + # Case 3: paired_position_list + paired_position_list = spec.get("paired_position_list", []) + if isinstance(paired_position_list, str): + paired_position_list = [paired_position_list] + if isinstance(paired_position_list, list): + for group_str in paired_position_list: + if not isinstance(group_str, str) or not group_str.strip(): + continue + pos_parts = [p.strip() for p in group_str.split(",") if p.strip()] + tok_indices: list[int] = [] + for pos_str in pos_parts: + tok = _pos_to_token_index(pos_str) + if tok is not None: + tok_indices.append(int(tok)) + for i in range(len(tok_indices)): + for j in range(i + 1, len(tok_indices)): + _add_pair(tok_indices[i], tok_indices[j]) + + # Case 4: loop_region_list + loop_region_list = spec.get("loop_region_list", []) + if isinstance(loop_region_list, str): + loop_region_list = [loop_region_list] + if isinstance(loop_region_list, list): + for region_str in loop_region_list: + if not isinstance(region_str, str) or not region_str.strip(): + continue + for tok in _region_to_token_indices(region_str): + loop_token_idxs.add(int(tok)) + + # Enforce loop tokens as unpaired: remove any pairs involving them + for i in list(loop_token_idxs): + if not (0 <= i < n_tokens): + continue + if not bool(is_na_token[int(i)]): + loop_token_idxs.discard(int(i)) + continue + for j in list(partners[i]): + partners[j].discard(i) + partners[i].clear() + + # Write lists of partner token_ids onto token-start atoms. + # Unspecified tokens remain unannotated (None) -> NA_SS_MASK. + for i in range(n_tokens): + atom_i = int(token_starts[i]) + if not bool(is_na_token[int(i)]): + continue + if len(partners[i]) > 0: + bp_partners_ann[atom_i] = [] + for j in sorted(partners[i]): + partner_token_id = int(token_ids[int(j)]) + bp_partners_ann[atom_i].append(partner_token_id) + elif int(i) in loop_token_idxs: + bp_partners_ann[atom_i] = [] + + atom_array.set_annotation("bp_partners", bp_partners_ann) + return atom_array + + +def annotate_na_ss_from_data_specification( + data: dict, + *, + overwrite: bool = True, +) -> AtomArray: + """Convenience wrapper: annotate bp partners from ``data['specification']``.""" + atom_array = data["atom_array"] + spec = data.get("specification", {}) or {} + return annotate_na_ss_from_specification(atom_array, spec, overwrite=overwrite) + diff --git a/models/rfd3/src/rfd3/transforms/pipelines.py b/models/rfd3/src/rfd3/transforms/pipelines.py index 0cd656b2..d4c3361f 100644 --- a/models/rfd3/src/rfd3/transforms/pipelines.py +++ b/models/rfd3/src/rfd3/transforms/pipelines.py @@ -72,6 +72,7 @@ ) from rfd3.transforms.design_transforms import ( AddAdditional1dFeaturesToFeats, + AddAdditional2dFeaturesToFeats, AddGroundTruthSequence, AddIsXFeats, AssignTypes, @@ -84,6 +85,7 @@ ) from rfd3.transforms.dna_crop import ProteinDNAContactContiguousCrop from rfd3.transforms.hbonds_hbplus import CalculateHbondsPlus +from rfd3.transforms.na_geom import CalculateNucleicAcidGeomFeats from rfd3.transforms.ppi_transforms import ( Add1DSSFeature, AddGlobalIsNonLoopyFeature, @@ -350,6 +352,7 @@ def build_atom14_base_pipeline_( center_option: str, atom_1d_features: dict | None, token_1d_features: dict | None, + token_2d_features: dict | None, # PPI features max_ppi_hotspots_frac_to_provide: float, ppi_hotspot_max_distance: float, @@ -357,6 +360,8 @@ def build_atom14_base_pipeline_( max_ss_frac_to_provide: float, min_ss_island_len: int, max_ss_island_len: int, + # Nucleic acid features + add_na_pair_features: bool, **_, # dump additional kwargs (e.g. msa stuff) ): """ @@ -437,6 +442,15 @@ def build_atom14_base_pipeline_( ), ) ) + # Add nucleic acid geometry features + if add_na_pair_features: + transforms.append( + CalculateNucleicAcidGeomFeats( + is_inference, + NA_only=False, + planar_only=True, + ) + ) # Design Transforms transforms += [ @@ -525,6 +539,11 @@ def build_atom14_base_pipeline_( atom_1d_features=atom_1d_features, association_scheme=association_scheme, ), + AddAdditional2dFeaturesToFeats( + autofill_zeros_if_not_present_in_atomarray=True, + token_2d_features=token_2d_features, + association_scheme=association_scheme, + ), AddAF3TokenBondFeatures(), AddGroundTruthSequence(sequence_encoding=af3_sequence_encoding), ConditionalRoute( @@ -615,6 +634,7 @@ def build_atom14_base_pipeline( kwargs.setdefault("min_ss_island_len", 0) kwargs.setdefault("max_ss_island_len", 999) kwargs.setdefault("max_binder_length", 999) + kwargs.setdefault("add_na_pair_features", False) kwargs.setdefault("b_factor_min", None) kwargs.setdefault("zero_occ_on_exposure_after_cropping", False) From f0ab0fedae1abe5862145290577e8495059de68f Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Sat, 31 Jan 2026 16:14:06 -0800 Subject: [PATCH 09/49] train and inference atom23 with custom datasets and na ss transform --- .../configs/datasets/design_base_rfd3na.yaml | 103 ++++++++++++++++++ .../train/rna_monomer_distillation.yaml | 39 +++++++ .../rfd3/configs/datasets/val/pseudoknot.yaml | 4 +- models/rfd3/configs/experiment/rfd3na-ss.yaml | 21 ++-- .../rfd3/src/rfd3/inference/input_parsing.py | 44 +++++--- .../rfd3/inference/legacy_input_parsing.py | 15 +-- .../rfd3/src/rfd3/metrics/design_metrics.py | 46 ++++++-- .../src/rfd3/transforms/conditioning_base.py | 13 ++- .../src/rfd3/transforms/design_transforms.py | 4 +- models/rfd3/src/rfd3/transforms/na_geom.py | 24 ++-- .../rfd3/src/rfd3/transforms/na_geom_utils.py | 2 + models/rfd3/src/rfd3/transforms/pipelines.py | 30 +++-- .../rfd3/transforms/training_conditions.py | 12 +- .../src/rfd3/transforms/util_transforms.py | 2 +- .../rfd3/src/rfd3/transforms/virtual_atoms.py | 8 +- 15 files changed, 286 insertions(+), 81 deletions(-) create mode 100644 models/rfd3/configs/datasets/design_base_rfd3na.yaml create mode 100644 models/rfd3/configs/datasets/train/rna_monomer_distillation.yaml diff --git a/models/rfd3/configs/datasets/design_base_rfd3na.yaml b/models/rfd3/configs/datasets/design_base_rfd3na.yaml new file mode 100644 index 00000000..71816174 --- /dev/null +++ b/models/rfd3/configs/datasets/design_base_rfd3na.yaml @@ -0,0 +1,103 @@ +# base training dataset for training AF3 design models (atom14 variants): +# protein subsampling only. + +defaults: + # Grab datasets + - train/pdb/rfd3_train_interface@train.pdb.sub_datasets.interface + - train/pdb/rfd3_train_pn_unit@train.pdb.sub_datasets.pn_unit + #- train/rfd3_monomer_distillation@train + - train/rna_monomer_distillation@train + + # Customized validation datasets + - val/unconditional@val.unconditional + - val/unconditional_deep@val.unconditional_deep + - val/indexed@val.indexed + - val/pseudoknot@val.pseudoknot + + # Customized train masks + - conditions/unconditional@global_transform_args.train_conditions.unconditional + - conditions/island@global_transform_args.train_conditions.island + - conditions/tipatom@global_transform_args.train_conditions.tipatom + - conditions/sequence_design@global_transform_args.train_conditions.sequence_design + - conditions/ppi@global_transform_args.train_conditions.ppi + + - _self_ + +# Create a dictionary used for transform arguments +pipeline_target: rfd3.transforms.pipelines.build_atom14_base_pipeline + +# Base config overrides: +diffusion_batch_size_train: 32 +diffusion_batch_size_inference: 8 +crop_size: 384 +n_recycles_train: 2 +n_recycles_validation: 1 +max_atoms_in_crop: 3840 # ~10x crop size. + +# Global transform arguments are necessary for arguments shared between training and inference +global_transform_args: + n_atoms_per_token: 14 + central_atom: CB + sigma_perturb: 2.0 + sigma_perturb_com: 1.0 + association_scheme: dense + center_option: diffuse # options are ["all", "motif", "diffuse"] + + # Reference conformer policy + generate_conformers: True + generate_conformers_for_non_protein_only: True + provide_reference_conformer_when_unmasked: True + ground_truth_conformer_policy: IGNORE # Other options: REPLACE, ADD, FALLBACK. See atomworks.enums for details + provide_elements_for_unindexed_components: True + use_element_for_atom_names_of_atomized_tokens: True # TODO: correct name, implies unindexed do too + + # PPI Cropping + keep_full_binder_in_spatial_crop: False + max_binder_length: 170 + + # PPI Hotspots + max_ppi_hotspots_frac_to_provide: 0.2 + ppi_hotspot_max_distance: 4.5 + + # Secondary structure features + max_ss_frac_to_provide: 0.4 + min_ss_island_len: 1 + max_ss_island_len: 10 + + # Nucleic acid features + add_na_pair_features: false + + train_conditions: + unconditional: + frequency: 5.0 + sequence_design: + frequency: 2.0 + island: + frequency: 1.0 + tipatom: + frequency: 0.0 + ppi: + frequency: 0.0 + + # Used to create simple boolean flags for downstream conditioning + meta_conditioning_probabilities: + calculate_NA_SS: 1.0 + calculate_hbonds: 0.2 + calculate_rasa: 0.6 + + keep_protein_motif_rasa: 0.1 # Small to prevent noisy input to model + hbond_subsample: 0.5 + + # fully indexed training + unindex_leak_global_index: 0.10 + unindex_insert_random_break: 0.10 + unindex_remove_random_break: 0.10 + + # Probability of adding 1d secondary structure conditioning + add_1d_ss_features: 0.1 + featurize_plddt: 0.9 # Applied for monomer distillation only + add_global_is_non_loopy_feature: 0.99 + + # PPI + add_ppi_hotspots: 0.75 + full_binder_crop: 0.75 diff --git a/models/rfd3/configs/datasets/train/rna_monomer_distillation.yaml b/models/rfd3/configs/datasets/train/rna_monomer_distillation.yaml new file mode 100644 index 00000000..05ac8472 --- /dev/null +++ b/models/rfd3/configs/datasets/train/rna_monomer_distillation.yaml @@ -0,0 +1,39 @@ +defaults: + - pdb/base_transform_args@rna_monomer_distillation + - _self_ + +rna_monomer_distillation: + dataset: + _target_: atomworks.ml.datasets.StructuralDatasetWrapper + save_failed_examples_to_dir: ${paths.data.failed_examples_dir} + + # cif parser arguments + cif_parser_args: + cache_dir: null + load_from_cache: False + save_to_cache: False + + # metadata parser + dataset_parser: + _target_: atomworks.ml.datasets.parsers.GenericDFParser + pn_unit_iid_colnames: null + + # metadata dataset + dataset: + _target_: atomworks.ml.datasets.PandasDataset + name: rna_monomer_distillation + id_column: example_id + data: /projects/ml/afavor/rna_distillation/rna_distillation_filtered_df.parquet + columns_to_load: + - example_id + - path + - cluster_id + - seq_hash + - overall_plddt + - overall_pde + - overall_pae + + transform: + crop_contiguous_probability: 0.67 + crop_spatial_probability: 0.33 + diff --git a/models/rfd3/configs/datasets/val/pseudoknot.yaml b/models/rfd3/configs/datasets/val/pseudoknot.yaml index 27b801c5..7cf5ce14 100644 --- a/models/rfd3/configs/datasets/val/pseudoknot.yaml +++ b/models/rfd3/configs/datasets/val/pseudoknot.yaml @@ -1,9 +1,9 @@ defaults: - - unconditional + - design_validation_base - _self_ dataset: name: pseudoknot eval_every_n: 1 - data: /home/afavor/git/RFD3/modelhub/projects/aa_design/tests/test_data/pseudoknot.json + data: ${paths.data.design_benchmark_data_dir}/pseudoknot.json diff --git a/models/rfd3/configs/experiment/rfd3na-ss.yaml b/models/rfd3/configs/experiment/rfd3na-ss.yaml index 614f20fc..6d2245c5 100644 --- a/models/rfd3/configs/experiment/rfd3na-ss.yaml +++ b/models/rfd3/configs/experiment/rfd3na-ss.yaml @@ -5,6 +5,7 @@ defaults: - /debug/default - override /model: rfd3_base - override /logger: null + - override /datasets: design_base_rfd3na - _self_ name: rfd3na-SScond @@ -54,7 +55,7 @@ datasets: max_atoms_in_crop: 2560 # ~10x crop size. global_transform_args: association_scheme: atom23 - add_na_pair_features: true + #add_na_pair_features: true train_conditions: unconditional: frequency: 2.0 @@ -69,19 +70,19 @@ datasets: train: # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. pdb: - probability: 1.0 - # rna_monomer_distillation: - # probability: 1.0 + probability: 0.5 + rna_monomer_distillation: + probability: 0.5 - # val: - # pseudoknot: - # dataset: - # # eval_every_n: 10 - # eval_every_n: 2 + val: + pseudoknot: + dataset: + # eval_every_n: 10 + eval_every_n: 1 trainer: devices_per_node: 1 limit_train_batches: 10 limit_val_batches: 1 validate_every_n_epochs: 5 - prevalidate: false + prevalidate: true diff --git a/models/rfd3/src/rfd3/inference/input_parsing.py b/models/rfd3/src/rfd3/inference/input_parsing.py index b3117fcc..17032e43 100644 --- a/models/rfd3/src/rfd3/inference/input_parsing.py +++ b/models/rfd3/src/rfd3/inference/input_parsing.py @@ -9,7 +9,7 @@ from typing import Any, Dict, List, Optional, Union import numpy as np -from atomworks.constants import STANDARD_AA +from atomworks.constants import STANDARD_AA, STANDARD_DNA, STANDARD_RNA from atomworks.io.parser import parse_atom_array # from atomworks.ml.datasets.datasets import BaseDataset @@ -119,7 +119,9 @@ class DesignInputSpecification(BaseModel): validate_assignment=False, str_strip_whitespace=True, str_min_length=1, - extra="forbid", + #extra="forbid", #################################################### + extra="allow" + ## for now allowing extra for rfd3na-ss purposes, can decide later ## ) # fmt: off # ======================================================================== @@ -494,6 +496,12 @@ def apply_selections(start, end): aa.is_motif_atom_with_fixed_seq[start:end] = np.full_like( is_bkbn, False, dtype=int ) + elif aa.res_name[start] in (STANDARD_DNA + STANDARD_RNA) and self.redesign_motif_sidechains: + is_bkbn = np.isin(aa.atom_name[start:end], backbone_atoms_RNA) + aa.is_motif_atom_with_fixed_coord[start:end] = is_bkbn.astype(int) + aa.is_motif_atom_with_fixed_seq[start:end] = np.full_like( + is_bkbn, False, dtype=int + ) # ... Apply selections on top apply_selections(start, end) @@ -509,17 +517,18 @@ def build(self, return_metadata=False): atom_array_input_annotated = copy.deepcopy(self.atom_array_input) ########## reorder NA atoms ########### - is_dna = np.isin(atom_array_input_annotated.res_name, ["DA", "DC", "DG", "DT"]) - is_rna = np.isin(atom_array_input_annotated.res_name, ["A", "C", "G", "U"]) - dna_array = atom_array_input_annotated[is_dna] - rna_array = atom_array_input_annotated[is_rna] - - atom_array_input_annotated[is_dna] = reorder_atoms_per_residue( - dna_array, backbone_atoms_DNA - ) - atom_array_input_annotated[is_rna] = reorder_atoms_per_residue( - rna_array, backbone_atoms_RNA - ) + if exists(atom_array_input_annotated): + is_dna = np.isin(atom_array_input_annotated.res_name, ["DA", "DC", "DG", "DT"]) + is_rna = np.isin(atom_array_input_annotated.res_name, ["A", "C", "G", "U"]) + dna_array = atom_array_input_annotated[is_dna] + rna_array = atom_array_input_annotated[is_rna] + + atom_array_input_annotated[is_dna] = reorder_atoms_per_residue( + dna_array, backbone_atoms_DNA + ) + atom_array_input_annotated[is_rna] = reorder_atoms_per_residue( + rna_array, backbone_atoms_RNA + ) ####################################### atom_array = self._build_init(atom_array_input_annotated) @@ -928,11 +937,11 @@ def create_diffused_residues(n, additional_annotations=None, polymer_type="P"): elif polymer_type == "R": res_name = "A" bb_len = len(backbone_atoms_RNA) - bb_atom_names = strip_list(backbone_atoms_RNA) + bb_atom_names = backbone_atoms_RNA elif polymer_type == "D": res_name = "DA" bb_len = len(backbone_atoms_DNA) - bb_atom_names = strip_list(backbone_atoms_DNA) + bb_atom_names = backbone_atoms_DNA else: raise ValueError( f"invalid polymer type detected: {polymer_type}, check contig!" @@ -955,12 +964,13 @@ def create_diffused_residues(n, additional_annotations=None, polymer_type="P"): for idx in range(1, n + 1) ] array = struc.array(atoms) - array.set_annotation("element", np.array(bb_elements * n, dtype=" 0.75 - - o["max_ca_deviation"] = float(deviation.max(-1).values.mean()) - o["fraction_chainbreaks"] = float(is_chainbreak.float().mean(-1).mean()) - o["n_chainbreaks"] = float(is_chainbreak.float().sum(-1).mean()) + xyz_protein = X_L.cpu()[:, protein_idx_mask] + xyz_na = X_L.cpu()[:, na_idx_mask] + + ca_dists_protein = torch.norm(xyz_protein[:, 1:] - xyz_protein[:, :-1], dim=-1) + ca_dists_na = torch.norm(xyz_na[:, 1:] - xyz_na[:, :-1], dim=-1) + + deviation_protein = torch.abs(ca_dists_protein - self.standard_ca_dist) # B, (I-1) + deviation_na = torch.abs(ca_dists_na - self.standard_PP_dist) # B, (I-1) + is_chainbreak_protein = deviation_protein > 0.75 + is_chainbreak_na = deviation_na > 1 + + try: + o["max_ca_deviation_protein"] = float(deviation_protein.max(-1).values.mean()) + o["fraction_chainbreaks_protein"] = float(is_chainbreak_protein.float().mean(-1).mean()) + o["n_chainbreaks_protein"] = float(is_chainbreak_protein.float().sum(-1).mean()) + except: + print("No protein in this example, skipping protein chainbreak metrics") + + try: + o["max_ca_deviation_na"] = float(deviation_na.max(-1).values.mean()) + o["fraction_chainbreaks_na"] = float(is_chainbreak_na.float().mean(-1).mean()) + o["n_chainbreaks_na"] = float(is_chainbreak_na.float().sum(-1).mean()) + except: + print("No NA in this example, skipping NA chainbreak metrics") return o - class PPIMetrics(Metric): """PPI-specific metrics""" diff --git a/models/rfd3/src/rfd3/transforms/conditioning_base.py b/models/rfd3/src/rfd3/transforms/conditioning_base.py index 25b51a70..4050041f 100644 --- a/models/rfd3/src/rfd3/transforms/conditioning_base.py +++ b/models/rfd3/src/rfd3/transforms/conditioning_base.py @@ -243,6 +243,10 @@ def __init__( ) self.meta_conditioning_probabilities = meta_conditioning_probabilities self.train_conditions = train_conditions + + for item in self.train_conditions: + self.train_conditions[item].association_scheme = association_scheme + self.sequence_encoding = sequence_encoding self.association_scheme = association_scheme @@ -261,12 +265,15 @@ def check_input(self, data: dict): assert "conditions" in data, "Conditioning dict not initialized" def forward(self, data): + #for item in self.train_conditions: + # print(self.train_conditions[item].is_valid_for_example(data)) + valid_conditions = [ cond for cond in self.train_conditions.values() - if cond.frequency > 0 and cond.is_valid_for_example(data) + if cond.is_valid_for_example(data) and cond.frequency > 0 ] - + if len(valid_conditions) == 0: raise InvalidSampledConditionException("No valid condition was found.") @@ -280,8 +287,6 @@ def forward(self, data): i_cond = np.random.choice(np.arange(len(p_cond)), p=p_cond) cond = valid_conditions[i_cond] - cond.association_scheme = self.association_scheme - data["sampled_condition"] = cond data["sampled_condition_name"] = cond.name data["sampled_condition_cls"] = cond.__class__ diff --git a/models/rfd3/src/rfd3/transforms/design_transforms.py b/models/rfd3/src/rfd3/transforms/design_transforms.py index 36a8b43d..8d8da604 100644 --- a/models/rfd3/src/rfd3/transforms/design_transforms.py +++ b/models/rfd3/src/rfd3/transforms/design_transforms.py @@ -71,8 +71,10 @@ class SubsampleToTypes(Transform): def __init__( self, allowed_types: list | str = ["is_protein"], + association_scheme: str = 'atom14' ): self.allowed_types = allowed_types + self.association_scheme = association_scheme if not self.allowed_types == "ALL": for k in allowed_types: if not k.startswith("is_"): @@ -104,7 +106,7 @@ def forward(self, data): ) ) - if atom_array.is_protein.sum() == 0: + if self.association_scheme != 'atom23' and atom_array.is_protein.sum() == 0: raise ValueError( "No protein atoms found in the atom array. Example ID: {}".format( data.get("example_id", "unknown") diff --git a/models/rfd3/src/rfd3/transforms/na_geom.py b/models/rfd3/src/rfd3/transforms/na_geom.py index ebb076d3..2cef7822 100644 --- a/models/rfd3/src/rfd3/transforms/na_geom.py +++ b/models/rfd3/src/rfd3/transforms/na_geom.py @@ -119,7 +119,7 @@ class CalculateNucleicAcidGeomFeats(Transform): def __init__( self, is_inference, - add_nucleic_ss_feats: bool = True, + meta_conditioning_probabilities, p_is_nucleic_ss_example: float = 0.3, p_show_partial_feats: float = 0.5, @@ -136,9 +136,18 @@ def __init__( ): # Critical, must always have to know how to handle self.is_inference = is_inference - + if not self.is_inference: + ## relevant in training + self.sampling_prob = meta_conditioning_probabilities['calculate_NA_SS'] + else: + ## irrelevant in inference + self.sampling_prob = 0 # For sampling whether we add nucleic-ss features (extra t2d) - self.add_nucleic_ss_feats = add_nucleic_ss_feats + + # relevant in training + self.add_nucleic_ss_feats = (self.sampling_prob > 0) + ###### + self.p_canonical_bp_filter = p_canonical_bp_filter # enforce that bp labels are only canonical self.p_is_nucleic_ss_example = p_is_nucleic_ss_example self.nucleic_ss_min_shown = nucleic_ss_min_shown @@ -189,7 +198,7 @@ def forward(self, data: dict) -> dict: n_tokens = len(token_starts) print(" DO I NEED TO CHANGE TO TOKEN_ID???") # Handle the training case with ground truth and masking: - if not self.is_inference: + if not self.is_inference and (np.random.rand() < self.sampling_prob): # First, annotate as usual # atom_array = annotate_na_ss(atom_array, **kwargs) @@ -226,8 +235,9 @@ def forward(self, data: dict) -> dict: - 3). Lists of paired indices """ - is_nucleic_ss_example=True - give_partial_feats=False + #is_nucleic_ss_example=True + #give_partial_feats=False + atom_array = annotate_na_ss_from_data_specification( data, overwrite=True, @@ -282,4 +292,4 @@ def _sample_where_to_show_ss(self, n_tokens: int, n_islands_max=self.n_islands_max, max_length=max_length, ) - \ No newline at end of file + diff --git a/models/rfd3/src/rfd3/transforms/na_geom_utils.py b/models/rfd3/src/rfd3/transforms/na_geom_utils.py index 61f80594..29be56f8 100644 --- a/models/rfd3/src/rfd3/transforms/na_geom_utils.py +++ b/models/rfd3/src/rfd3/transforms/na_geom_utils.py @@ -1321,6 +1321,8 @@ def bp_partner_to_ss_matrix( *, feature_info: Optional[dict] = None, mol_info: Optional[NucMolInfo] = None, + NA_only: Optional[bool] = False, + planar_only: Optional[bool] = False, include_loops: bool = True, token_level_data: Optional[dict] = None, ) -> np.ndarray: diff --git a/models/rfd3/src/rfd3/transforms/pipelines.py b/models/rfd3/src/rfd3/transforms/pipelines.py index d4c3361f..f7eb9a05 100644 --- a/models/rfd3/src/rfd3/transforms/pipelines.py +++ b/models/rfd3/src/rfd3/transforms/pipelines.py @@ -196,6 +196,7 @@ def get_crop_transform( max_binder_length: int, max_atoms_in_crop: int | None, allowed_types: List[str], + association_scheme: str, ): if ( crop_contiguous_probability > 0 @@ -215,7 +216,7 @@ def get_crop_transform( ), "Crop center cutoff distance must be greater than 0" pre_crop_transforms = [ - SubsampleToTypes(allowed_types=allowed_types), + SubsampleToTypes(allowed_types=allowed_types, association_scheme=association_scheme), ] cropping_transform = RandomRoute( @@ -360,8 +361,11 @@ def build_atom14_base_pipeline_( max_ss_frac_to_provide: float, min_ss_island_len: int, max_ss_island_len: int, - # Nucleic acid features - add_na_pair_features: bool, + + ## Nucleic acid features ##### + #add_na_pair_features: bool, + ## This should not be necessary, controlled through feature names in model, and meta conditioning probabilities, inference behavior handled in transform itself ##### + **_, # dump additional kwargs (e.g. msa stuff) ): """ @@ -411,6 +415,7 @@ def build_atom14_base_pipeline_( max_binder_length=max_binder_length, max_atoms_in_crop=max_atoms_in_crop, allowed_types=allowed_types, + association_scheme=association_scheme ) if zero_occ_on_exposure_after_cropping: @@ -443,14 +448,15 @@ def build_atom14_base_pipeline_( ) ) # Add nucleic acid geometry features - if add_na_pair_features: - transforms.append( - CalculateNucleicAcidGeomFeats( - is_inference, - NA_only=False, - planar_only=True, - ) + #if add_na_pair_features: + transforms.append( + CalculateNucleicAcidGeomFeats( + is_inference, + meta_conditioning_probabilities, + NA_only=False, + planar_only=True, ) + ) # Design Transforms transforms += [ @@ -618,7 +624,6 @@ def build_atom14_base_pipeline( Wrapper around pipeline construction to handle empty training args Sets default behaviour for inference to keep backward compatibility """ - if is_inference: # Provide explicit defaults for training-only args kwargs.setdefault("crop_size", 512) @@ -634,7 +639,8 @@ def build_atom14_base_pipeline( kwargs.setdefault("min_ss_island_len", 0) kwargs.setdefault("max_ss_island_len", 999) kwargs.setdefault("max_binder_length", 999) - kwargs.setdefault("add_na_pair_features", False) + # This should not be necessary. + #kwargs.setdefault("add_na_pair_features", False) kwargs.setdefault("b_factor_min", None) kwargs.setdefault("zero_occ_on_exposure_after_cropping", False) diff --git a/models/rfd3/src/rfd3/transforms/training_conditions.py b/models/rfd3/src/rfd3/transforms/training_conditions.py index dbb07b76..fae2768e 100644 --- a/models/rfd3/src/rfd3/transforms/training_conditions.py +++ b/models/rfd3/src/rfd3/transforms/training_conditions.py @@ -96,13 +96,13 @@ def is_valid_for_example(self, data) -> bool: is_rna = data["atom_array"].is_rna ### updating this to allow other polymers if self.association_scheme == "atom23": - if not np.any(is_protein | is_dna | is_rna): - return False + if np.any(is_protein | is_dna | is_rna): + return True else: - if not np.any(is_protein): - return False - - return True + if np.any(is_protein): + return True + + return False def sample_motif_tokens(self, atom_array): """ diff --git a/models/rfd3/src/rfd3/transforms/util_transforms.py b/models/rfd3/src/rfd3/transforms/util_transforms.py index 75d69ddf..37f08f19 100644 --- a/models/rfd3/src/rfd3/transforms/util_transforms.py +++ b/models/rfd3/src/rfd3/transforms/util_transforms.py @@ -37,7 +37,7 @@ def assert_single_representative(token, central_atom="CB"): mask = get_af3_token_representative_masks(token, central_atom=central_atom) assert ( np.sum(mask) == 1 - ), f"No representative atom (CB) found. mask: {mask}\nToken: {token}" + ), f"No representative atom ({central_atom}) found. mask: {mask}\nToken: {token}" def assert_single_token(token): diff --git a/models/rfd3/src/rfd3/transforms/virtual_atoms.py b/models/rfd3/src/rfd3/transforms/virtual_atoms.py index 6ce16759..07db4e11 100644 --- a/models/rfd3/src/rfd3/transforms/virtual_atoms.py +++ b/models/rfd3/src/rfd3/transforms/virtual_atoms.py @@ -225,17 +225,21 @@ def forward(self, data: dict) -> dict: # First, pad with virtual atoms if needed if self.association_scheme == "atom23" and atom_array[start].is_dna: n_atoms_per_token = 22 + central_atom = "C1'" elif self.association_scheme == "atom23" and atom_array[start].is_rna: n_atoms_per_token = 23 + central_atom = "C1'" else: n_atoms_per_token = self.n_atoms_per_token + central_atom = self.atom_to_pad_from + n_pad = n_atoms_per_token - len(token) if n_pad > 0: mask = get_af3_token_representative_masks( - token, central_atom=self.atom_to_pad_from + token, central_atom=central_atom ) - assert_single_representative(token) + assert_single_representative(token, central_atom=central_atom) # ... Create virtual atoms pad_atoms = token[mask].copy() From b5beff039eecbb8f2a03c5eedbff2df64e908db9 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Tue, 3 Feb 2026 12:54:16 -0800 Subject: [PATCH 10/49] inference fixes for legacy_input_parsing --- .../rfd3/inference/legacy_input_parsing.py | 44 +++++++++++++++---- 1 file changed, 35 insertions(+), 9 deletions(-) diff --git a/models/rfd3/src/rfd3/inference/legacy_input_parsing.py b/models/rfd3/src/rfd3/inference/legacy_input_parsing.py index 0d344fd7..7bf1a50b 100644 --- a/models/rfd3/src/rfd3/inference/legacy_input_parsing.py +++ b/models/rfd3/src/rfd3/inference/legacy_input_parsing.py @@ -268,36 +268,57 @@ def fetch_motif_residue_( return subarray -def create_diffused_residues_(n): +def create_diffused_residues_(n, polymer_type='p'): + from rfd3.constants import ( + ATOM23_ATOM_NAME_TO_ELEMENT, + backbone_atoms_DNA, + backbone_atoms_RNA, + ) if n <= 0: raise ValueError(f"Negative/null residue count ({n}) not allowed.") - + + if polymer_type == 'P': + res_name = 'ALA' + bb_len = 5 + bb_atom_names = ["N", "CA", "C", "O", "CB"] + elif polymer_type == 'R': + res_name = 'A' + bb_len = len(backbone_atoms_RNA) + bb_atom_names = backbone_atoms_RNA + elif polymer_type == 'D': + res_name = 'DA' + bb_len = len(backbone_atoms_DNA) + bb_atom_names = backbone_atoms_DNA + else: + raise ValueError(f"invalid polymer type detected: {polymer_type}, check contig!") + + bb_elements = [ATOM23_ATOM_NAME_TO_ELEMENT[item] for item in bb_atom_names] + atoms = [] [ atoms.extend( [ struc.Atom( np.array([0.0, 0.0, 0.0], dtype=np.float32), - res_name="ALA", + res_name=res_name, res_id=idx, ) - for _ in range(5) + for _ in range(bb_len) ] ) for idx in range(1, n + 1) ] array = struc.array(atoms) array.set_annotation( - "element", np.array(["N", "C", "C", "O", "C"] * n, dtype=" Date: Sun, 18 Jan 2026 15:41:00 -0800 Subject: [PATCH 11/49] feat: atom23 porting --- models/rfd3/src/rfd3/constants.py | 1 - models/rfd3/src/rfd3/transforms/conditioning_base.py | 4 ++++ models/rfd3/src/rfd3/transforms/design_transforms.py | 5 +++++ models/rfd3/src/rfd3/transforms/training_conditions.py | 5 +++++ models/rfd3/src/rfd3/transforms/virtual_atoms.py | 4 +--- 5 files changed, 15 insertions(+), 4 deletions(-) diff --git a/models/rfd3/src/rfd3/constants.py b/models/rfd3/src/rfd3/constants.py index 4d762621..bba1925d 100644 --- a/models/rfd3/src/rfd3/constants.py +++ b/models/rfd3/src/rfd3/constants.py @@ -401,7 +401,6 @@ ATOM23_ATOM_NAMES_RNA = np.array( [item.strip() for item in backbone_atomscheme_RNA] + [f"V{i}" for i in range(23 - len(backbone_atomscheme_RNA))] -) """Atom23 atom names (e.g. CA, V1)""" ATOM23_ATOM_ELEMENTS_RNA = np.array( diff --git a/models/rfd3/src/rfd3/transforms/conditioning_base.py b/models/rfd3/src/rfd3/transforms/conditioning_base.py index 4050041f..2bc598f3 100644 --- a/models/rfd3/src/rfd3/transforms/conditioning_base.py +++ b/models/rfd3/src/rfd3/transforms/conditioning_base.py @@ -287,6 +287,8 @@ def forward(self, data): i_cond = np.random.choice(np.arange(len(p_cond)), p=p_cond) cond = valid_conditions[i_cond] + cond.association_scheme = self.association_scheme + data["sampled_condition"] = cond data["sampled_condition_name"] = cond.name data["sampled_condition_cls"] = cond.__class__ @@ -304,6 +306,8 @@ class SampleConditioningFlags(Transform): "AssignTypes", "SampleConditioningType", ] # We use is_protein in the PPI training condition + def __init__(self, association_scheme): + self.association_scheme = association_scheme def __init__(self, association_scheme): self.association_scheme = association_scheme diff --git a/models/rfd3/src/rfd3/transforms/design_transforms.py b/models/rfd3/src/rfd3/transforms/design_transforms.py index 8d8da604..57e98b77 100644 --- a/models/rfd3/src/rfd3/transforms/design_transforms.py +++ b/models/rfd3/src/rfd3/transforms/design_transforms.py @@ -757,6 +757,11 @@ def forward(self, data: Dict[str, Any]) -> Dict[str, Any]: """ if "feats" not in data.keys(): data["feats"] = {} + + if association_scheme == 'atom23': + data['atom_array'].set_annotation('is_protein_token', data['atom_array'].is_protein) + data['atom_array'].set_annotation('is_dna_token', data['atom_array'].is_dna) + data['atom_array'].set_annotation('is_rna_token', data['atom_array'].is_rna) if self.association_scheme == "atom23": data["atom_array"].set_annotation( diff --git a/models/rfd3/src/rfd3/transforms/training_conditions.py b/models/rfd3/src/rfd3/transforms/training_conditions.py index fae2768e..389d0013 100644 --- a/models/rfd3/src/rfd3/transforms/training_conditions.py +++ b/models/rfd3/src/rfd3/transforms/training_conditions.py @@ -72,9 +72,11 @@ def __init__( p_fix_motif_coordinates, p_fix_motif_sequence, p_unindex_motif_tokens, + association_scheme = 'atom14', ): self.name = name self.frequency = frequency + self.association_scheme = association_scheme # Token selection self.island_sampling_kwargs = island_sampling_kwargs @@ -89,6 +91,8 @@ def __init__( self.p_fix_motif_coordinates = p_fix_motif_coordinates self.p_fix_motif_sequence = p_fix_motif_sequence self.p_unindex_motif_tokens = p_unindex_motif_tokens + + self.association_scheme = association_scheme def is_valid_for_example(self, data) -> bool: is_protein = data["atom_array"].is_protein @@ -521,6 +525,7 @@ def sample_is_motif_atom_with_fixed_seq( is_motif_atom_with_fixed_seq = ( is_motif_atom_with_fixed_seq | ~atom_array.is_protein ) + return is_motif_atom_with_fixed_seq diff --git a/models/rfd3/src/rfd3/transforms/virtual_atoms.py b/models/rfd3/src/rfd3/transforms/virtual_atoms.py index 07db4e11..9e9fea8a 100644 --- a/models/rfd3/src/rfd3/transforms/virtual_atoms.py +++ b/models/rfd3/src/rfd3/transforms/virtual_atoms.py @@ -53,7 +53,6 @@ def map_to_association_scheme( else: return ATOM_NAMES[idxs] - def map_names_to_elements( atom_names: list | str, default=VIRTUAL_ATOM_ELEMENT_NAME ) -> np.ndarray: @@ -180,7 +179,7 @@ def forward(self, data: dict) -> dict: token_ids = np.unique(atom_array.token_id) assert len(token_ids) == len( is_motif_atom_with_fixed_seq - ), "Token ids and token level array have different lengths!" + ), "Token ids and token level array have different lengths!" # Unindexed tokens are never fully atomized, but may be assigned as atomized to have repr atoms: if self.association_scheme == "atom23": @@ -212,7 +211,6 @@ def forward(self, data: dict) -> dict: is_non_paddable_residue = is_residue & ( is_motif_atom_with_fixed_seq | is_motif_token_unindexed ) - # Collect virtual atoms to insert (we will insert them all at once) virtual_atoms_to_insert = [] insert_positions = [] From 4caba95d327f92b48b8e6b1109a5dabe35b7f3ea Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Tue, 3 Feb 2026 15:45:00 -0800 Subject: [PATCH 12/49] fix: atom23 constants import error --- models/rfd3/src/rfd3/trainer/trainer_utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/models/rfd3/src/rfd3/trainer/trainer_utils.py b/models/rfd3/src/rfd3/trainer/trainer_utils.py index 9ee7939b..80d1f878 100644 --- a/models/rfd3/src/rfd3/trainer/trainer_utils.py +++ b/models/rfd3/src/rfd3/trainer/trainer_utils.py @@ -13,6 +13,7 @@ ATOM14_ATOM_NAMES, ATOM23_ATOM_NAMES_DNA, ATOM23_ATOM_NAMES_RNA, + backbone_atoms_RNA, VIRTUAL_ATOM_ELEMENT_NAME, association_schemes, association_schemes_stripped, From 45e8f837ce4b2497c39e4e4da65e67550b3ce104 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Thu, 5 Feb 2026 12:40:50 -0800 Subject: [PATCH 13/49] mor einference fixes --- .../rfd3/inference/legacy_input_parsing.py | 26 +++++++++++++------ .../rfd3/src/rfd3/transforms/virtual_atoms.py | 1 + 2 files changed, 19 insertions(+), 8 deletions(-) diff --git a/models/rfd3/src/rfd3/inference/legacy_input_parsing.py b/models/rfd3/src/rfd3/inference/legacy_input_parsing.py index 7bf1a50b..61cd34f7 100644 --- a/models/rfd3/src/rfd3/inference/legacy_input_parsing.py +++ b/models/rfd3/src/rfd3/inference/legacy_input_parsing.py @@ -117,7 +117,6 @@ def fetch_motif_residue_( NB: For glycines, we extend the array with a CB position so as to not leak whether the original residue is a glycine if sequence is masked during inference. """ - assert ( src_atom_array is not None ), "Motif provided in contigs, but no input provided. input={} contig={}".format( @@ -227,9 +226,15 @@ def fetch_motif_residue_( "is_motif_atom_with_fixed_coord", np.zeros(subarray.shape[0], dtype=int) ) else: - subarray.set_annotation( - "is_motif_atom_with_fixed_coord", subarray.is_motif_atom.copy() - ) + if "is_motif_atom" in subarray.get_annotation_categories(): + subarray.set_annotation( + "is_motif_atom_with_fixed_coord", subarray.is_motif_atom.copy() + ) + else: + subarray.set_annotation( + "is_motif_atom_with_fixed_coord", np.array([True]*len(subarray)) + ) + if flexible_backbone: backbone_atoms = ["N", "CA", "C", "O"] is_flexible_motif_atom = np.isin(subarray.atom_name, backbone_atoms) @@ -242,12 +247,17 @@ def fetch_motif_residue_( "is_flexible_motif_atom", np.zeros(subarray.shape[0], dtype=bool) ) if to_unindex: - subarray.set_annotation( - "is_motif_atom_unindexed", subarray.is_motif_atom.copy() - ) + if "is_motif_atom" in subarray.get_annotation_categories(): + subarray.set_annotation( + "is_motif_atom_unindexed", subarray.is_motif_atom.copy() + ) + else: + subarray.set_annotation( + "is_motif_atom_unindexed", np.array([True]*len(subarray)) + ) # Subset to desired motif atoms subarray = subarray[subarray.is_motif_atom.astype(bool)] - + # ... Relax sequence constraint if provided if ( exists(unfixed_sequence_components) diff --git a/models/rfd3/src/rfd3/transforms/virtual_atoms.py b/models/rfd3/src/rfd3/transforms/virtual_atoms.py index 9e9fea8a..31ec59ae 100644 --- a/models/rfd3/src/rfd3/transforms/virtual_atoms.py +++ b/models/rfd3/src/rfd3/transforms/virtual_atoms.py @@ -237,6 +237,7 @@ def forward(self, data: dict) -> dict: mask = get_af3_token_representative_masks( token, central_atom=central_atom ) + assert_single_representative(token, central_atom=central_atom) # ... Create virtual atoms From 335e58039060f9391f901f5d3ce03cec14822ee9 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Fri, 6 Feb 2026 11:45:31 -0800 Subject: [PATCH 14/49] fix: tiny legacy parsing bug --- models/rfd3/src/rfd3/inference/legacy_input_parsing.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/models/rfd3/src/rfd3/inference/legacy_input_parsing.py b/models/rfd3/src/rfd3/inference/legacy_input_parsing.py index 61cd34f7..aed3cc98 100644 --- a/models/rfd3/src/rfd3/inference/legacy_input_parsing.py +++ b/models/rfd3/src/rfd3/inference/legacy_input_parsing.py @@ -251,12 +251,12 @@ def fetch_motif_residue_( subarray.set_annotation( "is_motif_atom_unindexed", subarray.is_motif_atom.copy() ) + # Subset to desired motif atoms + subarray = subarray[subarray.is_motif_atom.astype(bool)] else: subarray.set_annotation( "is_motif_atom_unindexed", np.array([True]*len(subarray)) ) - # Subset to desired motif atoms - subarray = subarray[subarray.is_motif_atom.astype(bool)] # ... Relax sequence constraint if provided if ( From cff03801ed072113ed84313b1faf731883a56768 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Mon, 9 Feb 2026 11:37:38 -0800 Subject: [PATCH 15/49] inference fixes --- models/rfd3/src/rfd3/metrics/design_metrics.py | 15 +++++++++++---- .../rfd3/src/rfd3/transforms/design_transforms.py | 4 +++- models/rfd3/src/rfd3/transforms/na_geom.py | 2 +- 3 files changed, 15 insertions(+), 6 deletions(-) diff --git a/models/rfd3/src/rfd3/metrics/design_metrics.py b/models/rfd3/src/rfd3/metrics/design_metrics.py index cb9dad6a..67d4266b 100644 --- a/models/rfd3/src/rfd3/metrics/design_metrics.py +++ b/models/rfd3/src/rfd3/metrics/design_metrics.py @@ -12,9 +12,10 @@ from foundry.common import exists from foundry.metrics.metric import Metric +from rfd3.constants import backbone_atoms_RNA STANDARD_CACA_DIST = 3.8 - +STANDARD_P_P_DISTANCE = 6.4 ## average of B and A form 7 and 5.9 def get_clash_metrics( atom_array, @@ -28,7 +29,13 @@ def get_clash_metrics( ) def get_chainbreaks(): - ca_atoms = atom_array[atom_array.atom_name == "CA"] + if "CA" in atom_array.atom_name: + ca_atoms = atom_array[atom_array.atom_name == "CA"] + cut_off = STANDARD_CACA_DIST + elif "P" in atom_array.atom_name: + ca_atoms = atom_array[atom_array.atom_name == "P"] + cut_off = STANDARD_P_P_DISTANCE + xyz = ca_atoms.coord xyz = torch.from_numpy(xyz) ca_dists = torch.norm(xyz[1:] - xyz[:-1], dim=-1) @@ -45,7 +52,7 @@ def get_chainbreaks(): } def get_interresidue_clashes(backbone_only=False): - protein_array = atom_array[atom_array.is_protein] + protein_array = atom_array[atom_array.is_protein | atom_array.is_dna | atom_array.is_rna] resid = protein_array.res_id - protein_array.res_id.min() xyz = protein_array.coord dists = np.linalg.norm(xyz[:, None] - xyz[None], axis=-1) # N_atoms x N_atoms @@ -58,7 +65,7 @@ def get_interresidue_clashes(backbone_only=False): if backbone_only: # Block out non-backbone atoms - backbone_mask = np.isin(protein_array.atom_name, ["N", "CA", "C"]) + backbone_mask = np.isin(protein_array.atom_name, ["N", "CA", "C"] + backbone_atoms_RNA) mask = backbone_mask[:, None] & backbone_mask[None, :] dists[~mask] = 999 diff --git a/models/rfd3/src/rfd3/transforms/design_transforms.py b/models/rfd3/src/rfd3/transforms/design_transforms.py index 57e98b77..ca7482a9 100644 --- a/models/rfd3/src/rfd3/transforms/design_transforms.py +++ b/models/rfd3/src/rfd3/transforms/design_transforms.py @@ -823,9 +823,11 @@ def generate_token_feature(self, feature_name, n_dims, data): # Don't do this if we already have the feature if feature_name in data["feats"].keys(): return data - + # For these, we need to use a constructor function mapping, # since pair features may require custom logic/conventions. + + ## for old ckpt handling ## if feature_name in self.constructor_functions.keys(): feature_array = self.constructor_functions[feature_name](data["atom_array"]) else: diff --git a/models/rfd3/src/rfd3/transforms/na_geom.py b/models/rfd3/src/rfd3/transforms/na_geom.py index 2cef7822..2a93150b 100644 --- a/models/rfd3/src/rfd3/transforms/na_geom.py +++ b/models/rfd3/src/rfd3/transforms/na_geom.py @@ -196,7 +196,7 @@ def forward(self, data: dict) -> dict: token_level_array = atom_array[token_starts] token_ids = [int(t) for t in token_level_array.token_id] n_tokens = len(token_starts) - print(" DO I NEED TO CHANGE TO TOKEN_ID???") + #TODO print(" DO I NEED TO CHANGE TO TOKEN_ID???") # Handle the training case with ground truth and masking: if not self.is_inference and (np.random.rand() < self.sampling_prob): From d7fe89b8f54039ec57cee9082dce81d7a90d8969 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Tue, 10 Feb 2026 18:25:01 -0800 Subject: [PATCH 16/49] ligand chain fix; add ori_jitter option in new dialect --- models/rfd3/src/rfd3/inference/input_parsing.py | 12 +++++++++++- .../rfd3/src/rfd3/inference/legacy_input_parsing.py | 6 ++++++ models/rfd3/src/rfd3/utils/inference.py | 13 ++++++++++++- 3 files changed, 29 insertions(+), 2 deletions(-) diff --git a/models/rfd3/src/rfd3/inference/input_parsing.py b/models/rfd3/src/rfd3/inference/input_parsing.py index 17032e43..f3091805 100644 --- a/models/rfd3/src/rfd3/inference/input_parsing.py +++ b/models/rfd3/src/rfd3/inference/input_parsing.py @@ -184,6 +184,7 @@ class DesignInputSpecification(BaseModel): symmetry: Optional[SymmetryConfig] = Field(None, description="Symmetry specification, see docs/symmetry.md") # Centering & COM guidance ori_token: Optional[list[float]] = Field(None, description="Origin coordinates") + ori_jitter: Optional[float] = Field(None, description="Jitter ori in a random direction and use ori_jitter to sample distance via exponential distribution") infer_ori_strategy: Optional[str] = Field(None, description="Strategy for inferring origin; `com` or `hotspots`") # Additional global conditioning plddt_enhanced: Optional[bool] = Field(True, description="Enable pLDDT enhancement") @@ -725,6 +726,13 @@ def _append_ligand(self, atom_array, atom_array_input_annotated): ligand_array.set_annotation( annot, np.full(ligand_array.array_length(), default) ) + + chain_cand = 'X' + while chain_cand in atom_array.chain_id.tolist(): + chain_cand = chain_cand + chain_cand + ligand_chain = np.array([chain_cand]*len(ligand_array)) + ligand_array.chain_id = ligand_chain + atom_array = atom_array + ligand_array return atom_array @@ -749,8 +757,10 @@ def _set_origin(self, atom_array): "Partial diffusion with symmetry: skipping COM centering to preserve chain spacing" ) else: + if not exists(self.ori_jitter): + self.ori_jitter = None atom_array = set_com( - atom_array, ori_token=None, infer_ori_strategy="com" + atom_array, ori_token=None, infer_ori_strategy="com", ori_jitter=self.ori_jitter ) else: # Standard: set ori token, zero out diffused atoms diff --git a/models/rfd3/src/rfd3/inference/legacy_input_parsing.py b/models/rfd3/src/rfd3/inference/legacy_input_parsing.py index aed3cc98..fbee3463 100644 --- a/models/rfd3/src/rfd3/inference/legacy_input_parsing.py +++ b/models/rfd3/src/rfd3/inference/legacy_input_parsing.py @@ -736,6 +736,12 @@ def create_atom_array_from_design_specification_legacy( + np.max(atom_array.res_id) + 1 ) + chain_cand = 'X' + while chain_cand in atom_array.chain_id.tolist(): + chain_cand = chain_cand + chain_cand + ligand_chain = np.array([chain_cand]*len(ligand_array)) + ligand_array.chain_id = ligand_chain + atom_array = atom_array + ligand_array # ... Apply symmetry if it exists ahead of any other processing diff --git a/models/rfd3/src/rfd3/utils/inference.py b/models/rfd3/src/rfd3/utils/inference.py index 2d04e149..426eace2 100644 --- a/models/rfd3/src/rfd3/utils/inference.py +++ b/models/rfd3/src/rfd3/utils/inference.py @@ -452,7 +452,7 @@ def infer_ori_from_com(atom_array): def set_com( - atom_array, ori_token: list | None = None, infer_ori_strategy: str | None = None + atom_array, ori_token: list | None = None, infer_ori_strategy: str | None = None, ori_jitter: float | None = None ): if exists(ori_token): center = np.array([float(x) for x in ori_token], dtype=atom_array.coord.dtype) @@ -505,6 +505,17 @@ def set_com( atom_array.coord = np.zeros_like( atom_array.coord, dtype=atom_array.coord.dtype ) + if ori_jitter is not None: + # randomly jitter ori with given scale + direction = np.random.normal(size=3) + direction /= np.linalg.norm(direction) + + # Random length (mean ~ scale) + length = np.random.exponential(scale=scale) + jittered_offset = direction*length + + atom_array.coord -= jittered_offset + return atom_array From 22d24edc5be5c17ae788e4da45df6ea104c84f1d Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Mon, 16 Feb 2026 12:13:11 -0800 Subject: [PATCH 17/49] make 2oken_2d_features/embedder optional --- models/rfd3/src/rfd3/model/layers/encoders.py | 10 +++++++--- models/rfd3/src/rfd3/transforms/design_transforms.py | 4 +++- models/rfd3/src/rfd3/transforms/pipelines.py | 6 +++--- 3 files changed, 13 insertions(+), 7 deletions(-) diff --git a/models/rfd3/src/rfd3/model/layers/encoders.py b/models/rfd3/src/rfd3/model/layers/encoders.py index 363fc4de..a3c37cf6 100644 --- a/models/rfd3/src/rfd3/model/layers/encoders.py +++ b/models/rfd3/src/rfd3/model/layers/encoders.py @@ -50,9 +50,9 @@ def __init__( pairformer_block, downcast, token_1d_features, - token_2d_features, atom_1d_features, atom_transformer, + token_2d_features=None, use_chunked_pll=False, # New parameter for memory optimization ): super().__init__() @@ -64,7 +64,10 @@ def __init__( self.atom_1d_embedder_1 = OneDFeatureEmbedder(atom_1d_features, c_s) self.atom_1d_embedder_2 = OneDFeatureEmbedder(atom_1d_features, c_atom) self.token_1d_embedder = OneDFeatureEmbedder(token_1d_features, c_s) - self.token_2d_embedder = TwoDFeatureEmbedder(token_2d_features, c_z) + if token_2d_features != None: + self.token_2d_embedder = TwoDFeatureEmbedder(token_2d_features, c_z) + else: + self.token_2d_embedder = None self.downcast_atom = Downcast(c_atom=c_s, c_token=c_s, c_s=None, **downcast) self.transition_post_token = Transition(c=c_s, n=2) @@ -206,7 +209,8 @@ def init_tokens(): f["ref_pos"][f["is_ca"]], valid_mask ) # Add extra token pair features - Z_init_II = Z_init_II + self.token_2d_embedder(f, I) + if self.token_2d_embedder != None: + Z_init_II = Z_init_II + self.token_2d_embedder(f, I) # Run a small transformer to provide position encodings to single. for block in self.transformer_stack: diff --git a/models/rfd3/src/rfd3/transforms/design_transforms.py b/models/rfd3/src/rfd3/transforms/design_transforms.py index ca7482a9..1ee14ce3 100644 --- a/models/rfd3/src/rfd3/transforms/design_transforms.py +++ b/models/rfd3/src/rfd3/transforms/design_transforms.py @@ -803,7 +803,7 @@ def __init__( token_2d_features, autofill_zeros_if_not_present_in_atomarray=False, association_scheme="atom14", - ): + ): self.autofill = autofill_zeros_if_not_present_in_atomarray self.token_2d_features = token_2d_features self.association_scheme = association_scheme @@ -866,6 +866,8 @@ def forward(self, data: Dict[str, Any]) -> Dict[str, Any]: if "feats" not in data.keys(): data["feats"] = {} # Only apply for features that the model is expecting: + if self.token_2d_features == None: + return data for feature_name, n_dims in self.token_2d_features.items(): data = self.generate_token_feature(feature_name, n_dims, data) diff --git a/models/rfd3/src/rfd3/transforms/pipelines.py b/models/rfd3/src/rfd3/transforms/pipelines.py index f7eb9a05..940dfab6 100644 --- a/models/rfd3/src/rfd3/transforms/pipelines.py +++ b/models/rfd3/src/rfd3/transforms/pipelines.py @@ -353,7 +353,7 @@ def build_atom14_base_pipeline_( center_option: str, atom_1d_features: dict | None, token_1d_features: dict | None, - token_2d_features: dict | None, + token_2d_features: dict | None = None, # PPI features max_ppi_hotspots_frac_to_provide: float, ppi_hotspot_max_distance: float, @@ -373,7 +373,7 @@ def build_atom14_base_pipeline_( """ warnings.filterwarnings("ignore", category=RuntimeWarning) warnings.filterwarnings("ignore", category=DeprecationWarning) - + # Add any data necessary for downstream transforms transforms = [ AddData( @@ -653,7 +653,7 @@ def build_atom14_base_pipeline( kwargs.setdefault("residue_cache_dir", None) # TODO: Delete these once all checkpoints are updated with the latest defaults - kwargs.setdefault("generate_conformers_for_non_protein_only", True) + kwargs.setdefault("generate_conformers_for_non_protein_only", False) kwargs.setdefault("return_atom_array", True) kwargs.setdefault("provide_elements_for_unindexed_components", False) kwargs.setdefault("center_option", "all") From 4478253fa95a0f2af6b66c56d34fc24f72f1f816 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Mon, 16 Feb 2026 12:48:48 -0800 Subject: [PATCH 18/49] dll bug fix --- models/rfd3/src/rfd3/model/layers/block_utils.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/models/rfd3/src/rfd3/model/layers/block_utils.py b/models/rfd3/src/rfd3/model/layers/block_utils.py index aeac08c8..c2bbd56c 100644 --- a/models/rfd3/src/rfd3/model/layers/block_utils.py +++ b/models/rfd3/src/rfd3/model/layers/block_utils.py @@ -210,7 +210,7 @@ def create_attention_indices( chain_ids is not None and len(torch.unique(chain_ids)) > 3 ): # Multi-chain structure # Reserve 25% of attention keys for inter-chain interactions - k_inter_chain = max(32, k_actual // 4) # At least 32 inter-chain keys + k_inter_chain = min(max(32, k_actual // 4), k_actual) # At least 32 inter-chain keys k_intra_chain = k_actual - k_inter_chain attn_indices = get_sparse_attention_indices_with_inter_chain( @@ -413,7 +413,10 @@ def extend_index_mask_with_neighbours( # 2. Find k-nn excluding forced indices D_LL = torch.where(mask, inf, D_LL) - filler_idx = torch.topk(D_LL, k, dim=-1, largest=False).indices + try: + filler_idx = torch.topk(D_LL, k, dim=-1, largest=False).indices + except: + raise ValueError(f"DLL has nan?: {torch.isnan(D_LL).any()}, D_LL shape: {D_LL.shape}, k: {k}") # ... Reverse last axis s.t. best matched indices are last filler_idx = filler_idx.flip(dims=[-1]) From d8b8d0c04792d6da0756b9c88d008b7bc4d3d721 Mon Sep 17 00:00:00 2001 From: afavor Date: Mon, 16 Feb 2026 13:34:07 -0800 Subject: [PATCH 19/49] cleaned up NA-SS conditioning code before rebase --- models/rfd3/src/rfd3/constants.py | 67 +- .../src/rfd3/transforms/design_transforms.py | 4 +- models/rfd3/src/rfd3/transforms/na_geom.py | 255 +-- .../rfd3/src/rfd3/transforms/na_geom_utils.py | 1504 ++++++++--------- 4 files changed, 894 insertions(+), 936 deletions(-) diff --git a/models/rfd3/src/rfd3/constants.py b/models/rfd3/src/rfd3/constants.py index bba1925d..1e023eda 100644 --- a/models/rfd3/src/rfd3/constants.py +++ b/models/rfd3/src/rfd3/constants.py @@ -440,74 +440,73 @@ # Mapping from residue type to its backbone and sidechain atoms (for convenience) ATOM_REGION_BY_RESI = { - 'ALA': {'bb':('N','CA','C','O'), + 'ALA': {'bb':('N','CA','C','O'), 'sc':('CB')}, - 'ARG': {'bb':('N','CA','C','O'), + 'ARG': {'bb':('N','CA','C','O'), 'sc':('CB','CG','CD','NE','CZ','NH1','NH2')}, - 'ASN': {'bb':('N','CA','C','O'), + 'ASN': {'bb':('N','CA','C','O'), 'sc':('CB','CG','OD1','ND2')}, - 'ASP': {'bb':('N','CA','C','O'), + 'ASP': {'bb':('N','CA','C','O'), 'sc':('CB','CG','OD1','OD2')}, - 'CYS': {'bb':('N','CA','C','O'), + 'CYS': {'bb':('N','CA','C','O'), 'sc':('CB','SG')}, - 'GLN': {'bb':('N','CA','C','O'), + 'GLN': {'bb':('N','CA','C','O'), 'sc':('CB','CG','CD','OE1','NE2')}, - 'GLU': {'bb':('N','CA','C','O'), + 'GLU': {'bb':('N','CA','C','O'), 'sc':('CB','CG','CD','OE1','OE2')}, - 'GLY': {'bb':('N','CA','C','O'), + 'GLY': {'bb':('N','CA','C','O'), 'sc':()}, - 'HIS': {'bb':('N','CA','C','O'), + 'HIS': {'bb':('N','CA','C','O'), 'sc':('CB','CG','ND1','CD2','CE1','NE2')}, - 'ILE': {'bb':('N','CA','C','O'), + 'ILE': {'bb':('N','CA','C','O'), 'sc':('CB','CG1','CG2','CD1')}, - 'LEU': {'bb':('N','CA','C','O'), + 'LEU': {'bb':('N','CA','C','O'), 'sc':('CB','CG','CD1','CD2')}, - 'LYS': {'bb':('N','CA','C','O'), + 'LYS': {'bb':('N','CA','C','O'), 'sc':('CB','CG','CD','CE','NZ')}, - 'MET': {'bb':('N','CA','C','O'), + 'MET': {'bb':('N','CA','C','O'), 'sc':('CB','CG','SD','CE')}, - 'PHE': {'bb':('N','CA','C','O'), + 'PHE': {'bb':('N','CA','C','O'), 'sc':('CB','CG','CD1','CD2','CE1','CE2','CZ')}, - 'PRO': {'bb':('N','CA','C','O'), + 'PRO': {'bb':('N','CA','C','O'), 'sc':('CB','CG','CD')}, - 'SER': {'bb':('N','CA','C','O'), + 'SER': {'bb':('N','CA','C','O'), 'sc':('CB','OG')}, - 'THR': {'bb':('N','CA','C','O'), + 'THR': {'bb':('N','CA','C','O'), 'sc':('CB','OG1','CG2')}, - 'TRP': {'bb':('N','CA','C','O'), + 'TRP': {'bb':('N','CA','C','O'), 'sc':('CB','CG','CD1','CD2','CE2','CE3','NE1','CZ2','CZ3','CH2')}, - 'TYR': {'bb':('N','CA','C','O'), + 'TYR': {'bb':('N','CA','C','O'), 'sc':('CB','CG','CD1','CD2','CE1','CE2','CZ','OH')}, - 'VAL': {'bb':('N','CA','C','O'), + 'VAL': {'bb':('N','CA','C','O'), 'sc':('CB','CG1','CG2')}, - 'UNK': {'bb':('N','CA','C','O'), + 'UNK': {'bb':('N','CA','C','O'), 'sc':('CB')}, - 'MAS': {'bb':('N','CA','C','O'), + 'MAS': {'bb':('N','CA','C','O'), 'sc':('CB')}, - 'DA': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), + 'DA': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), 'sc':('N9','C4','N3','C2','N1','C6','C5','N7','C8','N6')}, - 'DC': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), + 'DC': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), 'sc':('N1','C2','O2','N3','C4','N4','C5','C6')}, - 'DG': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), + 'DG': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), 'sc':('N9','C4','N3','C2','N1','C6','C5','N7','C8','N2','O6')}, - 'DT': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), + 'DT': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), 'sc':('N1','C2','O2','N3','C4','O4','C5','C7','C6')}, - 'DX': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), + 'DX': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), 'sc':()}, - 'A': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), + 'A': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), 'sc':('N1','C2','N3','C4','C5','C6','N6','N7','C8','N9')}, - 'C': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), + 'C': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), 'sc':('N1','C2','O2','N3','C4','N4','C5','C6')}, - 'G': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), + 'G': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), 'sc':('N1','C2','N2','N3','C4','C5','C6','O6','N7','C8','N9')}, - 'U': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), + 'U': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), 'sc':('N1','C2','O2','N3','C4','O4','C5','C6')}, - 'X': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), + 'X': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), 'sc':()}, - 'HIS_D': {'bb':('N','CA','C','O'), + 'HIS_D': {'bb':('N','CA','C','O'), 'sc':('CB','CG','NE2','CD2','CE1','ND1')}, } - # Known planar sidechain atoms for each canonical residue type: PLANAR_ATOMS_BY_RESI = { 'ALA': [], diff --git a/models/rfd3/src/rfd3/transforms/design_transforms.py b/models/rfd3/src/rfd3/transforms/design_transforms.py index 1ee14ce3..c1193a30 100644 --- a/models/rfd3/src/rfd3/transforms/design_transforms.py +++ b/models/rfd3/src/rfd3/transforms/design_transforms.py @@ -45,7 +45,7 @@ get_af3_token_representative_masks, ) from rfd3.transforms.virtual_atoms import PadTokensWithVirtualAtoms -from rfd3.transforms.na_geom import get_bp_feats_from_atom_array +from rfd3.transforms.na_geom import na_ss_feats_from_annotation from foundry.utils.ddp import RankedLogger # noqa @@ -811,7 +811,7 @@ def __init__( # Need to pre-define custom constructor functions # to map from atomarray annotations to tensors. self.constructor_functions = { - 'bp_partners': get_bp_feats_from_atom_array, + 'bp_partners': na_ss_feats_from_annotation, } def check_input(self, data) -> None: diff --git a/models/rfd3/src/rfd3/transforms/na_geom.py b/models/rfd3/src/rfd3/transforms/na_geom.py index 2a93150b..a1c8565d 100644 --- a/models/rfd3/src/rfd3/transforms/na_geom.py +++ b/models/rfd3/src/rfd3/transforms/na_geom.py @@ -12,86 +12,80 @@ from rfd3.transforms.na_geom_utils import ( annotate_na_ss, annotate_na_ss_from_data_specification, - bp_partner_to_ss_matrix, + DEFAULT_NA_SS_FEATURE_INFO, ) from atomworks.ml.utils.token import spread_token_wise, get_token_starts -def get_bp_feats_from_atom_array( - atom_array: AtomArray, -) -> np.ndarray: - """Build NA-SS features from atom_array annotations, assuming 'bp_partners' is present. +def na_ss_feats_from_annotation(atom_array: AtomArray, + token_starts= None, + n_tokens = None, + return_as_onehot = True, + ) -> np.ndarray: + """ + Takes in atom array and constucts a base pair feature matrix from annotations, + according to to custom feature constuction + masking system. + This featurization utilizes info from BasePairEnum to assign int values + to paired, unpaired, and masked positions in the matrix. + + Args: + * atom_array: AtomArray with bp_partners annotation at atom level + * token_starts (optional): indices of token starts in the atom array + * n_tokens (optional): number of tokens (length of token_starts) + * return_as_onehot (optional): if False, return integer-encoded + matrix instead of one-hot encoded matrix + + returns: + * na_ss_matrix: + If ``return_as_onehot`` is True (default): + np.ndarray of shape (n_tokens, n_tokens, n_classes) + with one-hot encoded values according to BasePairEnum + + If ``return_as_onehot`` is False : + np.ndarray of shape (n_tokens, n_tokens) + with int values according to BasePairEnum + - This function reconstructs the SS matrix from the 'bp_partners' annotation on the atom_array, - then one-hot encodes it into a 3-class matrix (mask, pair, loop). """ - # Fixed feature info (inferred from usage in other functions) - feature_info = { - 'NA_SS_MASK': 0, # Unspecified - 'NA_SS_PAIR': 1, # Paired - 'NA_SS_LOOP': 2, # Loop / unpaired - 'num_classes_nucleic_ss': 3, - } - - # Check for required annotation - if "bp_partners" not in atom_array.get_annotation_categories(): - raise ValueError("atom_array must have 'bp_partners' annotation for NA-SS feature building.") - - # Reconstruct SS matrix from annotations - na_ss_matrix = np.asarray( - bp_partner_to_ss_matrix( - atom_array, - feature_info=feature_info, - NA_only=False, # Include all residues (logic from other utils) - planar_only=True, # Use planar interactions (common default) - include_loops=True, # Include loop states - ), - dtype=np.int64, - ) - - # One-hot encode the matrix - na_ss_matrix_int = np.asarray(na_ss_matrix, dtype=np.int64) - eye = np.eye(int(feature_info['num_classes_nucleic_ss']), dtype=np.int64) - return eye[na_ss_matrix_int] - - -def _build_na_ss_features_from_annotations( - atom_array: AtomArray, - *, - feature_info: dict, - num_classes: int, - NA_only: bool, - planar_only: bool, - is_nucleic_ss_example: bool, - give_partial_feats: bool, - get_feature_mask_fn, -) -> np.ndarray: - """Reconstruct SS matrix from annotations, optionally mask, then one-hot.""" - na_ss_matrix = np.asarray( - bp_partner_to_ss_matrix( - atom_array, - feature_info=feature_info, - NA_only=NA_only, - planar_only=planar_only, - include_loops=True, - ), - dtype=np.int64, - ) - - n_tokens = int(na_ss_matrix.shape[0]) - - if give_partial_feats: - is_shown = ( - np.asarray(get_feature_mask_fn(n_tokens), dtype=bool) - if is_nucleic_ss_example - else np.zeros((n_tokens,), dtype=bool) - ) - na_ss_matrix[~is_shown, :] = feature_info["NA_SS_MASK"] - na_ss_matrix[:, ~is_shown] = feature_info["NA_SS_MASK"] - - na_ss_matrix_int = np.asarray(na_ss_matrix, dtype=np.int64) - eye = np.eye(int(num_classes), dtype=np.int64) - return eye[na_ss_matrix_int] + # Get this info from atom_array, or avoid if given + if (token_starts is None) or (n_tokens is None): + token_starts = get_token_starts(atom_array) + n_tokens = len(token_starts) + + + # Collect token inds for paired or loop positions: + pair_inds = [] + loop_inds = [] + token_bp_partners = atom_array.get_annotation("bp_partners")[token_starts] # get bp_partners at token level + assert len(token_bp_partners) == n_tokens, "Length of token_bp_partners should match n_tokens" + for i, j_list in enumerate(token_bp_partners): + if j_list is not None: + if len(j_list) > 0: + for j in j_list: + pair_inds.append((i, j)) + else: + loop_inds.append(i) + + # The standard system for constructing meaningful base pair features: + # 0). Initialize with values of UNSPECIFIED (0): int matrix of shape (n_tokens, n_tokens) + na_ss_matrix = np.full((n_tokens, n_tokens), DEFAULT_NA_SS_FEATURE_INFO["NA_SS_MASK"], dtype=np.int64) + + # 1). Fill in with values of PAIR (1) at positions that have bp_partners annotated as a non-empty list + for pair_i, pair_j in pair_inds: + na_ss_matrix[pair_i, pair_j] = DEFAULT_NA_SS_FEATURE_INFO["NA_SS_PAIR"] + na_ss_matrix[pair_j, pair_i] = DEFAULT_NA_SS_FEATURE_INFO["NA_SS_PAIR"] # ensure symmetry + + # 2). Fill in with values of LOOP (2) at positions that have bp_partners annotated as an empty list (explicitly unpaired) + # (we make full stripes across that position's row/col to indicate that NONE of those other positions are paired ) + for loop_i in loop_inds: + na_ss_matrix[loop_i, :] = DEFAULT_NA_SS_FEATURE_INFO["NA_SS_LOOP"] + na_ss_matrix[:, loop_i] = DEFAULT_NA_SS_FEATURE_INFO["NA_SS_LOOP"] # ensure symmetry + + # Optional: convert NA-SS matrix to one-hot encoding according for model input: + if return_as_onehot: + na_ss_matrix = np.eye(len(DEFAULT_NA_SS_FEATURE_INFO), dtype=np.int64)[na_ss_matrix] + + return na_ss_matrix class CalculateNucleicAcidGeomFeats(Transform): @@ -119,11 +113,12 @@ class CalculateNucleicAcidGeomFeats(Transform): def __init__( self, is_inference, - meta_conditioning_probabilities, - + add_nucleic_ss_feats: bool = True, + # Conditional sampling parameters: p_is_nucleic_ss_example: float = 0.3, - p_show_partial_feats: float = 0.5, - nucleic_ss_min_shown: float = 0.0, + p_show_partial_feats: float = 0.7, + # Mask control paramerers: + nucleic_ss_min_shown: float = 0.2, nucleic_ss_max_shown: float = 1.0, n_islands_min: int = 1, n_islands_max: int = 6, @@ -136,39 +131,22 @@ def __init__( ): # Critical, must always have to know how to handle self.is_inference = is_inference - if not self.is_inference: - ## relevant in training - self.sampling_prob = meta_conditioning_probabilities['calculate_NA_SS'] - else: - ## irrelevant in inference - self.sampling_prob = 0 - # For sampling whether we add nucleic-ss features (extra t2d) - - # relevant in training - self.add_nucleic_ss_feats = (self.sampling_prob > 0) - ###### + self.add_nucleic_ss_feats = add_nucleic_ss_feats self.p_canonical_bp_filter = p_canonical_bp_filter # enforce that bp labels are only canonical self.p_is_nucleic_ss_example = p_is_nucleic_ss_example + self.p_show_partial_feats = p_show_partial_feats self.nucleic_ss_min_shown = nucleic_ss_min_shown self.nucleic_ss_max_shown = nucleic_ss_max_shown self.n_islands_min = n_islands_min self.n_islands_max = n_islands_max - self.p_show_partial_feats = p_show_partial_feats # Filters for what can be considered a planar contact interaction self.NA_only = NA_only # only annotate base-like interactions for nucleic acid residues self.planar_only = planar_only # only consider planar atoms in sidechains for geometry calculations, self.p_canonical_bp_filter = p_canonical_bp_filter # probability of enforcing canonical base pair filter - # Inds of annotation types in the nucleic-ss features (stack of 3 matrices): - self.feature_info = { - 'NA_SS_MASK' : 0, # Unspecified, or sm, or protein: - 'NA_SS_PAIR' : 1, - 'NA_SS_LOOP' : 2, - 'num_classes_nucleic_ss' : 3, - } def check_input(self, data: dict[str, Any]) -> None: @@ -179,10 +157,7 @@ def check_input(self, data: dict[str, Any]) -> None: def _sample_training_flags(self) -> tuple[bool, bool]: """Sample booleans controlling whether/how features are shown in training.""" - is_nucleic_ss_example = bool( - self.add_nucleic_ss_feats - and (np.random.rand() < self.p_is_nucleic_ss_example) - ) + is_nucleic_ss_example = bool(np.random.rand() < self.p_is_nucleic_ss_example) give_partial_feats = bool( np.random.rand() < self.p_show_partial_feats ) @@ -194,28 +169,41 @@ def forward(self, data: dict) -> dict: # Calculate n_tokens (assuming one token per residue for simplicity) token_starts = get_token_starts(atom_array) token_level_array = atom_array[token_starts] - token_ids = [int(t) for t in token_level_array.token_id] n_tokens = len(token_starts) - #TODO print(" DO I NEED TO CHANGE TO TOKEN_ID???") - # Handle the training case with ground truth and masking: - if not self.is_inference and (np.random.rand() < self.sampling_prob): + + # Defaults for feature visibility + is_nucleic_ss_example = True + give_partial_feats = False + token_mask_to_show = np.ones(n_tokens, dtype=bool) + + # Handle the training case with ground truth and masking + if not self.is_inference: # First, annotate as usual - # atom_array = annotate_na_ss(atom_array, **kwargs) atom_array = annotate_na_ss(atom_array, NA_only=self.NA_only, planar_only=self.planar_only, p_canonical_bp_filter=self.p_canonical_bp_filter, ) - - # Sample mask on token level: + + # Generate symmetric partner annotations at the token level for masking purposes. + # choice for object-consistency: if already masked/undefined: be a list mapping to self-index. + partner_sym_map = { + i: atom_array.bp_partners[ts_i] if atom_array.bp_partners[ts_i] is not None else [i] + for i, ts_i in enumerate(token_starts) + } + + # # Sample mask on token level: is_nucleic_ss_example, give_partial_feats = self._sample_training_flags() - is_ss_shown = self._sample_where_to_show_ss(n_tokens, - is_nucleic_ss_example=is_nucleic_ss_example, - give_partial_feats=give_partial_feats) # Mask vec for tokens where ss shown - # Spread mask to atom level - is_ss_shown = spread_token_wise(atom_array, is_ss_shown) + token_mask_to_show = self._sample_where_to_show_ss( + n_tokens, + is_nucleic_ss_example=is_nucleic_ss_example, + give_partial_feats=give_partial_feats, + partner_sym_map=partner_sym_map, + ) # Mask vec for tokens where ss shown + # Spread mask to atom level + is_ss_shown = spread_token_wise(atom_array, token_mask_to_show) # Extract the base pair annotations bp_partners_atom = atom_array.get_annotation("bp_partners") @@ -233,11 +221,7 @@ def forward(self, data: dict) -> dict: - 1). Single dot-bracket string - 2). multiple dot bracket strings with chain/ind ranges specified - 3). Lists of paired indices - """ - #is_nucleic_ss_example=True - #give_partial_feats=False - atom_array = annotate_na_ss_from_data_specification( data, overwrite=True, @@ -247,28 +231,34 @@ def forward(self, data: dict) -> dict: if "feats" not in data: data["feats"] = {} - # data["feats"].update(nucleic_features) data.setdefault("log_dict", {}) log_dict = data["log_dict"] data["log_dict"] = log_dict data["atom_array"] = atom_array - + return data def _sample_where_to_show_ss(self, n_tokens: int, is_nucleic_ss_example: bool = True, give_partial_feats: bool = True, + partner_sym_map: dict[int, list[int]] = None, ) -> np.ndarray: - """Sample token-level islands indicating which SS rows/cols to reveal.""" + """Sample token-level islands indicating which SS rows/cols to reveal. + This custom function allows for enforcing symmetry in the shown features according + to the partner_sym_map, which encodes which tokens are partners in the SS + matrix and thus should be masked/unmasked together to maintain consistency. + + """ # If NOT is_nucleic_ss_example, set is_shown to all False if not is_nucleic_ss_example: - return np.zeros((n_tokens,), dtype=bool) + token_mask_to_show = np.zeros((n_tokens,), dtype=bool) # If NOT give_partial_feats, set is_shown to all True if not give_partial_feats: - return np.ones((n_tokens,), dtype=bool) + token_mask_to_show = np.ones((n_tokens,), dtype=bool) else: + # Get numerical parameters for that govern the mask pattern frac_shown = ( self.nucleic_ss_min_shown + (self.nucleic_ss_max_shown - self.nucleic_ss_min_shown) * np.random.rand() @@ -276,15 +266,15 @@ def _sample_where_to_show_ss(self, n_tokens: int, frac_shown = float(np.clip(frac_shown, 0.0, 1.0)) max_length = int(np.ceil(frac_shown * n_tokens)) if max_length <= 0: - return np.zeros((n_tokens,), dtype=bool) - + token_mask_to_show = np.zeros((n_tokens,), dtype=bool) island_len_min = max(1, int(frac_shown * n_tokens // max(int(self.n_islands_max), 1))) island_len_max = max(1, int(frac_shown * n_tokens // max(int(self.n_islands_min), 1))) island_len_min = min(island_len_min, n_tokens) island_len_max = min(island_len_max, n_tokens) island_len_max = max(island_len_max, island_len_min) - - return sample_island_tokens( + + # Sample the actual mask using the utility function: + token_mask_to_show = sample_island_tokens( n_tokens, island_len_min=island_len_min, island_len_max=island_len_max, @@ -292,4 +282,17 @@ def _sample_where_to_show_ss(self, n_tokens: int, n_islands_max=self.n_islands_max, max_length=max_length, ) + + # Handle symmetry by iterating through the partner_sym_map items and setting + # `partner_mask_to_show` at partner positions to match `token_mask_to_show` + # initialize as all shown so effect comes from hiding + logical AND condition + partner_mask_to_show = np.ones_like(token_mask_to_show) + for token_i, partner_ind_list in partner_sym_map.items(): + for partner_ind in partner_ind_list: + partner_mask_to_show[partner_ind] = token_mask_to_show[token_i] + + # Combine the original mask with the partner mask to ensure symmetry + token_mask_to_show = token_mask_to_show & partner_mask_to_show + + return token_mask_to_show diff --git a/models/rfd3/src/rfd3/transforms/na_geom_utils.py b/models/rfd3/src/rfd3/transforms/na_geom_utils.py index 29be56f8..8ab2e71c 100644 --- a/models/rfd3/src/rfd3/transforms/na_geom_utils.py +++ b/models/rfd3/src/rfd3/transforms/na_geom_utils.py @@ -4,9 +4,11 @@ from typing import Dict, Optional import math import numpy as np +import biotite.structure as struc from biotite.structure import AtomArray from atomworks.constants import ( + STANDARD_AA, STANDARD_DNA, STANDARD_RNA, ) @@ -26,72 +28,63 @@ from atomworks.ml.encoding_definitions import AF3SequenceEncoding +from rfd3.constants import ( +ATOM_REGION_BY_RESI, +PLANAR_ATOMS_BY_RESI, +) + + +# Derived: True when the residue has any planar sidechain atoms +HAS_PLANAR_SC = {res: bool(atoms) for res, atoms in PLANAR_ATOMS_BY_RESI.items()} DEFAULT_NA_SS_FEATURE_INFO: dict[str, int] = { "NA_SS_MASK": 0, "NA_SS_PAIR": 1, "NA_SS_LOOP": 2, - "num_classes_nucleic_ss": 3, } +AA_PLANAR_ATOMS = sorted(set( + atom for res in STANDARD_AA if res in PLANAR_ATOMS_BY_RESI + for atom in PLANAR_ATOMS_BY_RESI[res] +)) -# Move to function scope to avoid module-level memory retention -def _get_sequence_encoding_data(): - """Get sequence encoding data on demand to avoid persistent module-level variables.""" - sequence_encoding = AF3SequenceEncoding() - return { - 'aa_like_res_names': sequence_encoding.all_res_names[sequence_encoding.is_aa_like], - 'rna_like_res_names': sequence_encoding.all_res_names[sequence_encoding.is_rna_like], - 'dna_like_res_names': sequence_encoding.all_res_names[sequence_encoding.is_dna_like], - 'sequence_encoding': sequence_encoding - } - +NA_PLANAR_ATOMS = sorted(set( + atom for res in (*STANDARD_RNA, *STANDARD_DNA) if res in PLANAR_ATOMS_BY_RESI + for atom in PLANAR_ATOMS_BY_RESI[res] +)) class NucMolInfo: - """ - Initializes constants and parameters relevant for computing nucleic acid geometry and interactions. - """ - def __init__(self, - cutoff_HA_dist = 2.5, - cutoff_DA_dist = 3.9, - ): - """ - Args: - kwargs: Optional keyword arguments for customization. - """ - - - # Optional parameters with default values - # self.incl_protein = True - self.eps = 1e-8 - # self.clamp_pairwise_params = True - # self.use_eigennormals = kwargs.get('use_eigennormals', True) - # self.use_all_base_atoms_for_MBD = kwargs.get('use_all_base_atoms_for_MBD', False) - self.edges_to_compute = ['S'] # list base edges to compute, if we want to analyze WC/Hoog/etc - self.perp_base_edge = 'S' # edge orthogonal to x- and z-directions in base frames (which is generally the sugar edge) - - self.cutoff_HA_dist = cutoff_HA_dist - self.cutoff_DA_dist = cutoff_DA_dist - self.seq_cutoff = 2 - self.gap_length = 200 + """Constants and parameters for nucleic-acid geometry and interaction scoring. + All parameters are set to empirically validated defaults. No constructor + arguments are currently accepted. + """ + def __init__(self) -> None: - # Hbond interaction type inds when counting: - self.BB_BB = 0 - self.BB_SC = 1 - self.SC_SC = 2 + # Hbond interaction-class indices of the `hbond_count`` array: + # `hbond_count`` array is (L, L, 3), where the last dimension + # encodes interaction type between tokens i & j + self.BB_BB = 0 # backbone-backbone hbond interactions + self.BB_SC = 1 # backbone-sidechain hbond interactions + self.SC_SC = 2 # sidechain-sidechain hbond interactions + # We sum over the last dimension of the hbond_count array, scaling + # count by the following weights to get the interaction score: self.bp_weight_BB_BB = 0.0 self.bp_weight_BB_SC = 0.5 self.bp_weight_SC_SC = 1.0 - self.bp_summation_weights = [self.bp_weight_BB_BB, self.bp_weight_BB_SC, self.bp_weight_SC_SC] + # Parameters fo sigmoid function that gives us a continuous step function for + # meeting basepair interaction criteria based on hbond counts alone (1st filter). + # Calibrated such that: + # >= 2 base-base H-bonds -> ~1.0 + # 1 base-base H-bond + 1 base-backbone H-bond -> ~0.5 self.min_hbonds_for_bp = 2.0 self.bp_hbond_coeff = 9.8 # determined heuristically self.bp_val_cutoff = 0.5 # minimum basepairing score for binarizing basepairs when needed @@ -102,91 +95,8 @@ def __init__(self, self.base_geometry_limits['P_ij'] = math.pi/5 self.base_geometry_limits['B_ij'] = math.pi/5 - # For interaction-edge classification (Watson-Crick, Hoogstein, Sugar, Base-other): - # self.edge_to_ind = {'W':0 , 'H':1 , 'S':2 ,'B':3} self.rep_atom_dict={"protein": "CA", "rna": "C1'", "dna": "C1'"} - self.has_planar_sc = { - 'ALA': False, - 'ARG': True, - 'ASN': True, - 'ASP': True, - 'CYS': False, - 'GLN': True, - 'GLU': True, - 'GLY': False, - 'HIS': True, - 'ILE': False, - 'LEU': False, - 'LYS': False, - 'MET': False, - 'PHE': True, - 'PRO': False, - 'SER': False, - 'THR': False, - 'TRP': True, - 'TYR': True, - 'VAL': False, - 'UNK': False, - 'MAS': False, - 'DA': True, - 'DC': True, - 'DG': True, - 'DT': True, - 'DX': False, - 'A': True, - 'C': True, - 'G': True, - 'U': True, - 'X': False, - 'HIS_D': True, - } - - - - # Make self.planar_atom_list_dict based on known planar atoms for each residue type: - self.planar_atom_list_dict = { - 'ALA': [], - 'ARG': ['NH1', 'NH2', 'CZ', 'NE', 'CD'], - 'ASN': ['OD1', 'ND2', 'CG', 'CB'], - 'ASP': ['OD1', 'OD2', 'CG', 'CB'], - 'CYS': [], - 'GLN': ['OE1', 'NE2', 'CD', 'CG'], - 'GLU': ['OE1', 'OE2', 'CD', 'CG'], - 'GLY': [], - 'HIS': ['ND1', 'CE1', 'NE2', 'CD2', 'CG', 'CB'], - 'ILE': [], - 'LEU': [], - 'LYS': [], - 'MET': [], - 'PHE': ['CZ', 'CE1', 'CE2', 'CD1', 'CD2', 'CG', 'CB'], - 'PRO': [], - 'SER': [], - 'THR': [], - 'TRP': ['CH2', 'CZ3', 'CZ2', 'CE3', 'CE2', 'CD2', 'NE1', 'CD1', 'CG', 'CB'], - 'TYR': ['OH', 'CZ', 'CE1', 'CE2', 'CD1', 'CD2', 'CG', 'CB'], - 'VAL': [], - 'UNK': [], - 'MAS': [], - 'DA': ['N6', 'C6', 'N1', 'C2', 'N3', 'C4', 'C5', 'N7', 'C8', 'N9'], - 'DC': ['N4', 'C4', 'N3', 'O2', 'C2', 'C5', 'C6', 'N1'], - 'DG': ['O6', 'C6', 'N1', 'N2', 'C2', 'N3', 'C4', 'C5', 'N7', 'C8', 'N9'], - 'DT': ['O4', 'O2', 'N3', 'C4', 'C2', 'C5', 'C6', 'N1', 'C7'], - 'DX': [], - 'A': ['N6', 'C6', 'N1', 'C2', 'N3', 'C4', 'C5', 'N7', 'C8', 'N9'], - 'C': ['N4', 'C4', 'N3', 'O2', 'C2', 'C5', 'C6', 'N1'], - 'G': ['O6', 'C6', 'N1', 'N2', 'C2', 'N3', 'C4', 'C5', 'N7', 'C8', 'N9'], - 'U': ['O4', 'O2', 'N3', 'C4', 'C2', 'C5', 'C6', 'N1'], - 'X': [], - 'HIS_D': ['ND1', 'CD2', 'CE1', 'NE2', 'CG', 'CB'], - } - - - # from pdb import set_trace; set_trace() - - self.nuc_resi_3letter = ["DA","DG","DC","DT","A","G","C","U"] - self.ring_atom_list = ["N1","C2","N3","C4","C6","C5"] - # go through self.vec_atom_dict and remove spaces from atom names (values in inner dicts), and remove spaces from keys + replace 'R' with '' in outer dict keys self.vec_atom_dict = { "DA": {"W_start":"N1", "W_stop":"N6", "H_start":"N7", "H_stop":"N6", "S_start":"C1'", "S_stop":"N3", "B_start":"C1'", "B_stop":"N9" }, @@ -201,96 +111,154 @@ def __init__(self, - self.atom_region_dict = { - 'ALA': {'bb':('N','CA','C','O'), - 'sc':('CB')}, - 'ARG': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD','NE','CZ','NH1','NH2')}, - 'ASN': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','OD1','ND2')}, - 'ASP': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','OD1','OD2')}, - 'CYS': {'bb':('N','CA','C','O'), - 'sc':('CB','SG')}, - 'GLN': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD','OE1','NE2')}, - 'GLU': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD','OE1','OE2')}, - 'GLY': {'bb':('N','CA','C','O'), - 'sc':()}, - 'HIS': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','ND1','CD2','CE1','NE2')}, - 'ILE': {'bb':('N','CA','C','O'), - 'sc':('CB','CG1','CG2','CD1')}, - 'LEU': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD1','CD2')}, - 'LYS': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD','CE','NZ')}, - 'MET': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','SD','CE')}, - 'PHE': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD1','CD2','CE1','CE2','CZ')}, - 'PRO': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD')}, - 'SER': {'bb':('N','CA','C','O'), - 'sc':('CB','OG')}, - 'THR': {'bb':('N','CA','C','O'), - 'sc':('CB','OG1','CG2')}, - 'TRP': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD1','CD2','CE2','CE3','NE1','CZ2','CZ3','CH2')}, - 'TYR': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD1','CD2','CE1','CE2','CZ','OH')}, - 'VAL': {'bb':('N','CA','C','O'), - 'sc':('CB','CG1','CG2')}, - 'UNK': {'bb':('N','CA','C','O'), - 'sc':('CB')}, - 'MAS': {'bb':('N','CA','C','O'), - 'sc':('CB')}, - 'DA': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), - 'sc':('N9','C4','N3','C2','N1','C6','C5','N7','C8','N6')}, - 'DC': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), - 'sc':('N1','C2','O2','N3','C4','N4','C5','C6')}, - 'DG': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), - 'sc':('N9','C4','N3','C2','N1','C6','C5','N7','C8','N2','O6')}, - 'DT': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), - 'sc':('N1','C2','O2','N3','C4','O4','C5','C7','C6')}, - 'DX': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), - 'sc':()}, - 'A': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), - 'sc':('N1','C2','N3','C4','C5','C6','N6','N7','C8','N9')}, - 'C': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), - 'sc':('N1','C2','O2','N3','C4','N4','C5','C6')}, - 'G': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), - 'sc':('N1','C2','N2','N3','C4','C5','C6','O6','N7','C8','N9')}, - 'U': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), - 'sc':('N1','C2','O2','N3','C4','O4','C5','C6')}, - 'X': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), - 'sc':()}, - 'HIS_D': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','NE2','CD2','CE1','ND1')}, - } - - - self.aa_planar_atoms = ['NH1', 'NH2', 'CZ', 'NE', 'OD1', 'ND2', - 'OD2', 'OE1', 'NE2', 'CD', 'OE2', 'ND1', - 'CD2', 'CE1', 'CD1', 'CE2', 'NE1', 'CZ2', - 'CZ3', 'CH2', 'CE3', 'OH', 'CG', 'CB',] - - self.na_planar_atoms = ['C4', 'N3', 'C2', 'C6', 'C5', 'N7', 'C8', - 'N6', 'O2', 'N4', 'N2', 'O6', 'O4', 'C7', - 'N9', 'N1'] +def calculate_hb_counts( + atom_array: AtomArray, + token_level_data: dict, + mol_info: NucMolInfo, + cutoff_HA_dist: float = 2.5, + cutoff_DA_dist: float = 3.9, + ): + """Count hydrogen bonds between residue pairs using HBPLUS. + + Args: + atom_array: Structure to analyse. + token_level_data: Token-level metadata dict (must contain + ``token_id_list`` and ``resi2index``). + mol_info: Molecular-info object for backbone/sidechain atom lookup. + cutoff_HA_dist: H–A distance cutoff (Å) passed to HBPLUS. + cutoff_DA_dist: D–A distance cutoff (Å) passed to HBPLUS. + + Returns: + np.ndarray of shape ``(I, I, 3)`` (int32) where the last axis + encodes: 0 = BB–BB, 1 = BB–SC, 2 = SC–SC H-bond counts. + """ + + dtstr = datetime.now().strftime("%Y%m%d%H%M%S") + pdb_path = f"{dtstr}_{np.random.randint(10000)}.pdb" + + atom_array, nan_mask, chain_map = save_atomarray_to_pdb(atom_array, pdb_path) + subprocess.call( + [ + "/projects/ml/hbplus", + "-h", + str(cutoff_HA_dist), + "-d", + str(cutoff_DA_dist), + pdb_path, + pdb_path, + ], + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) + + + num_resis_total = len(token_level_data["token_id_list"]) + + hbond_count = np.zeros((num_resis_total, num_resis_total, 3), dtype=np.int32) + + hb2_path = pdb_path.replace("pdb", "hb2") + with open(hb2_path, "r") as hb2_f: + for i, line in enumerate(hb2_f): + if i < 8: + continue + if len(line) < 28: + continue + + d_chain_iid = chain_map[line[0]] + d_resi = int(line[1:5].strip()) + d_resn = line[6:9].strip() + d_atom_name = line[9:13].strip() + + # Initialize donor/acceptor sidechain/backbone flags: + # then replace with True if valid for summation + d_is_sc = False + d_is_bb = False + a_is_sc = False + a_is_bb = False + + d_mask = ( + (atom_array.atom_name == d_atom_name) + & (atom_array.res_name == d_resn) + & (atom_array.res_id == d_resi) + & (atom_array.chain_iid == d_chain_iid) + ) + # d_atm = atom_array[d_mask] + # d_idx = d_atm.token_id + d_idx = token_level_data["resi2index"].get(f"{d_chain_iid}__{d_resi}", None) + if d_idx is None: + continue + + # Handle standard polymer residues for donor atom: + if d_resn in ATOM_REGION_BY_RESI.keys(): + d_is_sc = (d_atom_name in ATOM_REGION_BY_RESI[d_resn]['sc']) + d_is_bb = (d_atom_name in ATOM_REGION_BY_RESI[d_resn]['bb']) + else: + # If non-polymer, define any ligand HBonding atom as backbone: + if d_mask.sum() > 0: + d_is_bb = atom_array[d_mask][0].is_ligand + + a_chain_iid = chain_map[line[14]] + a_resi = int(line[15:19].strip()) + a_resn = line[20:23].strip() + a_atom_name = line[23:27].strip() + + a_mask = ( + (atom_array.atom_name == a_atom_name) + & (atom_array.res_name == a_resn) + & (atom_array.res_id == a_resi) + & (atom_array.chain_iid == a_chain_iid) + ) + a_idx = token_level_data["resi2index"].get(f"{a_chain_iid}__{a_resi}", None) + if a_idx is None: + continue + + # Handle standard polymer residues for acceptor atom: + if a_resn in ATOM_REGION_BY_RESI.keys(): + a_is_sc = (a_atom_name in ATOM_REGION_BY_RESI[a_resn]['sc']) + a_is_bb = (a_atom_name in ATOM_REGION_BY_RESI[a_resn]['bb']) + else: + # If non-polymer, define any ligand HBonding atom as backbone: + if a_mask.sum() > 0: + a_is_bb = atom_array[a_mask][0].is_ligand + + # 0 -> both backbone (BB-BB) + hbond_count[a_idx, d_idx, 0] += (a_is_bb * d_is_bb) + hbond_count[d_idx, a_idx, 0] += (d_is_bb * a_is_bb) + + # 1 -> one backbone, one sidechain (BB-SC) + hbond_count[a_idx, d_idx, 1] += (a_is_bb * d_is_sc) | (a_is_sc * d_is_bb) + hbond_count[d_idx, a_idx, 1] += (d_is_bb * a_is_sc) | (d_is_sc * a_is_bb) + + # 2 -> both sidechain (SC-SC) + hbond_count[a_idx, d_idx, 2] += (a_is_sc * d_is_sc) + hbond_count[d_idx, a_idx, 2] += (d_is_sc * a_is_sc) + + os.remove(pdb_path) + os.remove(hb2_path) + + return hbond_count + def find_planar_positions( atom_array: AtomArray, mol_info: NucMolInfo, tol: float = 1e-2, ) -> Dict: - """ - Finds residues with planar sidechains based on four tip-most atoms, - but also checks for valid atoms to use for this type of calculation. + """Identify residues with planar sidechains via known atom lists or PCA plane-fitting. + + For canonical residues the planar atoms are looked up from ``mol_info``; + for non-canonical residues a plane is fitted to the four tip-most sidechain + atoms, and all atoms within *tol* of that plane are returned. + + Args: + atom_array: Structure to analyse. + mol_info: Molecular-info object supplying per-residue planar atom lists. + tol: Distance tolerance (Å) from the fitted plane for an atom to be + considered planar. Returns: - dict of planar atom lists + Dictionary ``{(chain_iid, res_id): [atom_name, ...]}`` mapping each + unique residue position to its list of planar sidechain atom names. """ unique_positions_list = [] for atm in atom_array: @@ -312,11 +280,11 @@ def find_planar_positions( res_atoms = atom_array[mask] # If possible, speed up by using known planar atoms for this residue type: - if res_name in mol_info.planar_atom_list_dict.keys(): + if res_name in PLANAR_ATOMS_BY_RESI.keys(): # Shared atoms between residue and known planar atoms for that residue type: planar_atom_list = list( set([atm.atom_name for atm in res_atoms]) & - set(mol_info.planar_atom_list_dict[res_name]) + set(PLANAR_ATOMS_BY_RESI[res_name]) ) planar_atom_list_dict[(chain_iid, res_id)] = planar_atom_list @@ -327,11 +295,11 @@ def find_planar_positions( for atm in res_atoms: # Can pre-filter protein planar atoms: - if atm.is_protein and (atm.atom_name in mol_info.aa_planar_atoms): + if atm.is_protein and (atm.atom_name in AA_PLANAR_ATOMS): candidate_planar_atm_names.append(atm.atom_name) candidate_planar_atm_coords.append(atm.coord) # Can pre-filter nucleic acid planar atoms: - elif (atm.is_rna or atm.is_dna) and (atm.atom_name in mol_info.na_planar_atoms): + elif (atm.is_rna or atm.is_dna) and (atm.atom_name in NA_PLANAR_ATOMS): candidate_planar_atm_names.append(atm.atom_name) candidate_planar_atm_coords.append(atm.coord) # Otherwise, consider all atoms for plane fitting: @@ -389,144 +357,25 @@ def find_planar_positions( return planar_atom_list_dict - - -def calculate_hb_counts( - atom_array: AtomArray, - token_level_data: dict, - mol_info: NucMolInfo, - cutoff_HA_dist: float = 2.5, - cutoff_DA_dist: float = 3.9, - ): - """ - Compute hbond counts between residues and return an (L, L, 3) - numpy array where the last dimension encodes: - 0 -> both backbone (BB-BB) - 1 -> one backbone, one sidechain (BB-SC) - 2 -> both sidechain (SC-SC) - """ - dtstr = datetime.now().strftime("%Y%m%d%H%M%S") - pdb_path = f"{dtstr}_{np.random.randint(10000)}.pdb" - - atom_array, nan_mask, chain_map = save_atomarray_to_pdb(atom_array, pdb_path) - subprocess.call( - [ - "/projects/ml/hbplus", - "-h", - str(cutoff_HA_dist), - "-d", - str(cutoff_DA_dist), - pdb_path, - pdb_path, - ], - stdout=subprocess.DEVNULL, - stderr=subprocess.DEVNULL, - ) - - - num_resis_total = len(token_level_data["token_id_list"]) - - hbond_count = np.zeros((num_resis_total, num_resis_total, 3), dtype=np.int32) - - hb2_path = pdb_path.replace("pdb", "hb2") - with open(hb2_path, "r") as hb2_f: - for i, line in enumerate(hb2_f): - if i < 8: - continue - if len(line) < 28: - continue - - # Initialize donor/acceptor sidechain/backbone flags: - # then replace with True if valid for summation - d_is_sc = False - d_is_bb = False - a_is_sc = False - a_is_bb = False - - d_chain_iid = chain_map[line[0]] - d_resi = int(line[1:5].strip()) - d_resn = line[6:9].strip() - d_atom_name = line[9:13].strip() - - d_mask = ( - (atom_array.atom_name == d_atom_name) - & (atom_array.res_name == d_resn) - & (atom_array.res_id == d_resi) - & (atom_array.chain_iid == d_chain_iid) - ) - d_atm = atom_array[d_mask] - d_idx = d_atm.token_id - - # Handle standard polymer residues for donor atom: - if d_resn in mol_info.atom_region_dict.keys(): - d_is_sc = (d_atom_name in mol_info.atom_region_dict[d_resn]['sc']) - d_is_bb = (d_atom_name in mol_info.atom_region_dict[d_resn]['bb']) - else: - # If non-polymer, define any ligand HBonding atom as backbone: - if d_mask.sum() > 0: - d_is_bb = atom_array[d_mask][0].is_ligand - - a_chain_iid = chain_map[line[14]] - a_resi = int(line[15:19].strip()) - a_resn = line[20:23].strip() - a_atom_name = line[23:27].strip() - - a_mask = ( - (atom_array.atom_name == a_atom_name) - & (atom_array.res_name == a_resn) - & (atom_array.res_id == a_resi) - & (atom_array.chain_iid == a_chain_iid) - ) - a_atm = atom_array[a_mask] - a_idx = a_atm.token_id - - # Handle standard polymer residues for acceptor atom: - if a_resn in mol_info.atom_region_dict.keys(): - a_is_sc = (a_atom_name in mol_info.atom_region_dict[a_resn]['sc']) - a_is_bb = (a_atom_name in mol_info.atom_region_dict[a_resn]['bb']) - else: - # If non-polymer, define any ligand HBonding atom as backbone: - if a_mask.sum() > 0: - a_is_bb = atom_array[a_mask][0].is_ligand - - # 0 -> both backbone (BB-BB) - hbond_count[a_idx, d_idx, 0] += (a_is_bb * d_is_bb) - hbond_count[d_idx, a_idx, 0] += (d_is_bb * a_is_bb) - - # 1 -> one backbone, one sidechain (BB-SC) - hbond_count[a_idx, d_idx, 1] += (a_is_bb * d_is_sc) | (a_is_sc * d_is_bb) - hbond_count[d_idx, a_idx, 1] += (d_is_bb * a_is_sc) | (d_is_sc * a_is_bb) - - # 2 -> both sidechain (SC-SC) - hbond_count[a_idx, d_idx, 2] += (a_is_sc * d_is_sc) - hbond_count[d_idx, a_idx, 2] += (d_is_sc * a_is_sc) - - os.remove(pdb_path) - os.remove(hb2_path) - - return hbond_count - - - - def make_coord_list(atom_array: AtomArray, residue_list: list[str], chain_list: list[str], atom_list: list[str], ) -> list[list[str]]: - """ - Given an atom array, and lists of residues, chains, and atom names, - return a list of coordinates for the specified atoms in the specified residues and chains. - If the atom is not found, return [NaN, NaN, NaN] for that atom. - The the three input lists must be of the same length, and the output list will have the same length as well. + """Extract per-residue representative coordinates from an AtomArray. + + All three input lists must have the same length. Missing atoms are + filled with ``[NaN, NaN, NaN]``. + Args: - atom_array: BioTite atom_array object - residue_list: list of residue names to consider - chain_list: list of chain identifiers to consider - atom_list: list of atom names to extract coordinates for - Returns: - coord_list: list of lists of coordinates for the specified atoms + atom_array: Biotite AtomArray to query. + residue_list: Residue IDs (one per token). + chain_list: Chain identifiers (one per token). + atom_list: Atom names to extract (use ``"atomized"`` to take the + first atom of the residue). + Returns: + List of ``[x, y, z]`` coordinate lists, same length as input. """ coord_list = [] for res_id, chain_id, atom_name in zip(residue_list, chain_list, atom_list): @@ -565,31 +414,49 @@ def get_token_level_metadata( *, NA_only: bool = False, planar_only: bool = True, + seq_cutoff = 2, + gap_length = 200 ) -> dict: - """Lightweight token-level metadata. + """Build lightweight token-level metadata (no coordinate geometry). - This intentionally avoids expensive coordinate-derived computations - (e.g., planar plane-fitting and geometry coordinate extraction). + Sufficient for SS reconstruction, loop labeling from ``bp_partners``, + and inference-time SS specification parsing. For geometry keys + (``xyz_planar``, ``frame_xyz``, ``M_i``), follow up with + :func:`add_token_level_geometry_data`. - It is sufficient for: - - SS reconstruction / loop labeling from ``bp_partners`` - - inference-time SS specification parsing + Args: + atom_array: Structure to analyse. + mol_info: Molecular-info constants. + NA_only: If True, restrict filter_mask to nucleic-acid tokens. + planar_only: If True, restrict filter_mask to tokens with planar + sidechains. + seq_cutoff: Sequence-distance threshold for the ``seq_neighbors`` + boolean mask. + gap_length: Artificial gap inserted between chains for relative + sequence position computation. - If you later need geometry keys (``xyz_planar``, ``frame_xyz``, ``M_i``), - call :func:`add_token_level_geometry_data`. + Returns: + Dict with keys: ``token_starts``, ``token_index``, ``is_na``, + ``is_planar``, ``chain_list``, ``chain_iid_list``, ``resi_list``, + ``resn_list``, ``token_id_list``, ``resi2index``, ``len_s``, + ``seq_neighbors``, ``na_inds``, ``na_tensor_inds``, + ``filter_mask``, ``rep_atom_list``, ``S_start_atom_list``, + ``S_stop_atom_list``, ``include_geometry`` (False). """ - token_starts = get_token_starts(atom_array) + # Use residue starts (not token starts) so atomized atoms within one residue + # map to a single NA-SS position. + token_starts = struc.get_residue_starts(atom_array) token_level_array = atom_array[token_starts] token_index = np.arange(len(token_starts)) # molecule type flags - seq_data = _get_sequence_encoding_data() - is_protein = np.isin(token_level_array.res_name, seq_data["aa_like_res_names"]) - is_rna = np.isin(token_level_array.res_name, seq_data["rna_like_res_names"]) - is_dna = np.isin(token_level_array.res_name, seq_data["dna_like_res_names"]) - del seq_data + # Instantiate encoding locally to avoid retaining large arrays at module scope. + sequence_encoding = AF3SequenceEncoding() + is_protein = np.isin(token_level_array.res_name, sequence_encoding.all_res_names[sequence_encoding.is_aa_like]) + is_rna = np.isin(token_level_array.res_name, sequence_encoding.all_res_names[sequence_encoding.is_rna_like]) + is_dna = np.isin(token_level_array.res_name, sequence_encoding.all_res_names[sequence_encoding.is_dna_like]) is_na_arr = (is_dna | is_rna).astype(bool) @@ -613,8 +480,8 @@ def get_token_level_metadata( res_name_list.append(atm.res_name) token_id_list.append(str(atm.token_id)) - if atm.is_polymer and (atm.res_name in mol_info.has_planar_sc.keys()): - sc_planarity_list.append(bool(mol_info.has_planar_sc[atm.res_name])) + if atm.is_polymer and (atm.res_name in HAS_PLANAR_SC.keys()): + sc_planarity_list.append(bool(HAS_PLANAR_SC[atm.res_name])) else: sc_planarity_list.append(False) @@ -658,16 +525,16 @@ def get_token_level_metadata( # relative sequence positions w/ chain gaps rel_pos_list: list[int] = [] current_chain = "" - chn_bias = -mol_info.gap_length + chn_bias = -gap_length for r, c in zip(resi_list, chain_iid_list): if c != current_chain: - chn_bias += mol_info.gap_length + chn_bias += gap_length current_chain = c rel_pos_list.append(int(r + chn_bias)) rel_pos = np.asarray(rel_pos_list, dtype=np.int64) seq_neighbors = ( - np.abs(rel_pos[:, None] - rel_pos[None, :]) <= int(mol_info.seq_cutoff) + np.abs(rel_pos[:, None] - rel_pos[None, :]) <= int(seq_cutoff) ) na_inds = np.nonzero(is_na_arr)[0].tolist() @@ -717,13 +584,24 @@ def add_token_level_geometry_data( NA_only: bool = False, planar_only: bool = True, ) -> dict: - """Augment a metadata-only token_level_data dict with geometry fields. + """Augment token-level metadata with coordinate-derived geometry fields. + + Populates ``xyz_planar``, ``xyz_S_start``, ``xyz_S_stop``, + ``frame_xyz``, ``M_i`` and updates ``is_planar`` / ``filter_mask`` + using coordinate-derived planarity. Sets ``include_geometry=True``. - Populates: - - xyz_planar, xyz_S_start, xyz_S_stop - - frame_xyz, M_i - - updates is_planar and filter_mask using coordinate-derived planarity - - sets include_geometry=True + No-ops if geometry was already computed. + + Args: + atom_array: Structure to extract coordinates from. + mol_info: Molecular-info constants. + token_level_data: Dict produced by :func:`get_token_level_metadata` + (modified in-place and returned). + NA_only: Restrict filter_mask to nucleic-acid tokens. + planar_only: Restrict filter_mask to tokens with planar sidechains. + + Returns: + The same ``token_level_data`` dict, augmented with geometry keys. """ if bool(token_level_data.get("include_geometry", False)): @@ -755,12 +633,12 @@ def add_token_level_geometry_data( S_start_atom_list: list[str | None] = token_level_data["S_start_atom_list"] S_stop_atom_list: list[str | None] = token_level_data["S_stop_atom_list"] - planar_atom_list_dict = find_planar_positions(atom_array, mol_info) + planar_atom_list_dict = find_planar_positions(atom_array, mol_info) # {(chain_iid, res_id): [atom_name, ...]} has_planar_sc: list[bool] = [] - xyz_planar: list[list[list[float]]] = [] - xyz_S_start: list[list[float]] = [] - xyz_S_stop: list[list[float]] = [] + xyz_planar: list[list[list[float]]] = [] # list[I] of [K_i, 3] (K_i varies per residue) + xyz_S_start: list[list[float]] = [] # list[I] of [3] + xyz_S_stop: list[list[float]] = [] # list[I] of [3] for c, r, S_start_atm, S_stop_atm in zip( chain_iid_list, @@ -806,21 +684,21 @@ def add_token_level_geometry_data( del atom_array_i # frame coordinates and backbone direction - frame_xyz = np.asarray( + frame_xyz = np.asarray( # [I, 3] representative-atom coordinates make_coord_list(atom_array, resi_list, chain_list, rep_atom_list), dtype=np.float32, ) - padded_centers = np.concatenate([frame_xyz[:1], frame_xyz, frame_xyz[-1:]], axis=0) - M_i = ( + padded_centers = np.concatenate([frame_xyz[:1], frame_xyz, frame_xyz[-1:]], axis=0) # [I+2, 3] + M_i = ( # [I, 3] smoothed backbone-direction vectors (padded_centers[1:-1] - padded_centers[:-2]) + (padded_centers[2:] - padded_centers[1:-1]) ) / 2.0 - is_planar_arr = np.asarray(has_planar_sc, dtype=bool) + is_planar_arr = np.asarray(has_planar_sc, dtype=bool) # [I] token_level_data["is_planar"] = is_planar_arr - is_na_arr = np.asarray(token_level_data["is_na"], dtype=bool) + is_na_arr = np.asarray(token_level_data["is_na"], dtype=bool) # [I] if NA_only and planar_only: filter_mask = is_na_arr & is_planar_arr elif NA_only and (not planar_only): @@ -829,7 +707,7 @@ def add_token_level_geometry_data( filter_mask = is_planar_arr.copy() else: filter_mask = np.ones_like(is_na_arr, dtype=bool) - token_level_data["filter_mask"] = filter_mask + token_level_data["filter_mask"] = filter_mask # [I] bool token_level_data.update( { @@ -846,288 +724,414 @@ def add_token_level_geometry_data( return token_level_data -def _compute_nucleic_ss_impl( - mol_info, - token_level_data, - hbond_count, - clamp_pairwise_params=True, - eps=1e-8, - *, - return_local_params: bool = False, - return_pairwise_geometry: bool = False, - return_opening_angle: bool = False, - return_basepairs_only: bool = False, -): - """ - Compute nucleic secondary structure–related quantities and pairwise base params. - - Notes - ----- - This function is used in two modes: - - - Fast annotation mode (default): computes only what is needed to derive - ``basepairs_bool_ij`` and does *not* retain large intermediate pairwise - geometry arrays (X_ij/Y_ij/Z_ij/O_ij). - - Diagnostic mode: set ``return_pairwise_geometry=True`` (and optionally - ``return_local_params=True`` / ``return_opening_angle=True``) to also - return additional geometry arrays. - """ +# --------------------------------------------------------------------------- +# Sub-calculations used by compute_nucleic_ss +# --------------------------------------------------------------------------- - mask_1d = np.asarray(token_level_data["filter_mask"], dtype=bool) - len_mask = int(mask_1d.sum()) - # len_full = len(mask_1d) - - # unpack 1D data from token_level_data and apply filters - M_i = np.asarray(token_level_data["M_i"], dtype=np.float32)[mask_1d] - frame_xyz = np.asarray(token_level_data["frame_xyz"], dtype=np.float32)[mask_1d] - - is_na = np.asarray(token_level_data["is_na"], dtype=bool)[mask_1d] - - xyz_S_start = [xyz_list_i for xyz_list_i, keep_i in zip(token_level_data["xyz_S_start"], mask_1d) if keep_i] - xyz_S_stop = [xyz_list_i for xyz_list_i, keep_i in zip(token_level_data["xyz_S_stop"], mask_1d) if keep_i] - xyz_planar = [xyz_list_i for xyz_list_i, keep_i in zip(token_level_data["xyz_planar"], mask_1d) if keep_i] - - # unpack 2D data from token_level_data and apply filters - hbond_count = np.asarray(hbond_count)[mask_1d, :][:, mask_1d] - seq_neighbors = np.asarray(token_level_data["seq_neighbors"], dtype=bool)[mask_1d, :][:, mask_1d] +def _compute_local_frames( + xyz_planar: list[np.ndarray], + planar_centers: np.ndarray, + M_i: np.ndarray, + *, + xyz_S_start: list | None = None, + xyz_S_stop: list | None = None, + compute_full_frame: bool = False, + eps: float = 1e-8, +) -> dict[str, np.ndarray]: + """Build per-residue local coordinate frames from planar sidechain atoms. - # --- CALC 0: precompute displacement vectors / distances ---- - planar_centers = np.stack( - [ - np.nanmean(np.asarray(xyz_i, dtype=np.float32), axis=0) - for xyz_i in xyz_planar - ], - axis=0, - ).astype(np.float32) + The base-normal direction Z_i is always computed via PCA on the planar + atom cloud, corrected for backbone direction. When *compute_full_frame* + is True the sugar-edge vector is used to derive X_i and Y_i as well. - - frame_D_ij_vec = frame_xyz[None, :, :] - frame_xyz[:, None, :] # [L, L, 3] - sc_D_ij_vec = planar_centers[None, :, :] - planar_centers[:, None, :] # [L, L, 3] - # D_ij = frame_D_ij_vec.norm(dim=-1) # [L, L] + Args: + xyz_planar: Per-residue planar-atom coordinates, list[I] of [K_i, 3]. + planar_centers: Sidechain planar-atom centroids, [I, 3]. + M_i: Backbone-direction vectors, [I, 3]. + xyz_S_start: Sugar-edge start coordinates, list[I] of [3]. + Required when *compute_full_frame* is True. + xyz_S_stop: Sugar-edge stop coordinates, list[I] of [3]. + Required when *compute_full_frame* is True. + compute_full_frame: If True, also compute X_i and Y_i. + eps: Small constant for numerical stability. + Returns: + Dict with ``"Z_i"`` (always), and ``"X_i"``, ``"Y_i"`` when + *compute_full_frame* is True. Each array has shape ``[I, 3]``. + """ + n_tokens = len(xyz_planar) - # --- CALC I: local base params (canonical frames) ------------ - centered_points = [ + # Mean-centre the planar atoms per residue + centered_points = [ # list[I] of [K_i, 3] np.asarray(xyz_i, dtype=np.float32) - cen_i for xyz_i, cen_i in zip(xyz_planar, planar_centers) ] - # eigenvectors per residue: [L, 3, 3] (NaNs where invalid) - eigenvectors = np.full((len_mask, 3, 3), np.nan, dtype=np.float32) + # PCA → eigenvectors per residue + eigenvectors = np.full((n_tokens, 3, 3), np.nan, dtype=np.float32) # [I, 3, 3] for i, xyz_i in enumerate(centered_points): xyz_i = xyz_i[~np.isnan(xyz_i).any(axis=1)] if xyz_i.shape[0] >= 3: - cov_matrix = np.einsum("ij,ik->jk", xyz_i, xyz_i) / max( + cov_matrix = np.einsum("ij,ik->jk", xyz_i, xyz_i) / max( # [3, 3] xyz_i.shape[0] - 1, 1 ) - _, eigvecs = np.linalg.eigh(cov_matrix) + _, eigvecs = np.linalg.eigh(cov_matrix) # [3, 3] eigenvectors[i] = eigvecs - - # base-normal (principal) direction N_i, then corrected Z_i - N_i = eigenvectors[:, :, 0] + # Base-normal: smallest-eigenvalue direction, corrected for backbone dir + N_i = eigenvectors[:, :, 0] # [I, 3] N_i = N_i / (np.linalg.norm(N_i, axis=1, keepdims=True) + eps) - Z_i = N_i * np.sum(M_i * N_i, axis=-1, keepdims=True) + Z_i = N_i * np.sum(M_i * N_i, axis=-1, keepdims=True) # [I, 3] Z_i = Z_i / (np.linalg.norm(Z_i, axis=-1, keepdims=True) + eps) - # Only compute full local frames when requested. - # Basepair filters only need Z_i (via Z_ij) and do not require X_i/Y_i. - local_base_params = None - if return_local_params or return_opening_angle: - # Sugar-edge vectors X_s_i built from S_start/stop - X_s_i = ( + result: dict[str, np.ndarray] = {"Z_i": Z_i} + + if compute_full_frame: + if xyz_S_start is None or xyz_S_stop is None: + raise ValueError("xyz_S_start and xyz_S_stop are required for full frame") + + X_s_i = ( # [I, 3] sugar-edge direction np.asarray(xyz_S_stop, dtype=np.float32) - np.asarray(xyz_S_start, dtype=np.float32) ) X_s_i = X_s_i / (np.linalg.norm(X_s_i, axis=-1, keepdims=True) + eps) - X_i = np.cross(Z_i, X_s_i) + X_i = np.cross(Z_i, X_s_i) # [I, 3] X_i = X_i / (np.linalg.norm(X_i, axis=-1, keepdims=True) + eps) + result["X_i"] = X_i - if return_local_params: - Y_i = np.cross(X_i, Z_i) - Y_i = Y_i / (np.linalg.norm(Y_i, axis=-1, keepdims=True) + eps) - local_base_params = {"X_i": X_i, "Y_i": Y_i, "Z_i": Z_i} - else: - # Opening needs X_i but not the local params dict. - local_base_params = None + Y_i = np.cross(X_i, Z_i) # [I, 3] + Y_i = Y_i / (np.linalg.norm(Y_i, axis=-1, keepdims=True) + eps) + result["Y_i"] = Y_i - # --- CALC II: pairwise base parameters ----------------------- + return result - # stack mean Z-direction vectors for parallel (0) and antiparallel (1) - Z_sum = Z_i[:, None, :] + Z_i[None, :, :] - Z_diff = Z_i[:, None, :] - Z_i[None, :, :] - Z_ij_oris = 0.5 * np.stack((Z_sum, Z_diff), axis=0) # [2, L, L, 3] - base_ori_ij = ( +def _compute_pairwise_geometry( + Z_i: np.ndarray, + frame_D_ij_vec: np.ndarray, + sc_D_ij_vec: np.ndarray, + *, + X_i: np.ndarray | None = None, + clamp: bool = True, + compute_opening: bool = False, + eps: float = 1e-8, +) -> dict[str, np.ndarray]: + """Compute pairwise base-step geometry between all residue pairs. + + Derives the pairwise coordinate frame (X_ij, Y_ij, Z_ij) and the + base-pair geometry parameters: rise (H_ij), buckle (B_ij), propeller + (P_ij), and optionally opening angle (O_ij). + + Args: + Z_i: Per-residue base-normal vectors, [I, 3]. + frame_D_ij_vec: Pairwise backbone displacement vectors, [I, I, 3]. + sc_D_ij_vec: Pairwise sidechain-centroid displacement vectors, [I, I, 3]. + X_i: Per-residue local X-axis, [I, 3]. Required when + *compute_opening* is True. + clamp: Clamp cosines to [-1, 1] before ``arccos``. + compute_opening: If True, compute opening angle O_ij. + eps: Small constant for numerical stability. + + Returns: + Dict with keys ``"H_ij"`` [I, I], ``"B_ij"`` [I, I], + ``"P_ij"`` [I, I], ``"base_ori_ij"`` [I, I], + ``"X_ij"`` [I, I, 3], ``"Y_ij"`` [I, I, 3], + ``"Z_ij"`` [I, I, 3], and optionally ``"O_ij"`` [I, I]. + """ + # Orientation-selected pairwise Z-axis + Z_sum = Z_i[:, None, :] + Z_i[None, :, :] # [I, I, 3] + Z_diff = Z_i[:, None, :] - Z_i[None, :, :] # [I, I, 3] + Z_ij_oris = 0.5 * np.stack((Z_sum, Z_diff), axis=0) # [2, I, I, 3] + + base_ori_ij = ( # [I, I] 0=parallel, 1=antiparallel np.linalg.norm(Z_ij_oris[1], axis=-1) > np.linalg.norm(Z_ij_oris[0], axis=-1) - ).astype(np.int64) # [L, L] + ).astype(np.int64) - Z_ij = np.where(base_ori_ij[..., None] == 0, Z_ij_oris[0], Z_ij_oris[1]) + Z_ij = np.where(base_ori_ij[..., None] == 0, Z_ij_oris[0], Z_ij_oris[1]) # [I, I, 3] Z_ij = Z_ij / (np.linalg.norm(Z_ij, axis=-1, keepdims=True) + eps) - Y_ij = frame_D_ij_vec / (np.linalg.norm(frame_D_ij_vec, axis=-1, keepdims=True) + eps) - X_ij = np.cross(Z_ij, Y_ij) + # Pairwise Y (inter-residue direction) and X axes + Y_ij = frame_D_ij_vec / (np.linalg.norm(frame_D_ij_vec, axis=-1, keepdims=True) + eps) # [I, I, 3] + X_ij = np.cross(Z_ij, Y_ij) # [I, I, 3] X_ij = X_ij / (np.linalg.norm(X_ij, axis=-1, keepdims=True) + eps) - # vertical displacement using sidechain centroids - H_ij = np.sum(sc_D_ij_vec * Z_ij, axis=-1) - # H_ij_vec = H_ij[..., None] * Z_ij - - # Opening (O_ij) is purely diagnostic; compute only if requested. - O_ij = None - if return_opening_angle: - if not (return_local_params or return_opening_angle): - raise RuntimeError("Internal error: opening angle requested without local frame") - - proj_X_i_XY = ( - np.sum(X_i[:, None, :] * X_ij, axis=-1, keepdims=True) * X_ij - + np.sum(X_i[:, None, :] * Y_ij, axis=-1, keepdims=True) * Y_ij - ) - proj_X_i_XY_norm = proj_X_i_XY / ( - np.linalg.norm(proj_X_i_XY, axis=-1, keepdims=True) + eps - ) - cos_opening = np.sum( - proj_X_i_XY_norm * proj_X_i_XY_norm.swapaxes(0, 1), - axis=-1, - ) - if clamp_pairwise_params: - cos_opening = np.clip(cos_opening, -1.0, 1.0) - O_ij = np.arccos(cos_opening) + # Rise (H_ij) + H_ij = np.sum(sc_D_ij_vec * Z_ij, axis=-1) # [I, I] # Buckle (B_ij) - proj_Z_i_YZ = ( + proj_Z_i_YZ = ( # [I, I, 3] np.sum(Z_i[:, None, :] * Y_ij, axis=-1, keepdims=True) * Y_ij + np.sum(Z_i[:, None, :] * Z_ij, axis=-1, keepdims=True) * Z_ij ) - proj_Z_i_YZ_norm = proj_Z_i_YZ / ( - np.linalg.norm(proj_Z_i_YZ, axis=-1, keepdims=True) + eps - ) - cos_buckle = np.sum( - proj_Z_i_YZ_norm * (-proj_Z_i_YZ_norm.swapaxes(0, 1)), - axis=-1, - ) + proj_Z_i_YZ_norm = proj_Z_i_YZ / (np.linalg.norm(proj_Z_i_YZ, axis=-1, keepdims=True) + eps) + cos_buckle = np.sum(proj_Z_i_YZ_norm * (-proj_Z_i_YZ_norm.swapaxes(0, 1)), axis=-1) # [I, I] # Propeller (P_ij) - proj_Z_i_ZX = ( + proj_Z_i_ZX = ( # [I, I, 3] np.sum(Z_i[:, None, :] * Z_ij, axis=-1, keepdims=True) * Z_ij + np.sum(Z_i[:, None, :] * X_ij, axis=-1, keepdims=True) * X_ij ) - proj_Z_i_ZX_norm = proj_Z_i_ZX / ( - np.linalg.norm(proj_Z_i_ZX, axis=-1, keepdims=True) + eps - ) - cos_propeller = np.sum( - proj_Z_i_ZX_norm * (-proj_Z_i_ZX_norm.swapaxes(0, 1)), - axis=-1, - ) + proj_Z_i_ZX_norm = proj_Z_i_ZX / (np.linalg.norm(proj_Z_i_ZX, axis=-1, keepdims=True) + eps) + cos_propeller = np.sum(proj_Z_i_ZX_norm * (-proj_Z_i_ZX_norm.swapaxes(0, 1)), axis=-1) # [I, I] - if clamp_pairwise_params: + if clamp: cos_buckle = np.clip(cos_buckle, -1.0, 1.0) cos_propeller = np.clip(cos_propeller, -1.0, 1.0) - B_ij = np.arccos(cos_buckle) - P_ij = np.arccos(cos_propeller) + B_ij = np.arccos(cos_buckle) # [I, I] + P_ij = np.arccos(cos_propeller) # [I, I] + + result: dict[str, np.ndarray] = { + "H_ij": H_ij, + "B_ij": B_ij, + "P_ij": P_ij, + "base_ori_ij": base_ori_ij, + "X_ij": X_ij, + "Y_ij": Y_ij, + "Z_ij": Z_ij, + } - pair_params: dict | None - if return_basepairs_only: - pair_params = None - else: - pair_params = { - "H_ij": H_ij, - "B_ij": B_ij, - "P_ij": P_ij, - "base_ori_ij": base_ori_ij, - } + # Opening angle (O_ij) — purely diagnostic + if compute_opening: + if X_i is None: + raise ValueError("X_i is required to compute opening angle") + + proj_X_i_XY = ( # [I, I, 3] + np.sum(X_i[:, None, :] * X_ij, axis=-1, keepdims=True) * X_ij + + np.sum(X_i[:, None, :] * Y_ij, axis=-1, keepdims=True) * Y_ij + ) + proj_X_i_XY_norm = proj_X_i_XY / (np.linalg.norm(proj_X_i_XY, axis=-1, keepdims=True) + eps) + cos_opening = np.sum(proj_X_i_XY_norm * proj_X_i_XY_norm.swapaxes(0, 1), axis=-1) # [I, I] + if clamp: + cos_opening = np.clip(cos_opening, -1.0, 1.0) + result["O_ij"] = np.arccos(cos_opening) # [I, I] - if return_opening_angle and O_ij is not None: - pair_params["O_ij"] = O_ij + return result - if return_pairwise_geometry: - pair_params["X_ij"] = X_ij - pair_params["Y_ij"] = Y_ij - pair_params["Z_ij"] = Z_ij - # --- CALC III: basepair filters / probabilities -------------- - hbond_summation = np.tensordot( +def _compute_basepair_mask( + hbond_count: np.ndarray, + seq_neighbors: np.ndarray, + H_ij: np.ndarray, + B_ij: np.ndarray, + P_ij: np.ndarray, + mol_info, + *, + bool_only: bool = False, + eps: float = 1e-8, +) -> dict[str, np.ndarray] | np.ndarray: + """Identify base pairs by combining H-bond scores with geometry filters. + + Computes a sigmoid-based base-pair probability from weighted H-bond + counts and gates it with rise / buckle / propeller geometry limits. + + Args: + hbond_count: H-bond counts, [I, I, 3] (BB-BB / BB-SC / SC-SC). + seq_neighbors: Sequence-neighbor boolean mask, [I, I]. + H_ij: Rise displacement, [I, I]. + B_ij: Buckle angle (radians), [I, I]. + P_ij: Propeller angle (radians), [I, I]. + mol_info: Molecular-info object with ``bp_summation_weights``, + ``bp_hbond_coeff``, ``min_hbonds_for_bp``, ``bp_val_cutoff``, + and ``base_geometry_limits``. + bool_only: If True, return only the boolean mask array. + eps: Small constant for numerical stability. + + Returns: + If *bool_only*: ``np.ndarray`` of shape ``(I, I)`` (bool). + Otherwise: dict with ``"basepairs_bool_ij"`` [I, I] (bool), + ``"basepairs_ij"`` [I, I] (float), and + ``"hbond_summation"`` [I, I] (float). + """ + hbond_summation = np.tensordot( # [I, I] hbond_count.astype(np.float32), np.asarray(mol_info.bp_summation_weights, dtype=np.float32), axes=([2], [0]), - ) # [L, L] + ) - logits = mol_info.bp_hbond_coeff * ( + logits = mol_info.bp_hbond_coeff * ( # [I, I] hbond_summation - (mol_info.min_hbonds_for_bp - 1) ) - bp_preds = (1.0 / (1.0 + np.exp(-logits))) + eps + bp_preds = (1.0 / (1.0 + np.exp(-logits))) + eps # [I, I] - # Filter Height geometry - H_ij_filter = (H_ij >= -mol_info.base_geometry_limits["H_ij"]) & ( - H_ij <= mol_info.base_geometry_limits["H_ij"] + # Geometry filters + H_ij_filter = ( # [I, I] + (H_ij >= -mol_info.base_geometry_limits["H_ij"]) + & (H_ij <= mol_info.base_geometry_limits["H_ij"]) ) - # Filter Buckle geometry - B_ij_filter = (B_ij <= mol_info.base_geometry_limits["B_ij"]) | ( - B_ij >= math.pi - mol_info.base_geometry_limits["B_ij"] + B_ij_filter = ( # [I, I] + (B_ij <= mol_info.base_geometry_limits["B_ij"]) + | (B_ij >= math.pi - mol_info.base_geometry_limits["B_ij"]) ) - # Filter Propeller geometry - P_ij_filter = (P_ij <= mol_info.base_geometry_limits["P_ij"]) | ( - P_ij >= math.pi - mol_info.base_geometry_limits["P_ij"] + P_ij_filter = ( # [I, I] + (P_ij <= mol_info.base_geometry_limits["P_ij"]) + | (P_ij >= math.pi - mol_info.base_geometry_limits["P_ij"]) ) - - bp_geom_filter = (H_ij_filter & B_ij_filter & P_ij_filter) + bp_geom_filter = H_ij_filter & B_ij_filter & P_ij_filter # [I, I] - if return_basepairs_only: - # Avoid allocating basepairs_ij float matrix when only the boolean mask is needed. - basepairs_bool_ij = (~seq_neighbors) & bp_geom_filter & ( - bp_preds >= float(mol_info.bp_val_cutoff) + if bool_only: + basepairs_bool_ij = ( # [I, I] + (~seq_neighbors) & bp_geom_filter + & (bp_preds >= float(mol_info.bp_val_cutoff)) ) - basepairs_ij = None - else: - basepairs_ij = (~seq_neighbors).astype(np.float32) * ( - bp_geom_filter.astype(np.float32) * bp_preds.astype(np.float32) - ) - basepairs_bool_ij = basepairs_ij >= mol_info.bp_val_cutoff - - if return_basepairs_only: - # Cleanup intermediate tensors to free memory - del frame_D_ij_vec, sc_D_ij_vec - del hbond_summation, bp_preds - del H_ij_filter, B_ij_filter, P_ij_filter, bp_geom_filter - - # Explicitly drop the largest pairwise arrays. - del X_ij, Y_ij, Z_ij - if O_ij is not None: - del O_ij - if local_base_params is not None: - del local_base_params return basepairs_bool_ij - assert pair_params is not None + basepairs_ij = ( # [I, I] + (~seq_neighbors).astype(np.float32) + * bp_geom_filter.astype(np.float32) + * bp_preds.astype(np.float32) + ) + basepairs_bool_ij = basepairs_ij >= mol_info.bp_val_cutoff # [I, I] - pair_params["basepairs_bool_ij"] = basepairs_bool_ij - pair_params["hbond_summation"] = hbond_summation - pair_params["basepairs_ij"] = basepairs_ij + return { + "basepairs_bool_ij": basepairs_bool_ij, + "basepairs_ij": basepairs_ij, + "hbond_summation": hbond_summation, + } - nucleic_ss_data = {"pair_params": pair_params} - if return_local_params and local_base_params is not None: - nucleic_ss_data["local_params"] = local_base_params - # Cleanup intermediate tensors to free memory - del frame_D_ij_vec, sc_D_ij_vec - del hbond_summation, bp_preds - del H_ij_filter, B_ij_filter, P_ij_filter, bp_geom_filter +def compute_nucleic_ss( + mol_info, + token_level_data, + hbond_count, + clamp_pairwise_params=True, + eps=1e-8, + *, + return_local_params: bool = False, + return_pairwise_geometry: bool = False, + return_opening_angle: bool = False, + return_basepairs_only: bool = False, +): + """Compute nucleic-acid pairwise base-pair geometry and filters. + + Operates in two modes: + + * **Fast annotation** (default / ``return_basepairs_only=True``): returns + only ``basepairs_bool_ij`` and frees intermediate arrays. + * **Diagnostic**: additionally returns local/pairwise geometry when + ``return_pairwise_geometry``, ``return_local_params``, or + ``return_opening_angle`` are set. + + Args: + mol_info: Molecular-info constants (geometry limits, H-bond weights). + token_level_data: Token-level dict with geometry (from + :func:`add_token_level_geometry_data`). + hbond_count: H-bond count array, shape ``(I_full, I_full, 3)``. + clamp_pairwise_params: Clamp cosines to [-1, 1] before ``arccos``. + eps: Small constant for numerical stability. + return_local_params: Return per-residue X/Y/Z local frames. + return_pairwise_geometry: Return pairwise X_ij/Y_ij/Z_ij arrays. + return_opening_angle: Return pairwise opening angle O_ij. + return_basepairs_only: Return only the boolean base-pair mask + (fastest path). + + Returns: + If ``return_basepairs_only``: ``np.ndarray`` of shape ``(I, I)`` + (bool) — the base-pair boolean mask. + + Otherwise: dict ``{"pair_params": {...}, "local_params": {...}}`` + containing the requested geometry arrays (all shape ``(I, I)`` or + ``(I, 3)``). + """ + + mask_1d = np.asarray(token_level_data["filter_mask"], dtype=bool) # [I_full] + + # --- Unpack and filter token-level data ---------------------- + M_i = np.asarray(token_level_data["M_i"], dtype=np.float32)[mask_1d] # [I, 3] + frame_xyz = np.asarray(token_level_data["frame_xyz"], dtype=np.float32)[mask_1d] # [I, 3] + xyz_S_start = [v for v, k in zip(token_level_data["xyz_S_start"], mask_1d) if k] # list[I] of [3] + xyz_S_stop = [v for v, k in zip(token_level_data["xyz_S_stop"], mask_1d) if k] # list[I] of [3] + xyz_planar = [v for v, k in zip(token_level_data["xyz_planar"], mask_1d) if k] # list[I] of [K_i, 3] + + hbond_count = np.asarray(hbond_count)[mask_1d, :][:, mask_1d] # [I, I, 3] + seq_neighbors = np.asarray(token_level_data["seq_neighbors"], dtype=bool)[mask_1d, :][:, mask_1d] # [I, I] + + # --- Precompute centroids and displacement vectors ----------- + planar_centers = np.stack( # [I, 3] + [np.nanmean(np.asarray(xyz_i, dtype=np.float32), axis=0) for xyz_i in xyz_planar], + axis=0, + ).astype(np.float32) + + frame_D_ij_vec = frame_xyz[None, :, :] - frame_xyz[:, None, :] # [I, I, 3] + sc_D_ij_vec = planar_centers[None, :, :] - planar_centers[:, None, :] # [I, I, 3] + + # --- CALC I: per-residue local coordinate frames ------------- + need_full_frame = return_local_params or return_opening_angle + local_frames = _compute_local_frames( + xyz_planar, + planar_centers, + M_i, + xyz_S_start=xyz_S_start if need_full_frame else None, + xyz_S_stop=xyz_S_stop if need_full_frame else None, + compute_full_frame=need_full_frame, + eps=eps, + ) + Z_i = local_frames["Z_i"] # [I, 3] + X_i = local_frames.get("X_i") # [I, 3] or None + + # --- CALC II: pairwise base-step geometry -------------------- + pw_geom = _compute_pairwise_geometry( + Z_i, + frame_D_ij_vec, + sc_D_ij_vec, + X_i=X_i, + clamp=clamp_pairwise_params, + compute_opening=return_opening_angle, + eps=eps, + ) + + # --- CALC III: base-pair identification ---------------------- + bp_result = _compute_basepair_mask( + hbond_count, + seq_neighbors, + pw_geom["H_ij"], + pw_geom["B_ij"], + pw_geom["P_ij"], + mol_info, + bool_only=return_basepairs_only, + eps=eps, + ) - # If not returning, explicitly drop the largest pairwise arrays. - if not return_pairwise_geometry: - del X_ij, Y_ij, Z_ij - if not return_opening_angle and O_ij is not None: - del O_ij - if not return_local_params and local_base_params is not None: - del local_base_params + if return_basepairs_only: + return bp_result # np.ndarray [I, I] bool + + # --- Assemble output dict ------------------------------------ + assert isinstance(bp_result, dict) + + pair_params: dict[str, np.ndarray] = { + "H_ij": pw_geom["H_ij"], + "B_ij": pw_geom["B_ij"], + "P_ij": pw_geom["P_ij"], + "base_ori_ij": pw_geom["base_ori_ij"], + "basepairs_bool_ij": bp_result["basepairs_bool_ij"], + "basepairs_ij": bp_result["basepairs_ij"], + "hbond_summation": bp_result["hbond_summation"], + } + + if return_opening_angle and "O_ij" in pw_geom: + pair_params["O_ij"] = pw_geom["O_ij"] + + if return_pairwise_geometry: + pair_params["X_ij"] = pw_geom["X_ij"] + pair_params["Y_ij"] = pw_geom["Y_ij"] + pair_params["Z_ij"] = pw_geom["Z_ij"] + + nucleic_ss_data: dict = {"pair_params": pair_params} + if return_local_params and "Y_i" in local_frames: + nucleic_ss_data["local_params"] = { + "X_i": local_frames["X_i"], + "Y_i": local_frames["Y_i"], + "Z_i": local_frames["Z_i"], + } return nucleic_ss_data + + def annotate_na_ss( atom_array: AtomArray, *, @@ -1137,41 +1141,47 @@ def annotate_na_ss( mol_info: Optional[NucMolInfo] = None, overwrite: bool = True, token_level_data: Optional[dict] = None, + cutoff_HA_dist: float = 3.5, + cutoff_DA_dist: float = 3.5, ) -> AtomArray: - """Annotate base-pair partners directly onto the AtomArray. - - This computes nucleic-acid base pairing similarly to - :func:`get_gt_nucleic_geom_feats` but instead of returning an integer - secondary-structure matrix, it writes an AtomArray annotation - ``bp_partners``. - - The annotation is stored on the *full* ``atom_array`` (length N atoms), - but only nucleic-acid token-representative atoms (indices ``token_starts`` - from :func:`get_token_starts`) that are included in this call's - ``annotation_mask`` get a list value. - - Semantics: - - ``[]`` (empty list): explicitly unpaired nucleic-acid loop - - ``[token_id, ...]``: paired nucleic-acid token(s) - - ``None``: unannotated/masked (non-NA tokens, or tokens filtered out) - - Each list element is the partner token identifier (``token_id`` as int) - for the paired residue. This is sufficient to recover the partner's - token-representative atom via ``token_starts`` + token_id mapping. - - Notes - ----- - - ``token_level_data`` may be metadata-only; this function will augment it - with geometry as needed. - - If ``p_canonical_bp_filter > 0``, then with that probability we discard - any non-canonical NA basepairs (keeps only A-U, A-T, G-C). + """Compute base pairs and write a ``bp_partners`` annotation onto *atom_array*. + + Uses H-bond counts and pairwise geometry filters to identify base pairs, + then stores the result as a per-atom annotation with the following + semantics: + + * ``[]`` — explicitly unpaired (loop) + * ``[token_id, ...]`` — paired partner token IDs + * ``None`` — unannotated / masked (non-NA or filtered-out tokens) + + Args: + atom_array: Structure to annotate (modified in-place). + NA_only: Restrict geometry filter to nucleic-acid tokens. + planar_only: Restrict geometry filter to tokens with planar + sidechains. + p_canonical_bp_filter: Probability of discarding non-canonical + base pairs (keeps only A–U, A–T, G–C). + mol_info: Molecular-info constants; created if ``None``. + overwrite: If False, merge with existing ``bp_partners``. + token_level_data: Pre-computed metadata dict; augmented with + geometry as needed. + cutoff_HA_dist: H–A distance cutoff (Å) for HBPLUS. + cutoff_DA_dist: D–A distance cutoff (Å) for HBPLUS. + + Returns: + The same *atom_array* with the ``bp_partners`` annotation set. """ if mol_info is None: mol_info = NucMolInfo() - # Token representatives (0..L-1) and their corresponding atom indices (into atom_array) - token_starts = get_token_starts(atom_array) + # Residue representatives (0..L-1) and their corresponding atom indices. + # Keep this aligned with get_token_level_metadata(), which uses residue starts. + if token_level_data is not None and "token_starts" in token_level_data: + token_starts = np.asarray(token_level_data["token_starts"], dtype=int) + else: + token_starts = struc.get_residue_starts(atom_array) + residue_start_end = np.concatenate([token_starts, [atom_array.array_length()]]) token_level_array = atom_array[token_starts] # token_id is assigned token-wise and matches get_token_starts() segmentation. token_ids: list[int] = [int(t) for t in list(token_level_array.token_id)] @@ -1204,11 +1214,11 @@ def annotate_na_ss( atom_array, token_level_data, mol_info, - cutoff_HA_dist=mol_info.cutoff_HA_dist, - cutoff_DA_dist=mol_info.cutoff_DA_dist, + cutoff_HA_dist=cutoff_HA_dist, + cutoff_DA_dist=cutoff_DA_dist, ) bp_bool = np.asarray( - _compute_nucleic_ss_impl( + compute_nucleic_ss( mol_info, token_level_data, hbond_count, @@ -1222,6 +1232,18 @@ def annotate_na_ss( dtype=bool, ) + # Apply optional filters + if NA_only: + bp_bool &= is_na_full[:, None] + bp_bool &= is_na_full[None, :] + if planar_only: + n_tokens = bp_bool.shape[0] + has_planar_sc = np.asarray( + token_level_data.get("has_planar_sc", np.ones(n_tokens, dtype=bool)), dtype=bool + ) + bp_bool &= has_planar_sc[:, None] + bp_bool &= has_planar_sc[None, :] + # Optional: filter to canonical Watson-Crick basepairs only. # Sampled probabilistically to allow mixed supervision during training. do_canonical_filter = bool(p_canonical_bp_filter and (np.random.rand() < float(p_canonical_bp_filter))) @@ -1255,16 +1277,18 @@ def _base_letter(res_name: str) -> str | None: bp_bool = np.asarray(bp_bool, dtype=bool) bp_rows, bp_cols = np.nonzero(bp_bool) - # Prepare/overwrite annotation array + # Build residue-level annotation first, then spread to all atoms in each residue. if (not overwrite) and ("bp_partners" in atom_array.get_annotation_categories()): - bp_partners_ann = atom_array.bp_partners - if len(bp_partners_ann) != len(atom_array): - raise ValueError( - "Existing bp_partners annotation has wrong length" - ) + existing_ann = atom_array.bp_partners + if len(existing_ann) != len(atom_array): + raise ValueError("Existing bp_partners annotation has wrong length") + residue_bp_partners = np.empty(len(token_starts), dtype=object) + residue_bp_partners[:] = None + for i, start in enumerate(token_starts.tolist()): + residue_bp_partners[i] = existing_ann[int(start)] else: - bp_partners_ann = np.empty(len(atom_array), dtype=object) - bp_partners_ann[:] = None + residue_bp_partners = np.empty(len(token_starts), dtype=object) + residue_bp_partners[:] = None # Explicit-loop semantics: # - Only nucleic-acid token-start atoms *within subset_idxs* get a list container. @@ -1273,9 +1297,8 @@ def _base_letter(res_name: str) -> str | None: for full_i in subset_idxs.tolist(): if not bool(is_na_full[int(full_i)]): continue - atom_i = int(token_starts[int(full_i)]) - if bp_partners_ann[atom_i] is None: - bp_partners_ann[atom_i] = [] + if residue_bp_partners[int(full_i)] is None: + residue_bp_partners[int(full_i)] = [] # Populate partners using token_id ints # We only process each unordered pair once to avoid duplicates. @@ -1296,149 +1319,58 @@ def _base_letter(res_name: str) -> str | None: if full_j < full_i: continue - atom_i = int(token_starts[full_i]) - atom_j = int(token_starts[full_j]) partner_i = int(token_ids[full_j]) partner_j = int(token_ids[full_i]) - if bp_partners_ann[atom_i] is None: - bp_partners_ann[atom_i] = [] - if bp_partners_ann[atom_j] is None: - bp_partners_ann[atom_j] = [] + if residue_bp_partners[full_i] is None: + residue_bp_partners[full_i] = [] + if residue_bp_partners[full_j] is None: + residue_bp_partners[full_j] = [] # Add if not present - if partner_i not in bp_partners_ann[atom_i]: - bp_partners_ann[atom_i].append(partner_i) - if partner_j not in bp_partners_ann[atom_j]: - bp_partners_ann[atom_j].append(partner_j) + if partner_i not in residue_bp_partners[full_i]: + residue_bp_partners[full_i].append(partner_i) + if partner_j not in residue_bp_partners[full_j]: + residue_bp_partners[full_j].append(partner_j) + + # Project residue-level annotations back to atom-level storage: + # - atomized residues: spread to all atoms in that residue + # - non-atomized residues: keep only on token-start representative atom + bp_partners_ann = np.empty(len(atom_array), dtype=object) + bp_partners_ann[:] = None + for i, start in enumerate(token_starts.tolist()): + stop = int(residue_start_end[i + 1]) + value = residue_bp_partners[i] + if value is None: + continue + # A residue is treated as atomized if any atom in the residue carries atomize=True. + if "atomize" in atom_array.get_annotation_categories(): + residue_is_atomized = bool(np.any(np.asarray(atom_array.atomize[int(start):stop], dtype=bool))) + else: + residue_is_atomized = False + if residue_is_atomized: + for atom_idx in range(int(start), stop): + bp_partners_ann[atom_idx] = list(value) + else: + bp_partners_ann[int(start)] = list(value) atom_array.set_annotation("bp_partners", bp_partners_ann) return atom_array -def bp_partner_to_ss_matrix( - atom_array: AtomArray, - *, - feature_info: Optional[dict] = None, - mol_info: Optional[NucMolInfo] = None, - NA_only: Optional[bool] = False, - planar_only: Optional[bool] = False, - include_loops: bool = True, - token_level_data: Optional[dict] = None, -) -> np.ndarray: - """Reconstruct an integer NA secondary-structure matrix from annotations. - - Requires that ``atom_array`` has a ``bp_partners`` annotation created by - :func:`annotate_na_ss`. - - Returns - ------- - ss_matrix : np.ndarray - Shape (L, L) with values from ``feature_info``. - - Loop semantics: - - Only nucleic-acid tokens can be loops. - - Only tokens with an explicit empty list ``bp_partners == []`` are loops. - Unannotated tokens (``bp_partners is None``) remain masked. - """ - - if mol_info is None: - mol_info = NucMolInfo() - - if feature_info is None: - feature_info = DEFAULT_NA_SS_FEATURE_INFO - - if "bp_partners" not in atom_array.get_annotation_categories(): - raise ValueError( - "atom_array is missing bp_partners annotation; run annotate_na_ss() first" - ) - - token_starts = get_token_starts(atom_array) - token_level_array = atom_array[token_starts] - token_ids_int: list[int] = [int(t) for t in list(token_level_array.token_id)] - token_id_to_index_int = {int(tid): i for i, tid in enumerate(token_ids_int)} - L = len(token_starts) - - ss_matrix = feature_info["NA_SS_MASK"] * np.ones((L, L), dtype=np.int64) - - if token_level_data is None: - token_level_data = get_token_level_metadata( - atom_array, - mol_info, - # NA_only=NA_only, - # planar_only=planar_only, - ) - - mask_1d = np.asarray(token_level_data["filter_mask"], dtype=bool) - subset_idxs = np.nonzero(mask_1d)[0] - subset_set = set(int(x) for x in subset_idxs.tolist()) - is_na = np.asarray(token_level_data["is_na"], dtype=bool) - subset_na_idxs = subset_idxs[np.asarray(is_na[subset_idxs], dtype=bool)] - subset_na_set = set(int(x) for x in subset_na_idxs.tolist()) - - # Fill base-pair edges (only within subset, and only NA-NA) - bp_partners_ann = atom_array.bp_partners - for i in subset_idxs.tolist(): - if not bool(is_na[int(i)]): - continue - atom_i = int(token_starts[int(i)]) - partners = bp_partners_ann[atom_i] - if partners is None: - continue - if not isinstance(partners, (list, tuple, np.ndarray)): - continue - for partner_token_id in partners: - # Support int, numpy scalar, and legacy stringified token_id. - try: - partner_tid_int = int(partner_token_id) - except Exception: - partner_tid_int = None - j = token_id_to_index_int.get(partner_tid_int) if partner_tid_int is not None else None - if j is None or j == i: - continue - if int(j) not in subset_set: - continue - if not bool(is_na[int(j)]): - continue - ss_matrix[i, j] = feature_info["NA_SS_PAIR"] - ss_matrix[j, i] = feature_info["NA_SS_PAIR"] - - if not include_loops: - return ss_matrix - - # Loop labeling is explicit and NA-only: - # - only nucleic tokens can be loops - # - only tokens with an explicit empty list annotation are loops - loop_idxs_list: list[int] = [] - for i in subset_idxs.tolist(): - if not bool(is_na[int(i)]): - continue - atom_i = int(token_starts[int(i)]) - partners = bp_partners_ann[atom_i] - if not isinstance(partners, (list, tuple, np.ndarray)): - continue - if len(partners) == 0: - loop_idxs_list.append(int(i)) - - loop_idxs = np.asarray(loop_idxs_list, dtype=np.int64) - if loop_idxs.size > 0: - ss_matrix[loop_idxs[:, None], subset_na_idxs[None, :]] = feature_info["NA_SS_LOOP"] - ss_matrix[subset_na_idxs[:, None], loop_idxs[None, :]] = feature_info["NA_SS_LOOP"] - - return ss_matrix - - def parse_dot_bracket(dot_bracket: str) -> tuple[list[tuple[int, int]], list[int]]: """Parse a dot-bracket string into base pairs and unpaired positions. - Supports (), [], {}, <>, and A..E / a..e bracket pairs. + Supports standard ``()``, ``[]``, ``{}``, ``<>`` and pseudoknot + brackets ``A``–``E`` / ``a``–``e``. - Returns - ------- - pairs : list of (i, j) - 0-based indices in the string for paired positions. - unpaired : list of int - 0-based indices that are '.' (unpaired). + Args: + dot_bracket: Dot-bracket notation string. + + Returns: + Tuple of ``(pairs, unpaired)`` where *pairs* is a list of 0-based + ``(i, j)`` index tuples and *unpaired* is a list of 0-based indices + corresponding to ``.`` characters. """ stack: dict[str, list[int]] = {} @@ -1480,19 +1412,27 @@ def annotate_na_ss_from_specification( *, overwrite: bool = True, ) -> AtomArray: - """Annotate ``bp_partners`` from an inference-time specification. - - This is the inference analogue of :func:`annotate_na_ss`, except instead - of computing base pairs from geometry/H-bonds, it interprets a user-provided - specification (dot-bracket strings and/or residue ranges/positions) and - writes the same ``bp_partners`` annotation on token-representative atoms. - - Supported spec keys (all optional): - - ``ss_dbn``: global dot-bracket string applied to the first L tokens. - - ``ss_dbn_dict``: mapping like {"A5-15": dbn_str, ...}. - - ``paired_region_list``: list of "A5-15,B1-11" entries. - - ``paired_position_list``: list of "A19,A61,A20" groups. - - ``loop_region_list``: list of "A5-10" regions forced unpaired. + """Write ``bp_partners`` annotation from an inference-time specification. + + Inference analogue of :func:`annotate_na_ss`: interprets user-provided + dot-bracket strings and/or residue ranges rather than computing base + pairs from geometry. + + Supported *specification* keys (all optional): + + * ``ss_dbn``: global dot-bracket string (applied to the first *L* tokens). + * ``ss_dbn_dict``: ``{"-": dbn_str, ...}``. + * ``paired_region_list``: ``["A5-15,B1-11", ...]``. + * ``paired_position_list``: ``["A19,A61,A20", ...]``. + * ``loop_region_list``: ``["A5-10", ...]`` (forced unpaired). + + Args: + atom_array: Structure to annotate (modified in-place). + specification: Specification dict as described above. + overwrite: If False, merge with existing ``bp_partners``. + + Returns: + The same *atom_array* with the ``bp_partners`` annotation set. """ spec = specification or {} @@ -1502,11 +1442,17 @@ def annotate_na_ss_from_specification( n_tokens = len(token_starts) # Explicit loops are only meaningful for nucleic-acid tokens. - seq_data = _get_sequence_encoding_data() - is_rna_like = np.isin(token_level_array.res_name, seq_data["rna_like_res_names"]) - is_dna_like = np.isin(token_level_array.res_name, seq_data["dna_like_res_names"]) + # Instantiate encoding locally to avoid retaining large arrays at module scope. + sequence_encoding = AF3SequenceEncoding() + is_rna_like = np.isin( + token_level_array.res_name, + sequence_encoding.all_res_names[sequence_encoding.is_rna_like], + ) + is_dna_like = np.isin( + token_level_array.res_name, + sequence_encoding.all_res_names[sequence_encoding.is_dna_like], + ) is_na_token = np.asarray(is_rna_like | is_dna_like, dtype=bool) - del seq_data # Prepare/overwrite annotation array if (not overwrite) and ("bp_partners" in atom_array.get_annotation_categories()): @@ -1703,8 +1649,18 @@ def annotate_na_ss_from_data_specification( *, overwrite: bool = True, ) -> AtomArray: - """Convenience wrapper: annotate bp partners from ``data['specification']``.""" + """Annotate ``bp_partners`` from ``data["specification"]``. + + Convenience wrapper around :func:`annotate_na_ss_from_specification`. + + Args: + data: Pipeline data dict containing ``atom_array`` and optionally + ``specification``. + overwrite: If False, merge with existing ``bp_partners``. + + Returns: + The annotated AtomArray (also stored back in *data*). + """ atom_array = data["atom_array"] spec = data.get("specification", {}) or {} return annotate_na_ss_from_specification(atom_array, spec, overwrite=overwrite) - From fc8514bb041f0655f9ae9f9973eb4777ebc69e7b Mon Sep 17 00:00:00 2001 From: afavor Date: Mon, 16 Feb 2026 18:15:42 -0800 Subject: [PATCH 20/49] fixed behavior for conditional sampling of NA SS condition --- models/rfd3/configs/datasets/design_base.yaml | 5 + .../configs/datasets/design_base_rfd3na.yaml | 4 +- .../rfd3/configs/datasets/val/pseudoknot.yaml | 1 + models/rfd3/configs/experiment/rfd3na-ss.yaml | 21 ++- .../src/rfd3/metrics/nucleic_ss_metrics.py | 175 +++++++++--------- models/rfd3/src/rfd3/transforms/na_geom.py | 37 ++-- .../rfd3/src/rfd3/transforms/na_geom_utils.py | 54 ++++-- 7 files changed, 161 insertions(+), 136 deletions(-) diff --git a/models/rfd3/configs/datasets/design_base.yaml b/models/rfd3/configs/datasets/design_base.yaml index 02ee92a6..c862900c 100644 --- a/models/rfd3/configs/datasets/design_base.yaml +++ b/models/rfd3/configs/datasets/design_base.yaml @@ -98,3 +98,8 @@ global_transform_args: # PPI add_ppi_hotspots: 0.75 full_binder_crop: 0.75 + + # Nucleic SS: + p_is_nucleic_ss_example: 0.0 + p_nucleic_ss_show_partial_feats: 0.0 + \ No newline at end of file diff --git a/models/rfd3/configs/datasets/design_base_rfd3na.yaml b/models/rfd3/configs/datasets/design_base_rfd3na.yaml index 71816174..dbebee88 100644 --- a/models/rfd3/configs/datasets/design_base_rfd3na.yaml +++ b/models/rfd3/configs/datasets/design_base_rfd3na.yaml @@ -81,7 +81,9 @@ global_transform_args: # Used to create simple boolean flags for downstream conditioning meta_conditioning_probabilities: - calculate_NA_SS: 1.0 + # calculate_NA_SS: 1.0 + p_is_nucleic_ss_example: 0.1 + p_nucleic_ss_show_partial_feats: 0.7 calculate_hbonds: 0.2 calculate_rasa: 0.6 diff --git a/models/rfd3/configs/datasets/val/pseudoknot.yaml b/models/rfd3/configs/datasets/val/pseudoknot.yaml index 7cf5ce14..895e6c21 100644 --- a/models/rfd3/configs/datasets/val/pseudoknot.yaml +++ b/models/rfd3/configs/datasets/val/pseudoknot.yaml @@ -6,4 +6,5 @@ defaults: dataset: name: pseudoknot eval_every_n: 1 + # data: ${paths.data.design_benchmark_data_dir}/pseudoknot_debug.json data: ${paths.data.design_benchmark_data_dir}/pseudoknot.json diff --git a/models/rfd3/configs/experiment/rfd3na-ss.yaml b/models/rfd3/configs/experiment/rfd3na-ss.yaml index 6d2245c5..082ed819 100644 --- a/models/rfd3/configs/experiment/rfd3na-ss.yaml +++ b/models/rfd3/configs/experiment/rfd3na-ss.yaml @@ -2,6 +2,7 @@ # Training configuration for RFD3 defaults: + # - /debug/default - /debug/default - override /model: rfd3_base - override /logger: null @@ -54,6 +55,10 @@ datasets: crop_size: 256 max_atoms_in_crop: 2560 # ~10x crop size. global_transform_args: + meta_conditioning_probabilities: + p_is_nucleic_ss_example: 1.0 + p_nucleic_ss_show_partial_feats: 0.7 + p_canonical_bp_filter: 0.2 association_scheme: atom23 #add_na_pair_features: true train_conditions: @@ -70,19 +75,23 @@ datasets: train: # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. pdb: - probability: 0.5 + # probability: 0.5 + probability: 0.75 + # probability: 0.0 rna_monomer_distillation: - probability: 0.5 + # probability: 0.2 + probability: 0.25 + # probability: 1.0 val: pseudoknot: dataset: # eval_every_n: 10 - eval_every_n: 1 + eval_every_n: 5 trainer: - devices_per_node: 1 - limit_train_batches: 10 - limit_val_batches: 1 + #devices_per_node: 1 + #limit_train_batches: 10 + #limit_val_batches: 1 validate_every_n_epochs: 5 prevalidate: true diff --git a/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py b/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py index 6a0c8d44..62d077f5 100644 --- a/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py +++ b/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py @@ -16,6 +16,14 @@ global_logger = RankedLogger(__name__, rank_zero_only=False) +def _get_bp_partners_annotation(atom_array: AtomArray): + """Return bp-partners annotation.""" + categories = atom_array.get_annotation_categories() + if "bp_partners" in categories: + return atom_array.bp_partners + raise ValueError("atom_array missing bp_partners annotation") + + def _safe_f1_from_sizes(intersection_n: int, pred_n: int, gt_n: int) -> float: """Return F1 with sensible empty-set handling.""" if pred_n == 0 and gt_n == 0: @@ -76,19 +84,16 @@ def _extract_bp_pairs( *, allowed_token_ids: set[int], ) -> set[tuple[int, int]]: - """Extract unordered base-pair edges from bp_partner annotations. + """Extract unordered base-pair edges from bp-partner annotations. Pairs are represented as (min_token_id, max_token_id). """ - if "bp_partner" not in atom_array.get_annotation_categories(): - raise ValueError("atom_array missing bp_partner annotation") - token_starts = get_token_starts(atom_array) token_level_array = atom_array[token_starts] token_ids = np.asarray(token_level_array.token_id, dtype=int) token_id_to_pos = {int(tid): i for i, tid in enumerate(token_ids.tolist())} - bp_partner_ann = atom_array.bp_partner + bp_partner_ann = _get_bp_partners_annotation(atom_array) pairs: set[tuple[int, int]] = set() for pos, start_idx in enumerate(token_starts.tolist()): @@ -126,15 +131,12 @@ def _extract_loop_and_paired_token_ids( allowed_token_ids: set[int], ) -> tuple[set[int], set[int]]: """Return (loop_token_ids, paired_token_ids) within the allowed token set.""" - if "bp_partner" not in atom_array.get_annotation_categories(): - raise ValueError("atom_array missing bp_partner annotation") - token_starts = get_token_starts(atom_array) token_level_array = atom_array[token_starts] token_ids = np.asarray(token_level_array.token_id, dtype=int) token_id_to_pos = {int(tid): i for i, tid in enumerate(token_ids.tolist())} - bp_partner_ann = atom_array.bp_partner + bp_partner_ann = _get_bp_partners_annotation(atom_array) loop_token_ids: set[int] = set() paired_token_ids: set[int] = set() @@ -176,9 +178,9 @@ class NucleicSSSimilarityMetrics(Metric): """Secondary-structure similarity for nucleic acids. Reports: - - `pair_f1`: F1 over the set of basepair edges implied by token-level `bp_partner`. - - `loop_f1`: F1 over explicitly-unpaired loop tokens (`bp_partner == []`). - Unannotated tokens (`bp_partner is None`) are masked. + - `pair_f1`: F1 over basepair edges from token-level bp-partner annotation. + - `loop_f1`: F1 over explicitly-unpaired loop tokens (`bp_partners == []`). + Unannotated tokens (`bp_partners is None`) are masked. - `weighted_f1`: GT-weighted average of `pair_f1` and `loop_f1`, weighted by the prevalence of paired vs loop tokens in the GT. """ @@ -217,91 +219,84 @@ def compute(self, *, ground_truth_atom_array_stack, predicted_atom_array_stack): n_valid = 0 for gt_arr, pred_arr in zip(ground_truth_atom_array_stack, predicted_atom_array_stack): - try: - if "bp_partner" not in gt_arr.get_annotation_categories(): - continue - - # Important: predicted AtomArrays are built from a template AtomArray. - # If that template already carries bp_partner (often GT-derived), the - # prediction can inherit it, yielding artificially perfect scores. - # Optionally recompute bp_partner from the *predicted coordinates*. - - if self.annotate_predicted_fresh: - annotate_na_ss( - pred_arr, - NA_only=self.annotation_NA_only, - planar_only=self.annotation_planar_only, - overwrite=True, - p_canonical_bp_filter=0.0, - ) - - if "bp_partner" not in pred_arr.get_annotation_categories(): - continue - - # Basic sanity check: token counts should match for aligned comparisons. - gt_token_ids = _get_token_ids(gt_arr) - pred_token_ids = _get_token_ids(pred_arr) - if len(gt_token_ids) != len(pred_token_ids): - continue - - # Restrict to token_ids that are valid in both arrays. - gt_allowed = _get_candidate_token_ids( - gt_arr, - restrict_to_nucleic=self.restrict_to_nucleic, - compute_for_diffused_region_only=self.compute_for_diffused_region_only, - ) - pred_allowed = _get_candidate_token_ids( + gt_categories = gt_arr.get_annotation_categories() + if "bp_partners" not in gt_categories: + continue + + # Important: predicted AtomArrays are built from a template AtomArray. + # If that template already carries bp_partners (often GT-derived), the + # prediction can inherit it, yielding artificially perfect scores. + # Optionally recompute bp_partners from the *predicted coordinates*. + if self.annotate_predicted_fresh: + annotate_na_ss( pred_arr, - restrict_to_nucleic=self.restrict_to_nucleic, - compute_for_diffused_region_only=self.compute_for_diffused_region_only, + NA_only=self.annotation_NA_only, + planar_only=self.annotation_planar_only, + overwrite=True, + p_canonical_bp_filter=0.0, ) - allowed = gt_allowed & pred_allowed - if len(allowed) == 0: - continue + pred_categories = pred_arr.get_annotation_categories() + if "bp_partners" not in pred_categories: + continue - gt_pairs = _extract_bp_pairs(gt_arr, allowed_token_ids=allowed) - pred_pairs = _extract_bp_pairs(pred_arr, allowed_token_ids=allowed) + # Basic sanity check: token counts should match for aligned comparisons. + gt_token_ids = _get_token_ids(gt_arr) + pred_token_ids = _get_token_ids(pred_arr) + if len(gt_token_ids) != len(pred_token_ids): + continue - gt_loop, gt_paired_tokens = _extract_loop_and_paired_token_ids( - gt_arr, allowed_token_ids=allowed - ) - pred_loop, _pred_paired_tokens = _extract_loop_and_paired_token_ids( - pred_arr, allowed_token_ids=allowed + # Restrict to token_ids that are valid in both arrays. + gt_allowed = _get_candidate_token_ids( + gt_arr, + restrict_to_nucleic=self.restrict_to_nucleic, + compute_for_diffused_region_only=self.compute_for_diffused_region_only, + ) + pred_allowed = _get_candidate_token_ids( + pred_arr, + restrict_to_nucleic=self.restrict_to_nucleic, + compute_for_diffused_region_only=self.compute_for_diffused_region_only, + ) + allowed = gt_allowed & pred_allowed + + if len(allowed) == 0: + continue + + gt_pairs = _extract_bp_pairs(gt_arr, allowed_token_ids=allowed) + pred_pairs = _extract_bp_pairs(pred_arr, allowed_token_ids=allowed) + + gt_loop, gt_paired_tokens = _extract_loop_and_paired_token_ids( + gt_arr, allowed_token_ids=allowed + ) + pred_loop, _pred_paired_tokens = _extract_loop_and_paired_token_ids( + pred_arr, allowed_token_ids=allowed + ) + + pair_tp = len(gt_pairs & pred_pairs) + pair_pred_n = len(pred_pairs) + pair_gt_n = len(gt_pairs) + + loop_tp = len(gt_loop & pred_loop) + loop_pred_n = len(pred_loop) + loop_gt_n = len(gt_loop) + + pair_f1 = _safe_f1_from_sizes(pair_tp, pair_pred_n, pair_gt_n) + loop_f1 = _safe_f1_from_sizes(loop_tp, loop_pred_n, loop_gt_n) + + pair_weight = len(gt_paired_tokens) + loop_weight = len(gt_loop) + total_weight = pair_weight + loop_weight + if total_weight == 0: + weighted_f1 = 1.0 + else: + weighted_f1 = float( + (pair_weight * pair_f1 + loop_weight * loop_f1) / total_weight ) - pair_tp = len(gt_pairs & pred_pairs) - pair_pred_n = len(pred_pairs) - pair_gt_n = len(gt_pairs) - - loop_tp = len(gt_loop & pred_loop) - loop_pred_n = len(pred_loop) - loop_gt_n = len(gt_loop) - - pair_f1 = _safe_f1_from_sizes(pair_tp, pair_pred_n, pair_gt_n) - loop_f1 = _safe_f1_from_sizes(loop_tp, loop_pred_n, loop_gt_n) - - pair_weight = len(gt_paired_tokens) - loop_weight = len(gt_loop) - total_weight = pair_weight + loop_weight - if total_weight == 0: - weighted_f1 = 1.0 - else: - weighted_f1 = float( - (pair_weight * pair_f1 + loop_weight * loop_f1) / total_weight - ) - - pair_f1_list.append(pair_f1) - loop_f1_list.append(loop_f1) - weighted_f1_list.append(weighted_f1) - n_valid += 1 - - except bdb.BdbQuit: - # Allow interactive debuggers (pdb) to cleanly abort without being swallowed. - raise - except Exception as e: - global_logger.error(f"Error computing nucleic-SS similarity: {e} | Skipping") - continue + pair_f1_list.append(pair_f1) + loop_f1_list.append(loop_f1) + weighted_f1_list.append(weighted_f1) + n_valid += 1 if n_valid == 0: return {} diff --git a/models/rfd3/src/rfd3/transforms/na_geom.py b/models/rfd3/src/rfd3/transforms/na_geom.py index a1c8565d..f1f400a9 100644 --- a/models/rfd3/src/rfd3/transforms/na_geom.py +++ b/models/rfd3/src/rfd3/transforms/na_geom.py @@ -100,12 +100,12 @@ class CalculateNucleicAcidGeomFeats(Transform): Training: - Computes geometry/H-bond-based base pairs and writes them onto the AtomArray - via the ``bp_partner`` annotation (annotation-first), then reconstructs the + via the ``bp_partners`` annotation (annotation-first), then reconstructs the matrix (and optionally masks parts of it) before one-hot encoding. Inference: - Interprets user-provided secondary-structure specifications, writes the same - ``bp_partner`` annotation, then follows the same matrix + one-hot path. + ``bp_partners`` annotation, then follows the same matrix + one-hot path. Note: helical-parameter features are not implemented/used in this refactored path. """ @@ -113,16 +113,14 @@ class CalculateNucleicAcidGeomFeats(Transform): def __init__( self, is_inference, - add_nucleic_ss_feats: bool = True, - # Conditional sampling parameters: - p_is_nucleic_ss_example: float = 0.3, - p_show_partial_feats: float = 0.7, + # Conditional sampling parameters all stored in this dict: + meta_conditioning_probabilities: dict[str, float] = None, + # Mask control paramerers: nucleic_ss_min_shown: float = 0.2, nucleic_ss_max_shown: float = 1.0, n_islands_min: int = 1, n_islands_max: int = 6, - p_canonical_bp_filter: float = 0.0, # USE_RF2AA_NAMES: bool = False, NA_only: bool = False, @@ -131,21 +129,27 @@ def __init__( ): # Critical, must always have to know how to handle self.is_inference = is_inference + + self.meta_conditioning_probabilities = meta_conditioning_probabilities or {} + + # Control whether we show some nucleic SS or default to full 2D mask + self.p_is_nucleic_ss_example = self.meta_conditioning_probabilities.get("p_is_nucleic_ss_example", 0.0) + + # Control whether we define full SS or just part of it (only applies if is NA SS example) + self.p_show_partial_feats = self.meta_conditioning_probabilities.get("p_nucleic_ss_show_partial_feats", 0.0) - self.add_nucleic_ss_feats = add_nucleic_ss_feats - self.p_canonical_bp_filter = p_canonical_bp_filter # enforce that bp labels are only canonical - self.p_is_nucleic_ss_example = p_is_nucleic_ss_example - self.p_show_partial_feats = p_show_partial_feats + # Some frac of time default to only showing canonical base pairs + self.p_canonical_bp_filter = self.meta_conditioning_probabilities.get("p_canonical_bp_filter", 0.5) + + # mask patterning control to make things resemble design scenarios self.nucleic_ss_min_shown = nucleic_ss_min_shown self.nucleic_ss_max_shown = nucleic_ss_max_shown self.n_islands_min = n_islands_min self.n_islands_max = n_islands_max - # Filters for what can be considered a planar contact interaction self.NA_only = NA_only # only annotate base-like interactions for nucleic acid residues self.planar_only = planar_only # only consider planar atoms in sidechains for geometry calculations, - self.p_canonical_bp_filter = p_canonical_bp_filter # probability of enforcing canonical base pair filter @@ -168,13 +172,8 @@ def forward(self, data: dict) -> dict: # Calculate n_tokens (assuming one token per residue for simplicity) token_starts = get_token_starts(atom_array) - token_level_array = atom_array[token_starts] n_tokens = len(token_starts) - - # Defaults for feature visibility - is_nucleic_ss_example = True - give_partial_feats = False - token_mask_to_show = np.ones(n_tokens, dtype=bool) + # token_level_array = atom_array[token_starts] # Handle the training case with ground truth and masking if not self.is_inference: diff --git a/models/rfd3/src/rfd3/transforms/na_geom_utils.py b/models/rfd3/src/rfd3/transforms/na_geom_utils.py index 8ab2e71c..23292a87 100644 --- a/models/rfd3/src/rfd3/transforms/na_geom_utils.py +++ b/models/rfd3/src/rfd3/transforms/na_geom_utils.py @@ -1050,6 +1050,40 @@ def compute_nucleic_ss( hbond_count = np.asarray(hbond_count)[mask_1d, :][:, mask_1d] # [I, I, 3] seq_neighbors = np.asarray(token_level_data["seq_neighbors"], dtype=bool)[mask_1d, :][:, mask_1d] # [I, I] + # Nothing passed NA/planar filtering for this structure. + # Return empty outputs instead of failing downstream on np.stack([]). + if len(xyz_planar) == 0: + if return_basepairs_only: + return np.zeros((0, 0), dtype=bool) + + pair_params: dict[str, np.ndarray] = { + "H_ij": np.zeros((0, 0), dtype=np.float32), + "B_ij": np.zeros((0, 0), dtype=np.float32), + "P_ij": np.zeros((0, 0), dtype=np.float32), + "base_ori_ij": np.zeros((0, 0), dtype=np.float32), + "basepairs_bool_ij": np.zeros((0, 0), dtype=bool), + "basepairs_ij": np.zeros((0, 0), dtype=np.float32), + "hbond_summation": np.zeros((0, 0), dtype=np.float32), + } + + if return_opening_angle: + pair_params["O_ij"] = np.zeros((0, 0), dtype=np.float32) + + if return_pairwise_geometry: + pair_params["X_ij"] = np.zeros((0, 0), dtype=np.float32) + pair_params["Y_ij"] = np.zeros((0, 0), dtype=np.float32) + pair_params["Z_ij"] = np.zeros((0, 0), dtype=np.float32) + + nucleic_ss_data: dict = {"pair_params": pair_params} + if return_local_params: + nucleic_ss_data["local_params"] = { + "X_i": np.zeros((0, 3), dtype=np.float32), + "Y_i": np.zeros((0, 3), dtype=np.float32), + "Z_i": np.zeros((0, 3), dtype=np.float32), + } + + return nucleic_ss_data + # --- Precompute centroids and displacement vectors ----------- planar_centers = np.stack( # [I, 3] [np.nanmean(np.asarray(xyz_i, dtype=np.float32), axis=0) for xyz_i in xyz_planar], @@ -1441,19 +1475,6 @@ def annotate_na_ss_from_specification( token_ids: list[int] = [int(t) for t in list(token_level_array.token_id)] n_tokens = len(token_starts) - # Explicit loops are only meaningful for nucleic-acid tokens. - # Instantiate encoding locally to avoid retaining large arrays at module scope. - sequence_encoding = AF3SequenceEncoding() - is_rna_like = np.isin( - token_level_array.res_name, - sequence_encoding.all_res_names[sequence_encoding.is_rna_like], - ) - is_dna_like = np.isin( - token_level_array.res_name, - sequence_encoding.all_res_names[sequence_encoding.is_dna_like], - ) - is_na_token = np.asarray(is_rna_like | is_dna_like, dtype=bool) - # Prepare/overwrite annotation array if (not overwrite) and ("bp_partners" in atom_array.get_annotation_categories()): bp_partners_ann = atom_array.bp_partners @@ -1528,8 +1549,6 @@ def _add_pair(i: int, j: int) -> None: return if i == j: return - if (not bool(is_na_token[int(i)])) or (not bool(is_na_token[int(j)])): - return if i in loop_token_idxs or j in loop_token_idxs: return partners[i].add(j) @@ -1619,9 +1638,6 @@ def _add_pair(i: int, j: int) -> None: for i in list(loop_token_idxs): if not (0 <= i < n_tokens): continue - if not bool(is_na_token[int(i)]): - loop_token_idxs.discard(int(i)) - continue for j in list(partners[i]): partners[j].discard(i) partners[i].clear() @@ -1630,8 +1646,6 @@ def _add_pair(i: int, j: int) -> None: # Unspecified tokens remain unannotated (None) -> NA_SS_MASK. for i in range(n_tokens): atom_i = int(token_starts[i]) - if not bool(is_na_token[int(i)]): - continue if len(partners[i]) > 0: bp_partners_ann[atom_i] = [] for j in sorted(partners[i]): From 1f0d9115f6f6d49dd36d1978b881e251ac95822d Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Mon, 16 Feb 2026 18:53:46 -0800 Subject: [PATCH 21/49] val metric logging fix --- models/rfd3/configs/callbacks/design_callbacks.yaml | 1 + models/rfd3/configs/datasets/design_base_rfd3na.yaml | 8 ++++---- models/rfd3/configs/experiment/rfd3na-ss.yaml | 11 ++++++----- 3 files changed, 11 insertions(+), 9 deletions(-) diff --git a/models/rfd3/configs/callbacks/design_callbacks.yaml b/models/rfd3/configs/callbacks/design_callbacks.yaml index 309b492a..68c9a4ce 100644 --- a/models/rfd3/configs/callbacks/design_callbacks.yaml +++ b/models/rfd3/configs/callbacks/design_callbacks.yaml @@ -1,5 +1,6 @@ defaults: - train_logging + - metrics_logging - _self_ log_learning_rate_callback: diff --git a/models/rfd3/configs/datasets/design_base_rfd3na.yaml b/models/rfd3/configs/datasets/design_base_rfd3na.yaml index dbebee88..46bfb852 100644 --- a/models/rfd3/configs/datasets/design_base_rfd3na.yaml +++ b/models/rfd3/configs/datasets/design_base_rfd3na.yaml @@ -9,9 +9,9 @@ defaults: - train/rna_monomer_distillation@train # Customized validation datasets - - val/unconditional@val.unconditional - - val/unconditional_deep@val.unconditional_deep - - val/indexed@val.indexed + #- val/unconditional@val.unconditional + #- val/unconditional_deep@val.unconditional_deep + #- val/indexed@val.indexed - val/pseudoknot@val.pseudoknot # Customized train masks @@ -81,9 +81,9 @@ global_transform_args: # Used to create simple boolean flags for downstream conditioning meta_conditioning_probabilities: - # calculate_NA_SS: 1.0 p_is_nucleic_ss_example: 0.1 p_nucleic_ss_show_partial_feats: 0.7 + calculate_NA_SS: 0.5 calculate_hbonds: 0.2 calculate_rasa: 0.6 diff --git a/models/rfd3/configs/experiment/rfd3na-ss.yaml b/models/rfd3/configs/experiment/rfd3na-ss.yaml index 082ed819..1c1a7454 100644 --- a/models/rfd3/configs/experiment/rfd3na-ss.yaml +++ b/models/rfd3/configs/experiment/rfd3na-ss.yaml @@ -2,14 +2,13 @@ # Training configuration for RFD3 defaults: - # - /debug/default - - /debug/default + #- /debug/default - override /model: rfd3_base - - override /logger: null + - override /logger: wandb - override /datasets: design_base_rfd3na - _self_ -name: rfd3na-SScond +name: rfd3na tags: [print-model] ckpt_path: null @@ -59,6 +58,8 @@ datasets: p_is_nucleic_ss_example: 1.0 p_nucleic_ss_show_partial_feats: 0.7 p_canonical_bp_filter: 0.2 + calculate_NA_SS: 0.3 + association_scheme: atom23 #add_na_pair_features: true train_conditions: @@ -94,4 +95,4 @@ trainer: #limit_train_batches: 10 #limit_val_batches: 1 validate_every_n_epochs: 5 - prevalidate: true + prevalidate: false From 3dfa04ac20737437374eeeb61e73b6434d1537fe Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Mon, 16 Feb 2026 19:19:29 -0800 Subject: [PATCH 22/49] prevalidate on --- models/rfd3/configs/experiment/rfd3na-ss.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/models/rfd3/configs/experiment/rfd3na-ss.yaml b/models/rfd3/configs/experiment/rfd3na-ss.yaml index 1c1a7454..cfed3a6d 100644 --- a/models/rfd3/configs/experiment/rfd3na-ss.yaml +++ b/models/rfd3/configs/experiment/rfd3na-ss.yaml @@ -95,4 +95,4 @@ trainer: #limit_train_batches: 10 #limit_val_batches: 1 validate_every_n_epochs: 5 - prevalidate: false + prevalidate: true From 91a0eb22ec366cfb104d4a86576c658cd1850c94 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Tue, 17 Feb 2026 11:06:26 -0800 Subject: [PATCH 23/49] skip ss computation when not ss example --- models/rfd3/src/rfd3/transforms/na_geom.py | 61 ++++++++++++---------- 1 file changed, 33 insertions(+), 28 deletions(-) diff --git a/models/rfd3/src/rfd3/transforms/na_geom.py b/models/rfd3/src/rfd3/transforms/na_geom.py index f1f400a9..ba45c7f4 100644 --- a/models/rfd3/src/rfd3/transforms/na_geom.py +++ b/models/rfd3/src/rfd3/transforms/na_geom.py @@ -179,39 +179,44 @@ def forward(self, data: dict) -> dict: if not self.is_inference: # First, annotate as usual - atom_array = annotate_na_ss(atom_array, + is_nucleic_ss_example, give_partial_feats = self._sample_training_flags() + + if is_nucleic_ss_example: + + atom_array = annotate_na_ss(atom_array, NA_only=self.NA_only, planar_only=self.planar_only, p_canonical_bp_filter=self.p_canonical_bp_filter, ) - # Generate symmetric partner annotations at the token level for masking purposes. - # choice for object-consistency: if already masked/undefined: be a list mapping to self-index. - partner_sym_map = { - i: atom_array.bp_partners[ts_i] if atom_array.bp_partners[ts_i] is not None else [i] - for i, ts_i in enumerate(token_starts) - } - - # # Sample mask on token level: - is_nucleic_ss_example, give_partial_feats = self._sample_training_flags() - token_mask_to_show = self._sample_where_to_show_ss( - n_tokens, - is_nucleic_ss_example=is_nucleic_ss_example, - give_partial_feats=give_partial_feats, - partner_sym_map=partner_sym_map, - ) # Mask vec for tokens where ss shown - - # Spread mask to atom level - is_ss_shown = spread_token_wise(atom_array, token_mask_to_show) - - # Extract the base pair annotations - bp_partners_atom = atom_array.get_annotation("bp_partners") - - # Remove unshown positions from bp_partners annotation - bp_partners_atom[~is_ss_shown] = None - - # Reset the annotation with newly hidden positions - atom_array.set_annotation("bp_partners", bp_partners_atom) + # Generate symmetric partner annotations at the token level for masking purposes. + # choice for object-consistency: if already masked/undefined: be a list mapping to self-index. + partner_sym_map = { + i: atom_array.bp_partners[ts_i] if atom_array.bp_partners[ts_i] is not None else [i] + for i, ts_i in enumerate(token_starts) + } + + # # Sample mask on token level: + token_mask_to_show = self._sample_where_to_show_ss( + n_tokens, + is_nucleic_ss_example=is_nucleic_ss_example, + give_partial_feats=give_partial_feats, + partner_sym_map=partner_sym_map, + ) # Mask vec for tokens where ss shown + + # Spread mask to atom level + is_ss_shown = spread_token_wise(atom_array, token_mask_to_show) + + # Extract the base pair annotations + bp_partners_atom = atom_array.get_annotation("bp_partners") + + # Remove unshown positions from bp_partners annotation + bp_partners_atom[~is_ss_shown] = None + + # Reset the annotation with newly hidden positions + atom_array.set_annotation("bp_partners", bp_partners_atom) + else: + atom_array.set_annotation("bp_partners", np.array([None]*len(atom_array))) # Inference case: create from commandline args else: From de06df8cbfcc02a88f31d6af837c4fadf78efa60 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Tue, 17 Feb 2026 12:26:13 -0800 Subject: [PATCH 24/49] cleanup; aptamer metric; monomer dist on; send trainininggit status --- .../configs/datasets/design_base_rfd3na.yaml | 2 +- .../train/pdb/rfd3_train_interface.yaml | 3 +- .../train/pdb/rfd3_train_pn_unit.yaml | 1 + models/rfd3/configs/experiment/rfd3na-ss.yaml | 98 ---------------- models/rfd3/configs/experiment/rfd3na.yaml | 42 ++++--- .../trainer/metrics/design_metrics.yaml | 4 + .../src/rfd3/metrics/rna_aptamer_metrics.py | 109 ++++++++++++++++++ 7 files changed, 145 insertions(+), 114 deletions(-) delete mode 100644 models/rfd3/configs/experiment/rfd3na-ss.yaml create mode 100644 models/rfd3/src/rfd3/metrics/rna_aptamer_metrics.py diff --git a/models/rfd3/configs/datasets/design_base_rfd3na.yaml b/models/rfd3/configs/datasets/design_base_rfd3na.yaml index 46bfb852..661b2fb0 100644 --- a/models/rfd3/configs/datasets/design_base_rfd3na.yaml +++ b/models/rfd3/configs/datasets/design_base_rfd3na.yaml @@ -5,7 +5,7 @@ defaults: # Grab datasets - train/pdb/rfd3_train_interface@train.pdb.sub_datasets.interface - train/pdb/rfd3_train_pn_unit@train.pdb.sub_datasets.pn_unit - #- train/rfd3_monomer_distillation@train + - train/rfd3_monomer_distillation@train - train/rna_monomer_distillation@train # Customized validation datasets diff --git a/models/rfd3/configs/datasets/train/pdb/rfd3_train_interface.yaml b/models/rfd3/configs/datasets/train/pdb/rfd3_train_interface.yaml index d5df571b..9ff698a8 100644 --- a/models/rfd3/configs/datasets/train/pdb/rfd3_train_interface.yaml +++ b/models/rfd3/configs/datasets/train/pdb/rfd3_train_interface.yaml @@ -12,6 +12,7 @@ dataset: # filters common across all PDB datasets - 'pdb_id not in ["7rte", "7m5w", "7n5u"]' - 'pdb_id not in ["3di3", "5o45", "1z92", "2gy5", "4zxb"]' + - 'pdb_id not in ["1drz", "2m8k", "2miy", "3q3z", "4oqu", "4plx", "4znp", "7kd1", "7kga", "7qr4"]' - "deposition_date < '2024-12-16'" - "resolution < 9.0" - "num_polymer_pn_units <= 300" @@ -19,4 +20,4 @@ dataset: # interface specific filters - "~(pn_unit_1_non_polymer_res_names.notnull() and pn_unit_1_non_polymer_res_names.str.contains('${resolve_import:atomworks.constants,AF3_EXCLUDED_LIGANDS_REGEX}', regex=True))" - "~(pn_unit_2_non_polymer_res_names.notnull() and pn_unit_2_non_polymer_res_names.str.contains('${resolve_import:atomworks.constants,AF3_EXCLUDED_LIGANDS_REGEX}', regex=True))" - - "is_inter_molecule" \ No newline at end of file + - "is_inter_molecule" diff --git a/models/rfd3/configs/datasets/train/pdb/rfd3_train_pn_unit.yaml b/models/rfd3/configs/datasets/train/pdb/rfd3_train_pn_unit.yaml index dfd548fc..19d31e8b 100644 --- a/models/rfd3/configs/datasets/train/pdb/rfd3_train_pn_unit.yaml +++ b/models/rfd3/configs/datasets/train/pdb/rfd3_train_pn_unit.yaml @@ -15,6 +15,7 @@ dataset: # filters common across all PDB datasets - 'pdb_id not in ["7rte", "7m5w", "7n5u"]' - 'pdb_id not in ["3di3", "5o45", "1z92", "2gy5", "4zxb"]' + - 'pdb_id not in ["1drz", "2m8k", "2miy", "3q3z", "4oqu", "4plx", "4znp", "7kd1", "7kga", "7qr4"]' - "deposition_date < '2024-12-16'" - "resolution < 9.0" - "num_polymer_pn_units <= 300" diff --git a/models/rfd3/configs/experiment/rfd3na-ss.yaml b/models/rfd3/configs/experiment/rfd3na-ss.yaml deleted file mode 100644 index cfed3a6d..00000000 --- a/models/rfd3/configs/experiment/rfd3na-ss.yaml +++ /dev/null @@ -1,98 +0,0 @@ -# @package _global_ -# Training configuration for RFD3 - -defaults: - #- /debug/default - - override /model: rfd3_base - - override /logger: wandb - - override /datasets: design_base_rfd3na - - _self_ - -name: rfd3na -tags: [print-model] -ckpt_path: null - -model: - net: - token_initializer: - token_1d_features: - ref_motif_token_type: 3 - restype: 32 - is_dna_token: 1 - is_rna_token: 1 - is_protein_token: 1 - token_2d_features: - bp_partners: 3 # Unspecified, pair, loop - atom_1d_features: - ref_atom_name_chars: 256 - ref_element: 128 - ref_charge: 1 - ref_mask: 1 - ref_is_motif_atom_with_fixed_coord: 1 - ref_is_motif_atom_unindexed: 1 - has_zero_occupancy: 1 - ref_pos: 3 - - # Guided features - ref_atomwise_rasa: 3 - active_donor: 1 - active_acceptor: 1 - is_atom_level_hotspot: 1 - diffusion_module: - n_recycle: 2 - use_local_token_attention: True - diffusion_transformer: - n_local_tokens: 32 - n_keys: 128 - - inference_sampler: - num_timesteps: 100 - - -datasets: - diffusion_batch_size_train: 16 - crop_size: 256 - max_atoms_in_crop: 2560 # ~10x crop size. - global_transform_args: - meta_conditioning_probabilities: - p_is_nucleic_ss_example: 1.0 - p_nucleic_ss_show_partial_feats: 0.7 - p_canonical_bp_filter: 0.2 - calculate_NA_SS: 0.3 - - association_scheme: atom23 - #add_na_pair_features: true - train_conditions: - unconditional: - frequency: 2.0 - island: - frequency: 2.0 - sequence_design: - frequency: 0.5 - tipatom: - frequency: 5.0 - ppi: - frequency: 0.0 - train: - # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. - pdb: - # probability: 0.5 - probability: 0.75 - # probability: 0.0 - rna_monomer_distillation: - # probability: 0.2 - probability: 0.25 - # probability: 1.0 - - val: - pseudoknot: - dataset: - # eval_every_n: 10 - eval_every_n: 5 - -trainer: - #devices_per_node: 1 - #limit_train_batches: 10 - #limit_val_batches: 1 - validate_every_n_epochs: 5 - prevalidate: true diff --git a/models/rfd3/configs/experiment/rfd3na.yaml b/models/rfd3/configs/experiment/rfd3na.yaml index 6d807209..32e43827 100644 --- a/models/rfd3/configs/experiment/rfd3na.yaml +++ b/models/rfd3/configs/experiment/rfd3na.yaml @@ -2,14 +2,13 @@ # Training configuration for RFD3 defaults: - - /debug/default + #- /debug/default - override /model: rfd3_base - #- override /datasets: all - - override /logger: csv - #- override /logger: wandb + - override /logger: wandb + - override /datasets: design_base_rfd3na - _self_ -name: train-base +name: rfd3na_scratch tags: [print-model] ckpt_path: null @@ -22,6 +21,8 @@ model: is_dna_token: 1 is_rna_token: 1 is_protein_token: 1 + token_2d_features: + bp_partners: 3 # Unspecified, pair, loop atom_1d_features: ref_atom_name_chars: 256 ref_element: 128 @@ -53,7 +54,14 @@ datasets: crop_size: 256 max_atoms_in_crop: 2560 # ~10x crop size. global_transform_args: + meta_conditioning_probabilities: + p_is_nucleic_ss_example: 0.25 + p_nucleic_ss_show_partial_feats: 0.7 + p_canonical_bp_filter: 0.2 + #calculate_NA_SS: 0.3 + association_scheme: atom23 + #add_na_pair_features: true train_conditions: unconditional: frequency: 2.0 @@ -67,16 +75,22 @@ datasets: frequency: 0.0 train: # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. - #pdb: - #probability: 0.10 - #monomer_distillation: - #probability: 0.90 pdb: - probability: 1.0 + probability: 0.5 + rna_monomer_distillation: + probability: 0.3 + monomer_distillation: + probability: 0.2 + + val: + pseudoknot: + dataset: + # eval_every_n: 10 + eval_every_n: 5 trainer: - devices_per_node: 1 - limit_train_batches: 10 - limit_val_batches: 1 + #devices_per_node: 1 + #limit_train_batches: 10 + #limit_val_batches: 1 validate_every_n_epochs: 5 - prevalidate: false + prevalidate: true diff --git a/models/rfd3/configs/trainer/metrics/design_metrics.yaml b/models/rfd3/configs/trainer/metrics/design_metrics.yaml index b73bd840..c0bde00d 100644 --- a/models/rfd3/configs/trainer/metrics/design_metrics.yaml +++ b/models/rfd3/configs/trainer/metrics/design_metrics.yaml @@ -29,3 +29,7 @@ nucleic_ss_similarity: annotation_NA_only: False annotation_planar_only: True +rna_aptamer_contacts: + _target_: rfd3.metrics.rna_aptamer_metrics.LigandContactMetrics + restrict_to_nucleic: True + diff --git a/models/rfd3/src/rfd3/metrics/rna_aptamer_metrics.py b/models/rfd3/src/rfd3/metrics/rna_aptamer_metrics.py new file mode 100644 index 00000000..c4071ca3 --- /dev/null +++ b/models/rfd3/src/rfd3/metrics/rna_aptamer_metrics.py @@ -0,0 +1,109 @@ +import logging + + +from foundry.metrics.metric import Metric +from foundry.utils.ddp import RankedLogger + +logging.basicConfig(level=logging.INFO) +global_logger = RankedLogger(__name__, rank_zero_only=False) + + +import numpy as np + + +def calculate_ligand_contacts( + atom_array_stack, + cutoff_distance=4.0, +): + """ + Count number of atom contacts within cutoff of any ligand atom. + + Parameters + ---------- + atom_array_stack : AtomArrayStack + Shape: (n_models, n_atoms) + cutoff_distance : float + Distance cutoff in Å + + Returns + ------- + total_contacts : int + mean_contacts_per_model : float + """ + + cutoff_sq = cutoff_distance ** 2 + contacts_per_model = [] + + n_models = len(atom_array_stack) + + for i in range(n_models): + + atoms = atom_array_stack[i] + + coords = atoms.coord + hetero_mask = atoms.hetero.astype(bool) + + # Skip if no ligand + if not np.any(hetero_mask): + contacts_per_model.append(0) + continue + + ligand_coords = coords[hetero_mask] + non_ligand_coords = coords[~hetero_mask] + + if len(non_ligand_coords) == 0: + contacts_per_model.append(0) + continue + + # Pairwise squared distances + diff = non_ligand_coords[:, None, :] - ligand_coords[None, :, :] + dist_sq = np.sum(diff ** 2, axis=-1) + + # Any ligand within cutoff + contact_mask = np.any(dist_sq < cutoff_sq, axis=1) + + n_contacts = np.sum(contact_mask) + contacts_per_model.append(n_contacts) + + contacts_per_model = np.array(contacts_per_model) + + return int(np.sum(contacts_per_model)), float(np.mean(contacts_per_model)), float(np.mean(contacts_per_model))/hetero_mask.sum() + + +class LigandContactMetrics(Metric): + def __init__( + self, + *, + cutoff_distance: float = 4.0, + restrict_to_nucleic: bool = True, + ): + super().__init__() + self.cutoff_distance = cutoff_distance + self.restrict_to_nucleic = restrict_to_nucleic + + @property + def kwargs_to_compute_args(self): + return { + "predicted_atom_array_stack": ("predicted_atom_array_stack",), + } + + def compute(self, *, predicted_atom_array_stack): + if self.restrict_to_nucleic: + if (predicted_atom_array_stack[0].is_rna.sum() + predicted_atom_array_stack[0].is_dna.sum()== 0): + return {} + try: + total_contacts, mean_contacts, mean_contacts_per_atom = calculate_ligand_contacts( + atom_array_stack=predicted_atom_array_stack, + cutoff_distance=self.cutoff_distance, + ) + except Exception as e: + global_logger.error( + f"Error calculating ligand contact metrics: {e} | Skipping" + ) + return {} + + return { + "mean_ligand_contacts_per_model": float(mean_contacts), + "mean_ligand_contacts_per_atom": float(mean_contacts_per_atom), + } + From bcaa6b79a8beb1dd91a8418c72bf3b3a7c4e59b8 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Wed, 18 Feb 2026 12:59:23 -0800 Subject: [PATCH 25/49] run time fixes; inference fix for readout seq from struct --- models/rfd3/src/rfd3/trainer/trainer_utils.py | 12 +- .../src/rfd3/transforms/design_transforms.py | 1 - .../rfd3/src/rfd3/transforms/hbonds_hbplus.py | 2 + .../rfd3/src/rfd3/transforms/na_geom_utils.py | 220 ++++++++++-------- 4 files changed, 129 insertions(+), 106 deletions(-) diff --git a/models/rfd3/src/rfd3/trainer/trainer_utils.py b/models/rfd3/src/rfd3/trainer/trainer_utils.py index 80d1f878..652665ad 100644 --- a/models/rfd3/src/rfd3/trainer/trainer_utils.py +++ b/models/rfd3/src/rfd3/trainer/trainer_utils.py @@ -25,6 +25,7 @@ from foundry.common import exists from foundry.utils.ddp import RankedLogger +from atomworks.constants import STANDARD_DNA, STANDARD_RNA global_logger = RankedLogger(__name__, rank_zero_only=False) @@ -221,11 +222,15 @@ def _readout_seq_from_struc( # There might be a better way to do this. CA_coord = cur_res_atom_array.coord[cur_res_atom_array.atom_name == "CA"] CB_coord = cur_res_atom_array.coord[cur_res_atom_array.atom_name == "CB"] - if np.linalg.norm(CA_coord - CB_coord) < threshold: + + if cur_res_atom_array.is_dna[0] or cur_res_atom_array.is_rna[0]: + cur_central_atom = "C1'" + elif np.linalg.norm(CA_coord - CB_coord) < threshold: cur_central_atom = "CA" else: cur_central_atom = central_atom + central_mask = cur_res_atom_array.atom_name == cur_central_atom # ... Calculate the distance to the central atom @@ -258,8 +263,12 @@ def _readout_seq_from_struc( ATOM_NAMES = ATOM14_ATOM_NAMES if restype in STANDARD_DNA: ATOM_NAMES = ATOM23_ATOM_NAMES_DNA + if not cur_res_atom_array.is_dna[0]: + continue if restype in STANDARD_RNA: ATOM_NAMES = ATOM23_ATOM_NAMES_RNA + if not cur_res_atom_array.is_rna[0]: + continue atom_name_idx_in_atom14_scheme = np.array( [ @@ -269,7 +278,6 @@ def _readout_seq_from_struc( ) # five backbone atoms + some virtual atoms, returning e.g. [0, 1, 2, 3, 4, 11, 7] atom14_scheme_mask = np.zeros_like(ATOM_NAMES, dtype=bool) atom14_scheme_mask[atom_name_idx_in_atom14_scheme] = True - # ... Find the matched restype by checking if all the non-None posititons and None positions match # This is designed to keep virtual atoms and doesn't assign the atom names for now, which will be handled later. if all(x is not None for x in atom_names[atom14_scheme_mask]) and all( diff --git a/models/rfd3/src/rfd3/transforms/design_transforms.py b/models/rfd3/src/rfd3/transforms/design_transforms.py index c1193a30..04f1500d 100644 --- a/models/rfd3/src/rfd3/transforms/design_transforms.py +++ b/models/rfd3/src/rfd3/transforms/design_transforms.py @@ -870,7 +870,6 @@ def forward(self, data: Dict[str, Any]) -> Dict[str, Any]: return data for feature_name, n_dims in self.token_2d_features.items(): data = self.generate_token_feature(feature_name, n_dims, data) - return data diff --git a/models/rfd3/src/rfd3/transforms/hbonds_hbplus.py b/models/rfd3/src/rfd3/transforms/hbonds_hbplus.py index 58e855cf..f49dcdb8 100644 --- a/models/rfd3/src/rfd3/transforms/hbonds_hbplus.py +++ b/models/rfd3/src/rfd3/transforms/hbonds_hbplus.py @@ -248,3 +248,5 @@ def subsample_one_hot_np(array, fraction): new_array[i, j] = 1 return new_array + + diff --git a/models/rfd3/src/rfd3/transforms/na_geom_utils.py b/models/rfd3/src/rfd3/transforms/na_geom_utils.py index 23292a87..5a055779 100644 --- a/models/rfd3/src/rfd3/transforms/na_geom_utils.py +++ b/models/rfd3/src/rfd3/transforms/na_geom_utils.py @@ -32,7 +32,7 @@ ATOM_REGION_BY_RESI, PLANAR_ATOMS_BY_RESI, ) - +import tempfile # Derived: True when the residue has any planar sidechain atoms HAS_PLANAR_SC = {res: bool(atoms) for res, atoms in PLANAR_ATOMS_BY_RESI.items()} @@ -132,110 +132,124 @@ def calculate_hb_counts( np.ndarray of shape ``(I, I, 3)`` (int32) where the last axis encodes: 0 = BB–BB, 1 = BB–SC, 2 = SC–SC H-bond counts. """ + hbplus_exe = os.environ.get("HBPLUS_PATH") - dtstr = datetime.now().strftime("%Y%m%d%H%M%S") - pdb_path = f"{dtstr}_{np.random.randint(10000)}.pdb" - - atom_array, nan_mask, chain_map = save_atomarray_to_pdb(atom_array, pdb_path) - subprocess.call( - [ - "/projects/ml/hbplus", - "-h", - str(cutoff_HA_dist), - "-d", - str(cutoff_DA_dist), - pdb_path, - pdb_path, - ], - stdout=subprocess.DEVNULL, - stderr=subprocess.DEVNULL, - ) - - - num_resis_total = len(token_level_data["token_id_list"]) - - hbond_count = np.zeros((num_resis_total, num_resis_total, 3), dtype=np.int32) - - hb2_path = pdb_path.replace("pdb", "hb2") - with open(hb2_path, "r") as hb2_f: - for i, line in enumerate(hb2_f): - if i < 8: - continue - if len(line) < 28: - continue - - d_chain_iid = chain_map[line[0]] - d_resi = int(line[1:5].strip()) - d_resn = line[6:9].strip() - d_atom_name = line[9:13].strip() - - # Initialize donor/acceptor sidechain/backbone flags: - # then replace with True if valid for summation - d_is_sc = False - d_is_bb = False - a_is_sc = False - a_is_bb = False - - d_mask = ( - (atom_array.atom_name == d_atom_name) - & (atom_array.res_name == d_resn) - & (atom_array.res_id == d_resi) - & (atom_array.chain_iid == d_chain_iid) - ) - # d_atm = atom_array[d_mask] - # d_idx = d_atm.token_id - d_idx = token_level_data["resi2index"].get(f"{d_chain_iid}__{d_resi}", None) - if d_idx is None: - continue - - # Handle standard polymer residues for donor atom: - if d_resn in ATOM_REGION_BY_RESI.keys(): - d_is_sc = (d_atom_name in ATOM_REGION_BY_RESI[d_resn]['sc']) - d_is_bb = (d_atom_name in ATOM_REGION_BY_RESI[d_resn]['bb']) - else: - # If non-polymer, define any ligand HBonding atom as backbone: - if d_mask.sum() > 0: - d_is_bb = atom_array[d_mask][0].is_ligand - - a_chain_iid = chain_map[line[14]] - a_resi = int(line[15:19].strip()) - a_resn = line[20:23].strip() - a_atom_name = line[23:27].strip() - - a_mask = ( - (atom_array.atom_name == a_atom_name) - & (atom_array.res_name == a_resn) - & (atom_array.res_id == a_resi) - & (atom_array.chain_iid == a_chain_iid) - ) - a_idx = token_level_data["resi2index"].get(f"{a_chain_iid}__{a_resi}", None) - if a_idx is None: - continue - - # Handle standard polymer residues for acceptor atom: - if a_resn in ATOM_REGION_BY_RESI.keys(): - a_is_sc = (a_atom_name in ATOM_REGION_BY_RESI[a_resn]['sc']) - a_is_bb = (a_atom_name in ATOM_REGION_BY_RESI[a_resn]['bb']) - else: - # If non-polymer, define any ligand HBonding atom as backbone: - if a_mask.sum() > 0: - a_is_bb = atom_array[a_mask][0].is_ligand - - # 0 -> both backbone (BB-BB) - hbond_count[a_idx, d_idx, 0] += (a_is_bb * d_is_bb) - hbond_count[d_idx, a_idx, 0] += (d_is_bb * a_is_bb) - - # 1 -> one backbone, one sidechain (BB-SC) - hbond_count[a_idx, d_idx, 1] += (a_is_bb * d_is_sc) | (a_is_sc * d_is_bb) - hbond_count[d_idx, a_idx, 1] += (d_is_bb * a_is_sc) | (d_is_sc * a_is_bb) - - # 2 -> both sidechain (SC-SC) - hbond_count[a_idx, d_idx, 2] += (a_is_sc * d_is_sc) - hbond_count[d_idx, a_idx, 2] += (d_is_sc * a_is_sc) - - os.remove(pdb_path) - os.remove(hb2_path) + if hbplus_exe is None or hbplus_exe == "": + raise ValueError( + "HBPLUS_PATH environment variable not set. " + "Please set it to the path of the hbplus executable in order to calculate hydrogen bonds." + ) + with tempfile.TemporaryDirectory() as tmpdir: + dtstr = datetime.now().strftime("%Y%m%d%H%M%S") + pdb_filename = f"{dtstr}_{np.random.randint(10000)}.pdb" + pdb_path = os.path.join(tmpdir, pdb_filename) + atom_array, nan_mask, chain_map = save_atomarray_to_pdb(atom_array, pdb_path) + + subprocess.call( + [ + hbplus_exe, + "-h", + str(cutoff_HA_dist), + "-d", + str(cutoff_DA_dist), + pdb_path, + pdb_path, + ], + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + cwd=tmpdir, + ) + num_resis_total = len(token_level_data["token_id_list"]) + + hbond_count = np.zeros((num_resis_total, num_resis_total, 3), dtype=np.int32) + + hb2_path = pdb_path.replace("pdb", "hb2") + if not os.path.exists(hb2_path): + print("WARNING: HB2 file could not be found; skipping NA SS metric") + return hbond_count + with open(hb2_path, "r") as hb2_f: + for i, line in enumerate(hb2_f): + if i < 8: + continue + if len(line) < 28: + continue + + d_chain_iid = chain_map[line[0]] + d_resi = int(line[1:5].strip()) + d_resn = line[6:9].strip() + d_atom_name = line[9:13].strip() + + # Initialize donor/acceptor sidechain/backbone flags: + # then replace with True if valid for summation + d_is_sc = False + d_is_bb = False + a_is_sc = False + a_is_bb = False + + d_mask = ( + (atom_array.atom_name == d_atom_name) + & (atom_array.res_name == d_resn) + & (atom_array.res_id == d_resi) + & (atom_array.chain_iid == d_chain_iid) + ) + # d_atm = atom_array[d_mask] + # d_idx = d_atm.token_id + d_idx = token_level_data["resi2index"].get(f"{d_chain_iid}__{d_resi}", None) + if d_idx is None: + continue + + # Handle standard polymer residues for donor atom: + if d_resn in ATOM_REGION_BY_RESI.keys(): + d_is_sc = (d_atom_name in ATOM_REGION_BY_RESI[d_resn]['sc']) + d_is_bb = (d_atom_name in ATOM_REGION_BY_RESI[d_resn]['bb']) + else: + # If non-polymer, define any ligand HBonding atom as backbone: + if d_mask.sum() > 0: + d_is_bb = atom_array[d_mask][0].is_ligand + + a_chain_iid = chain_map[line[14]] + a_resi = int(line[15:19].strip()) + a_resn = line[20:23].strip() + a_atom_name = line[23:27].strip() + + a_mask = ( + (atom_array.atom_name == a_atom_name) + & (atom_array.res_name == a_resn) + & (atom_array.res_id == a_resi) + & (atom_array.chain_iid == a_chain_iid) + ) + a_idx = token_level_data["resi2index"].get(f"{a_chain_iid}__{a_resi}", None) + if a_idx is None: + continue + + # Handle standard polymer residues for acceptor atom: + if a_resn in ATOM_REGION_BY_RESI.keys(): + a_is_sc = (a_atom_name in ATOM_REGION_BY_RESI[a_resn]['sc']) + a_is_bb = (a_atom_name in ATOM_REGION_BY_RESI[a_resn]['bb']) + else: + # If non-polymer, define any ligand HBonding atom as backbone: + if a_mask.sum() > 0: + a_is_bb = atom_array[a_mask][0].is_ligand + + # 0 -> both backbone (BB-BB) + hbond_count[a_idx, d_idx, 0] += (a_is_bb * d_is_bb) + hbond_count[d_idx, a_idx, 0] += (d_is_bb * a_is_bb) + + # 1 -> one backbone, one sidechain (BB-SC) + hbond_count[a_idx, d_idx, 1] += (a_is_bb * d_is_sc) | (a_is_sc * d_is_bb) + hbond_count[d_idx, a_idx, 1] += (d_is_bb * a_is_sc) | (d_is_sc * a_is_bb) + + # 2 -> both sidechain (SC-SC) + hbond_count[a_idx, d_idx, 2] += (a_is_sc * d_is_sc) + hbond_count[d_idx, a_idx, 2] += (d_is_sc * a_is_sc) + ''' + try: + os.remove(pdb_path) + os.remove(hb2_path) + except: + print("temp pdb/hb already removed or not created to begin with") + ''' return hbond_count From c8d1e6be1033e449cbbd1373f7c516c07b63b892 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Wed, 18 Feb 2026 17:47:44 -0800 Subject: [PATCH 26/49] time to fix ss metric line 242 --- .../rfd3/configs/datasets/val/pseudoknot.yaml | 4 +- models/rfd3/configs/experiment/rfd3na.yaml | 13 ++- .../configs/experiment/rfd3na_fine_tune.yaml | 96 +++++++++++++++++++ .../configs/experiment/rfd3na_no_distill.yaml | 96 +++++++++++++++++++ models/rfd3/configs/experiment/rfd3na_rm.yaml | 90 +++++++++++++++++ .../src/rfd3/metrics/nucleic_ss_metrics.py | 6 +- models/rfd3/src/rfd3/trainer/trainer_utils.py | 1 - 7 files changed, 297 insertions(+), 9 deletions(-) create mode 100644 models/rfd3/configs/experiment/rfd3na_fine_tune.yaml create mode 100644 models/rfd3/configs/experiment/rfd3na_no_distill.yaml create mode 100644 models/rfd3/configs/experiment/rfd3na_rm.yaml diff --git a/models/rfd3/configs/datasets/val/pseudoknot.yaml b/models/rfd3/configs/datasets/val/pseudoknot.yaml index 895e6c21..d3fa6f7e 100644 --- a/models/rfd3/configs/datasets/val/pseudoknot.yaml +++ b/models/rfd3/configs/datasets/val/pseudoknot.yaml @@ -6,5 +6,5 @@ defaults: dataset: name: pseudoknot eval_every_n: 1 - # data: ${paths.data.design_benchmark_data_dir}/pseudoknot_debug.json - data: ${paths.data.design_benchmark_data_dir}/pseudoknot.json + data: ${paths.data.design_benchmark_data_dir}/pseudoknot_debug.json + #data: ${paths.data.design_benchmark_data_dir}/pseudoknot.json diff --git a/models/rfd3/configs/experiment/rfd3na.yaml b/models/rfd3/configs/experiment/rfd3na.yaml index 32e43827..e1194be6 100644 --- a/models/rfd3/configs/experiment/rfd3na.yaml +++ b/models/rfd3/configs/experiment/rfd3na.yaml @@ -2,15 +2,18 @@ # Training configuration for RFD3 defaults: - #- /debug/default + - /debug/default - override /model: rfd3_base - - override /logger: wandb + #- override /logger: wandb - override /datasets: design_base_rfd3na - _self_ -name: rfd3na_scratch +name: rfd3na_scratch_clean_test tags: [print-model] -ckpt_path: null + +#ckpt_path: /net/scratch/raktim/training/logs/train/rfd3na-fine-tune/2026-02-17_15-21_JOB_3608285/ckpt/epoch-0590.ckpt +#ckpt_path: /net/scratch/raktim/training/logs/train/rfd3na_scratch/2026-02-17_17-56_JOB_3620867/ckpt/epoch-0030.ckpt + model: net: @@ -55,7 +58,7 @@ datasets: max_atoms_in_crop: 2560 # ~10x crop size. global_transform_args: meta_conditioning_probabilities: - p_is_nucleic_ss_example: 0.25 + p_is_nucleic_ss_example: 1.0 p_nucleic_ss_show_partial_feats: 0.7 p_canonical_bp_filter: 0.2 #calculate_NA_SS: 0.3 diff --git a/models/rfd3/configs/experiment/rfd3na_fine_tune.yaml b/models/rfd3/configs/experiment/rfd3na_fine_tune.yaml new file mode 100644 index 00000000..e7310633 --- /dev/null +++ b/models/rfd3/configs/experiment/rfd3na_fine_tune.yaml @@ -0,0 +1,96 @@ +# @package _global_ +# Training configuration for RFD3 + +defaults: + #- /debug/default + - override /model: rfd3_base + - override /logger: wandb + - override /datasets: design_base_rfd3na + - _self_ + +name: rfd3na-fine-tune +tags: [print-model] +ckpt_path: /projects/ml/aa_design/models/rfd3_latest_foundry.ckpt + +model: + net: + token_initializer: + token_1d_features: + ref_motif_token_type: 3 + restype: 32 + is_dna_token: 1 + is_rna_token: 1 + is_protein_token: 1 + token_2d_features: + bp_partners: 3 # Unspecified, pair, loop + atom_1d_features: + ref_atom_name_chars: 256 + ref_element: 128 + ref_charge: 1 + ref_mask: 1 + ref_is_motif_atom_with_fixed_coord: 1 + ref_is_motif_atom_unindexed: 1 + has_zero_occupancy: 1 + ref_pos: 3 + + # Guided features + ref_atomwise_rasa: 3 + active_donor: 1 + active_acceptor: 1 + is_atom_level_hotspot: 1 + diffusion_module: + n_recycle: 2 + use_local_token_attention: True + diffusion_transformer: + n_local_tokens: 32 + n_keys: 128 + + inference_sampler: + num_timesteps: 100 + + +datasets: + diffusion_batch_size_train: 16 + crop_size: 256 + max_atoms_in_crop: 2560 # ~10x crop size. + global_transform_args: + meta_conditioning_probabilities: + p_is_nucleic_ss_example: 0.25 + p_nucleic_ss_show_partial_feats: 0.7 + p_canonical_bp_filter: 0.2 + #calculate_NA_SS: 0.3 + + association_scheme: atom23 + #add_na_pair_features: true + train_conditions: + unconditional: + frequency: 2.0 + island: + frequency: 2.0 + sequence_design: + frequency: 0.5 + tipatom: + frequency: 5.0 + ppi: + frequency: 0.0 + train: + # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. + pdb: + probability: 0.5 + rna_monomer_distillation: + probability: 0.3 + monomer_distillation: + probability: 0.2 + + val: + pseudoknot: + dataset: + # eval_every_n: 10 + eval_every_n: 5 + +trainer: + #devices_per_node: 1 + #limit_train_batches: 10 + #limit_val_batches: 1 + validate_every_n_epochs: 5 + prevalidate: true diff --git a/models/rfd3/configs/experiment/rfd3na_no_distill.yaml b/models/rfd3/configs/experiment/rfd3na_no_distill.yaml new file mode 100644 index 00000000..e3642cce --- /dev/null +++ b/models/rfd3/configs/experiment/rfd3na_no_distill.yaml @@ -0,0 +1,96 @@ +# @package _global_ +# Training configuration for RFD3 + +defaults: + #- /debug/default + - override /model: rfd3_base + - override /logger: wandb + - override /datasets: design_base_rfd3na + - _self_ + +name: rfd3na_no_distill +tags: [print-model] +ckpt_path: /net/scratch/raktim/training/logs/train/rfd3na_no_distill/2026-02-17_15-21_JOB_3608348/ckpt/epoch-0020.ckpt + +model: + net: + token_initializer: + token_1d_features: + ref_motif_token_type: 3 + restype: 32 + is_dna_token: 1 + is_rna_token: 1 + is_protein_token: 1 + token_2d_features: + bp_partners: 3 # Unspecified, pair, loop + atom_1d_features: + ref_atom_name_chars: 256 + ref_element: 128 + ref_charge: 1 + ref_mask: 1 + ref_is_motif_atom_with_fixed_coord: 1 + ref_is_motif_atom_unindexed: 1 + has_zero_occupancy: 1 + ref_pos: 3 + + # Guided features + ref_atomwise_rasa: 3 + active_donor: 1 + active_acceptor: 1 + is_atom_level_hotspot: 1 + diffusion_module: + n_recycle: 2 + use_local_token_attention: True + diffusion_transformer: + n_local_tokens: 32 + n_keys: 128 + + inference_sampler: + num_timesteps: 100 + + +datasets: + diffusion_batch_size_train: 16 + crop_size: 256 + max_atoms_in_crop: 2560 # ~10x crop size. + global_transform_args: + meta_conditioning_probabilities: + p_is_nucleic_ss_example: 0.25 + p_nucleic_ss_show_partial_feats: 0.7 + p_canonical_bp_filter: 0.2 + #calculate_NA_SS: 0.3 + + association_scheme: atom23 + #add_na_pair_features: true + train_conditions: + unconditional: + frequency: 2.0 + island: + frequency: 2.0 + sequence_design: + frequency: 0.5 + tipatom: + frequency: 5.0 + ppi: + frequency: 0.0 + train: + # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. + pdb: + probability: 0.6 + rna_monomer_distillation: + probability: 0.4 + monomer_distillation: + probability: 0.0 + + val: + pseudoknot: + dataset: + # eval_every_n: 10 + eval_every_n: 5 + +trainer: + #devices_per_node: 1 + #limit_train_batches: 10 + #limit_val_batches: 1 + validate_every_n_epochs: 5 + prevalidate: true diff --git a/models/rfd3/configs/experiment/rfd3na_rm.yaml b/models/rfd3/configs/experiment/rfd3na_rm.yaml new file mode 100644 index 00000000..1596cb12 --- /dev/null +++ b/models/rfd3/configs/experiment/rfd3na_rm.yaml @@ -0,0 +1,90 @@ +# @package _global_ +# Training configuration for RFD3 + +defaults: + #- /debug/default + - override /model: rfd3_base + - override /logger: wandb + - override /datasets: design_base_rfd3na + - _self_ + +name: rfd3na +tags: [print-model] +ckpt_path: null + +model: + net: + token_initializer: + token_1d_features: + ref_motif_token_type: 3 + restype: 32 + is_dna_token: 1 + is_rna_token: 1 + is_protein_token: 1 + #token_2d_features: + #bp_partners: 3 # Unspecified, pair, loop + atom_1d_features: + ref_atom_name_chars: 256 + ref_element: 128 + ref_charge: 1 + ref_mask: 1 + ref_is_motif_atom_with_fixed_coord: 1 + ref_is_motif_atom_unindexed: 1 + has_zero_occupancy: 1 + ref_pos: 3 + + # Guided features + ref_atomwise_rasa: 3 + active_donor: 1 + active_acceptor: 1 + is_atom_level_hotspot: 1 + diffusion_module: + n_recycle: 2 + use_local_token_attention: True + diffusion_transformer: + n_local_tokens: 32 + n_keys: 128 + + inference_sampler: + num_timesteps: 100 + + +datasets: + diffusion_batch_size_train: 16 + crop_size: 256 + max_atoms_in_crop: 2560 # ~10x crop size. + global_transform_args: + meta_conditioning_probabilities: + calculate_NA_SS: 0.0 + association_scheme: atom23 + #add_na_pair_features: true + train_conditions: + unconditional: + frequency: 2.0 + island: + frequency: 2.0 + sequence_design: + frequency: 0.5 + tipatom: + frequency: 5.0 + ppi: + frequency: 0.0 + train: + # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. + pdb: + probability: 0.5 + rna_monomer_distillation: + probability: 0.5 + + val: + pseudoknot: + dataset: + # eval_every_n: 10 + eval_every_n: 5 + +trainer: + #devices_per_node: 1 + #limit_train_batches: 10 + limit_val_batches: 1 + validate_every_n_epochs: 5 + prevalidate: false diff --git a/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py b/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py index 62d077f5..cc7633e1 100644 --- a/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py +++ b/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py @@ -12,6 +12,8 @@ from foundry.metrics.metric import Metric from foundry.utils.ddp import RankedLogger +from rfd3.trainer.trainer_utils import _readout_seq_from_struc, _cleanup_virtual_atoms_and_assign_atom_name_elements + logging.basicConfig(level=logging.INFO) global_logger = RankedLogger(__name__, rank_zero_only=False) @@ -228,6 +230,8 @@ def compute(self, *, ground_truth_atom_array_stack, predicted_atom_array_stack): # prediction can inherit it, yielding artificially perfect scores. # Optionally recompute bp_partners from the *predicted coordinates*. if self.annotate_predicted_fresh: + #pred_arr = _cleanup_virtual_atoms_and_assign_atom_name_elements(pred_arr, association_scheme = "atom23") + pred_arr = _readout_seq_from_struc(pred_arr, central_atom="C1'", threshold=0.5, association_scheme = "atom23") annotate_na_ss( pred_arr, NA_only=self.annotation_NA_only, @@ -235,7 +239,7 @@ def compute(self, *, ground_truth_atom_array_stack, predicted_atom_array_stack): overwrite=True, p_canonical_bp_filter=0.0, ) - + import pdb; pdb.set_trace()#TODO pred_categories = pred_arr.get_annotation_categories() if "bp_partners" not in pred_categories: continue diff --git a/models/rfd3/src/rfd3/trainer/trainer_utils.py b/models/rfd3/src/rfd3/trainer/trainer_utils.py index 652665ad..436d80e5 100644 --- a/models/rfd3/src/rfd3/trainer/trainer_utils.py +++ b/models/rfd3/src/rfd3/trainer/trainer_utils.py @@ -152,7 +152,6 @@ def _cleanup_virtual_atoms_and_assign_atom_name_elements( is_seq_known = all( np.array(res_array.is_motif_atom_with_fixed_seq, dtype=bool) ) or all(np.array(res_array.is_motif_atom_unindexed, dtype=bool)) - # ... If sequence is known for the original atom array, just skip if is_seq_known: ret_mask += [True] * len(res_array) From 3578002956a6baf78fe799f29e2d2da8d1b85f78 Mon Sep 17 00:00:00 2001 From: afavor Date: Wed, 18 Feb 2026 21:53:29 -0800 Subject: [PATCH 27/49] fixed na ss metric calculation --- models/rfd3/configs/experiment/rfd3na_af.yaml | 99 +++++++++++++++++++ .../src/rfd3/metrics/nucleic_ss_metrics.py | 30 +++++- 2 files changed, 125 insertions(+), 4 deletions(-) create mode 100644 models/rfd3/configs/experiment/rfd3na_af.yaml diff --git a/models/rfd3/configs/experiment/rfd3na_af.yaml b/models/rfd3/configs/experiment/rfd3na_af.yaml new file mode 100644 index 00000000..e9331b7a --- /dev/null +++ b/models/rfd3/configs/experiment/rfd3na_af.yaml @@ -0,0 +1,99 @@ +# @package _global_ +# Training configuration for RFD3 + +defaults: + - /debug/default + - override /model: rfd3_base + # - override /logger: wandb + - override /datasets: design_base_rfd3na + - _self_ + +name: rfd3na-fine-tune +tags: [print-model] +ckpt_path: /projects/ml/aa_design/models/rfd3_latest_foundry.ckpt + +model: + net: + token_initializer: + token_1d_features: + ref_motif_token_type: 3 + restype: 32 + is_dna_token: 1 + is_rna_token: 1 + is_protein_token: 1 + token_2d_features: + bp_partners: 3 # Unspecified, pair, loop + atom_1d_features: + ref_atom_name_chars: 256 + ref_element: 128 + ref_charge: 1 + ref_mask: 1 + ref_is_motif_atom_with_fixed_coord: 1 + ref_is_motif_atom_unindexed: 1 + has_zero_occupancy: 1 + ref_pos: 3 + + # Guided features + ref_atomwise_rasa: 3 + active_donor: 1 + active_acceptor: 1 + is_atom_level_hotspot: 1 + diffusion_module: + n_recycle: 2 + use_local_token_attention: True + diffusion_transformer: + n_local_tokens: 32 + n_keys: 128 + + inference_sampler: + num_timesteps: 100 + + +datasets: + diffusion_batch_size_train: 16 + crop_size: 256 + max_atoms_in_crop: 2560 # ~10x crop size. + global_transform_args: + meta_conditioning_probabilities: + # p_is_nucleic_ss_example: 0.25 + # p_nucleic_ss_show_partial_feats: 0.7 + # p_canonical_bp_filter: 0.2 + p_is_nucleic_ss_example: 1.0 + p_nucleic_ss_show_partial_feats: 0.0 + p_canonical_bp_filter: 0.0 + #calculate_NA_SS: 0.3 + + association_scheme: atom23 + #add_na_pair_features: true + train_conditions: + unconditional: + frequency: 2.0 + island: + frequency: 2.0 + sequence_design: + frequency: 0.5 + tipatom: + frequency: 5.0 + ppi: + frequency: 0.0 + train: + # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. + pdb: + probability: 0.0 + rna_monomer_distillation: + probability: 1.0 + monomer_distillation: + probability: 0.0 + + val: + pseudoknot: + dataset: + # eval_every_n: 10 + eval_every_n: 5 + +trainer: + #devices_per_node: 1 + #limit_train_batches: 10 + #limit_val_batches: 1 + validate_every_n_epochs: 5 + prevalidate: true diff --git a/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py b/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py index cc7633e1..345c26b0 100644 --- a/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py +++ b/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py @@ -230,8 +230,24 @@ def compute(self, *, ground_truth_atom_array_stack, predicted_atom_array_stack): # prediction can inherit it, yielding artificially perfect scores. # Optionally recompute bp_partners from the *predicted coordinates*. if self.annotate_predicted_fresh: - #pred_arr = _cleanup_virtual_atoms_and_assign_atom_name_elements(pred_arr, association_scheme = "atom23") - pred_arr = _readout_seq_from_struc(pred_arr, central_atom="C1'", threshold=0.5, association_scheme = "atom23") + + # Infer res name from geometry first + pred_arr = _readout_seq_from_struc( + pred_arr, + central_atom="C1'", + threshold=0.5, + association_scheme="atom23", + ) + # strip virtuals and set final atom names/elements + pred_arr = _cleanup_virtual_atoms_and_assign_atom_name_elements( + pred_arr, + association_scheme="atom23", + ) + # clear annotation to avoid potential info leak + if "bp_partners" in pred_arr.get_annotation_categories(): + pred_arr.del_annotation("bp_partners") + + # add nucleic-ss annotations annotate_na_ss( pred_arr, NA_only=self.annotation_NA_only, @@ -239,7 +255,7 @@ def compute(self, *, ground_truth_atom_array_stack, predicted_atom_array_stack): overwrite=True, p_canonical_bp_filter=0.0, ) - import pdb; pdb.set_trace()#TODO + pred_categories = pred_arr.get_annotation_categories() if "bp_partners" not in pred_categories: continue @@ -304,7 +320,13 @@ def compute(self, *, ground_truth_atom_array_stack, predicted_atom_array_stack): if n_valid == 0: return {} - + aaa = { + "pair_f1": float(np.mean(pair_f1_list)), + "loop_f1": float(np.mean(loop_f1_list)), + "weighted_f1": float(np.mean(weighted_f1_list)), + "n_valid_samples": int(n_valid), + } + print(aaa) return { "pair_f1": float(np.mean(pair_f1_list)), "loop_f1": float(np.mean(loop_f1_list)), From 3d016cd38965df669d2e64a2b4628f345ad8b0ac Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Wed, 18 Feb 2026 22:51:57 -0800 Subject: [PATCH 28/49] fix atom14 scheme cases --- models/rfd3/src/rfd3/trainer/trainer_utils.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/models/rfd3/src/rfd3/trainer/trainer_utils.py b/models/rfd3/src/rfd3/trainer/trainer_utils.py index 436d80e5..84b8829c 100644 --- a/models/rfd3/src/rfd3/trainer/trainer_utils.py +++ b/models/rfd3/src/rfd3/trainer/trainer_utils.py @@ -264,10 +264,14 @@ def _readout_seq_from_struc( ATOM_NAMES = ATOM23_ATOM_NAMES_DNA if not cur_res_atom_array.is_dna[0]: continue - if restype in STANDARD_RNA: + elif restype in STANDARD_RNA: ATOM_NAMES = ATOM23_ATOM_NAMES_RNA if not cur_res_atom_array.is_rna[0]: continue + else: + #ATOM_NAMES = ATOM23_ATOM_NAMES_RNA + if not cur_res_atom_array.is_protein[0]: + continue atom_name_idx_in_atom14_scheme = np.array( [ From 84648be95a98af88da0a098c32eceb91aab7449f Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Thu, 19 Feb 2026 00:05:08 -0800 Subject: [PATCH 29/49] fix: permute U in association_schemes_stripped --- models/rfd3/src/rfd3/constants.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/models/rfd3/src/rfd3/constants.py b/models/rfd3/src/rfd3/constants.py index 1e023eda..c7d43c62 100644 --- a/models/rfd3/src/rfd3/constants.py +++ b/models/rfd3/src/rfd3/constants.py @@ -226,10 +226,7 @@ strip_list = lambda x: [(x.strip() if x is not None else None) for x in x] # noqa -association_schemes_stripped = { - name: {k: strip_list(v) for k, v in scheme.items()} - for name, scheme in association_schemes.items() -} + SELECTION_PROTEIN = ["POLYPEPTIDE(D)", "POLYPEPTIDE(L)"] SELECTION_NONPROTEIN = [ "POLYDEOXYRIBONUCLEOTIDE", From 212082581b1c0df27ea04398b3b3f8adb930e115 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Wed, 18 Feb 2026 17:47:44 -0800 Subject: [PATCH 30/49] time to fix ss metric line 242 --- models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py b/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py index 345c26b0..302e6e1e 100644 --- a/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py +++ b/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py @@ -230,7 +230,6 @@ def compute(self, *, ground_truth_atom_array_stack, predicted_atom_array_stack): # prediction can inherit it, yielding artificially perfect scores. # Optionally recompute bp_partners from the *predicted coordinates*. if self.annotate_predicted_fresh: - # Infer res name from geometry first pred_arr = _readout_seq_from_struc( pred_arr, @@ -255,7 +254,6 @@ def compute(self, *, ground_truth_atom_array_stack, predicted_atom_array_stack): overwrite=True, p_canonical_bp_filter=0.0, ) - pred_categories = pred_arr.get_annotation_categories() if "bp_partners" not in pred_categories: continue From d04989f65f6bae4bcb2aaef06568b827dabf0cae Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Thu, 19 Feb 2026 01:59:11 -0800 Subject: [PATCH 31/49] add failsafe for nucleic ss metric --- .../rfd3/configs/datasets/val/pseudoknot.yaml | 4 +-- .../src/rfd3/metrics/nucleic_ss_metrics.py | 30 +++++++++++-------- 2 files changed, 20 insertions(+), 14 deletions(-) diff --git a/models/rfd3/configs/datasets/val/pseudoknot.yaml b/models/rfd3/configs/datasets/val/pseudoknot.yaml index d3fa6f7e..bb312c05 100644 --- a/models/rfd3/configs/datasets/val/pseudoknot.yaml +++ b/models/rfd3/configs/datasets/val/pseudoknot.yaml @@ -6,5 +6,5 @@ defaults: dataset: name: pseudoknot eval_every_n: 1 - data: ${paths.data.design_benchmark_data_dir}/pseudoknot_debug.json - #data: ${paths.data.design_benchmark_data_dir}/pseudoknot.json + #data: ${paths.data.design_benchmark_data_dir}/pseudoknot_debug.json + data: ${paths.data.design_benchmark_data_dir}/pseudoknot.json diff --git a/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py b/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py index 302e6e1e..f0216390 100644 --- a/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py +++ b/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py @@ -230,18 +230,24 @@ def compute(self, *, ground_truth_atom_array_stack, predicted_atom_array_stack): # prediction can inherit it, yielding artificially perfect scores. # Optionally recompute bp_partners from the *predicted coordinates*. if self.annotate_predicted_fresh: - # Infer res name from geometry first - pred_arr = _readout_seq_from_struc( - pred_arr, - central_atom="C1'", - threshold=0.5, - association_scheme="atom23", - ) - # strip virtuals and set final atom names/elements - pred_arr = _cleanup_virtual_atoms_and_assign_atom_name_elements( - pred_arr, - association_scheme="atom23", - ) + try: + # Infer res name from geometry first + pred_arr = _readout_seq_from_struc( + pred_arr, + central_atom="C1'", + threshold=0.5, + association_scheme="atom23", + ) + + # strip virtuals and set final atom names/elements + pred_arr = _cleanup_virtual_atoms_and_assign_atom_name_elements( + pred_arr, + association_scheme="atom23", + ) + except: + # this can fail early in training + print("could not cleanup virtuals for nucleic ss metric compute") + pass # clear annotation to avoid potential info leak if "bp_partners" in pred_arr.get_annotation_categories(): pred_arr.del_annotation("bp_partners") From ad3d95f351fd68aa510bb807995e83146bbe5387 Mon Sep 17 00:00:00 2001 From: afavor Date: Wed, 25 Feb 2026 21:03:30 -0800 Subject: [PATCH 32/49] extra filter for huge base pair centroid distances, and also cleaned inference specification code --- .../rfd3/src/rfd3/transforms/na_geom_utils.py | 31 +++++++++++++++---- 1 file changed, 25 insertions(+), 6 deletions(-) diff --git a/models/rfd3/src/rfd3/transforms/na_geom_utils.py b/models/rfd3/src/rfd3/transforms/na_geom_utils.py index 5a055779..453e3640 100644 --- a/models/rfd3/src/rfd3/transforms/na_geom_utils.py +++ b/models/rfd3/src/rfd3/transforms/na_geom_utils.py @@ -90,7 +90,7 @@ def __init__(self) -> None: self.bp_val_cutoff = 0.5 # minimum basepairing score for binarizing basepairs when needed self.base_geometry_limits = {} - self.base_geometry_limits['D_ij'] = 20.0 + self.base_geometry_limits['D_ij'] = 16.0 self.base_geometry_limits['H_ij'] = 1.5 self.base_geometry_limits['P_ij'] = math.pi/5 self.base_geometry_limits['B_ij'] = math.pi/5 @@ -875,6 +875,7 @@ def _compute_pairwise_geometry( # Rise (H_ij) H_ij = np.sum(sc_D_ij_vec * Z_ij, axis=-1) # [I, I] + D_ij = np.linalg.norm(sc_D_ij_vec, axis=-1) # [I, I] # Buckle (B_ij) proj_Z_i_YZ = ( # [I, I, 3] @@ -903,6 +904,7 @@ def _compute_pairwise_geometry( "H_ij": H_ij, "B_ij": B_ij, "P_ij": P_ij, + "D_ij": D_ij, "base_ori_ij": base_ori_ij, "X_ij": X_ij, "Y_ij": Y_ij, @@ -933,6 +935,7 @@ def _compute_basepair_mask( H_ij: np.ndarray, B_ij: np.ndarray, P_ij: np.ndarray, + D_ij: np.ndarray, mol_info, *, bool_only: bool = False, @@ -985,7 +988,10 @@ def _compute_basepair_mask( (P_ij <= mol_info.base_geometry_limits["P_ij"]) | (P_ij >= math.pi - mol_info.base_geometry_limits["P_ij"]) ) - bp_geom_filter = H_ij_filter & B_ij_filter & P_ij_filter # [I, I] + + D_ij_filter = (D_ij <= mol_info.base_geometry_limits["D_ij"]) + + bp_geom_filter = H_ij_filter & B_ij_filter & P_ij_filter & D_ij_filter # [I, I] if bool_only: basepairs_bool_ij = ( # [I, I] @@ -1074,6 +1080,7 @@ def compute_nucleic_ss( "H_ij": np.zeros((0, 0), dtype=np.float32), "B_ij": np.zeros((0, 0), dtype=np.float32), "P_ij": np.zeros((0, 0), dtype=np.float32), + "D_ij": np.zeros((0, 0), dtype=np.float32), "base_ori_ij": np.zeros((0, 0), dtype=np.float32), "basepairs_bool_ij": np.zeros((0, 0), dtype=bool), "basepairs_ij": np.zeros((0, 0), dtype=np.float32), @@ -1139,6 +1146,7 @@ def compute_nucleic_ss( pw_geom["H_ij"], pw_geom["B_ij"], pw_geom["P_ij"], + pw_geom["D_ij"], mol_info, bool_only=return_basepairs_only, eps=eps, @@ -1498,12 +1506,23 @@ def annotate_na_ss_from_specification( bp_partners_ann = np.empty(len(atom_array), dtype=object) bp_partners_ann[:] = None - # Build chain/res -> token index map for region/position specs + # Build chain/res -> token index map for region/position specs. + # Accept both chain_iid-like keys (e.g. "A_1") and plain chain IDs (e.g. "A") + # so CLI/json specs like "A1,B3" work reliably in inference. chain_iid_list: list[str] = [str(atm.chain_iid) for atm in token_level_array] + chain_id_list: list[str] = [str(atm.chain_id) for atm in token_level_array] resi_list: list[int] = [int(atm.res_id) for atm in token_level_array] - chain_res_to_tok: dict[tuple[str, int], int] = { - (c, r): i for i, (c, r) in enumerate(zip(chain_iid_list, resi_list)) - } + chain_res_to_tok: dict[tuple[str, int], int] = {} + for i, (chain_iid, chain_id, res_id) in enumerate( + zip(chain_iid_list, chain_id_list, resi_list) + ): + key_iid = (chain_iid, int(res_id)) + key_chain = (chain_id, int(res_id)) + chain_res_to_tok.setdefault(key_iid, int(i)) + chain_res_to_tok.setdefault(key_chain, int(i)) + # Also support the short alias from chain_iid (e.g. "A_1" -> "A") + short_chain = chain_iid.split("_", 1)[0] + chain_res_to_tok.setdefault((short_chain, int(res_id)), int(i)) def _parse_region(region_str: str) -> tuple[str, int, int] | None: region_str = str(region_str).strip() From 60b18c281b7c4bada96d66203f74fe153c4334ed Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Thu, 26 Feb 2026 10:41:52 -0800 Subject: [PATCH 33/49] mc: yaml changes --- models/rfd3/configs/experiment/rfd3na.yaml | 13 +++++++------ .../rfd3/configs/experiment/rfd3na_fine_tune.yaml | 11 ++++++----- .../rfd3/configs/experiment/rfd3na_no_distill.yaml | 7 ++++--- 3 files changed, 17 insertions(+), 14 deletions(-) diff --git a/models/rfd3/configs/experiment/rfd3na.yaml b/models/rfd3/configs/experiment/rfd3na.yaml index e1194be6..8739992e 100644 --- a/models/rfd3/configs/experiment/rfd3na.yaml +++ b/models/rfd3/configs/experiment/rfd3na.yaml @@ -2,17 +2,18 @@ # Training configuration for RFD3 defaults: - - /debug/default + #- /debug/default - override /model: rfd3_base - #- override /logger: wandb + - override /logger: wandb - override /datasets: design_base_rfd3na - _self_ name: rfd3na_scratch_clean_test tags: [print-model] +#ckpt: null #ckpt_path: /net/scratch/raktim/training/logs/train/rfd3na-fine-tune/2026-02-17_15-21_JOB_3608285/ckpt/epoch-0590.ckpt -#ckpt_path: /net/scratch/raktim/training/logs/train/rfd3na_scratch/2026-02-17_17-56_JOB_3620867/ckpt/epoch-0030.ckpt +ckpt_path: /net/scratch/raktim/training/logs/train/rfd3na_scratch_clean_test/2026-02-19_01-58_JOB_3986244/ckpt/epoch-0180.ckpt model: @@ -58,7 +59,7 @@ datasets: max_atoms_in_crop: 2560 # ~10x crop size. global_transform_args: meta_conditioning_probabilities: - p_is_nucleic_ss_example: 1.0 + p_is_nucleic_ss_example: 0.25 p_nucleic_ss_show_partial_feats: 0.7 p_canonical_bp_filter: 0.2 #calculate_NA_SS: 0.3 @@ -79,11 +80,11 @@ datasets: train: # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. pdb: - probability: 0.5 + probability: 0.6 rna_monomer_distillation: probability: 0.3 monomer_distillation: - probability: 0.2 + probability: 0.1 val: pseudoknot: diff --git a/models/rfd3/configs/experiment/rfd3na_fine_tune.yaml b/models/rfd3/configs/experiment/rfd3na_fine_tune.yaml index e7310633..3210c5cb 100644 --- a/models/rfd3/configs/experiment/rfd3na_fine_tune.yaml +++ b/models/rfd3/configs/experiment/rfd3na_fine_tune.yaml @@ -8,9 +8,10 @@ defaults: - override /datasets: design_base_rfd3na - _self_ -name: rfd3na-fine-tune +name: rfd3na-fine-tune-ss0.5 tags: [print-model] -ckpt_path: /projects/ml/aa_design/models/rfd3_latest_foundry.ckpt +#ckpt_path: /projects/ml/aa_design/models/rfd3_latest_foundry.ckpt +ckpt_path: /net/scratch/raktim/training/logs/train/rfd3na-fine-tune-ss0.5/2026-02-23_23-57_JOB_4717793/ckpt/epoch-0820.ckpt model: net: @@ -55,7 +56,7 @@ datasets: max_atoms_in_crop: 2560 # ~10x crop size. global_transform_args: meta_conditioning_probabilities: - p_is_nucleic_ss_example: 0.25 + p_is_nucleic_ss_example: 0.5 p_nucleic_ss_show_partial_feats: 0.7 p_canonical_bp_filter: 0.2 #calculate_NA_SS: 0.3 @@ -76,11 +77,11 @@ datasets: train: # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. pdb: - probability: 0.5 + probability: 0.6 rna_monomer_distillation: probability: 0.3 monomer_distillation: - probability: 0.2 + probability: 0.1 val: pseudoknot: diff --git a/models/rfd3/configs/experiment/rfd3na_no_distill.yaml b/models/rfd3/configs/experiment/rfd3na_no_distill.yaml index e3642cce..abb302b4 100644 --- a/models/rfd3/configs/experiment/rfd3na_no_distill.yaml +++ b/models/rfd3/configs/experiment/rfd3na_no_distill.yaml @@ -10,7 +10,8 @@ defaults: name: rfd3na_no_distill tags: [print-model] -ckpt_path: /net/scratch/raktim/training/logs/train/rfd3na_no_distill/2026-02-17_15-21_JOB_3608348/ckpt/epoch-0020.ckpt +#ckpt_path: null +ckpt_path: /net/scratch/raktim/training/logs/train/rfd3na_no_distill/2026-02-19_02-16_JOB_3988845/ckpt/epoch-0170.ckpt model: net: @@ -76,9 +77,9 @@ datasets: train: # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. pdb: - probability: 0.6 + probability: 0.7 rna_monomer_distillation: - probability: 0.4 + probability: 0.3 monomer_distillation: probability: 0.0 From bfe513ab178307f5ba58c7a0e0ef486d13b34109 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Thu, 26 Feb 2026 12:25:41 -0800 Subject: [PATCH 34/49] refactor ss metric computatino section to be called during inference to write in the json --- .../src/rfd3/metrics/nucleic_ss_metrics.py | 146 +++++++++++------- models/rfd3/src/rfd3/trainer/rfd3.py | 10 ++ 2 files changed, 98 insertions(+), 58 deletions(-) diff --git a/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py b/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py index f0216390..55bd8cb1 100644 --- a/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py +++ b/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py @@ -175,6 +175,88 @@ def _extract_loop_and_paired_token_ids( return loop_token_ids, paired_token_ids +def compute_from_two_arr(gt_arr, pred_arr, restrict_to_nucleic=True, compute_for_diffused_region_only = False): + gt_token_ids = _get_token_ids(gt_arr) + pred_token_ids = _get_token_ids(pred_arr) + if len(gt_token_ids) != len(pred_token_ids): + None + + # Restrict to token_ids that are valid in both arrays. + gt_allowed = _get_candidate_token_ids( + gt_arr, + restrict_to_nucleic=restrict_to_nucleic, + compute_for_diffused_region_only=compute_for_diffused_region_only, + ) + pred_allowed = _get_candidate_token_ids( + pred_arr, + restrict_to_nucleic=restrict_to_nucleic, + compute_for_diffused_region_only=compute_for_diffused_region_only, + ) + allowed = gt_allowed & pred_allowed + + if len(allowed) == 0: + return None + + gt_pairs = _extract_bp_pairs(gt_arr, allowed_token_ids=allowed) + pred_pairs = _extract_bp_pairs(pred_arr, allowed_token_ids=allowed) + + gt_loop, gt_paired_tokens = _extract_loop_and_paired_token_ids( + gt_arr, allowed_token_ids=allowed + ) + pred_loop, _pred_paired_tokens = _extract_loop_and_paired_token_ids( + pred_arr, allowed_token_ids=allowed + ) + + pair_tp = len(gt_pairs & pred_pairs) + pair_pred_n = len(pred_pairs) + pair_gt_n = len(gt_pairs) + + loop_tp = len(gt_loop & pred_loop) + loop_pred_n = len(pred_loop) + loop_gt_n = len(gt_loop) + + pair_f1 = _safe_f1_from_sizes(pair_tp, pair_pred_n, pair_gt_n) + loop_f1 = _safe_f1_from_sizes(loop_tp, loop_pred_n, loop_gt_n) + + pair_weight = len(gt_paired_tokens) + loop_weight = len(gt_loop) + total_weight = pair_weight + loop_weight + if total_weight == 0: + weighted_f1 = 1.0 + else: + weighted_f1 = float( + (pair_weight * pair_f1 + loop_weight * loop_f1) / total_weight + ) + + + return pair_f1, loop_f1, weighted_f1 + +def get_NA_SS_F1(pred_array): + + ## save the original bop_partner annotation + gt_array = pred_array.copy() + + ## replace by annotating again + pred_array = annotate_na_ss( + pred_array, + NA_only=True, + planar_only=True, + overwrite=True, + p_canonical_bp_filter=0.0, + ) + + try: + pair_f1, loop_f1, weighted_f1 = compute_from_two_arr(gt_array, pred_array) + except: + # fails when returns None because expects three returns + return {} + + return { + "pair_f1": pair_f1, + "loop_f1": loop_f1, + "weighted_f1": weighted_f1, + } + class NucleicSSSimilarityMetrics(Metric): """Secondary-structure similarity for nucleic acids. @@ -264,59 +346,13 @@ def compute(self, *, ground_truth_atom_array_stack, predicted_atom_array_stack): if "bp_partners" not in pred_categories: continue - # Basic sanity check: token counts should match for aligned comparisons. - gt_token_ids = _get_token_ids(gt_arr) - pred_token_ids = _get_token_ids(pred_arr) - if len(gt_token_ids) != len(pred_token_ids): - continue - - # Restrict to token_ids that are valid in both arrays. - gt_allowed = _get_candidate_token_ids( - gt_arr, - restrict_to_nucleic=self.restrict_to_nucleic, - compute_for_diffused_region_only=self.compute_for_diffused_region_only, - ) - pred_allowed = _get_candidate_token_ids( - pred_arr, - restrict_to_nucleic=self.restrict_to_nucleic, - compute_for_diffused_region_only=self.compute_for_diffused_region_only, - ) - allowed = gt_allowed & pred_allowed - - if len(allowed) == 0: + # Basic sanity check: token counts should match for aligned comparisons + try: + pair_f1, loop_f1, weighted_f1 = compute_from_two_arr(gt_arr, pred_arr, restrict_to_nucleic=self.restrict_to_nucleic, compute_for_diffused_region_only = self.compute_for_diffused_region_only) + except: + # fails when returns None because expects three returns continue - gt_pairs = _extract_bp_pairs(gt_arr, allowed_token_ids=allowed) - pred_pairs = _extract_bp_pairs(pred_arr, allowed_token_ids=allowed) - - gt_loop, gt_paired_tokens = _extract_loop_and_paired_token_ids( - gt_arr, allowed_token_ids=allowed - ) - pred_loop, _pred_paired_tokens = _extract_loop_and_paired_token_ids( - pred_arr, allowed_token_ids=allowed - ) - - pair_tp = len(gt_pairs & pred_pairs) - pair_pred_n = len(pred_pairs) - pair_gt_n = len(gt_pairs) - - loop_tp = len(gt_loop & pred_loop) - loop_pred_n = len(pred_loop) - loop_gt_n = len(gt_loop) - - pair_f1 = _safe_f1_from_sizes(pair_tp, pair_pred_n, pair_gt_n) - loop_f1 = _safe_f1_from_sizes(loop_tp, loop_pred_n, loop_gt_n) - - pair_weight = len(gt_paired_tokens) - loop_weight = len(gt_loop) - total_weight = pair_weight + loop_weight - if total_weight == 0: - weighted_f1 = 1.0 - else: - weighted_f1 = float( - (pair_weight * pair_f1 + loop_weight * loop_f1) / total_weight - ) - pair_f1_list.append(pair_f1) loop_f1_list.append(loop_f1) weighted_f1_list.append(weighted_f1) @@ -324,13 +360,7 @@ def compute(self, *, ground_truth_atom_array_stack, predicted_atom_array_stack): if n_valid == 0: return {} - aaa = { - "pair_f1": float(np.mean(pair_f1_list)), - "loop_f1": float(np.mean(loop_f1_list)), - "weighted_f1": float(np.mean(weighted_f1_list)), - "n_valid_samples": int(n_valid), - } - print(aaa) + return { "pair_f1": float(np.mean(pair_f1_list)), "loop_f1": float(np.mean(loop_f1_list)), diff --git a/models/rfd3/src/rfd3/trainer/rfd3.py b/models/rfd3/src/rfd3/trainer/rfd3.py index 9735978e..6701f8d6 100644 --- a/models/rfd3/src/rfd3/trainer/rfd3.py +++ b/models/rfd3/src/rfd3/trainer/rfd3.py @@ -8,6 +8,7 @@ from omegaconf import DictConfig from rfd3.metrics.design_metrics import get_all_backbone_metrics from rfd3.metrics.hbonds_hbplus_metrics import get_hbond_metrics +from rfd3.metrics.nucleic_ss_metrics import get_NA_SS_F1 from rfd3.trainer.recycling import get_recycle_schedule from rfd3.trainer.trainer_utils import ( _build_atom_array_stack, @@ -449,6 +450,15 @@ def _build_predicted_atom_array_stack( ): metadata_dict[i]["metrics"] |= get_hbond_metrics(atom_array) + if ( + "bp_partners" in atom_array.get_annotation_categories() + ): + if not np.all(atom_array.bp_partners == None): + try: + metadata_dict[i]["metrics"] |= get_NA_SS_F1(atom_array) + except: + pass + if "partial_t" in f: # Try calcualte a CA RMSD to input: aa_in = example["atom_array"] From 42680bdf1e84521709031e604e1f6b64f87fb02a Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Fri, 27 Feb 2026 15:17:09 -0800 Subject: [PATCH 35/49] cfg for bp_partners feature --- .../rfd3/configs/inference_engine/rfdiffusion3.yaml | 7 ++++--- models/rfd3/src/rfd3/model/cfg_utils.py | 11 ++++++++--- 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/models/rfd3/configs/inference_engine/rfdiffusion3.yaml b/models/rfd3/configs/inference_engine/rfdiffusion3.yaml index c5a6a72b..80dcc37f 100644 --- a/models/rfd3/configs/inference_engine/rfdiffusion3.yaml +++ b/models/rfd3/configs/inference_engine/rfdiffusion3.yaml @@ -25,9 +25,10 @@ inference_sampler: kind: "default" # "default" or "symmetry" to choose the sampler # Classifier-free guidance args: cfg_features: # set to 0 in the reference CFG step - - active_donor - - active_acceptor - - ref_atomwise_rasa + #- active_donor + #- active_acceptor + #- ref_atomwise_rasa + - bp_partners use_classifier_free_guidance: False cfg_t_max: null # max t to apply cfg guidance diff --git a/models/rfd3/src/rfd3/model/cfg_utils.py b/models/rfd3/src/rfd3/model/cfg_utils.py index d99c2fa0..f84ce453 100644 --- a/models/rfd3/src/rfd3/model/cfg_utils.py +++ b/models/rfd3/src/rfd3/model/cfg_utils.py @@ -57,9 +57,14 @@ def strip_f( # set the feature to default value if it is in the cfg_features if k in cfg_features: - v_cropped = torch.zeros_like(v_cropped).to( - v_cropped.device, dtype=v_cropped.dtype - ) + if k not in ["bp_partners"]: + v_cropped = torch.zeros_like(v_cropped).to( + v_cropped.device, dtype=v_cropped.dtype + ) + else: + ## for bp_partners default is a mask feature + v_cropped[:,:,0] = 1 + v_cropped[:,:,1:] = 0 # update the feature in the dictionary f_stripped[k] = v_cropped From 5a73df5c2f9b54001823df57e044ab20251b37d5 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Thu, 19 Mar 2026 12:56:45 -0700 Subject: [PATCH 36/49] unindex fix final for real --- models/rfd3/src/rfd3/trainer/trainer_utils.py | 5 ++++- models/rfd3/src/rfd3/transforms/conditioning_base.py | 1 - .../rfd3/src/rfd3/transforms/training_conditions.py | 12 ++++++------ 3 files changed, 10 insertions(+), 8 deletions(-) diff --git a/models/rfd3/src/rfd3/trainer/trainer_utils.py b/models/rfd3/src/rfd3/trainer/trainer_utils.py index 84b8829c..bf135b20 100644 --- a/models/rfd3/src/rfd3/trainer/trainer_utils.py +++ b/models/rfd3/src/rfd3/trainer/trainer_utils.py @@ -470,8 +470,11 @@ def process_unindexed_outputs( ) else: dist = float(dists[row_ind[join_atom], col_ind[join_atom]]) - + + try: metadata["join_point_rmsd_by_token"][token_pdb_id] = dist + except: + pass metadata["diffused_index_map"][token_pdb_id] = f"{chain_id}{res_id}" diff --git a/models/rfd3/src/rfd3/transforms/conditioning_base.py b/models/rfd3/src/rfd3/transforms/conditioning_base.py index 2bc598f3..65514c52 100644 --- a/models/rfd3/src/rfd3/transforms/conditioning_base.py +++ b/models/rfd3/src/rfd3/transforms/conditioning_base.py @@ -434,7 +434,6 @@ def expand_unindexed_motifs( f"Failed to create uniquely recognised tokens after concatenation.\n" f"Concatenated tokens: {get_token_count(atom_array_full)}, unindexed: {n_unindexed_tokens}" ) - return atom_array_full def create_unindexed_masks( diff --git a/models/rfd3/src/rfd3/transforms/training_conditions.py b/models/rfd3/src/rfd3/transforms/training_conditions.py index 389d0013..f2173397 100644 --- a/models/rfd3/src/rfd3/transforms/training_conditions.py +++ b/models/rfd3/src/rfd3/transforms/training_conditions.py @@ -19,6 +19,8 @@ sample_subgraph_atoms, ) +from rfd3.constants import backbone_atoms_RNA + nx.from_numpy_matrix = nx.from_numpy_array logger = logging.getLogger(__name__) @@ -158,7 +160,9 @@ def sample_motif_atoms(self, atom_array): is_motif_atom = np.asarray(atom_array.is_motif_token, dtype=bool).copy() if random_condition(self.p_diffuse_motif_sidechains): - backbone_atoms = ["N", "C", "CA"] + backbone_atoms = backbone_atoms_RNA.copy() + backbone_atoms.remove("C1'") + backbone_atoms = ["N", "C", "CA"] + backbone_atoms #covers DNA also if random_condition(self.p_include_oxygen_in_backbone_mask): backbone_atoms.append("O") is_motif_atom = is_motif_atom & np.isin( @@ -173,7 +177,6 @@ def sample_motif_atoms(self, atom_array): # We also only want resolved atoms to be motif is_motif_atom = (is_motif_atom) & (atom_array.occupancy > 0.0) - return is_motif_atom def sample(self, data): @@ -202,7 +205,6 @@ def sample(self, data): leak_global_index=data["conditions"]["unindex_leak_global_index"], ), ) - return atom_array @@ -498,10 +500,9 @@ def sample_conditioning_strategy( atom_array.set_annotation( "is_motif_atom_unindexed", sample_unindexed_atoms( - atom_array, p_unindex_motif_tokens=p_unindex_motif_tokens + atom_array, p_unindex_motif_tokens=p_unindex_motif_tokens, association_scheme=association_scheme ), ) - return atom_array @@ -557,7 +558,6 @@ def sample_unindexed_atoms( is_motif_atom_unindexed = atom_array.is_motif_atom.copy() else: is_motif_atom_unindexed = np.zeros(atom_array.array_length(), dtype=bool) - # ensure non-residue atoms are not already flagged if association_scheme == "atom23": is_motif_atom_unindexed = np.logical_and( From fe817ccc7a9e877cf832a675dd3082451fdeb8fe Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Fri, 20 Mar 2026 11:15:25 -0700 Subject: [PATCH 37/49] unindex train fix --- models/rfd3/src/rfd3/trainer/trainer_utils.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/models/rfd3/src/rfd3/trainer/trainer_utils.py b/models/rfd3/src/rfd3/trainer/trainer_utils.py index bf135b20..24b05071 100644 --- a/models/rfd3/src/rfd3/trainer/trainer_utils.py +++ b/models/rfd3/src/rfd3/trainer/trainer_utils.py @@ -423,7 +423,11 @@ def process_unindexed_outputs( row_ind, col_ind = linear_sum_assignment(dists) res_id_, chain_id_, _ = indices_to_components_(atom_array_diffused, col_ind) - assert (res_id_ == res_id) & (chain_id_ == chain_id) + try: + assert (res_id_ == res_id) & (chain_id_ == chain_id) + except: + global_logger.warning("Unindexed mapping did not work properly, res_id, chain_id") + inserted_mask = np.logical_or(inserted_mask, token_match) # ... Compute metrics based on the new distances @@ -454,7 +458,7 @@ def process_unindexed_outputs( elif not np.any( np.isin( - token.atom_name, [item.replace(" ", "") for item in backbone_atoms_RNA] + token.atom_name, backbone_atoms_RNA ) ): if np.sum(token.atomize) == 1: From 001709433c51ef92989b6746cf3659f4b25e8eec Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Fri, 20 Mar 2026 16:04:16 -0700 Subject: [PATCH 38/49] remove rf3 config which tagegd along --- .../experiment/rf3_run11-with-confidence.yaml | 56 ------------------- 1 file changed, 56 deletions(-) delete mode 100644 models/rf3/configs/experiment/rf3_run11-with-confidence.yaml diff --git a/models/rf3/configs/experiment/rf3_run11-with-confidence.yaml b/models/rf3/configs/experiment/rf3_run11-with-confidence.yaml deleted file mode 100644 index 9738564c..00000000 --- a/models/rf3/configs/experiment/rf3_run11-with-confidence.yaml +++ /dev/null @@ -1,56 +0,0 @@ -# @package _global_ - -name: rf3-with-confidence -project: rf3 - -tags: - # list of tags to add to the run ( & on wandb to easily find & filter runs) - - full - -defaults: - - override /datasets: pdb_only - - override /model: rf3_with_confidence - - override /trainer: rf3_with_confidence - -ckpt_config: - _target_: foundry.utils.weights.CheckpointConfig - path: null - reset_optimizer: true - -model: - lr_scheduler: - base_lr: 0.9e-3 # 1/2 of original learning rate (1.8e-3) - -# load confidence features -# remove within_20_closest_chains from loaders -datasets: - run_confidence_head: true - train: - pdb: - sub_datasets: - pn_unit: - dataset: - dataset: - filters: - # filters common across all PDB datasets - - "deposition_date < '2024-01-01'" - - "resolution < 9.0" - - "num_polymer_pn_units <= 300" - - "cluster.notnull()" - - "pdb_id not in ${resolve_import:atomworks.constants,PDB_IDS_WITH_UNPHYSICAL_BONDS}" - # pn_unit specific filters - - "~(q_pn_unit_non_polymer_res_names.notnull() and q_pn_unit_non_polymer_res_names.str.contains('${resolve_import:atomworks.constants,AF3_EXCLUDED_LIGANDS_REGEX}', regex=True))" - interface: - dataset: - dataset: - filters: - # filters common across all PDB datasets - - "deposition_date < '2024-01-01'" - - "resolution < 9.0" - - "num_polymer_pn_units <= 300" - - "cluster.notnull()" - - "pdb_id not in ${resolve_import:atomworks.constants,PDB_IDS_WITH_UNPHYSICAL_BONDS}" - # interface specific filters - - "~(pn_unit_1_non_polymer_res_names.notnull() and pn_unit_1_non_polymer_res_names.str.contains('${resolve_import:atomworks.constants,AF3_EXCLUDED_LIGANDS_REGEX}', regex=True))" - - "~(pn_unit_2_non_polymer_res_names.notnull() and pn_unit_2_non_polymer_res_names.str.contains('${resolve_import:atomworks.constants,AF3_EXCLUDED_LIGANDS_REGEX}', regex=True))" - - "is_inter_molecule" From e94ea147a6ebdf34320de1a93d702a55078225db Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Fri, 20 Mar 2026 16:07:07 -0700 Subject: [PATCH 39/49] remove extra rfd3na configs --- models/rfd3/configs/experiment/rfd3na.yaml | 100 ------------------ models/rfd3/configs/experiment/rfd3na_af.yaml | 99 ----------------- .../configs/experiment/rfd3na_no_distill.yaml | 97 ----------------- models/rfd3/configs/experiment/rfd3na_rm.yaml | 90 ---------------- 4 files changed, 386 deletions(-) delete mode 100644 models/rfd3/configs/experiment/rfd3na.yaml delete mode 100644 models/rfd3/configs/experiment/rfd3na_af.yaml delete mode 100644 models/rfd3/configs/experiment/rfd3na_no_distill.yaml delete mode 100644 models/rfd3/configs/experiment/rfd3na_rm.yaml diff --git a/models/rfd3/configs/experiment/rfd3na.yaml b/models/rfd3/configs/experiment/rfd3na.yaml deleted file mode 100644 index 8739992e..00000000 --- a/models/rfd3/configs/experiment/rfd3na.yaml +++ /dev/null @@ -1,100 +0,0 @@ -# @package _global_ -# Training configuration for RFD3 - -defaults: - #- /debug/default - - override /model: rfd3_base - - override /logger: wandb - - override /datasets: design_base_rfd3na - - _self_ - -name: rfd3na_scratch_clean_test -tags: [print-model] - -#ckpt: null -#ckpt_path: /net/scratch/raktim/training/logs/train/rfd3na-fine-tune/2026-02-17_15-21_JOB_3608285/ckpt/epoch-0590.ckpt -ckpt_path: /net/scratch/raktim/training/logs/train/rfd3na_scratch_clean_test/2026-02-19_01-58_JOB_3986244/ckpt/epoch-0180.ckpt - - -model: - net: - token_initializer: - token_1d_features: - ref_motif_token_type: 3 - restype: 32 - is_dna_token: 1 - is_rna_token: 1 - is_protein_token: 1 - token_2d_features: - bp_partners: 3 # Unspecified, pair, loop - atom_1d_features: - ref_atom_name_chars: 256 - ref_element: 128 - ref_charge: 1 - ref_mask: 1 - ref_is_motif_atom_with_fixed_coord: 1 - ref_is_motif_atom_unindexed: 1 - has_zero_occupancy: 1 - ref_pos: 3 - - # Guided features - ref_atomwise_rasa: 3 - active_donor: 1 - active_acceptor: 1 - is_atom_level_hotspot: 1 - diffusion_module: - n_recycle: 2 - use_local_token_attention: True - diffusion_transformer: - n_local_tokens: 32 - n_keys: 128 - - inference_sampler: - num_timesteps: 100 - - -datasets: - diffusion_batch_size_train: 16 - crop_size: 256 - max_atoms_in_crop: 2560 # ~10x crop size. - global_transform_args: - meta_conditioning_probabilities: - p_is_nucleic_ss_example: 0.25 - p_nucleic_ss_show_partial_feats: 0.7 - p_canonical_bp_filter: 0.2 - #calculate_NA_SS: 0.3 - - association_scheme: atom23 - #add_na_pair_features: true - train_conditions: - unconditional: - frequency: 2.0 - island: - frequency: 2.0 - sequence_design: - frequency: 0.5 - tipatom: - frequency: 5.0 - ppi: - frequency: 0.0 - train: - # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. - pdb: - probability: 0.6 - rna_monomer_distillation: - probability: 0.3 - monomer_distillation: - probability: 0.1 - - val: - pseudoknot: - dataset: - # eval_every_n: 10 - eval_every_n: 5 - -trainer: - #devices_per_node: 1 - #limit_train_batches: 10 - #limit_val_batches: 1 - validate_every_n_epochs: 5 - prevalidate: true diff --git a/models/rfd3/configs/experiment/rfd3na_af.yaml b/models/rfd3/configs/experiment/rfd3na_af.yaml deleted file mode 100644 index e9331b7a..00000000 --- a/models/rfd3/configs/experiment/rfd3na_af.yaml +++ /dev/null @@ -1,99 +0,0 @@ -# @package _global_ -# Training configuration for RFD3 - -defaults: - - /debug/default - - override /model: rfd3_base - # - override /logger: wandb - - override /datasets: design_base_rfd3na - - _self_ - -name: rfd3na-fine-tune -tags: [print-model] -ckpt_path: /projects/ml/aa_design/models/rfd3_latest_foundry.ckpt - -model: - net: - token_initializer: - token_1d_features: - ref_motif_token_type: 3 - restype: 32 - is_dna_token: 1 - is_rna_token: 1 - is_protein_token: 1 - token_2d_features: - bp_partners: 3 # Unspecified, pair, loop - atom_1d_features: - ref_atom_name_chars: 256 - ref_element: 128 - ref_charge: 1 - ref_mask: 1 - ref_is_motif_atom_with_fixed_coord: 1 - ref_is_motif_atom_unindexed: 1 - has_zero_occupancy: 1 - ref_pos: 3 - - # Guided features - ref_atomwise_rasa: 3 - active_donor: 1 - active_acceptor: 1 - is_atom_level_hotspot: 1 - diffusion_module: - n_recycle: 2 - use_local_token_attention: True - diffusion_transformer: - n_local_tokens: 32 - n_keys: 128 - - inference_sampler: - num_timesteps: 100 - - -datasets: - diffusion_batch_size_train: 16 - crop_size: 256 - max_atoms_in_crop: 2560 # ~10x crop size. - global_transform_args: - meta_conditioning_probabilities: - # p_is_nucleic_ss_example: 0.25 - # p_nucleic_ss_show_partial_feats: 0.7 - # p_canonical_bp_filter: 0.2 - p_is_nucleic_ss_example: 1.0 - p_nucleic_ss_show_partial_feats: 0.0 - p_canonical_bp_filter: 0.0 - #calculate_NA_SS: 0.3 - - association_scheme: atom23 - #add_na_pair_features: true - train_conditions: - unconditional: - frequency: 2.0 - island: - frequency: 2.0 - sequence_design: - frequency: 0.5 - tipatom: - frequency: 5.0 - ppi: - frequency: 0.0 - train: - # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. - pdb: - probability: 0.0 - rna_monomer_distillation: - probability: 1.0 - monomer_distillation: - probability: 0.0 - - val: - pseudoknot: - dataset: - # eval_every_n: 10 - eval_every_n: 5 - -trainer: - #devices_per_node: 1 - #limit_train_batches: 10 - #limit_val_batches: 1 - validate_every_n_epochs: 5 - prevalidate: true diff --git a/models/rfd3/configs/experiment/rfd3na_no_distill.yaml b/models/rfd3/configs/experiment/rfd3na_no_distill.yaml deleted file mode 100644 index abb302b4..00000000 --- a/models/rfd3/configs/experiment/rfd3na_no_distill.yaml +++ /dev/null @@ -1,97 +0,0 @@ -# @package _global_ -# Training configuration for RFD3 - -defaults: - #- /debug/default - - override /model: rfd3_base - - override /logger: wandb - - override /datasets: design_base_rfd3na - - _self_ - -name: rfd3na_no_distill -tags: [print-model] -#ckpt_path: null -ckpt_path: /net/scratch/raktim/training/logs/train/rfd3na_no_distill/2026-02-19_02-16_JOB_3988845/ckpt/epoch-0170.ckpt - -model: - net: - token_initializer: - token_1d_features: - ref_motif_token_type: 3 - restype: 32 - is_dna_token: 1 - is_rna_token: 1 - is_protein_token: 1 - token_2d_features: - bp_partners: 3 # Unspecified, pair, loop - atom_1d_features: - ref_atom_name_chars: 256 - ref_element: 128 - ref_charge: 1 - ref_mask: 1 - ref_is_motif_atom_with_fixed_coord: 1 - ref_is_motif_atom_unindexed: 1 - has_zero_occupancy: 1 - ref_pos: 3 - - # Guided features - ref_atomwise_rasa: 3 - active_donor: 1 - active_acceptor: 1 - is_atom_level_hotspot: 1 - diffusion_module: - n_recycle: 2 - use_local_token_attention: True - diffusion_transformer: - n_local_tokens: 32 - n_keys: 128 - - inference_sampler: - num_timesteps: 100 - - -datasets: - diffusion_batch_size_train: 16 - crop_size: 256 - max_atoms_in_crop: 2560 # ~10x crop size. - global_transform_args: - meta_conditioning_probabilities: - p_is_nucleic_ss_example: 0.25 - p_nucleic_ss_show_partial_feats: 0.7 - p_canonical_bp_filter: 0.2 - #calculate_NA_SS: 0.3 - - association_scheme: atom23 - #add_na_pair_features: true - train_conditions: - unconditional: - frequency: 2.0 - island: - frequency: 2.0 - sequence_design: - frequency: 0.5 - tipatom: - frequency: 5.0 - ppi: - frequency: 0.0 - train: - # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. - pdb: - probability: 0.7 - rna_monomer_distillation: - probability: 0.3 - monomer_distillation: - probability: 0.0 - - val: - pseudoknot: - dataset: - # eval_every_n: 10 - eval_every_n: 5 - -trainer: - #devices_per_node: 1 - #limit_train_batches: 10 - #limit_val_batches: 1 - validate_every_n_epochs: 5 - prevalidate: true diff --git a/models/rfd3/configs/experiment/rfd3na_rm.yaml b/models/rfd3/configs/experiment/rfd3na_rm.yaml deleted file mode 100644 index 1596cb12..00000000 --- a/models/rfd3/configs/experiment/rfd3na_rm.yaml +++ /dev/null @@ -1,90 +0,0 @@ -# @package _global_ -# Training configuration for RFD3 - -defaults: - #- /debug/default - - override /model: rfd3_base - - override /logger: wandb - - override /datasets: design_base_rfd3na - - _self_ - -name: rfd3na -tags: [print-model] -ckpt_path: null - -model: - net: - token_initializer: - token_1d_features: - ref_motif_token_type: 3 - restype: 32 - is_dna_token: 1 - is_rna_token: 1 - is_protein_token: 1 - #token_2d_features: - #bp_partners: 3 # Unspecified, pair, loop - atom_1d_features: - ref_atom_name_chars: 256 - ref_element: 128 - ref_charge: 1 - ref_mask: 1 - ref_is_motif_atom_with_fixed_coord: 1 - ref_is_motif_atom_unindexed: 1 - has_zero_occupancy: 1 - ref_pos: 3 - - # Guided features - ref_atomwise_rasa: 3 - active_donor: 1 - active_acceptor: 1 - is_atom_level_hotspot: 1 - diffusion_module: - n_recycle: 2 - use_local_token_attention: True - diffusion_transformer: - n_local_tokens: 32 - n_keys: 128 - - inference_sampler: - num_timesteps: 100 - - -datasets: - diffusion_batch_size_train: 16 - crop_size: 256 - max_atoms_in_crop: 2560 # ~10x crop size. - global_transform_args: - meta_conditioning_probabilities: - calculate_NA_SS: 0.0 - association_scheme: atom23 - #add_na_pair_features: true - train_conditions: - unconditional: - frequency: 2.0 - island: - frequency: 2.0 - sequence_design: - frequency: 0.5 - tipatom: - frequency: 5.0 - ppi: - frequency: 0.0 - train: - # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. - pdb: - probability: 0.5 - rna_monomer_distillation: - probability: 0.5 - - val: - pseudoknot: - dataset: - # eval_every_n: 10 - eval_every_n: 5 - -trainer: - #devices_per_node: 1 - #limit_train_batches: 10 - limit_val_batches: 1 - validate_every_n_epochs: 5 - prevalidate: false From f10771f98d1ff4f585d5d75b97aff779da8d936c Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Fri, 20 Mar 2026 16:11:19 -0700 Subject: [PATCH 40/49] debug cleanup --- models/rfd3/src/rfd3/model/layers/block_utils.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/models/rfd3/src/rfd3/model/layers/block_utils.py b/models/rfd3/src/rfd3/model/layers/block_utils.py index c2bbd56c..67621a4f 100644 --- a/models/rfd3/src/rfd3/model/layers/block_utils.py +++ b/models/rfd3/src/rfd3/model/layers/block_utils.py @@ -413,10 +413,7 @@ def extend_index_mask_with_neighbours( # 2. Find k-nn excluding forced indices D_LL = torch.where(mask, inf, D_LL) - try: - filler_idx = torch.topk(D_LL, k, dim=-1, largest=False).indices - except: - raise ValueError(f"DLL has nan?: {torch.isnan(D_LL).any()}, D_LL shape: {D_LL.shape}, k: {k}") + filler_idx = torch.topk(D_LL, k, dim=-1, largest=False).indices # ... Reverse last axis s.t. best matched indices are last filler_idx = filler_idx.flip(dims=[-1]) From 67921fa6c753328d515b1ec93f77744b76c1326a Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Fri, 20 Mar 2026 16:48:45 -0700 Subject: [PATCH 41/49] leftover rebase bugs cleanup, atom14 inference demo working --- models/rfd3/src/rfd3/constants.py | 1 + models/rfd3/src/rfd3/transforms/design_transforms.py | 12 ------------ 2 files changed, 1 insertion(+), 12 deletions(-) diff --git a/models/rfd3/src/rfd3/constants.py b/models/rfd3/src/rfd3/constants.py index c7d43c62..3ec144c8 100644 --- a/models/rfd3/src/rfd3/constants.py +++ b/models/rfd3/src/rfd3/constants.py @@ -398,6 +398,7 @@ ATOM23_ATOM_NAMES_RNA = np.array( [item.strip() for item in backbone_atomscheme_RNA] + [f"V{i}" for i in range(23 - len(backbone_atomscheme_RNA))] +) """Atom23 atom names (e.g. CA, V1)""" ATOM23_ATOM_ELEMENTS_RNA = np.array( diff --git a/models/rfd3/src/rfd3/transforms/design_transforms.py b/models/rfd3/src/rfd3/transforms/design_transforms.py index 04f1500d..aeadcc2b 100644 --- a/models/rfd3/src/rfd3/transforms/design_transforms.py +++ b/models/rfd3/src/rfd3/transforms/design_transforms.py @@ -758,18 +758,6 @@ def forward(self, data: Dict[str, Any]) -> Dict[str, Any]: if "feats" not in data.keys(): data["feats"] = {} - if association_scheme == 'atom23': - data['atom_array'].set_annotation('is_protein_token', data['atom_array'].is_protein) - data['atom_array'].set_annotation('is_dna_token', data['atom_array'].is_dna) - data['atom_array'].set_annotation('is_rna_token', data['atom_array'].is_rna) - - if self.association_scheme == "atom23": - data["atom_array"].set_annotation( - "is_protein_token", data["atom_array"].is_protein - ) - data["atom_array"].set_annotation("is_dna_token", data["atom_array"].is_dna) - data["atom_array"].set_annotation("is_rna_token", data["atom_array"].is_rna) - if self.association_scheme == "atom23": data["atom_array"].set_annotation( "is_protein_token", data["atom_array"].is_protein From fdf3f98a9f416cc36c647d94984844cdc85ac397 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Mon, 23 Mar 2026 13:33:50 -0700 Subject: [PATCH 42/49] cleanup configs --- .../components/rf3_net_with_confidence_head.yaml | 3 +-- models/rfd3/configs/datasets/design_base.yaml | 8 -------- models/rfd3/configs/datasets/train/pdb/base.yaml | 2 +- models/rfd3/configs/datasets/val/pseudoknot.yaml | 1 - models/rfd3/configs/experiment/rfd3na_fine_tune.yaml | 12 ++++++++++-- .../rfd3/configs/inference_engine/rfdiffusion3.yaml | 6 +++--- 6 files changed, 15 insertions(+), 17 deletions(-) diff --git a/models/rf3/configs/model/components/rf3_net_with_confidence_head.yaml b/models/rf3/configs/model/components/rf3_net_with_confidence_head.yaml index e7c0d3c0..4714de10 100644 --- a/models/rf3/configs/model/components/rf3_net_with_confidence_head.yaml +++ b/models/rf3/configs/model/components/rf3_net_with_confidence_head.yaml @@ -36,11 +36,10 @@ confidence_head: d_hidden: 32 attention_pair_bias: n_head: 16 - kq_norm: true n_bins_pae: 64 n_bins_pde: 64 n_bins_plddt: 50 n_bins_exp_resolved: 2 use_Cb_distances: False use_af3_style_binning_and_final_layer_norms: True - symmetrize_Cb_logits: True \ No newline at end of file + symmetrize_Cb_logits: True diff --git a/models/rfd3/configs/datasets/design_base.yaml b/models/rfd3/configs/datasets/design_base.yaml index c862900c..a6995893 100644 --- a/models/rfd3/configs/datasets/design_base.yaml +++ b/models/rfd3/configs/datasets/design_base.yaml @@ -62,9 +62,6 @@ global_transform_args: min_ss_island_len: 1 max_ss_island_len: 10 - # Nucleic acid features - add_na_pair_features: false - train_conditions: unconditional: frequency: 5.0 @@ -98,8 +95,3 @@ global_transform_args: # PPI add_ppi_hotspots: 0.75 full_binder_crop: 0.75 - - # Nucleic SS: - p_is_nucleic_ss_example: 0.0 - p_nucleic_ss_show_partial_feats: 0.0 - \ No newline at end of file diff --git a/models/rfd3/configs/datasets/train/pdb/base.yaml b/models/rfd3/configs/datasets/train/pdb/base.yaml index 9c0a8c87..d3f45628 100644 --- a/models/rfd3/configs/datasets/train/pdb/base.yaml +++ b/models/rfd3/configs/datasets/train/pdb/base.yaml @@ -9,6 +9,6 @@ weights: beta: 0.5 alphas: a_prot: 3.0 # 3 for AF-3 - a_nuc: 3.0 # 3 for AF-3 + a_nuc: 0.0 # 3 for AF-3 a_ligand: 1.0 # 1 for AF-3 a_loi: 5.0 # 5 for AF-3 diff --git a/models/rfd3/configs/datasets/val/pseudoknot.yaml b/models/rfd3/configs/datasets/val/pseudoknot.yaml index bb312c05..7cf5ce14 100644 --- a/models/rfd3/configs/datasets/val/pseudoknot.yaml +++ b/models/rfd3/configs/datasets/val/pseudoknot.yaml @@ -6,5 +6,4 @@ defaults: dataset: name: pseudoknot eval_every_n: 1 - #data: ${paths.data.design_benchmark_data_dir}/pseudoknot_debug.json data: ${paths.data.design_benchmark_data_dir}/pseudoknot.json diff --git a/models/rfd3/configs/experiment/rfd3na_fine_tune.yaml b/models/rfd3/configs/experiment/rfd3na_fine_tune.yaml index 3210c5cb..3f2dfbe1 100644 --- a/models/rfd3/configs/experiment/rfd3na_fine_tune.yaml +++ b/models/rfd3/configs/experiment/rfd3na_fine_tune.yaml @@ -10,8 +10,7 @@ defaults: name: rfd3na-fine-tune-ss0.5 tags: [print-model] -#ckpt_path: /projects/ml/aa_design/models/rfd3_latest_foundry.ckpt -ckpt_path: /net/scratch/raktim/training/logs/train/rfd3na-fine-tune-ss0.5/2026-02-23_23-57_JOB_4717793/ckpt/epoch-0820.ckpt +ckpt_path: null model: net: @@ -78,6 +77,15 @@ datasets: # These are the ratios used in the preprint but we set all pdb sampling by default since not everyone might download the distillation data. pdb: probability: 0.6 + sub_datasets: + pn_unit: + weights: + alphas: + a_nuc: 3.0 + interface: + weights: + alphas: + a_nuc: 3.0 rna_monomer_distillation: probability: 0.3 monomer_distillation: diff --git a/models/rfd3/configs/inference_engine/rfdiffusion3.yaml b/models/rfd3/configs/inference_engine/rfdiffusion3.yaml index 80dcc37f..fe4a00fb 100644 --- a/models/rfd3/configs/inference_engine/rfdiffusion3.yaml +++ b/models/rfd3/configs/inference_engine/rfdiffusion3.yaml @@ -25,9 +25,9 @@ inference_sampler: kind: "default" # "default" or "symmetry" to choose the sampler # Classifier-free guidance args: cfg_features: # set to 0 in the reference CFG step - #- active_donor - #- active_acceptor - #- ref_atomwise_rasa + - active_donor + - active_acceptor + - ref_atomwise_rasa - bp_partners use_classifier_free_guidance: False From b9e050c35b9b67c8e5e355df49b2d674912260c0 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Fri, 27 Mar 2026 11:22:23 -0700 Subject: [PATCH 43/49] ruff format --- models/rfd3/src/rfd3/constants.py | 348 +++++++++---- .../rfd3/src/rfd3/inference/input_parsing.py | 26 +- .../rfd3/inference/legacy_input_parsing.py | 53 +- .../rfd3/src/rfd3/metrics/design_metrics.py | 50 +- .../src/rfd3/metrics/nucleic_ss_metrics.py | 71 +-- .../src/rfd3/metrics/rna_aptamer_metrics.py | 30 +- models/rfd3/src/rfd3/model/cfg_utils.py | 4 +- .../rfd3/src/rfd3/model/layers/block_utils.py | 4 +- models/rfd3/src/rfd3/model/layers/blocks.py | 7 +- models/rfd3/src/rfd3/model/layers/encoders.py | 6 +- models/rfd3/src/rfd3/trainer/rfd3.py | 9 +- models/rfd3/src/rfd3/trainer/trainer_utils.py | 27 +- .../src/rfd3/transforms/conditioning_base.py | 15 +- .../src/rfd3/transforms/design_transforms.py | 24 +- .../rfd3/src/rfd3/transforms/hbonds_hbplus.py | 2 - models/rfd3/src/rfd3/transforms/na_geom.py | 197 ++++---- .../rfd3/src/rfd3/transforms/na_geom_utils.py | 477 ++++++++++++------ models/rfd3/src/rfd3/transforms/pipelines.py | 18 +- .../rfd3/transforms/training_conditions.py | 16 +- .../rfd3/src/rfd3/transforms/virtual_atoms.py | 7 +- models/rfd3/src/rfd3/utils/inference.py | 7 +- 21 files changed, 878 insertions(+), 520 deletions(-) diff --git a/models/rfd3/src/rfd3/constants.py b/models/rfd3/src/rfd3/constants.py index 3ec144c8..5407e681 100644 --- a/models/rfd3/src/rfd3/constants.py +++ b/models/rfd3/src/rfd3/constants.py @@ -438,114 +438,254 @@ # Mapping from residue type to its backbone and sidechain atoms (for convenience) ATOM_REGION_BY_RESI = { - 'ALA': {'bb':('N','CA','C','O'), - 'sc':('CB')}, - 'ARG': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD','NE','CZ','NH1','NH2')}, - 'ASN': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','OD1','ND2')}, - 'ASP': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','OD1','OD2')}, - 'CYS': {'bb':('N','CA','C','O'), - 'sc':('CB','SG')}, - 'GLN': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD','OE1','NE2')}, - 'GLU': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD','OE1','OE2')}, - 'GLY': {'bb':('N','CA','C','O'), - 'sc':()}, - 'HIS': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','ND1','CD2','CE1','NE2')}, - 'ILE': {'bb':('N','CA','C','O'), - 'sc':('CB','CG1','CG2','CD1')}, - 'LEU': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD1','CD2')}, - 'LYS': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD','CE','NZ')}, - 'MET': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','SD','CE')}, - 'PHE': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD1','CD2','CE1','CE2','CZ')}, - 'PRO': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD')}, - 'SER': {'bb':('N','CA','C','O'), - 'sc':('CB','OG')}, - 'THR': {'bb':('N','CA','C','O'), - 'sc':('CB','OG1','CG2')}, - 'TRP': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD1','CD2','CE2','CE3','NE1','CZ2','CZ3','CH2')}, - 'TYR': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','CD1','CD2','CE1','CE2','CZ','OH')}, - 'VAL': {'bb':('N','CA','C','O'), - 'sc':('CB','CG1','CG2')}, - 'UNK': {'bb':('N','CA','C','O'), - 'sc':('CB')}, - 'MAS': {'bb':('N','CA','C','O'), - 'sc':('CB')}, - 'DA': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), - 'sc':('N9','C4','N3','C2','N1','C6','C5','N7','C8','N6')}, - 'DC': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), - 'sc':('N1','C2','O2','N3','C4','N4','C5','C6')}, - 'DG': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), - 'sc':('N9','C4','N3','C2','N1','C6','C5','N7','C8','N2','O6')}, - 'DT': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), - 'sc':('N1','C2','O2','N3','C4','O4','C5','C7','C6')}, - 'DX': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'"), - 'sc':()}, - 'A': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), - 'sc':('N1','C2','N3','C4','C5','C6','N6','N7','C8','N9')}, - 'C': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), - 'sc':('N1','C2','O2','N3','C4','N4','C5','C6')}, - 'G': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), - 'sc':('N1','C2','N2','N3','C4','C5','C6','O6','N7','C8','N9')}, - 'U': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), - 'sc':('N1','C2','O2','N3','C4','O4','C5','C6')}, - 'X': {'bb':("O4'", "C1'", "C2'",'OP1','P','OP2', "O5'", "C5'", "C4'", "C3'", "O3'", "O2'"), - 'sc':()}, - 'HIS_D': {'bb':('N','CA','C','O'), - 'sc':('CB','CG','NE2','CD2','CE1','ND1')}, + "ALA": {"bb": ("N", "CA", "C", "O"), "sc": ("CB")}, + "ARG": { + "bb": ("N", "CA", "C", "O"), + "sc": ("CB", "CG", "CD", "NE", "CZ", "NH1", "NH2"), + }, + "ASN": {"bb": ("N", "CA", "C", "O"), "sc": ("CB", "CG", "OD1", "ND2")}, + "ASP": {"bb": ("N", "CA", "C", "O"), "sc": ("CB", "CG", "OD1", "OD2")}, + "CYS": {"bb": ("N", "CA", "C", "O"), "sc": ("CB", "SG")}, + "GLN": {"bb": ("N", "CA", "C", "O"), "sc": ("CB", "CG", "CD", "OE1", "NE2")}, + "GLU": {"bb": ("N", "CA", "C", "O"), "sc": ("CB", "CG", "CD", "OE1", "OE2")}, + "GLY": {"bb": ("N", "CA", "C", "O"), "sc": ()}, + "HIS": { + "bb": ("N", "CA", "C", "O"), + "sc": ("CB", "CG", "ND1", "CD2", "CE1", "NE2"), + }, + "ILE": {"bb": ("N", "CA", "C", "O"), "sc": ("CB", "CG1", "CG2", "CD1")}, + "LEU": {"bb": ("N", "CA", "C", "O"), "sc": ("CB", "CG", "CD1", "CD2")}, + "LYS": {"bb": ("N", "CA", "C", "O"), "sc": ("CB", "CG", "CD", "CE", "NZ")}, + "MET": {"bb": ("N", "CA", "C", "O"), "sc": ("CB", "CG", "SD", "CE")}, + "PHE": { + "bb": ("N", "CA", "C", "O"), + "sc": ("CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ"), + }, + "PRO": {"bb": ("N", "CA", "C", "O"), "sc": ("CB", "CG", "CD")}, + "SER": {"bb": ("N", "CA", "C", "O"), "sc": ("CB", "OG")}, + "THR": {"bb": ("N", "CA", "C", "O"), "sc": ("CB", "OG1", "CG2")}, + "TRP": { + "bb": ("N", "CA", "C", "O"), + "sc": ("CB", "CG", "CD1", "CD2", "CE2", "CE3", "NE1", "CZ2", "CZ3", "CH2"), + }, + "TYR": { + "bb": ("N", "CA", "C", "O"), + "sc": ("CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "OH"), + }, + "VAL": {"bb": ("N", "CA", "C", "O"), "sc": ("CB", "CG1", "CG2")}, + "UNK": {"bb": ("N", "CA", "C", "O"), "sc": ("CB")}, + "MAS": {"bb": ("N", "CA", "C", "O"), "sc": ("CB")}, + "DA": { + "bb": ( + "O4'", + "C1'", + "C2'", + "OP1", + "P", + "OP2", + "O5'", + "C5'", + "C4'", + "C3'", + "O3'", + ), + "sc": ("N9", "C4", "N3", "C2", "N1", "C6", "C5", "N7", "C8", "N6"), + }, + "DC": { + "bb": ( + "O4'", + "C1'", + "C2'", + "OP1", + "P", + "OP2", + "O5'", + "C5'", + "C4'", + "C3'", + "O3'", + ), + "sc": ("N1", "C2", "O2", "N3", "C4", "N4", "C5", "C6"), + }, + "DG": { + "bb": ( + "O4'", + "C1'", + "C2'", + "OP1", + "P", + "OP2", + "O5'", + "C5'", + "C4'", + "C3'", + "O3'", + ), + "sc": ("N9", "C4", "N3", "C2", "N1", "C6", "C5", "N7", "C8", "N2", "O6"), + }, + "DT": { + "bb": ( + "O4'", + "C1'", + "C2'", + "OP1", + "P", + "OP2", + "O5'", + "C5'", + "C4'", + "C3'", + "O3'", + ), + "sc": ("N1", "C2", "O2", "N3", "C4", "O4", "C5", "C7", "C6"), + }, + "DX": { + "bb": ( + "O4'", + "C1'", + "C2'", + "OP1", + "P", + "OP2", + "O5'", + "C5'", + "C4'", + "C3'", + "O3'", + ), + "sc": (), + }, + "A": { + "bb": ( + "O4'", + "C1'", + "C2'", + "OP1", + "P", + "OP2", + "O5'", + "C5'", + "C4'", + "C3'", + "O3'", + "O2'", + ), + "sc": ("N1", "C2", "N3", "C4", "C5", "C6", "N6", "N7", "C8", "N9"), + }, + "C": { + "bb": ( + "O4'", + "C1'", + "C2'", + "OP1", + "P", + "OP2", + "O5'", + "C5'", + "C4'", + "C3'", + "O3'", + "O2'", + ), + "sc": ("N1", "C2", "O2", "N3", "C4", "N4", "C5", "C6"), + }, + "G": { + "bb": ( + "O4'", + "C1'", + "C2'", + "OP1", + "P", + "OP2", + "O5'", + "C5'", + "C4'", + "C3'", + "O3'", + "O2'", + ), + "sc": ("N1", "C2", "N2", "N3", "C4", "C5", "C6", "O6", "N7", "C8", "N9"), + }, + "U": { + "bb": ( + "O4'", + "C1'", + "C2'", + "OP1", + "P", + "OP2", + "O5'", + "C5'", + "C4'", + "C3'", + "O3'", + "O2'", + ), + "sc": ("N1", "C2", "O2", "N3", "C4", "O4", "C5", "C6"), + }, + "X": { + "bb": ( + "O4'", + "C1'", + "C2'", + "OP1", + "P", + "OP2", + "O5'", + "C5'", + "C4'", + "C3'", + "O3'", + "O2'", + ), + "sc": (), + }, + "HIS_D": { + "bb": ("N", "CA", "C", "O"), + "sc": ("CB", "CG", "NE2", "CD2", "CE1", "ND1"), + }, } # Known planar sidechain atoms for each canonical residue type: PLANAR_ATOMS_BY_RESI = { - 'ALA': [], - 'ARG': ['NH1', 'NH2', 'CZ', 'NE', 'CD'], - 'ASN': ['OD1', 'ND2', 'CG', 'CB'], - 'ASP': ['OD1', 'OD2', 'CG', 'CB'], - 'CYS': [], - 'GLN': ['OE1', 'NE2', 'CD', 'CG'], - 'GLU': ['OE1', 'OE2', 'CD', 'CG'], - 'GLY': [], - 'HIS': ['ND1', 'CE1', 'NE2', 'CD2', 'CG', 'CB'], - 'ILE': [], - 'LEU': [], - 'LYS': [], - 'MET': [], - 'PHE': ['CZ', 'CE1', 'CE2', 'CD1', 'CD2', 'CG', 'CB'], - 'PRO': [], - 'SER': [], - 'THR': [], - 'TRP': ['CH2', 'CZ3', 'CZ2', 'CE3', 'CE2', 'CD2', 'NE1', 'CD1', 'CG', 'CB'], - 'TYR': ['OH', 'CZ', 'CE1', 'CE2', 'CD1', 'CD2', 'CG', 'CB'], - 'VAL': [], - 'UNK': [], - 'MAS': [], - 'DA': ['N6', 'C6', 'N1', 'C2', 'N3', 'C4', 'C5', 'N7', 'C8', 'N9'], - 'DC': ['N4', 'C4', 'N3', 'O2', 'C2', 'C5', 'C6', 'N1'], - 'DG': ['O6', 'C6', 'N1', 'N2', 'C2', 'N3', 'C4', 'C5', 'N7', 'C8', 'N9'], - 'DT': ['O4', 'O2', 'N3', 'C4', 'C2', 'C5', 'C6', 'N1', 'C7'], - 'DX': [], - 'A': ['N6', 'C6', 'N1', 'C2', 'N3', 'C4', 'C5', 'N7', 'C8', 'N9'], - 'C': ['N4', 'C4', 'N3', 'O2', 'C2', 'C5', 'C6', 'N1'], - 'G': ['O6', 'C6', 'N1', 'N2', 'C2', 'N3', 'C4', 'C5', 'N7', 'C8', 'N9'], - 'U': ['O4', 'O2', 'N3', 'C4', 'C2', 'C5', 'C6', 'N1'], - 'X': [], - 'HIS_D': ['ND1', 'CD2', 'CE1', 'NE2', 'CG', 'CB'], - } + "ALA": [], + "ARG": ["NH1", "NH2", "CZ", "NE", "CD"], + "ASN": ["OD1", "ND2", "CG", "CB"], + "ASP": ["OD1", "OD2", "CG", "CB"], + "CYS": [], + "GLN": ["OE1", "NE2", "CD", "CG"], + "GLU": ["OE1", "OE2", "CD", "CG"], + "GLY": [], + "HIS": ["ND1", "CE1", "NE2", "CD2", "CG", "CB"], + "ILE": [], + "LEU": [], + "LYS": [], + "MET": [], + "PHE": ["CZ", "CE1", "CE2", "CD1", "CD2", "CG", "CB"], + "PRO": [], + "SER": [], + "THR": [], + "TRP": ["CH2", "CZ3", "CZ2", "CE3", "CE2", "CD2", "NE1", "CD1", "CG", "CB"], + "TYR": ["OH", "CZ", "CE1", "CE2", "CD1", "CD2", "CG", "CB"], + "VAL": [], + "UNK": [], + "MAS": [], + "DA": ["N6", "C6", "N1", "C2", "N3", "C4", "C5", "N7", "C8", "N9"], + "DC": ["N4", "C4", "N3", "O2", "C2", "C5", "C6", "N1"], + "DG": ["O6", "C6", "N1", "N2", "C2", "N3", "C4", "C5", "N7", "C8", "N9"], + "DT": ["O4", "O2", "N3", "C4", "C2", "C5", "C6", "N1", "C7"], + "DX": [], + "A": ["N6", "C6", "N1", "C2", "N3", "C4", "C5", "N7", "C8", "N9"], + "C": ["N4", "C4", "N3", "O2", "C2", "C5", "C6", "N1"], + "G": ["O6", "C6", "N1", "N2", "C2", "N3", "C4", "C5", "N7", "C8", "N9"], + "U": ["O4", "O2", "N3", "C4", "C2", "C5", "C6", "N1"], + "X": [], + "HIS_D": ["ND1", "CD2", "CE1", "NE2", "CG", "CB"], +} # fix C/U symmetry -temp = list(association_schemes['atom23']['U']) +temp = list(association_schemes["atom23"]["U"]) temp[19], temp[20] = temp[20], temp[19] -association_schemes['atom23']['U'] = tuple(temp) +association_schemes["atom23"]["U"] = tuple(temp) association_schemes_stripped = { name: {k: strip_list(v) for k, v in scheme.items()} @@ -553,4 +693,6 @@ } if __name__ == "__main__": - import pdb; pdb.set_trace() + import pdb + + pdb.set_trace() diff --git a/models/rfd3/src/rfd3/inference/input_parsing.py b/models/rfd3/src/rfd3/inference/input_parsing.py index f3091805..8293ca74 100644 --- a/models/rfd3/src/rfd3/inference/input_parsing.py +++ b/models/rfd3/src/rfd3/inference/input_parsing.py @@ -119,8 +119,8 @@ class DesignInputSpecification(BaseModel): validate_assignment=False, str_strip_whitespace=True, str_min_length=1, - #extra="forbid", #################################################### - extra="allow" + # extra="forbid", #################################################### + extra="allow", ## for now allowing extra for rfd3na-ss purposes, can decide later ## ) # fmt: off @@ -497,7 +497,10 @@ def apply_selections(start, end): aa.is_motif_atom_with_fixed_seq[start:end] = np.full_like( is_bkbn, False, dtype=int ) - elif aa.res_name[start] in (STANDARD_DNA + STANDARD_RNA) and self.redesign_motif_sidechains: + elif ( + aa.res_name[start] in (STANDARD_DNA + STANDARD_RNA) + and self.redesign_motif_sidechains + ): is_bkbn = np.isin(aa.atom_name[start:end], backbone_atoms_RNA) aa.is_motif_atom_with_fixed_coord[start:end] = is_bkbn.astype(int) aa.is_motif_atom_with_fixed_seq[start:end] = np.full_like( @@ -519,7 +522,9 @@ def build(self, return_metadata=False): ########## reorder NA atoms ########### if exists(atom_array_input_annotated): - is_dna = np.isin(atom_array_input_annotated.res_name, ["DA", "DC", "DG", "DT"]) + is_dna = np.isin( + atom_array_input_annotated.res_name, ["DA", "DC", "DG", "DT"] + ) is_rna = np.isin(atom_array_input_annotated.res_name, ["A", "C", "G", "U"]) dna_array = atom_array_input_annotated[is_dna] rna_array = atom_array_input_annotated[is_rna] @@ -726,11 +731,11 @@ def _append_ligand(self, atom_array, atom_array_input_annotated): ligand_array.set_annotation( annot, np.full(ligand_array.array_length(), default) ) - - chain_cand = 'X' + + chain_cand = "X" while chain_cand in atom_array.chain_id.tolist(): chain_cand = chain_cand + chain_cand - ligand_chain = np.array([chain_cand]*len(ligand_array)) + ligand_chain = np.array([chain_cand] * len(ligand_array)) ligand_array.chain_id = ligand_chain atom_array = atom_array + ligand_array @@ -758,9 +763,12 @@ def _set_origin(self, atom_array): ) else: if not exists(self.ori_jitter): - self.ori_jitter = None + self.ori_jitter = None atom_array = set_com( - atom_array, ori_token=None, infer_ori_strategy="com", ori_jitter=self.ori_jitter + atom_array, + ori_token=None, + infer_ori_strategy="com", + ori_jitter=self.ori_jitter, ) else: # Standard: set ori token, zero out diffused atoms diff --git a/models/rfd3/src/rfd3/inference/legacy_input_parsing.py b/models/rfd3/src/rfd3/inference/legacy_input_parsing.py index fbee3463..a15c4145 100644 --- a/models/rfd3/src/rfd3/inference/legacy_input_parsing.py +++ b/models/rfd3/src/rfd3/inference/legacy_input_parsing.py @@ -220,7 +220,6 @@ def fetch_motif_residue_( "Please check your input and contig specification." ) - if unfix_all or f"{src_chain}{src_resid}" in unfix_residues: subarray.set_annotation( "is_motif_atom_with_fixed_coord", np.zeros(subarray.shape[0], dtype=int) @@ -232,7 +231,7 @@ def fetch_motif_residue_( ) else: subarray.set_annotation( - "is_motif_atom_with_fixed_coord", np.array([True]*len(subarray)) + "is_motif_atom_with_fixed_coord", np.array([True] * len(subarray)) ) if flexible_backbone: @@ -255,9 +254,9 @@ def fetch_motif_residue_( subarray = subarray[subarray.is_motif_atom.astype(bool)] else: subarray.set_annotation( - "is_motif_atom_unindexed", np.array([True]*len(subarray)) + "is_motif_atom_unindexed", np.array([True] * len(subarray)) ) - + # ... Relax sequence constraint if provided if ( exists(unfixed_sequence_components) @@ -278,32 +277,35 @@ def fetch_motif_residue_( return subarray -def create_diffused_residues_(n, polymer_type='p'): +def create_diffused_residues_(n, polymer_type="p"): from rfd3.constants import ( ATOM23_ATOM_NAME_TO_ELEMENT, backbone_atoms_DNA, backbone_atoms_RNA, ) + if n <= 0: raise ValueError(f"Negative/null residue count ({n}) not allowed.") - - if polymer_type == 'P': - res_name = 'ALA' + + if polymer_type == "P": + res_name = "ALA" bb_len = 5 bb_atom_names = ["N", "CA", "C", "O", "CB"] - elif polymer_type == 'R': - res_name = 'A' + elif polymer_type == "R": + res_name = "A" bb_len = len(backbone_atoms_RNA) bb_atom_names = backbone_atoms_RNA - elif polymer_type == 'D': - res_name = 'DA' + elif polymer_type == "D": + res_name = "DA" bb_len = len(backbone_atoms_DNA) bb_atom_names = backbone_atoms_DNA else: - raise ValueError(f"invalid polymer type detected: {polymer_type}, check contig!") - + raise ValueError( + f"invalid polymer type detected: {polymer_type}, check contig!" + ) + bb_elements = [ATOM23_ATOM_NAME_TO_ELEMENT[item] for item in bb_atom_names] - + atoms = [] [ atoms.extend( @@ -319,16 +321,13 @@ def create_diffused_residues_(n, polymer_type='p'): for idx in range(1, n + 1) ] array = struc.array(atoms) - array.set_annotation( - "element", np.array(bb_elements * n, dtype=" 0.75 is_chainbreak_na = deviation_na > 1 try: - o["max_ca_deviation_protein"] = float(deviation_protein.max(-1).values.mean()) - o["fraction_chainbreaks_protein"] = float(is_chainbreak_protein.float().mean(-1).mean()) - o["n_chainbreaks_protein"] = float(is_chainbreak_protein.float().sum(-1).mean()) - except: + o["max_ca_deviation_protein"] = float( + deviation_protein.max(-1).values.mean() + ) + o["fraction_chainbreaks_protein"] = float( + is_chainbreak_protein.float().mean(-1).mean() + ) + o["n_chainbreaks_protein"] = float( + is_chainbreak_protein.float().sum(-1).mean() + ) + except Exception: print("No protein in this example, skipping protein chainbreak metrics") try: o["max_ca_deviation_na"] = float(deviation_na.max(-1).values.mean()) - o["fraction_chainbreaks_na"] = float(is_chainbreak_na.float().mean(-1).mean()) + o["fraction_chainbreaks_na"] = float( + is_chainbreak_na.float().mean(-1).mean() + ) o["n_chainbreaks_na"] = float(is_chainbreak_na.float().sum(-1).mean()) - except: + except Exception: print("No NA in this example, skipping NA chainbreak metrics") return o + class PPIMetrics(Metric): """PPI-specific metrics""" diff --git a/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py b/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py index 55bd8cb1..53c21194 100644 --- a/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py +++ b/models/rfd3/src/rfd3/metrics/nucleic_ss_metrics.py @@ -1,19 +1,19 @@ import logging -import bdb import numpy as np -from biotite.structure import AtomArray from atomworks.ml.utils.token import ( get_token_starts, ) - +from biotite.structure import AtomArray +from rfd3.trainer.trainer_utils import ( + _cleanup_virtual_atoms_and_assign_atom_name_elements, + _readout_seq_from_struc, +) from rfd3.transforms.na_geom_utils import annotate_na_ss from foundry.metrics.metric import Metric from foundry.utils.ddp import RankedLogger -from rfd3.trainer.trainer_utils import _readout_seq_from_struc, _cleanup_virtual_atoms_and_assign_atom_name_elements - logging.basicConfig(level=logging.INFO) global_logger = RankedLogger(__name__, rank_zero_only=False) @@ -70,7 +70,9 @@ def _get_candidate_token_ids( if hasattr(token_level_array, "is_dna") else np.zeros(len(token_ids), dtype=bool) ) - token_mask &= (is_rna | is_dna) if (is_rna.any() or is_dna.any()) else token_mask + token_mask &= ( + (is_rna | is_dna) if (is_rna.any() or is_dna.any()) else token_mask + ) if compute_for_diffused_region_only: if hasattr(token_level_array, "is_motif_atom"): @@ -175,7 +177,10 @@ def _extract_loop_and_paired_token_ids( return loop_token_ids, paired_token_ids -def compute_from_two_arr(gt_arr, pred_arr, restrict_to_nucleic=True, compute_for_diffused_region_only = False): + +def compute_from_two_arr( + gt_arr, pred_arr, restrict_to_nucleic=True, compute_for_diffused_region_only=False +): gt_token_ids = _get_token_ids(gt_arr) pred_token_ids = _get_token_ids(pred_arr) if len(gt_token_ids) != len(pred_token_ids): @@ -228,26 +233,25 @@ def compute_from_two_arr(gt_arr, pred_arr, restrict_to_nucleic=True, compute_for (pair_weight * pair_f1 + loop_weight * loop_f1) / total_weight ) - return pair_f1, loop_f1, weighted_f1 + def get_NA_SS_F1(pred_array): - ## save the original bop_partner annotation gt_array = pred_array.copy() ## replace by annotating again pred_array = annotate_na_ss( - pred_array, - NA_only=True, - planar_only=True, - overwrite=True, - p_canonical_bp_filter=0.0, - ) - + pred_array, + NA_only=True, + planar_only=True, + overwrite=True, + p_canonical_bp_filter=0.0, + ) + try: pair_f1, loop_f1, weighted_f1 = compute_from_two_arr(gt_array, pred_array) - except: + except Exception: # fails when returns None because expects three returns return {} @@ -261,13 +265,13 @@ def get_NA_SS_F1(pred_array): class NucleicSSSimilarityMetrics(Metric): """Secondary-structure similarity for nucleic acids. - Reports: - - `pair_f1`: F1 over basepair edges from token-level bp-partner annotation. - - `loop_f1`: F1 over explicitly-unpaired loop tokens (`bp_partners == []`). - Unannotated tokens (`bp_partners is None`) are masked. - - `weighted_f1`: GT-weighted average of `pair_f1` and `loop_f1`, weighted by - the prevalence of paired vs loop tokens in the GT. - """ + Reports: + - `pair_f1`: F1 over basepair edges from token-level bp-partner annotation. + - `loop_f1`: F1 over explicitly-unpaired loop tokens (`bp_partners == []`). + Unannotated tokens (`bp_partners is None`) are masked. + - `weighted_f1`: GT-weighted average of `pair_f1` and `loop_f1`, weighted by + the prevalence of paired vs loop tokens in the GT. + """ def __init__( self, @@ -302,7 +306,9 @@ def compute(self, *, ground_truth_atom_array_stack, predicted_atom_array_stack): n_valid = 0 - for gt_arr, pred_arr in zip(ground_truth_atom_array_stack, predicted_atom_array_stack): + for gt_arr, pred_arr in zip( + ground_truth_atom_array_stack, predicted_atom_array_stack + ): gt_categories = gt_arr.get_annotation_categories() if "bp_partners" not in gt_categories: continue @@ -326,14 +332,14 @@ def compute(self, *, ground_truth_atom_array_stack, predicted_atom_array_stack): pred_arr, association_scheme="atom23", ) - except: + except Exception: # this can fail early in training print("could not cleanup virtuals for nucleic ss metric compute") pass # clear annotation to avoid potential info leak if "bp_partners" in pred_arr.get_annotation_categories(): pred_arr.del_annotation("bp_partners") - + # add nucleic-ss annotations annotate_na_ss( pred_arr, @@ -348,8 +354,13 @@ def compute(self, *, ground_truth_atom_array_stack, predicted_atom_array_stack): # Basic sanity check: token counts should match for aligned comparisons try: - pair_f1, loop_f1, weighted_f1 = compute_from_two_arr(gt_arr, pred_arr, restrict_to_nucleic=self.restrict_to_nucleic, compute_for_diffused_region_only = self.compute_for_diffused_region_only) - except: + pair_f1, loop_f1, weighted_f1 = compute_from_two_arr( + gt_arr, + pred_arr, + restrict_to_nucleic=self.restrict_to_nucleic, + compute_for_diffused_region_only=self.compute_for_diffused_region_only, + ) + except Exception: # fails when returns None because expects three returns continue @@ -360,7 +371,7 @@ def compute(self, *, ground_truth_atom_array_stack, predicted_atom_array_stack): if n_valid == 0: return {} - + return { "pair_f1": float(np.mean(pair_f1_list)), "loop_f1": float(np.mean(loop_f1_list)), diff --git a/models/rfd3/src/rfd3/metrics/rna_aptamer_metrics.py b/models/rfd3/src/rfd3/metrics/rna_aptamer_metrics.py index c4071ca3..bf1c7a36 100644 --- a/models/rfd3/src/rfd3/metrics/rna_aptamer_metrics.py +++ b/models/rfd3/src/rfd3/metrics/rna_aptamer_metrics.py @@ -1,5 +1,6 @@ import logging +import numpy as np from foundry.metrics.metric import Metric from foundry.utils.ddp import RankedLogger @@ -8,9 +9,6 @@ global_logger = RankedLogger(__name__, rank_zero_only=False) -import numpy as np - - def calculate_ligand_contacts( atom_array_stack, cutoff_distance=4.0, @@ -31,13 +29,12 @@ def calculate_ligand_contacts( mean_contacts_per_model : float """ - cutoff_sq = cutoff_distance ** 2 + cutoff_sq = cutoff_distance**2 contacts_per_model = [] n_models = len(atom_array_stack) for i in range(n_models): - atoms = atom_array_stack[i] coords = atoms.coord @@ -57,7 +54,7 @@ def calculate_ligand_contacts( # Pairwise squared distances diff = non_ligand_coords[:, None, :] - ligand_coords[None, :, :] - dist_sq = np.sum(diff ** 2, axis=-1) + dist_sq = np.sum(diff**2, axis=-1) # Any ligand within cutoff contact_mask = np.any(dist_sq < cutoff_sq, axis=1) @@ -67,7 +64,11 @@ def calculate_ligand_contacts( contacts_per_model = np.array(contacts_per_model) - return int(np.sum(contacts_per_model)), float(np.mean(contacts_per_model)), float(np.mean(contacts_per_model))/hetero_mask.sum() + return ( + int(np.sum(contacts_per_model)), + float(np.mean(contacts_per_model)), + float(np.mean(contacts_per_model)) / hetero_mask.sum(), + ) class LigandContactMetrics(Metric): @@ -89,12 +90,18 @@ def kwargs_to_compute_args(self): def compute(self, *, predicted_atom_array_stack): if self.restrict_to_nucleic: - if (predicted_atom_array_stack[0].is_rna.sum() + predicted_atom_array_stack[0].is_dna.sum()== 0): + if ( + predicted_atom_array_stack[0].is_rna.sum() + + predicted_atom_array_stack[0].is_dna.sum() + == 0 + ): return {} try: - total_contacts, mean_contacts, mean_contacts_per_atom = calculate_ligand_contacts( - atom_array_stack=predicted_atom_array_stack, - cutoff_distance=self.cutoff_distance, + total_contacts, mean_contacts, mean_contacts_per_atom = ( + calculate_ligand_contacts( + atom_array_stack=predicted_atom_array_stack, + cutoff_distance=self.cutoff_distance, + ) ) except Exception as e: global_logger.error( @@ -106,4 +113,3 @@ def compute(self, *, predicted_atom_array_stack): "mean_ligand_contacts_per_model": float(mean_contacts), "mean_ligand_contacts_per_atom": float(mean_contacts_per_atom), } - diff --git a/models/rfd3/src/rfd3/model/cfg_utils.py b/models/rfd3/src/rfd3/model/cfg_utils.py index f84ce453..2b9f860d 100644 --- a/models/rfd3/src/rfd3/model/cfg_utils.py +++ b/models/rfd3/src/rfd3/model/cfg_utils.py @@ -63,8 +63,8 @@ def strip_f( ) else: ## for bp_partners default is a mask feature - v_cropped[:,:,0] = 1 - v_cropped[:,:,1:] = 0 + v_cropped[:, :, 0] = 1 + v_cropped[:, :, 1:] = 0 # update the feature in the dictionary f_stripped[k] = v_cropped diff --git a/models/rfd3/src/rfd3/model/layers/block_utils.py b/models/rfd3/src/rfd3/model/layers/block_utils.py index 67621a4f..3dfa0d56 100644 --- a/models/rfd3/src/rfd3/model/layers/block_utils.py +++ b/models/rfd3/src/rfd3/model/layers/block_utils.py @@ -210,7 +210,9 @@ def create_attention_indices( chain_ids is not None and len(torch.unique(chain_ids)) > 3 ): # Multi-chain structure # Reserve 25% of attention keys for inter-chain interactions - k_inter_chain = min(max(32, k_actual // 4), k_actual) # At least 32 inter-chain keys + k_inter_chain = min( + max(32, k_actual // 4), k_actual + ) # At least 32 inter-chain keys k_intra_chain = k_actual - k_inter_chain attn_indices = get_sparse_attention_indices_with_inter_chain( diff --git a/models/rfd3/src/rfd3/model/layers/blocks.py b/models/rfd3/src/rfd3/model/layers/blocks.py index f5cfe34f..3290b9e0 100644 --- a/models/rfd3/src/rfd3/model/layers/blocks.py +++ b/models/rfd3/src/rfd3/model/layers/blocks.py @@ -143,6 +143,7 @@ def forward(self, f, collapse_length): ) ) + class TwoDFeatureEmbedder(nn.Module): """ Embeds 2D features into a single vector. @@ -164,18 +165,22 @@ def __init__(self, features, output_channels): for feature, n_channels in self.features.items() } ) + def collapse2D(self, x, L): return x.reshape((L, L, x.numel() // (L * L))) def forward(self, f, collapse_length): return sum( tuple( - self.embedders[feature](self.collapse2D(f[feature].float(), collapse_length)) + self.embedders[feature]( + self.collapse2D(f[feature].float(), collapse_length) + ) for feature, n_channels in self.features.items() if exists(n_channels) ) ) + class SinusoidalDistEmbed(nn.Module): """ Applies sinusoidal embedding to pairwise distances and projects to c_atompair. diff --git a/models/rfd3/src/rfd3/model/layers/encoders.py b/models/rfd3/src/rfd3/model/layers/encoders.py index a3c37cf6..fc19a95c 100644 --- a/models/rfd3/src/rfd3/model/layers/encoders.py +++ b/models/rfd3/src/rfd3/model/layers/encoders.py @@ -11,10 +11,10 @@ Downcast, LocalAtomTransformer, OneDFeatureEmbedder, - TwoDFeatureEmbedder, PositionPairDistEmbedder, RelativePositionEncodingWithIndexRemoval, SinusoidalDistEmbed, + TwoDFeatureEmbedder, ) from rfd3.model.layers.chunked_pairwise import ( ChunkedPairwiseEmbedder, @@ -64,7 +64,7 @@ def __init__( self.atom_1d_embedder_1 = OneDFeatureEmbedder(atom_1d_features, c_s) self.atom_1d_embedder_2 = OneDFeatureEmbedder(atom_1d_features, c_atom) self.token_1d_embedder = OneDFeatureEmbedder(token_1d_features, c_s) - if token_2d_features != None: + if token_2d_features is not None: self.token_2d_embedder = TwoDFeatureEmbedder(token_2d_features, c_z) else: self.token_2d_embedder = None @@ -209,7 +209,7 @@ def init_tokens(): f["ref_pos"][f["is_ca"]], valid_mask ) # Add extra token pair features - if self.token_2d_embedder != None: + if self.token_2d_embedder is not None: Z_init_II = Z_init_II + self.token_2d_embedder(f, I) # Run a small transformer to provide position encodings to single. diff --git a/models/rfd3/src/rfd3/trainer/rfd3.py b/models/rfd3/src/rfd3/trainer/rfd3.py index 6701f8d6..93a8f18c 100644 --- a/models/rfd3/src/rfd3/trainer/rfd3.py +++ b/models/rfd3/src/rfd3/trainer/rfd3.py @@ -450,15 +450,12 @@ def _build_predicted_atom_array_stack( ): metadata_dict[i]["metrics"] |= get_hbond_metrics(atom_array) - if ( - "bp_partners" in atom_array.get_annotation_categories() - ): - if not np.all(atom_array.bp_partners == None): + if "bp_partners" in atom_array.get_annotation_categories(): + if not np.all(atom_array.bp_partners == None): # noqa: E711 try: metadata_dict[i]["metrics"] |= get_NA_SS_F1(atom_array) - except: + except Exception: pass - if "partial_t" in f: # Try calcualte a CA RMSD to input: aa_in = example["atom_array"] diff --git a/models/rfd3/src/rfd3/trainer/trainer_utils.py b/models/rfd3/src/rfd3/trainer/trainer_utils.py index 24b05071..a2cd07c9 100644 --- a/models/rfd3/src/rfd3/trainer/trainer_utils.py +++ b/models/rfd3/src/rfd3/trainer/trainer_utils.py @@ -2,6 +2,7 @@ import numpy as np import torch +from atomworks.constants import STANDARD_DNA, STANDARD_RNA from atomworks.ml.encoding_definitions import AF3SequenceEncoding from atomworks.ml.utils.token import ( get_token_starts, @@ -13,10 +14,10 @@ ATOM14_ATOM_NAMES, ATOM23_ATOM_NAMES_DNA, ATOM23_ATOM_NAMES_RNA, - backbone_atoms_RNA, VIRTUAL_ATOM_ELEMENT_NAME, association_schemes, association_schemes_stripped, + backbone_atoms_RNA, ) from rfd3.utils.io import ( build_stack_from_atom_array_and_batched_coords, @@ -25,7 +26,6 @@ from foundry.common import exists from foundry.utils.ddp import RankedLogger -from atomworks.constants import STANDARD_DNA, STANDARD_RNA global_logger = RankedLogger(__name__, rank_zero_only=False) @@ -221,7 +221,7 @@ def _readout_seq_from_struc( # There might be a better way to do this. CA_coord = cur_res_atom_array.coord[cur_res_atom_array.atom_name == "CA"] CB_coord = cur_res_atom_array.coord[cur_res_atom_array.atom_name == "CB"] - + if cur_res_atom_array.is_dna[0] or cur_res_atom_array.is_rna[0]: cur_central_atom = "C1'" elif np.linalg.norm(CA_coord - CB_coord) < threshold: @@ -229,7 +229,6 @@ def _readout_seq_from_struc( else: cur_central_atom = central_atom - central_mask = cur_res_atom_array.atom_name == cur_central_atom # ... Calculate the distance to the central atom @@ -269,7 +268,7 @@ def _readout_seq_from_struc( if not cur_res_atom_array.is_rna[0]: continue else: - #ATOM_NAMES = ATOM23_ATOM_NAMES_RNA + # ATOM_NAMES = ATOM23_ATOM_NAMES_RNA if not cur_res_atom_array.is_protein[0]: continue @@ -425,9 +424,11 @@ def process_unindexed_outputs( try: assert (res_id_ == res_id) & (chain_id_ == chain_id) - except: - global_logger.warning("Unindexed mapping did not work properly, res_id, chain_id") - + except Exception: + global_logger.warning( + "Unindexed mapping did not work properly, res_id, chain_id" + ) + inserted_mask = np.logical_or(inserted_mask, token_match) # ... Compute metrics based on the new distances @@ -456,11 +457,7 @@ def process_unindexed_outputs( else: dist = float(dists[row_ind[join_atom], col_ind[join_atom]]) - elif not np.any( - np.isin( - token.atom_name, backbone_atoms_RNA - ) - ): + elif not np.any(np.isin(token.atom_name, backbone_atoms_RNA)): if np.sum(token.atomize) == 1: join_atom = np.where(token.atomize)[0][0] elif "C1'" in token.atom_name: @@ -474,10 +471,10 @@ def process_unindexed_outputs( ) else: dist = float(dists[row_ind[join_atom], col_ind[join_atom]]) - + try: metadata["join_point_rmsd_by_token"][token_pdb_id] = dist - except: + except Exception: pass metadata["diffused_index_map"][token_pdb_id] = f"{chain_id}{res_id}" diff --git a/models/rfd3/src/rfd3/transforms/conditioning_base.py b/models/rfd3/src/rfd3/transforms/conditioning_base.py index 65514c52..682005bb 100644 --- a/models/rfd3/src/rfd3/transforms/conditioning_base.py +++ b/models/rfd3/src/rfd3/transforms/conditioning_base.py @@ -243,7 +243,7 @@ def __init__( ) self.meta_conditioning_probabilities = meta_conditioning_probabilities self.train_conditions = train_conditions - + for item in self.train_conditions: self.train_conditions[item].association_scheme = association_scheme @@ -265,15 +265,15 @@ def check_input(self, data: dict): assert "conditions" in data, "Conditioning dict not initialized" def forward(self, data): - #for item in self.train_conditions: + # for item in self.train_conditions: # print(self.train_conditions[item].is_valid_for_example(data)) valid_conditions = [ cond for cond in self.train_conditions.values() - if cond.is_valid_for_example(data) and cond.frequency > 0 + if cond.is_valid_for_example(data) and cond.frequency > 0 ] - + if len(valid_conditions) == 0: raise InvalidSampledConditionException("No valid condition was found.") @@ -288,7 +288,7 @@ def forward(self, data): cond = valid_conditions[i_cond] cond.association_scheme = self.association_scheme - + data["sampled_condition"] = cond data["sampled_condition_name"] = cond.name data["sampled_condition_cls"] = cond.__class__ @@ -306,11 +306,6 @@ class SampleConditioningFlags(Transform): "AssignTypes", "SampleConditioningType", ] # We use is_protein in the PPI training condition - def __init__(self, association_scheme): - self.association_scheme = association_scheme - - def __init__(self, association_scheme): - self.association_scheme = association_scheme def __init__(self, association_scheme): self.association_scheme = association_scheme diff --git a/models/rfd3/src/rfd3/transforms/design_transforms.py b/models/rfd3/src/rfd3/transforms/design_transforms.py index aeadcc2b..e7d2d4cb 100644 --- a/models/rfd3/src/rfd3/transforms/design_transforms.py +++ b/models/rfd3/src/rfd3/transforms/design_transforms.py @@ -38,6 +38,7 @@ UnindexFlaggedTokens, get_motif_features, ) +from rfd3.transforms.na_geom import na_ss_feats_from_annotation from rfd3.transforms.rasa import discretize_rasa from rfd3.transforms.util_transforms import ( AssignTypes, @@ -45,7 +46,6 @@ get_af3_token_representative_masks, ) from rfd3.transforms.virtual_atoms import PadTokensWithVirtualAtoms -from rfd3.transforms.na_geom import na_ss_feats_from_annotation from foundry.utils.ddp import RankedLogger # noqa @@ -71,7 +71,7 @@ class SubsampleToTypes(Transform): def __init__( self, allowed_types: list | str = ["is_protein"], - association_scheme: str = 'atom14' + association_scheme: str = "atom14", ): self.allowed_types = allowed_types self.association_scheme = association_scheme @@ -106,7 +106,7 @@ def forward(self, data): ) ) - if self.association_scheme != 'atom23' and atom_array.is_protein.sum() == 0: + if self.association_scheme != "atom23" and atom_array.is_protein.sum() == 0: raise ValueError( "No protein atoms found in the atom array. Example ID: {}".format( data.get("example_id", "unknown") @@ -757,7 +757,7 @@ def forward(self, data: Dict[str, Any]) -> Dict[str, Any]: """ if "feats" not in data.keys(): data["feats"] = {} - + if self.association_scheme == "atom23": data["atom_array"].set_annotation( "is_protein_token", data["atom_array"].is_protein @@ -772,7 +772,6 @@ def forward(self, data: Dict[str, Any]) -> Dict[str, Any]: data = self.generate_feature(feature_name, n_dims, data, "atom") return data - class AddAdditional2dFeaturesToFeats(Transform): @@ -791,15 +790,15 @@ def __init__( token_2d_features, autofill_zeros_if_not_present_in_atomarray=False, association_scheme="atom14", - ): + ): self.autofill = autofill_zeros_if_not_present_in_atomarray self.token_2d_features = token_2d_features self.association_scheme = association_scheme - # Need to pre-define custom constructor functions + # Need to pre-define custom constructor functions # to map from atomarray annotations to tensors. self.constructor_functions = { - 'bp_partners': na_ss_feats_from_annotation, + "bp_partners": na_ss_feats_from_annotation, } def check_input(self, data) -> None: @@ -807,11 +806,10 @@ def check_input(self, data) -> None: check_is_instance(data, "atom_array", AtomArray) def generate_token_feature(self, feature_name, n_dims, data): - # Don't do this if we already have the feature if feature_name in data["feats"].keys(): return data - + # For these, we need to use a constructor function mapping, # since pair features may require custom logic/conventions. @@ -822,11 +820,11 @@ def generate_token_feature(self, feature_name, n_dims, data): raise ValueError( f"No constructor function found for 2d feature `{feature_name}`" ) - + # We can fix shape issues here: if len(feature_array.shape) == 2 and n_dims == 1: feature_array = feature_array.unsqueeze(1) - + # ensure that feature_array is a 3d array with third dim == n_dims: if len(feature_array.shape) != 3: raise ValueError( @@ -854,7 +852,7 @@ def forward(self, data: Dict[str, Any]) -> Dict[str, Any]: if "feats" not in data.keys(): data["feats"] = {} # Only apply for features that the model is expecting: - if self.token_2d_features == None: + if self.token_2d_features is None: return data for feature_name, n_dims in self.token_2d_features.items(): data = self.generate_token_feature(feature_name, n_dims, data) diff --git a/models/rfd3/src/rfd3/transforms/hbonds_hbplus.py b/models/rfd3/src/rfd3/transforms/hbonds_hbplus.py index f49dcdb8..58e855cf 100644 --- a/models/rfd3/src/rfd3/transforms/hbonds_hbplus.py +++ b/models/rfd3/src/rfd3/transforms/hbonds_hbplus.py @@ -248,5 +248,3 @@ def subsample_one_hot_np(array, fraction): new_array[i, j] = 1 return new_array - - diff --git a/models/rfd3/src/rfd3/transforms/na_geom.py b/models/rfd3/src/rfd3/transforms/na_geom.py index ba45c7f4..ee264f2b 100644 --- a/models/rfd3/src/rfd3/transforms/na_geom.py +++ b/models/rfd3/src/rfd3/transforms/na_geom.py @@ -1,48 +1,49 @@ from typing import Any + import numpy as np -from functools import partial -from biotite.structure import AtomArray from atomworks.ml.transforms._checks import ( check_atom_array_annotation, check_contains_keys, check_is_instance, ) from atomworks.ml.transforms.base import Transform +from atomworks.ml.utils.token import get_token_starts, spread_token_wise +from biotite.structure import AtomArray from rfd3.transforms.conditioning_utils import sample_island_tokens from rfd3.transforms.na_geom_utils import ( - annotate_na_ss, - annotate_na_ss_from_data_specification, DEFAULT_NA_SS_FEATURE_INFO, + annotate_na_ss, + annotate_na_ss_from_data_specification, ) -from atomworks.ml.utils.token import spread_token_wise, get_token_starts -def na_ss_feats_from_annotation(atom_array: AtomArray, - token_starts= None, - n_tokens = None, - return_as_onehot = True, - ) -> np.ndarray: +def na_ss_feats_from_annotation( + atom_array: AtomArray, + token_starts=None, + n_tokens=None, + return_as_onehot=True, +) -> np.ndarray: """ Takes in atom array and constucts a base pair feature matrix from annotations, according to to custom feature constuction + masking system. - This featurization utilizes info from BasePairEnum to assign int values + This featurization utilizes info from BasePairEnum to assign int values to paired, unpaired, and masked positions in the matrix. Args: * atom_array: AtomArray with bp_partners annotation at atom level * token_starts (optional): indices of token starts in the atom array * n_tokens (optional): number of tokens (length of token_starts) - * return_as_onehot (optional): if False, return integer-encoded + * return_as_onehot (optional): if False, return integer-encoded matrix instead of one-hot encoded matrix returns: * na_ss_matrix: If ``return_as_onehot`` is True (default): - np.ndarray of shape (n_tokens, n_tokens, n_classes) + np.ndarray of shape (n_tokens, n_tokens, n_classes) with one-hot encoded values according to BasePairEnum If ``return_as_onehot`` is False : - np.ndarray of shape (n_tokens, n_tokens) + np.ndarray of shape (n_tokens, n_tokens) with int values according to BasePairEnum @@ -51,13 +52,16 @@ def na_ss_feats_from_annotation(atom_array: AtomArray, if (token_starts is None) or (n_tokens is None): token_starts = get_token_starts(atom_array) n_tokens = len(token_starts) - # Collect token inds for paired or loop positions: pair_inds = [] loop_inds = [] - token_bp_partners = atom_array.get_annotation("bp_partners")[token_starts] # get bp_partners at token level - assert len(token_bp_partners) == n_tokens, "Length of token_bp_partners should match n_tokens" + token_bp_partners = atom_array.get_annotation("bp_partners")[ + token_starts + ] # get bp_partners at token level + assert ( + len(token_bp_partners) == n_tokens + ), "Length of token_bp_partners should match n_tokens" for i, j_list in enumerate(token_bp_partners): if j_list is not None: if len(j_list) > 0: @@ -68,46 +72,54 @@ def na_ss_feats_from_annotation(atom_array: AtomArray, # The standard system for constructing meaningful base pair features: # 0). Initialize with values of UNSPECIFIED (0): int matrix of shape (n_tokens, n_tokens) - na_ss_matrix = np.full((n_tokens, n_tokens), DEFAULT_NA_SS_FEATURE_INFO["NA_SS_MASK"], dtype=np.int64) + na_ss_matrix = np.full( + (n_tokens, n_tokens), DEFAULT_NA_SS_FEATURE_INFO["NA_SS_MASK"], dtype=np.int64 + ) # 1). Fill in with values of PAIR (1) at positions that have bp_partners annotated as a non-empty list for pair_i, pair_j in pair_inds: na_ss_matrix[pair_i, pair_j] = DEFAULT_NA_SS_FEATURE_INFO["NA_SS_PAIR"] - na_ss_matrix[pair_j, pair_i] = DEFAULT_NA_SS_FEATURE_INFO["NA_SS_PAIR"] # ensure symmetry + na_ss_matrix[pair_j, pair_i] = DEFAULT_NA_SS_FEATURE_INFO[ + "NA_SS_PAIR" + ] # ensure symmetry # 2). Fill in with values of LOOP (2) at positions that have bp_partners annotated as an empty list (explicitly unpaired) # (we make full stripes across that position's row/col to indicate that NONE of those other positions are paired ) for loop_i in loop_inds: na_ss_matrix[loop_i, :] = DEFAULT_NA_SS_FEATURE_INFO["NA_SS_LOOP"] - na_ss_matrix[:, loop_i] = DEFAULT_NA_SS_FEATURE_INFO["NA_SS_LOOP"] # ensure symmetry - + na_ss_matrix[:, loop_i] = DEFAULT_NA_SS_FEATURE_INFO[ + "NA_SS_LOOP" + ] # ensure symmetry + # Optional: convert NA-SS matrix to one-hot encoding according for model input: if return_as_onehot: - na_ss_matrix = np.eye(len(DEFAULT_NA_SS_FEATURE_INFO), dtype=np.int64)[na_ss_matrix] - + na_ss_matrix = np.eye(len(DEFAULT_NA_SS_FEATURE_INFO), dtype=np.int64)[ + na_ss_matrix + ] + return na_ss_matrix class CalculateNucleicAcidGeomFeats(Transform): """ - Transform for constructing nucleic-acid conditioning features. + Transform for constructing nucleic-acid conditioning features. - This transform currently produces only nucleic-acid secondary-structure (NA-SS) - features as a 2D token-token matrix with 3 bins: - * 0: mask / unspecified - * 1: paired - * 2: loop / explicitly unpaired + This transform currently produces only nucleic-acid secondary-structure (NA-SS) + features as a 2D token-token matrix with 3 bins: + * 0: mask / unspecified + * 1: paired + * 2: loop / explicitly unpaired - Training: - - Computes geometry/H-bond-based base pairs and writes them onto the AtomArray - via the ``bp_partners`` annotation (annotation-first), then reconstructs the - matrix (and optionally masks parts of it) before one-hot encoding. + Training: + - Computes geometry/H-bond-based base pairs and writes them onto the AtomArray + via the ``bp_partners`` annotation (annotation-first), then reconstructs the + matrix (and optionally masks parts of it) before one-hot encoding. - Inference: - - Interprets user-provided secondary-structure specifications, writes the same - ``bp_partners`` annotation, then follows the same matrix + one-hot path. + Inference: + - Interprets user-provided secondary-structure specifications, writes the same + ``bp_partners`` annotation, then follows the same matrix + one-hot path. - Note: helical-parameter features are not implemented/used in this refactored path. + Note: helical-parameter features are not implemented/used in this refactored path. """ def __init__( @@ -115,43 +127,46 @@ def __init__( is_inference, # Conditional sampling parameters all stored in this dict: meta_conditioning_probabilities: dict[str, float] = None, - # Mask control paramerers: nucleic_ss_min_shown: float = 0.2, nucleic_ss_max_shown: float = 1.0, n_islands_min: int = 1, n_islands_max: int = 6, - # USE_RF2AA_NAMES: bool = False, NA_only: bool = False, - planar_only : bool = True, - + planar_only: bool = True, ): # Critical, must always have to know how to handle - self.is_inference = is_inference - + self.is_inference = is_inference + self.meta_conditioning_probabilities = meta_conditioning_probabilities or {} - + # Control whether we show some nucleic SS or default to full 2D mask - self.p_is_nucleic_ss_example = self.meta_conditioning_probabilities.get("p_is_nucleic_ss_example", 0.0) + self.p_is_nucleic_ss_example = self.meta_conditioning_probabilities.get( + "p_is_nucleic_ss_example", 0.0 + ) # Control whether we define full SS or just part of it (only applies if is NA SS example) - self.p_show_partial_feats = self.meta_conditioning_probabilities.get("p_nucleic_ss_show_partial_feats", 0.0) + self.p_show_partial_feats = self.meta_conditioning_probabilities.get( + "p_nucleic_ss_show_partial_feats", 0.0 + ) # Some frac of time default to only showing canonical base pairs - self.p_canonical_bp_filter = self.meta_conditioning_probabilities.get("p_canonical_bp_filter", 0.5) - + self.p_canonical_bp_filter = self.meta_conditioning_probabilities.get( + "p_canonical_bp_filter", 0.5 + ) + # mask patterning control to make things resemble design scenarios - self.nucleic_ss_min_shown = nucleic_ss_min_shown - self.nucleic_ss_max_shown = nucleic_ss_max_shown - self.n_islands_min = n_islands_min - self.n_islands_max = n_islands_max + self.nucleic_ss_min_shown = nucleic_ss_min_shown + self.nucleic_ss_max_shown = nucleic_ss_max_shown + self.n_islands_min = n_islands_min + self.n_islands_max = n_islands_max # Filters for what can be considered a planar contact interaction - self.NA_only = NA_only # only annotate base-like interactions for nucleic acid residues - self.planar_only = planar_only # only consider planar atoms in sidechains for geometry calculations, - - + self.NA_only = ( + NA_only # only annotate base-like interactions for nucleic acid residues + ) + self.planar_only = planar_only # only consider planar atoms in sidechains for geometry calculations, def check_input(self, data: dict[str, Any]) -> None: check_contains_keys(data, ["atom_array"]) @@ -162,9 +177,7 @@ def check_input(self, data: dict[str, Any]) -> None: def _sample_training_flags(self) -> tuple[bool, bool]: """Sample booleans controlling whether/how features are shown in training.""" is_nucleic_ss_example = bool(np.random.rand() < self.p_is_nucleic_ss_example) - give_partial_feats = bool( - np.random.rand() < self.p_show_partial_feats - ) + give_partial_feats = bool(np.random.rand() < self.p_show_partial_feats) return is_nucleic_ss_example, give_partial_feats def forward(self, data: dict) -> dict: @@ -177,24 +190,25 @@ def forward(self, data: dict) -> dict: # Handle the training case with ground truth and masking if not self.is_inference: - # First, annotate as usual is_nucleic_ss_example, give_partial_feats = self._sample_training_flags() if is_nucleic_ss_example: - - atom_array = annotate_na_ss(atom_array, - NA_only=self.NA_only, - planar_only=self.planar_only, - p_canonical_bp_filter=self.p_canonical_bp_filter, - ) + atom_array = annotate_na_ss( + atom_array, + NA_only=self.NA_only, + planar_only=self.planar_only, + p_canonical_bp_filter=self.p_canonical_bp_filter, + ) # Generate symmetric partner annotations at the token level for masking purposes. # choice for object-consistency: if already masked/undefined: be a list mapping to self-index. partner_sym_map = { - i: atom_array.bp_partners[ts_i] if atom_array.bp_partners[ts_i] is not None else [i] + i: atom_array.bp_partners[ts_i] + if atom_array.bp_partners[ts_i] is not None + else [i] for i, ts_i in enumerate(token_starts) - } + } # # Sample mask on token level: token_mask_to_show = self._sample_where_to_show_ss( @@ -206,17 +220,19 @@ def forward(self, data: dict) -> dict: # Spread mask to atom level is_ss_shown = spread_token_wise(atom_array, token_mask_to_show) - + # Extract the base pair annotations bp_partners_atom = atom_array.get_annotation("bp_partners") # Remove unshown positions from bp_partners annotation bp_partners_atom[~is_ss_shown] = None - + # Reset the annotation with newly hidden positions atom_array.set_annotation("bp_partners", bp_partners_atom) else: - atom_array.set_annotation("bp_partners", np.array([None]*len(atom_array))) + atom_array.set_annotation( + "bp_partners", np.array([None] * len(atom_array)) + ) # Inference case: create from commandline args else: @@ -239,25 +255,26 @@ def forward(self, data: dict) -> dict: log_dict = data["log_dict"] data["log_dict"] = log_dict data["atom_array"] = atom_array - - return data + return data - def _sample_where_to_show_ss(self, n_tokens: int, - is_nucleic_ss_example: bool = True, - give_partial_feats: bool = True, - partner_sym_map: dict[int, list[int]] = None, - ) -> np.ndarray: + def _sample_where_to_show_ss( + self, + n_tokens: int, + is_nucleic_ss_example: bool = True, + give_partial_feats: bool = True, + partner_sym_map: dict[int, list[int]] = None, + ) -> np.ndarray: """Sample token-level islands indicating which SS rows/cols to reveal. - This custom function allows for enforcing symmetry in the shown features according - to the partner_sym_map, which encodes which tokens are partners in the SS - matrix and thus should be masked/unmasked together to maintain consistency. - + This custom function allows for enforcing symmetry in the shown features according + to the partner_sym_map, which encodes which tokens are partners in the SS + matrix and thus should be masked/unmasked together to maintain consistency. + """ # If NOT is_nucleic_ss_example, set is_shown to all False if not is_nucleic_ss_example: token_mask_to_show = np.zeros((n_tokens,), dtype=bool) - + # If NOT give_partial_feats, set is_shown to all True if not give_partial_feats: token_mask_to_show = np.ones((n_tokens,), dtype=bool) @@ -265,14 +282,19 @@ def _sample_where_to_show_ss(self, n_tokens: int, # Get numerical parameters for that govern the mask pattern frac_shown = ( self.nucleic_ss_min_shown - + (self.nucleic_ss_max_shown - self.nucleic_ss_min_shown) * np.random.rand() + + (self.nucleic_ss_max_shown - self.nucleic_ss_min_shown) + * np.random.rand() ) frac_shown = float(np.clip(frac_shown, 0.0, 1.0)) max_length = int(np.ceil(frac_shown * n_tokens)) if max_length <= 0: token_mask_to_show = np.zeros((n_tokens,), dtype=bool) - island_len_min = max(1, int(frac_shown * n_tokens // max(int(self.n_islands_max), 1))) - island_len_max = max(1, int(frac_shown * n_tokens // max(int(self.n_islands_min), 1))) + island_len_min = max( + 1, int(frac_shown * n_tokens // max(int(self.n_islands_max), 1)) + ) + island_len_max = max( + 1, int(frac_shown * n_tokens // max(int(self.n_islands_min), 1)) + ) island_len_min = min(island_len_min, n_tokens) island_len_max = min(island_len_max, n_tokens) island_len_max = max(island_len_max, island_len_min) @@ -287,7 +309,7 @@ def _sample_where_to_show_ss(self, n_tokens: int, max_length=max_length, ) - # Handle symmetry by iterating through the partner_sym_map items and setting + # Handle symmetry by iterating through the partner_sym_map items and setting # `partner_mask_to_show` at partner positions to match `token_mask_to_show` # initialize as all shown so effect comes from hiding + logical AND condition partner_mask_to_show = np.ones_like(token_mask_to_show) @@ -299,4 +321,3 @@ def _sample_where_to_show_ss(self, n_tokens: int, token_mask_to_show = token_mask_to_show & partner_mask_to_show return token_mask_to_show - diff --git a/models/rfd3/src/rfd3/transforms/na_geom_utils.py b/models/rfd3/src/rfd3/transforms/na_geom_utils.py index 453e3640..7a4dd911 100644 --- a/models/rfd3/src/rfd3/transforms/na_geom_utils.py +++ b/models/rfd3/src/rfd3/transforms/na_geom_utils.py @@ -1,22 +1,22 @@ +import math import os import subprocess +import tempfile from datetime import datetime from typing import Dict, Optional -import math -import numpy as np -import biotite.structure as struc -from biotite.structure import AtomArray +import biotite.structure as struc +import numpy as np from atomworks.constants import ( - STANDARD_AA, + STANDARD_AA, STANDARD_DNA, STANDARD_RNA, ) - from atomworks.io.utils.sequence import ( is_purine, is_pyrimidine, ) +from atomworks.ml.encoding_definitions import AF3SequenceEncoding from atomworks.ml.utils.token import ( get_token_starts, is_glycine, @@ -24,15 +24,12 @@ is_standard_aa_not_glycine, is_unknown_nucleotide, ) -from rfd3.transforms.hbonds_hbplus import save_atomarray_to_pdb - -from atomworks.ml.encoding_definitions import AF3SequenceEncoding - +from biotite.structure import AtomArray from rfd3.constants import ( -ATOM_REGION_BY_RESI, -PLANAR_ATOMS_BY_RESI, + ATOM_REGION_BY_RESI, + PLANAR_ATOMS_BY_RESI, ) -import tempfile +from rfd3.transforms.hbonds_hbplus import save_atomarray_to_pdb # Derived: True when the residue has any planar sidechain atoms HAS_PLANAR_SC = {res: bool(atoms) for res, atoms in PLANAR_ATOMS_BY_RESI.items()} @@ -43,15 +40,23 @@ "NA_SS_LOOP": 2, } -AA_PLANAR_ATOMS = sorted(set( - atom for res in STANDARD_AA if res in PLANAR_ATOMS_BY_RESI - for atom in PLANAR_ATOMS_BY_RESI[res] -)) +AA_PLANAR_ATOMS = sorted( + set( + atom + for res in STANDARD_AA + if res in PLANAR_ATOMS_BY_RESI + for atom in PLANAR_ATOMS_BY_RESI[res] + ) +) -NA_PLANAR_ATOMS = sorted(set( - atom for res in (*STANDARD_RNA, *STANDARD_DNA) if res in PLANAR_ATOMS_BY_RESI - for atom in PLANAR_ATOMS_BY_RESI[res] -)) +NA_PLANAR_ATOMS = sorted( + set( + atom + for res in (*STANDARD_RNA, *STANDARD_DNA) + if res in PLANAR_ATOMS_BY_RESI + for atom in PLANAR_ATOMS_BY_RESI[res] + ) +) class NucMolInfo: @@ -62,53 +67,126 @@ class NucMolInfo: """ def __init__(self) -> None: - - # Hbond interaction-class indices of the `hbond_count`` array: - # `hbond_count`` array is (L, L, 3), where the last dimension + # `hbond_count`` array is (L, L, 3), where the last dimension # encodes interaction type between tokens i & j - self.BB_BB = 0 # backbone-backbone hbond interactions - self.BB_SC = 1 # backbone-sidechain hbond interactions - self.SC_SC = 2 # sidechain-sidechain hbond interactions + self.BB_BB = 0 # backbone-backbone hbond interactions + self.BB_SC = 1 # backbone-sidechain hbond interactions + self.SC_SC = 2 # sidechain-sidechain hbond interactions - # We sum over the last dimension of the hbond_count array, scaling + # We sum over the last dimension of the hbond_count array, scaling # count by the following weights to get the interaction score: self.bp_weight_BB_BB = 0.0 self.bp_weight_BB_SC = 0.5 self.bp_weight_SC_SC = 1.0 - self.bp_summation_weights = [self.bp_weight_BB_BB, - self.bp_weight_BB_SC, - self.bp_weight_SC_SC] + self.bp_summation_weights = [ + self.bp_weight_BB_BB, + self.bp_weight_BB_SC, + self.bp_weight_SC_SC, + ] - # Parameters fo sigmoid function that gives us a continuous step function for + # Parameters fo sigmoid function that gives us a continuous step function for # meeting basepair interaction criteria based on hbond counts alone (1st filter). # Calibrated such that: # >= 2 base-base H-bonds -> ~1.0 # 1 base-base H-bond + 1 base-backbone H-bond -> ~0.5 self.min_hbonds_for_bp = 2.0 - self.bp_hbond_coeff = 9.8 # determined heuristically - self.bp_val_cutoff = 0.5 # minimum basepairing score for binarizing basepairs when needed + self.bp_hbond_coeff = 9.8 # determined heuristically + self.bp_val_cutoff = ( + 0.5 # minimum basepairing score for binarizing basepairs when needed + ) self.base_geometry_limits = {} - self.base_geometry_limits['D_ij'] = 16.0 - self.base_geometry_limits['H_ij'] = 1.5 - self.base_geometry_limits['P_ij'] = math.pi/5 - self.base_geometry_limits['B_ij'] = math.pi/5 + self.base_geometry_limits["D_ij"] = 16.0 + self.base_geometry_limits["H_ij"] = 1.5 + self.base_geometry_limits["P_ij"] = math.pi / 5 + self.base_geometry_limits["B_ij"] = math.pi / 5 - self.rep_atom_dict={"protein": "CA", "rna": "C1'", "dna": "C1'"} + self.rep_atom_dict = {"protein": "CA", "rna": "C1'", "dna": "C1'"} # go through self.vec_atom_dict and remove spaces from atom names (values in inner dicts), and remove spaces from keys + replace 'R' with '' in outer dict keys self.vec_atom_dict = { - "DA": {"W_start":"N1", "W_stop":"N6", "H_start":"N7", "H_stop":"N6", "S_start":"C1'", "S_stop":"N3", "B_start":"C1'", "B_stop":"N9" }, - "DG": {"W_start":"N1", "W_stop":"O6", "H_start":"N7", "H_stop":"O6", "S_start":"C1'", "S_stop":"N3", "B_start":"C1'", "B_stop":"N9" }, - "DC": {"W_start":"N3", "W_stop":"N4", "H_start":"C5", "H_stop":"N4", "S_start":"C1'", "S_stop":"O2", "B_start":"C1'", "B_stop":"N1" }, - "DT": {"W_start":"N3", "W_stop":"O4", "H_start":"C5", "H_stop":"O4", "S_start":"C1'", "S_stop":"O2", "B_start":"C1'", "B_stop":"N1" }, - "A": {"W_start":"N1", "W_stop":"N6", "H_start":"N7", "H_stop":"N6", "S_start":"C1'", "S_stop":"N3", "B_start":"C1'", "B_stop":"N9" }, - "G": {"W_start":"N1", "W_stop":"O6", "H_start":"N7", "H_stop":"O6", "S_start":"C1'", "S_stop":"N3", "B_start":"C1'", "B_stop":"N9" }, - "C": {"W_start":"N3", "W_stop":"N4", "H_start":"C5", "H_stop":"N4", "S_start":"C1'", "S_stop":"O2", "B_start":"C1'", "B_stop":"N1" }, - "U": {"W_start":"N3", "W_stop":"O4", "H_start":"C5", "H_stop":"O4", "S_start":"C1'", "S_stop":"O2", "B_start":"C1'", "B_stop":"N1" }, - } - + "DA": { + "W_start": "N1", + "W_stop": "N6", + "H_start": "N7", + "H_stop": "N6", + "S_start": "C1'", + "S_stop": "N3", + "B_start": "C1'", + "B_stop": "N9", + }, + "DG": { + "W_start": "N1", + "W_stop": "O6", + "H_start": "N7", + "H_stop": "O6", + "S_start": "C1'", + "S_stop": "N3", + "B_start": "C1'", + "B_stop": "N9", + }, + "DC": { + "W_start": "N3", + "W_stop": "N4", + "H_start": "C5", + "H_stop": "N4", + "S_start": "C1'", + "S_stop": "O2", + "B_start": "C1'", + "B_stop": "N1", + }, + "DT": { + "W_start": "N3", + "W_stop": "O4", + "H_start": "C5", + "H_stop": "O4", + "S_start": "C1'", + "S_stop": "O2", + "B_start": "C1'", + "B_stop": "N1", + }, + "A": { + "W_start": "N1", + "W_stop": "N6", + "H_start": "N7", + "H_stop": "N6", + "S_start": "C1'", + "S_stop": "N3", + "B_start": "C1'", + "B_stop": "N9", + }, + "G": { + "W_start": "N1", + "W_stop": "O6", + "H_start": "N7", + "H_stop": "O6", + "S_start": "C1'", + "S_stop": "N3", + "B_start": "C1'", + "B_stop": "N9", + }, + "C": { + "W_start": "N3", + "W_stop": "N4", + "H_start": "C5", + "H_stop": "N4", + "S_start": "C1'", + "S_stop": "O2", + "B_start": "C1'", + "B_stop": "N1", + }, + "U": { + "W_start": "N3", + "W_stop": "O4", + "H_start": "C5", + "H_stop": "O4", + "S_start": "C1'", + "S_stop": "O2", + "B_start": "C1'", + "B_stop": "N1", + }, + } def calculate_hb_counts( @@ -117,7 +195,7 @@ def calculate_hb_counts( mol_info: NucMolInfo, cutoff_HA_dist: float = 2.5, cutoff_DA_dist: float = 3.9, - ): +): """Count hydrogen bonds between residue pairs using HBPLUS. Args: @@ -195,14 +273,16 @@ def calculate_hb_counts( ) # d_atm = atom_array[d_mask] # d_idx = d_atm.token_id - d_idx = token_level_data["resi2index"].get(f"{d_chain_iid}__{d_resi}", None) + d_idx = token_level_data["resi2index"].get( + f"{d_chain_iid}__{d_resi}", None + ) if d_idx is None: continue # Handle standard polymer residues for donor atom: if d_resn in ATOM_REGION_BY_RESI.keys(): - d_is_sc = (d_atom_name in ATOM_REGION_BY_RESI[d_resn]['sc']) - d_is_bb = (d_atom_name in ATOM_REGION_BY_RESI[d_resn]['bb']) + d_is_sc = d_atom_name in ATOM_REGION_BY_RESI[d_resn]["sc"] + d_is_bb = d_atom_name in ATOM_REGION_BY_RESI[d_resn]["bb"] else: # If non-polymer, define any ligand HBonding atom as backbone: if d_mask.sum() > 0: @@ -219,45 +299,51 @@ def calculate_hb_counts( & (atom_array.res_id == a_resi) & (atom_array.chain_iid == a_chain_iid) ) - a_idx = token_level_data["resi2index"].get(f"{a_chain_iid}__{a_resi}", None) + a_idx = token_level_data["resi2index"].get( + f"{a_chain_iid}__{a_resi}", None + ) if a_idx is None: continue # Handle standard polymer residues for acceptor atom: if a_resn in ATOM_REGION_BY_RESI.keys(): - a_is_sc = (a_atom_name in ATOM_REGION_BY_RESI[a_resn]['sc']) - a_is_bb = (a_atom_name in ATOM_REGION_BY_RESI[a_resn]['bb']) + a_is_sc = a_atom_name in ATOM_REGION_BY_RESI[a_resn]["sc"] + a_is_bb = a_atom_name in ATOM_REGION_BY_RESI[a_resn]["bb"] else: # If non-polymer, define any ligand HBonding atom as backbone: if a_mask.sum() > 0: a_is_bb = atom_array[a_mask][0].is_ligand # 0 -> both backbone (BB-BB) - hbond_count[a_idx, d_idx, 0] += (a_is_bb * d_is_bb) - hbond_count[d_idx, a_idx, 0] += (d_is_bb * a_is_bb) + hbond_count[a_idx, d_idx, 0] += a_is_bb * d_is_bb + hbond_count[d_idx, a_idx, 0] += d_is_bb * a_is_bb # 1 -> one backbone, one sidechain (BB-SC) - hbond_count[a_idx, d_idx, 1] += (a_is_bb * d_is_sc) | (a_is_sc * d_is_bb) - hbond_count[d_idx, a_idx, 1] += (d_is_bb * a_is_sc) | (d_is_sc * a_is_bb) + hbond_count[a_idx, d_idx, 1] += (a_is_bb * d_is_sc) | ( + a_is_sc * d_is_bb + ) + hbond_count[d_idx, a_idx, 1] += (d_is_bb * a_is_sc) | ( + d_is_sc * a_is_bb + ) # 2 -> both sidechain (SC-SC) - hbond_count[a_idx, d_idx, 2] += (a_is_sc * d_is_sc) - hbond_count[d_idx, a_idx, 2] += (d_is_sc * a_is_sc) - ''' + hbond_count[a_idx, d_idx, 2] += a_is_sc * d_is_sc + hbond_count[d_idx, a_idx, 2] += d_is_sc * a_is_sc + """ try: os.remove(pdb_path) os.remove(hb2_path) except: print("temp pdb/hb already removed or not created to begin with") - ''' + """ return hbond_count def find_planar_positions( - atom_array: AtomArray, - mol_info: NucMolInfo, - tol: float = 1e-2, - ) -> Dict: + atom_array: AtomArray, + mol_info: NucMolInfo, + tol: float = 1e-2, +) -> Dict: """Identify residues with planar sidechains via known atom lists or PCA plane-fitting. For canonical residues the planar atoms are looked up from ``mol_info``; @@ -285,11 +371,10 @@ def find_planar_positions( # for chain_iid, res_id in unique_positions_list: for chain_iid, res_id, res_name in unique_positions_list: - mask = ( - (atom_array.chain_iid == chain_iid) & - (atom_array.res_id == res_id) & - (atom_array.res_name == res_name) + (atom_array.chain_iid == chain_iid) + & (atom_array.res_id == res_id) + & (atom_array.res_name == res_name) ) res_atoms = atom_array[mask] @@ -297,9 +382,9 @@ def find_planar_positions( if res_name in PLANAR_ATOMS_BY_RESI.keys(): # Shared atoms between residue and known planar atoms for that residue type: planar_atom_list = list( - set([atm.atom_name for atm in res_atoms]) & - set(PLANAR_ATOMS_BY_RESI[res_name]) - ) + set([atm.atom_name for atm in res_atoms]) + & set(PLANAR_ATOMS_BY_RESI[res_name]) + ) planar_atom_list_dict[(chain_iid, res_id)] = planar_atom_list # If unknown or noncanonical residue, compute planar atoms geometrically: @@ -337,7 +422,9 @@ def find_planar_positions( all_quad_centered = coords - quad_center quad_centered = quad_coords - quad_center # covariance matrix - quad_cov = (quad_centered.T @ quad_centered) / max(quad_coords.shape[0] - 1, 1) + quad_cov = (quad_centered.T @ quad_centered) / max( + quad_coords.shape[0] - 1, 1 + ) # eigen decomposition _, quad_eigvecs = np.linalg.eigh(quad_cov) quad_normal = quad_eigvecs[:, 0] # eigenvector with smallest eigenvalue @@ -348,34 +435,39 @@ def find_planar_positions( quad_valid_mask = quad_dists <= tol # Filter for if we have a valid plane in the first place: - valid_plane_filter = (np.nanmax(quad_dists[:4]) < tol) + valid_plane_filter = np.nanmax(quad_dists[:4]) < tol # Filter for if we have enough atoms in the plane: - plane_atom_filter = (int(np.sum(quad_valid_mask)) >= 4) + plane_atom_filter = int(np.sum(quad_valid_mask)) >= 4 if valid_plane_filter and plane_atom_filter: - # Set the planar atom list for this position to those that are within tol of the plane: + # Set the planar atom list for this position to those that are within tol of the plane: # using quad_valid_mask and candidate_planar_atm_names: - planar_atom_list = [n for n, keep in zip(candidate_planar_atm_names, quad_valid_mask.tolist()) if keep] - + planar_atom_list = [ + n + for n, keep in zip( + candidate_planar_atm_names, quad_valid_mask.tolist() + ) + if keep + ] + # not enough atoms close to a common plane else: planar_atom_list = [] else: - # need at least 4 atoms to define a robust plane planar_atom_list = [] - - planar_atom_list_dict[(chain_iid, res_id)] = planar_atom_list + planar_atom_list_dict[(chain_iid, res_id)] = planar_atom_list return planar_atom_list_dict -def make_coord_list(atom_array: AtomArray, - residue_list: list[str], - chain_list: list[str], - atom_list: list[str], - ) -> list[list[str]]: +def make_coord_list( + atom_array: AtomArray, + residue_list: list[str], + chain_list: list[str], + atom_list: list[str], +) -> list[list[str]]: """Extract per-residue representative coordinates from an AtomArray. All three input lists must have the same length. Missing atoms are @@ -393,24 +485,20 @@ def make_coord_list(atom_array: AtomArray, """ coord_list = [] for res_id, chain_id, atom_name in zip(residue_list, chain_list, atom_list): - # Check if the residue exists in the atom array if atom_name == "atomized": # Check for atomized residue, in which case we take the first atom of the residue # full mask should be length-1 if atomized - mask = ( - (atom_array.chain_id == chain_id) & - (atom_array.res_id == res_id) - ) + mask = (atom_array.chain_id == chain_id) & (atom_array.res_id == res_id) else: # General case for non-atomized residues # should have a unique solution, but we take the first entry either way. mask = ( - (atom_array.chain_id == chain_id) & - (atom_array.res_id == res_id) & - (atom_array.atom_name == atom_name) - ) - + (atom_array.chain_id == chain_id) + & (atom_array.res_id == res_id) + & (atom_array.atom_name == atom_name) + ) + # Get the coordinates for the masked atoms coords = atom_array.coord[mask][0:1] @@ -428,8 +516,8 @@ def get_token_level_metadata( *, NA_only: bool = False, planar_only: bool = True, - seq_cutoff = 2, - gap_length = 200 + seq_cutoff=2, + gap_length=200, ) -> dict: """Build lightweight token-level metadata (no coordinate geometry). @@ -468,9 +556,21 @@ def get_token_level_metadata( # molecule type flags # Instantiate encoding locally to avoid retaining large arrays at module scope. sequence_encoding = AF3SequenceEncoding() - is_protein = np.isin(token_level_array.res_name, sequence_encoding.all_res_names[sequence_encoding.is_aa_like]) - is_rna = np.isin(token_level_array.res_name, sequence_encoding.all_res_names[sequence_encoding.is_rna_like]) - is_dna = np.isin(token_level_array.res_name, sequence_encoding.all_res_names[sequence_encoding.is_dna_like]) + + ################### + # is_protein = np.isin( + # token_level_array.res_name, + # sequence_encoding.all_res_names[sequence_encoding.is_aa_like], + # ) + ################## + is_rna = np.isin( + token_level_array.res_name, + sequence_encoding.all_res_names[sequence_encoding.is_rna_like], + ) + is_dna = np.isin( + token_level_array.res_name, + sequence_encoding.all_res_names[sequence_encoding.is_dna_like], + ) is_na_arr = (is_dna | is_rna).astype(bool) @@ -500,7 +600,7 @@ def get_token_level_metadata( sc_planarity_list.append(False) # representative & sugar-edge atoms - if (is_glycine(atm.res_name) | is_protein_unknown(atm.res_name)): + if is_glycine(atm.res_name) | is_protein_unknown(atm.res_name): rep_atom_i = "CA" S_start_atom_i = None S_stop_atom_i = None @@ -534,7 +634,9 @@ def get_token_level_metadata( S_stop_atom_list.append(S_stop_atom_i) # residue index <-> token index map - resi2index = {f"{c}__{r}": i for c, r, i in zip(chain_iid_list, resi_list, ind_list)} + resi2index = { + f"{c}__{r}": i for c, r, i in zip(chain_iid_list, resi_list, ind_list) + } # relative sequence positions w/ chain gaps rel_pos_list: list[int] = [] @@ -547,9 +649,7 @@ def get_token_level_metadata( rel_pos_list.append(int(r + chn_bias)) rel_pos = np.asarray(rel_pos_list, dtype=np.int64) - seq_neighbors = ( - np.abs(rel_pos[:, None] - rel_pos[None, :]) <= int(seq_cutoff) - ) + seq_neighbors = np.abs(rel_pos[:, None] - rel_pos[None, :]) <= int(seq_cutoff) na_inds = np.nonzero(is_na_arr)[0].tolist() na_tensor_inds = {na_i: i for i, na_i in enumerate(na_inds)} @@ -647,12 +747,16 @@ def add_token_level_geometry_data( S_start_atom_list: list[str | None] = token_level_data["S_start_atom_list"] S_stop_atom_list: list[str | None] = token_level_data["S_stop_atom_list"] - planar_atom_list_dict = find_planar_positions(atom_array, mol_info) # {(chain_iid, res_id): [atom_name, ...]} + planar_atom_list_dict = find_planar_positions( + atom_array, mol_info + ) # {(chain_iid, res_id): [atom_name, ...]} has_planar_sc: list[bool] = [] - xyz_planar: list[list[list[float]]] = [] # list[I] of [K_i, 3] (K_i varies per residue) - xyz_S_start: list[list[float]] = [] # list[I] of [3] - xyz_S_stop: list[list[float]] = [] # list[I] of [3] + xyz_planar: list[ + list[list[float]] + ] = [] # list[I] of [K_i, 3] (K_i varies per residue) + xyz_S_start: list[list[float]] = [] # list[I] of [3] + xyz_S_stop: list[list[float]] = [] # list[I] of [3] for c, r, S_start_atm, S_stop_atm in zip( chain_iid_list, @@ -663,7 +767,9 @@ def add_token_level_geometry_data( planar_atoms_i = planar_atom_list_dict[(c, r)] has_planar_sc.append(bool(len(planar_atoms_i) >= 4)) - atom_array_i = atom_array[(atom_array.chain_iid == c) & (atom_array.res_id == r)] + atom_array_i = atom_array[ + (atom_array.chain_iid == c) & (atom_array.res_id == r) + ] planar_coords_i: list[list[float]] = [] for pl_atm_name_j in planar_atoms_i: @@ -673,7 +779,9 @@ def add_token_level_geometry_data( else: planar_coords_i.append(pl_atom_array_ij[0].coord) - xyz_planar.append(planar_coords_i if len(planar_coords_i) > 3 else [[float("nan")] * 3]) + xyz_planar.append( + planar_coords_i if len(planar_coords_i) > 3 else [[float("nan")] * 3] + ) if S_start_atm is None: xyz_S_start.append([float("nan"), float("nan"), float("nan")]) @@ -698,21 +806,26 @@ def add_token_level_geometry_data( del atom_array_i # frame coordinates and backbone direction - frame_xyz = np.asarray( # [I, 3] representative-atom coordinates + frame_xyz = np.asarray( # [I, 3] representative-atom coordinates make_coord_list(atom_array, resi_list, chain_list, rep_atom_list), dtype=np.float32, ) - padded_centers = np.concatenate([frame_xyz[:1], frame_xyz, frame_xyz[-1:]], axis=0) # [I+2, 3] - M_i = ( # [I, 3] smoothed backbone-direction vectors - (padded_centers[1:-1] - padded_centers[:-2]) - + (padded_centers[2:] - padded_centers[1:-1]) - ) / 2.0 + padded_centers = np.concatenate( + [frame_xyz[:1], frame_xyz, frame_xyz[-1:]], axis=0 + ) # [I+2, 3] + M_i = ( + ( # [I, 3] smoothed backbone-direction vectors + (padded_centers[1:-1] - padded_centers[:-2]) + + (padded_centers[2:] - padded_centers[1:-1]) + ) + / 2.0 + ) - is_planar_arr = np.asarray(has_planar_sc, dtype=bool) # [I] + is_planar_arr = np.asarray(has_planar_sc, dtype=bool) # [I] token_level_data["is_planar"] = is_planar_arr - is_na_arr = np.asarray(token_level_data["is_na"], dtype=bool) # [I] + is_na_arr = np.asarray(token_level_data["is_na"], dtype=bool) # [I] if NA_only and planar_only: filter_mask = is_na_arr & is_planar_arr elif NA_only and (not planar_only): @@ -721,7 +834,7 @@ def add_token_level_geometry_data( filter_mask = is_planar_arr.copy() else: filter_mask = np.ones_like(is_na_arr, dtype=bool) - token_level_data["filter_mask"] = filter_mask # [I] bool + token_level_data["filter_mask"] = filter_mask # [I] bool token_level_data.update( { @@ -777,7 +890,7 @@ def _compute_local_frames( n_tokens = len(xyz_planar) # Mean-centre the planar atoms per residue - centered_points = [ # list[I] of [K_i, 3] + centered_points = [ # list[I] of [K_i, 3] np.asarray(xyz_i, dtype=np.float32) - cen_i for xyz_i, cen_i in zip(xyz_planar, planar_centers) ] @@ -857,19 +970,23 @@ def _compute_pairwise_geometry( ``"Z_ij"`` [I, I, 3], and optionally ``"O_ij"`` [I, I]. """ # Orientation-selected pairwise Z-axis - Z_sum = Z_i[:, None, :] + Z_i[None, :, :] # [I, I, 3] - Z_diff = Z_i[:, None, :] - Z_i[None, :, :] # [I, I, 3] + Z_sum = Z_i[:, None, :] + Z_i[None, :, :] # [I, I, 3] + Z_diff = Z_i[:, None, :] - Z_i[None, :, :] # [I, I, 3] Z_ij_oris = 0.5 * np.stack((Z_sum, Z_diff), axis=0) # [2, I, I, 3] base_ori_ij = ( # [I, I] 0=parallel, 1=antiparallel np.linalg.norm(Z_ij_oris[1], axis=-1) > np.linalg.norm(Z_ij_oris[0], axis=-1) ).astype(np.int64) - Z_ij = np.where(base_ori_ij[..., None] == 0, Z_ij_oris[0], Z_ij_oris[1]) # [I, I, 3] + Z_ij = np.where( + base_ori_ij[..., None] == 0, Z_ij_oris[0], Z_ij_oris[1] + ) # [I, I, 3] Z_ij = Z_ij / (np.linalg.norm(Z_ij, axis=-1, keepdims=True) + eps) # Pairwise Y (inter-residue direction) and X axes - Y_ij = frame_D_ij_vec / (np.linalg.norm(frame_D_ij_vec, axis=-1, keepdims=True) + eps) # [I, I, 3] + Y_ij = frame_D_ij_vec / ( + np.linalg.norm(frame_D_ij_vec, axis=-1, keepdims=True) + eps + ) # [I, I, 3] X_ij = np.cross(Z_ij, Y_ij) # [I, I, 3] X_ij = X_ij / (np.linalg.norm(X_ij, axis=-1, keepdims=True) + eps) @@ -882,22 +999,30 @@ def _compute_pairwise_geometry( np.sum(Z_i[:, None, :] * Y_ij, axis=-1, keepdims=True) * Y_ij + np.sum(Z_i[:, None, :] * Z_ij, axis=-1, keepdims=True) * Z_ij ) - proj_Z_i_YZ_norm = proj_Z_i_YZ / (np.linalg.norm(proj_Z_i_YZ, axis=-1, keepdims=True) + eps) - cos_buckle = np.sum(proj_Z_i_YZ_norm * (-proj_Z_i_YZ_norm.swapaxes(0, 1)), axis=-1) # [I, I] + proj_Z_i_YZ_norm = proj_Z_i_YZ / ( + np.linalg.norm(proj_Z_i_YZ, axis=-1, keepdims=True) + eps + ) + cos_buckle = np.sum( + proj_Z_i_YZ_norm * (-proj_Z_i_YZ_norm.swapaxes(0, 1)), axis=-1 + ) # [I, I] # Propeller (P_ij) proj_Z_i_ZX = ( # [I, I, 3] np.sum(Z_i[:, None, :] * Z_ij, axis=-1, keepdims=True) * Z_ij + np.sum(Z_i[:, None, :] * X_ij, axis=-1, keepdims=True) * X_ij ) - proj_Z_i_ZX_norm = proj_Z_i_ZX / (np.linalg.norm(proj_Z_i_ZX, axis=-1, keepdims=True) + eps) - cos_propeller = np.sum(proj_Z_i_ZX_norm * (-proj_Z_i_ZX_norm.swapaxes(0, 1)), axis=-1) # [I, I] + proj_Z_i_ZX_norm = proj_Z_i_ZX / ( + np.linalg.norm(proj_Z_i_ZX, axis=-1, keepdims=True) + eps + ) + cos_propeller = np.sum( + proj_Z_i_ZX_norm * (-proj_Z_i_ZX_norm.swapaxes(0, 1)), axis=-1 + ) # [I, I] if clamp: cos_buckle = np.clip(cos_buckle, -1.0, 1.0) cos_propeller = np.clip(cos_propeller, -1.0, 1.0) - B_ij = np.arccos(cos_buckle) # [I, I] + B_ij = np.arccos(cos_buckle) # [I, I] P_ij = np.arccos(cos_propeller) # [I, I] result: dict[str, np.ndarray] = { @@ -920,8 +1045,12 @@ def _compute_pairwise_geometry( np.sum(X_i[:, None, :] * X_ij, axis=-1, keepdims=True) * X_ij + np.sum(X_i[:, None, :] * Y_ij, axis=-1, keepdims=True) * Y_ij ) - proj_X_i_XY_norm = proj_X_i_XY / (np.linalg.norm(proj_X_i_XY, axis=-1, keepdims=True) + eps) - cos_opening = np.sum(proj_X_i_XY_norm * proj_X_i_XY_norm.swapaxes(0, 1), axis=-1) # [I, I] + proj_X_i_XY_norm = proj_X_i_XY / ( + np.linalg.norm(proj_X_i_XY, axis=-1, keepdims=True) + eps + ) + cos_opening = np.sum( + proj_X_i_XY_norm * proj_X_i_XY_norm.swapaxes(0, 1), axis=-1 + ) # [I, I] if clamp: cos_opening = np.clip(cos_opening, -1.0, 1.0) result["O_ij"] = np.arccos(cos_opening) # [I, I] @@ -989,13 +1118,14 @@ def _compute_basepair_mask( | (P_ij >= math.pi - mol_info.base_geometry_limits["P_ij"]) ) - D_ij_filter = (D_ij <= mol_info.base_geometry_limits["D_ij"]) + D_ij_filter = D_ij <= mol_info.base_geometry_limits["D_ij"] bp_geom_filter = H_ij_filter & B_ij_filter & P_ij_filter & D_ij_filter # [I, I] if bool_only: basepairs_bool_ij = ( # [I, I] - (~seq_neighbors) & bp_geom_filter + (~seq_neighbors) + & bp_geom_filter & (bp_preds >= float(mol_info.bp_val_cutoff)) ) return basepairs_bool_ij @@ -1015,7 +1145,7 @@ def _compute_basepair_mask( def compute_nucleic_ss( - mol_info, + mol_info, token_level_data, hbond_count, clamp_pairwise_params=True, @@ -1061,14 +1191,24 @@ def compute_nucleic_ss( mask_1d = np.asarray(token_level_data["filter_mask"], dtype=bool) # [I_full] # --- Unpack and filter token-level data ---------------------- - M_i = np.asarray(token_level_data["M_i"], dtype=np.float32)[mask_1d] # [I, 3] - frame_xyz = np.asarray(token_level_data["frame_xyz"], dtype=np.float32)[mask_1d] # [I, 3] - xyz_S_start = [v for v, k in zip(token_level_data["xyz_S_start"], mask_1d) if k] # list[I] of [3] - xyz_S_stop = [v for v, k in zip(token_level_data["xyz_S_stop"], mask_1d) if k] # list[I] of [3] - xyz_planar = [v for v, k in zip(token_level_data["xyz_planar"], mask_1d) if k] # list[I] of [K_i, 3] - - hbond_count = np.asarray(hbond_count)[mask_1d, :][:, mask_1d] # [I, I, 3] - seq_neighbors = np.asarray(token_level_data["seq_neighbors"], dtype=bool)[mask_1d, :][:, mask_1d] # [I, I] + M_i = np.asarray(token_level_data["M_i"], dtype=np.float32)[mask_1d] # [I, 3] + frame_xyz = np.asarray(token_level_data["frame_xyz"], dtype=np.float32)[ + mask_1d + ] # [I, 3] + xyz_S_start = [ + v for v, k in zip(token_level_data["xyz_S_start"], mask_1d) if k + ] # list[I] of [3] + xyz_S_stop = [ + v for v, k in zip(token_level_data["xyz_S_stop"], mask_1d) if k + ] # list[I] of [3] + xyz_planar = [ + v for v, k in zip(token_level_data["xyz_planar"], mask_1d) if k + ] # list[I] of [K_i, 3] + + hbond_count = np.asarray(hbond_count)[mask_1d, :][:, mask_1d] # [I, I, 3] + seq_neighbors = np.asarray(token_level_data["seq_neighbors"], dtype=bool)[ + mask_1d, : + ][:, mask_1d] # [I, I] # Nothing passed NA/planar filtering for this structure. # Return empty outputs instead of failing downstream on np.stack([]). @@ -1107,12 +1247,15 @@ def compute_nucleic_ss( # --- Precompute centroids and displacement vectors ----------- planar_centers = np.stack( # [I, 3] - [np.nanmean(np.asarray(xyz_i, dtype=np.float32), axis=0) for xyz_i in xyz_planar], + [ + np.nanmean(np.asarray(xyz_i, dtype=np.float32), axis=0) + for xyz_i in xyz_planar + ], axis=0, ).astype(np.float32) - frame_D_ij_vec = frame_xyz[None, :, :] - frame_xyz[:, None, :] # [I, I, 3] - sc_D_ij_vec = planar_centers[None, :, :] - planar_centers[:, None, :] # [I, I, 3] + frame_D_ij_vec = frame_xyz[None, :, :] - frame_xyz[:, None, :] # [I, I, 3] + sc_D_ij_vec = planar_centers[None, :, :] - planar_centers[:, None, :] # [I, I, 3] # --- CALC I: per-residue local coordinate frames ------------- need_full_frame = return_local_params or return_opening_angle @@ -1125,8 +1268,8 @@ def compute_nucleic_ss( compute_full_frame=need_full_frame, eps=eps, ) - Z_i = local_frames["Z_i"] # [I, 3] - X_i = local_frames.get("X_i") # [I, 3] or None + Z_i = local_frames["Z_i"] # [I, 3] + X_i = local_frames.get("X_i") # [I, 3] or None # --- CALC II: pairwise base-step geometry -------------------- pw_geom = _compute_pairwise_geometry( @@ -1187,7 +1330,6 @@ def compute_nucleic_ss( return nucleic_ss_data - def annotate_na_ss( atom_array: AtomArray, *, @@ -1258,10 +1400,10 @@ def annotate_na_ss( NA_only=NA_only, planar_only=planar_only, ) - # Note: this mask gives positions that are *chemically valid* for forming + # Note: this mask gives positions that are *chemically valid* for forming # base pairs, which is different from custom mask-generation for features mask_1d = np.asarray(token_level_data["filter_mask"], dtype=bool) - + subset_idxs = np.nonzero(mask_1d)[0] is_na_full = np.asarray(token_level_data["is_na"], dtype=bool) @@ -1295,15 +1437,19 @@ def annotate_na_ss( if planar_only: n_tokens = bp_bool.shape[0] has_planar_sc = np.asarray( - token_level_data.get("has_planar_sc", np.ones(n_tokens, dtype=bool)), dtype=bool + token_level_data.get("has_planar_sc", np.ones(n_tokens, dtype=bool)), + dtype=bool, ) bp_bool &= has_planar_sc[:, None] bp_bool &= has_planar_sc[None, :] # Optional: filter to canonical Watson-Crick basepairs only. # Sampled probabilistically to allow mixed supervision during training. - do_canonical_filter = bool(p_canonical_bp_filter and (np.random.rand() < float(p_canonical_bp_filter))) + do_canonical_filter = bool( + p_canonical_bp_filter and (np.random.rand() < float(p_canonical_bp_filter)) + ) if do_canonical_filter: + def _base_letter(res_name: str) -> str | None: rn = str(res_name).strip().upper() if rn in STANDARD_RNA: @@ -1313,11 +1459,16 @@ def _base_letter(res_name: str) -> str | None: return None allowed_pairs = { - ("A", "U"), ("U", "A"), - ("A", "T"), ("T", "A"), - ("G", "C"), ("C", "G"), + ("A", "U"), + ("U", "A"), + ("A", "T"), + ("T", "A"), + ("G", "C"), + ("C", "G"), } - base_letters_full: list[str | None] = [_base_letter(rn) for rn in token_res_names] + base_letters_full: list[str | None] = [ + _base_letter(rn) for rn in token_res_names + ] bp_bool = np.asarray(bp_bool, dtype=bool) bp_rows_tmp, bp_cols_tmp = np.nonzero(bp_bool) @@ -1401,7 +1552,9 @@ def _base_letter(res_name: str) -> str | None: continue # A residue is treated as atomized if any atom in the residue carries atomize=True. if "atomize" in atom_array.get_annotation_categories(): - residue_is_atomized = bool(np.any(np.asarray(atom_array.atomize[int(start):stop], dtype=bool))) + residue_is_atomized = bool( + np.any(np.asarray(atom_array.atomize[int(start) : stop], dtype=bool)) + ) else: residue_is_atomized = False if residue_is_atomized: diff --git a/models/rfd3/src/rfd3/transforms/pipelines.py b/models/rfd3/src/rfd3/transforms/pipelines.py index 940dfab6..8d8fee10 100644 --- a/models/rfd3/src/rfd3/transforms/pipelines.py +++ b/models/rfd3/src/rfd3/transforms/pipelines.py @@ -216,7 +216,9 @@ def get_crop_transform( ), "Crop center cutoff distance must be greater than 0" pre_crop_transforms = [ - SubsampleToTypes(allowed_types=allowed_types, association_scheme=association_scheme), + SubsampleToTypes( + allowed_types=allowed_types, association_scheme=association_scheme + ), ] cropping_transform = RandomRoute( @@ -361,11 +363,9 @@ def build_atom14_base_pipeline_( max_ss_frac_to_provide: float, min_ss_island_len: int, max_ss_island_len: int, - ## Nucleic acid features ##### - #add_na_pair_features: bool, + # add_na_pair_features: bool, ## This should not be necessary, controlled through feature names in model, and meta conditioning probabilities, inference behavior handled in transform itself ##### - **_, # dump additional kwargs (e.g. msa stuff) ): """ @@ -373,7 +373,7 @@ def build_atom14_base_pipeline_( """ warnings.filterwarnings("ignore", category=RuntimeWarning) warnings.filterwarnings("ignore", category=DeprecationWarning) - + # Add any data necessary for downstream transforms transforms = [ AddData( @@ -415,7 +415,7 @@ def build_atom14_base_pipeline_( max_binder_length=max_binder_length, max_atoms_in_crop=max_atoms_in_crop, allowed_types=allowed_types, - association_scheme=association_scheme + association_scheme=association_scheme, ) if zero_occ_on_exposure_after_cropping: @@ -448,7 +448,7 @@ def build_atom14_base_pipeline_( ) ) # Add nucleic acid geometry features - #if add_na_pair_features: + # if add_na_pair_features: transforms.append( CalculateNucleicAcidGeomFeats( is_inference, @@ -639,8 +639,8 @@ def build_atom14_base_pipeline( kwargs.setdefault("min_ss_island_len", 0) kwargs.setdefault("max_ss_island_len", 999) kwargs.setdefault("max_binder_length", 999) - # This should not be necessary. - #kwargs.setdefault("add_na_pair_features", False) + # This should not be necessary. + # kwargs.setdefault("add_na_pair_features", False) kwargs.setdefault("b_factor_min", None) kwargs.setdefault("zero_occ_on_exposure_after_cropping", False) diff --git a/models/rfd3/src/rfd3/transforms/training_conditions.py b/models/rfd3/src/rfd3/transforms/training_conditions.py index f2173397..67ced33a 100644 --- a/models/rfd3/src/rfd3/transforms/training_conditions.py +++ b/models/rfd3/src/rfd3/transforms/training_conditions.py @@ -13,14 +13,13 @@ spread_token_wise, ) from biotite.structure import AtomArray, get_residue_starts +from rfd3.constants import backbone_atoms_RNA from rfd3.transforms.conditioning_utils import ( random_condition, sample_island_tokens, sample_subgraph_atoms, ) -from rfd3.constants import backbone_atoms_RNA - nx.from_numpy_matrix = nx.from_numpy_array logger = logging.getLogger(__name__) @@ -74,7 +73,7 @@ def __init__( p_fix_motif_coordinates, p_fix_motif_sequence, p_unindex_motif_tokens, - association_scheme = 'atom14', + association_scheme="atom14", ): self.name = name self.frequency = frequency @@ -93,7 +92,7 @@ def __init__( self.p_fix_motif_coordinates = p_fix_motif_coordinates self.p_fix_motif_sequence = p_fix_motif_sequence self.p_unindex_motif_tokens = p_unindex_motif_tokens - + self.association_scheme = association_scheme def is_valid_for_example(self, data) -> bool: @@ -107,7 +106,7 @@ def is_valid_for_example(self, data) -> bool: else: if np.any(is_protein): return True - + return False def sample_motif_tokens(self, atom_array): @@ -162,7 +161,7 @@ def sample_motif_atoms(self, atom_array): if random_condition(self.p_diffuse_motif_sidechains): backbone_atoms = backbone_atoms_RNA.copy() backbone_atoms.remove("C1'") - backbone_atoms = ["N", "C", "CA"] + backbone_atoms #covers DNA also + backbone_atoms = ["N", "C", "CA"] + backbone_atoms # covers DNA also if random_condition(self.p_include_oxygen_in_backbone_mask): backbone_atoms.append("O") is_motif_atom = is_motif_atom & np.isin( @@ -500,7 +499,9 @@ def sample_conditioning_strategy( atom_array.set_annotation( "is_motif_atom_unindexed", sample_unindexed_atoms( - atom_array, p_unindex_motif_tokens=p_unindex_motif_tokens, association_scheme=association_scheme + atom_array, + p_unindex_motif_tokens=p_unindex_motif_tokens, + association_scheme=association_scheme, ), ) return atom_array @@ -526,7 +527,6 @@ def sample_is_motif_atom_with_fixed_seq( is_motif_atom_with_fixed_seq = ( is_motif_atom_with_fixed_seq | ~atom_array.is_protein ) - return is_motif_atom_with_fixed_seq diff --git a/models/rfd3/src/rfd3/transforms/virtual_atoms.py b/models/rfd3/src/rfd3/transforms/virtual_atoms.py index 31ec59ae..d9097fea 100644 --- a/models/rfd3/src/rfd3/transforms/virtual_atoms.py +++ b/models/rfd3/src/rfd3/transforms/virtual_atoms.py @@ -53,6 +53,7 @@ def map_to_association_scheme( else: return ATOM_NAMES[idxs] + def map_names_to_elements( atom_names: list | str, default=VIRTUAL_ATOM_ELEMENT_NAME ) -> np.ndarray: @@ -179,7 +180,7 @@ def forward(self, data: dict) -> dict: token_ids = np.unique(atom_array.token_id) assert len(token_ids) == len( is_motif_atom_with_fixed_seq - ), "Token ids and token level array have different lengths!" + ), "Token ids and token level array have different lengths!" # Unindexed tokens are never fully atomized, but may be assigned as atomized to have repr atoms: if self.association_scheme == "atom23": @@ -230,14 +231,14 @@ def forward(self, data: dict) -> dict: else: n_atoms_per_token = self.n_atoms_per_token central_atom = self.atom_to_pad_from - + n_pad = n_atoms_per_token - len(token) if n_pad > 0: mask = get_af3_token_representative_masks( token, central_atom=central_atom ) - + assert_single_representative(token, central_atom=central_atom) # ... Create virtual atoms diff --git a/models/rfd3/src/rfd3/utils/inference.py b/models/rfd3/src/rfd3/utils/inference.py index 426eace2..1e00f566 100644 --- a/models/rfd3/src/rfd3/utils/inference.py +++ b/models/rfd3/src/rfd3/utils/inference.py @@ -452,7 +452,10 @@ def infer_ori_from_com(atom_array): def set_com( - atom_array, ori_token: list | None = None, infer_ori_strategy: str | None = None, ori_jitter: float | None = None + atom_array, + ori_token: list | None = None, + infer_ori_strategy: str | None = None, + ori_jitter: float | None = None, ): if exists(ori_token): center = np.array([float(x) for x in ori_token], dtype=atom_array.coord.dtype) @@ -512,7 +515,7 @@ def set_com( # Random length (mean ~ scale) length = np.random.exponential(scale=scale) - jittered_offset = direction*length + jittered_offset = direction * length atom_array.coord -= jittered_offset From 3da4cdfe191b293d1ac43c99556852f3b0a0c569 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Fri, 27 Mar 2026 11:25:56 -0700 Subject: [PATCH 44/49] ruff_format_2 --- src/foundry_cli/download_checkpoints.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/foundry_cli/download_checkpoints.py b/src/foundry_cli/download_checkpoints.py index 8a27c1b7..ef367a1b 100644 --- a/src/foundry_cli/download_checkpoints.py +++ b/src/foundry_cli/download_checkpoints.py @@ -132,9 +132,7 @@ def install_model(model_name: str, checkpoint_dir: Path, force: bool = False) -> ) try: - download_file( - checkpoint_info.url, dest_path, checkpoint_info.sha256 - ) + download_file(checkpoint_info.url, dest_path, checkpoint_info.sha256) console.print( f"[green]✓[/green] Successfully installed {model_name} to {dest_path}" ) From 28d1375f96db824f73489dc9055cf06cf3ffeb92 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Fri, 27 Mar 2026 11:32:08 -0700 Subject: [PATCH 45/49] cleanup debug comment --- models/rf3/src/rf3/loss/af3_losses.py | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/models/rf3/src/rf3/loss/af3_losses.py b/models/rf3/src/rf3/loss/af3_losses.py index 1bb0cd27..974db267 100644 --- a/models/rf3/src/rf3/loss/af3_losses.py +++ b/models/rf3/src/rf3/loss/af3_losses.py @@ -349,13 +349,9 @@ def forward(self, network_output, loss_input, symm_input): x_native = symm_input["coord_atom_lvl"].to(x_pred.device) mask_native = symm_input["mask_atom_lvl"].to(x_pred.device) - try: - x_native_aln, x_native_mask = self._resolve_subunits( - mol_entities, mol_iid, crop_mask, x_native, mask_native, x_pred - ) - except Exception: - # fd ... TO DO: DEBUG! - return loss_input + x_native_aln, x_native_mask = self._resolve_subunits( + mol_entities, mol_iid, crop_mask, x_native, mask_native, x_pred + ) loss_input["X_gt_L"] = x_native_aln loss_input["crd_mask_L"] = x_native_mask From da7b89cc14c0b9097e9ff56bb664f21fa5de9f95 Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Fri, 27 Mar 2026 12:27:56 -0700 Subject: [PATCH 46/49] docs and json progress --- models/rfd3/README.md | 14 +++- models/rfd3/docs/.assets/multipolymer.png | Bin 0 -> 711047 bytes models/rfd3/docs/.assets/overview.png | Bin 158871 -> 480735 bytes models/rfd3/docs/examples/atom23_design.json | 73 +++++++++++++++++++ models/rfd3/docs/input.md | 3 +- 5 files changed, 85 insertions(+), 5 deletions(-) create mode 100644 models/rfd3/docs/.assets/multipolymer.png create mode 100644 models/rfd3/docs/examples/atom23_design.json diff --git a/models/rfd3/README.md b/models/rfd3/README.md index ecb4f34c..0886afc0 100644 --- a/models/rfd3/README.md +++ b/models/rfd3/README.md @@ -74,10 +74,6 @@ you will need to change the path in the `.json` file(s) before running. - + + + +
-

Nucleic acid binder design

- -

Small molecule binder design

@@ -86,6 +82,11 @@ you will need to change the path in the `.json` file(s) before running.

Protein binder design

 +

Nucleic acid binder design

+ +
@@ -96,6 +97,11 @@ you will need to change the path in the `.json` file(s) before running.

Symmetric design

 +

Multipolymer design

+ +
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z%R3xb!j`X#ki!Hb{#I4nT%GegRsEfad;sXk2ic3r6+oS;zAUEb&~?CAOtAEPRsD|J zZWboJ-blCaf5!wmenVBiS}=}p0t+zJhkt*((|uZ_+G4R-PC!ItG{$S}!FZEb0H0IU j^nQ1=SS%LHapC_1Zi7UUuS3P|00000NkvXXu0mjfm*3JC diff --git a/models/rfd3/docs/examples/atom23_design.json b/models/rfd3/docs/examples/atom23_design.json new file mode 100644 index 00000000..18911450 --- /dev/null +++ b/models/rfd3/docs/examples/atom23_design.json @@ -0,0 +1,73 @@ +{ + "W05": { + "ss_dbn": ".(((((((((((((((((((..[[[[[[.)))))(((....)))(((....)))))))))))))))))((((((..]]]]]].)))))).", + "select_fixed_atoms": false, + "contig": "90-90R", + "length": "90-90", + "input": "../input_pdbs/AMP.pdb" + }, + "AMP_aptamer": { + "input": "../input_pdbs/AMP.pdb", + "ligand": "AMP", + "contig": "40-50R", + "length": "40-50", + "ori_jitter": 1, + "select_buried": {"AMP": "ALL"}, + "select_hbond_acceptor": { + "AMP": "N7,O4',O1P,O2P,O3P,N3,N1" + }, + "select_hbond_donor": { + "AMP": "N6,O3',O2'" + } + }, + "FMN_aptamer": { + "input": "../input_pdbs/FMN_3x21.pdb", + "ligand": "FMN", + "contig": "40-50R", + "length": "40-50", + "ori_jitter": 1, + "select_buried": {"FMN": "ALL"}, + "select_hbond_acceptor": { + "FMN": "O2,O4,N1,N5,O5',O2P,O3P" + }, + "select_hbond_donor": { + "FMN": "N3,O2',O3',O4'" + } + }, + "AMP_aptamer_noh": { + "input": "../input_pdbs/AMP.pdb", + "ligand": "AMP", + "contig": "40-50R", + "length": "40-50", + "ori_jitter": 1, + "select_buried": {"AMP": "ALL"} + }, + "FMN_aptamer_noh": { + "input": "../input_pdbs/FMN_3x21.pdb", + "ligand": "FMN", + "contig": "40-50R", + "length": "40-50", + "ori_jitter": 1, + "select_buried": {"FMN": "ALL"} + }, + "unindexed_rnasep": { + "input": "../input_pdbs/rnase_p_3q1q_active_site_small.pdb", + "contig": "50-80R,/0,100-120,/0,C1-4,C79-86", + "length": "162-212", + "ligand": "MG,PO4", + "unindex": "B49,B50,B51,B52,B321,/0,A56-58,/0", + "select_fixed_atoms": { + "B49": "ALL", + "B50": "ALL", + "B51": "ALL", + "B52": "ALL", + "B321": "ALL", + "A56-58": "ALL", + "C1-4": "ALL", + "C79-86": "ALL" + } + } + + + +} diff --git a/models/rfd3/docs/input.md b/models/rfd3/docs/input.md index 9253abc5..384101c6 100644 --- a/models/rfd3/docs/input.md +++ b/models/rfd3/docs/input.md @@ -120,7 +120,7 @@ Below is a table of all of the inputs that the `InputSpecification` accepts. Use | -------------------------------------------------------------- | ----------------- | --------------------------------------------------------------------- | | `input` | `str` | Path to and file name of **PDB/CIF**. Required if you provide contig+length. | | `atom_array_input` | internal | Pre-loaded [`AtomArray`](https://www.biotite-python.org/latest/apidoc/biotite.structure.AtomArray.html) (not recommended). | -| `contig` | `InputSelection` | (Can only pass a contig string.) Indexed motif specification, e.g., `"A1-80,10,/0,B5-12"`. | +| `contig` | `InputSelection` | (Can only pass a contig string.) Indexed motif specification, e.g., `"A1-80,10,/0,B5-12"`. When running inference with an atom223 checkpoint, contigs can include `R` or `D` suffixes to denote designed polymer type is RNA or DNA e.g. "A1-80,10-10R,20-20D,30-30,/0,B5-12" (designs a 10 length RNA chain, 20 length DNA chain and a 30 length protein chain). | | `unindex` | `InputSelection` | (Can only pass a contig string or dictionary.) Unindexed motif components, the specified residues can be anywhere in the final sequence. See [Unindexing Specifics](#unindexing-specifics) for more information. | | `length` | `str` | Total design length constraint; `"min-max"` or int for specified length. | | `ligand` | `str` | Ligand(s) by chemical component name (from [RSCB PDB](https://www.rcsb.org/)) or index. | @@ -136,6 +136,7 @@ Below is a table of all of the inputs that the `InputSpecification` accepts. Use | `symmetry` | `SymmetryConfig` | See {doc}`examples/symmetry`. | | `ori_token` | `list[float]` | `[x,y,z]` origin override to control COM (center of mass) placement of designed structure. | | `infer_ori_strategy` | `str` | `"com"` or `"hotspots"`. The center of mass of the diffused region will typically be within 5Å of the ORI token. Using `hotspots` will place the ORI token 10Å outward from the center of mass of the specified hotspots. Using `com` will place the token at the center of mass of the input structure.| +| `ori_jitter` | `float` | default `None`. Per batch, move the ori token in a random direction by a distance sampled from a geomtric distribution with the mean as the specified float value in Angstrom.| | `plddt_enhanced` | `bool` | Default `True`. Enables pLDDT (predicted Local Distance Difference Test) enhancement. | | `is_non_loopy` | `bool \| None` | Default `None`. If `True`/`False`, produces output structures with fewer/more loops.| | `partial_t` | `float` | Noise (Å) for partial diffusion, enables partial diffusion (sets the noise level.) Recommended values are 5.0-15.0 Å. See [Partial Diffusion](#partial-diffusion) for more information. | From b04cc1d72793a803203fc244e7944049bcf0710c Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Sat, 28 Mar 2026 11:51:34 -0700 Subject: [PATCH 47/49] more extensive documentation --- models/rfd3/docs/examples/atom23_design.json | 31 ++- models/rfd3/docs/examples/atom23_design.md | 228 +++++++++++++++++++ 2 files changed, 256 insertions(+), 3 deletions(-) create mode 100644 models/rfd3/docs/examples/atom23_design.md diff --git a/models/rfd3/docs/examples/atom23_design.json b/models/rfd3/docs/examples/atom23_design.json index 18911450..4632fef8 100644 --- a/models/rfd3/docs/examples/atom23_design.json +++ b/models/rfd3/docs/examples/atom23_design.json @@ -1,4 +1,9 @@ { + "multipolymer": { + "contig": "40-50R,/0,10-20D,/0,80-110", + "length": "130-180", + "input": "../input_pdbs/AMP.pdb" + }, "W05": { "ss_dbn": ".(((((((((((((((((((..[[[[[[.)))))(((....)))(((....)))))))))))))))))((((((..]]]]]].)))))).", "select_fixed_atoms": false, @@ -66,8 +71,28 @@ "C1-4": "ALL", "C79-86": "ALL" } - } - - + }, + "dict_input_ss": { + "ss_dbn_dict": { + "A6-25":"(((..)))....(((..)))", + "B1-20":"((((..))))...((...))" + }, + "contig":"30-30R,/0,30-30R", + "length":"60-60", + "input":"../input_pdbs/AMP.pdb" + }, + "paired_region_input_ss": { + "paired_region_list": ["A20-25,B10-15"], + "loop_region_list":["A10-19","B20-30"], + "contig":"50-50R,/0,50-50R", + "length":"100-100", + "input":"../input_pdbs/AMP.pdb" + }, + "paired_position_input_ss": { + "paired_position_list": ["A3,B3","A5,B5","A7,B7","A9,B9","A11,B11","A13,B13","A15,B15","A17,B17","A19,B19"], + "contig":"20-20R,/0,20-20R", + "length":"40-40", + "input":"../input_pdbs/AMP.pdb" + } } diff --git a/models/rfd3/docs/examples/atom23_design.md b/models/rfd3/docs/examples/atom23_design.md new file mode 100644 index 00000000..ce6b1789 --- /dev/null +++ b/models/rfd3/docs/examples/atom23_design.md @@ -0,0 +1,228 @@ +# RNA / DNA Design in RFdiffusion3 + +This guide describes extensions to RFdiffusion3 for nucleic acid and hybrid RNA–protein design, including: + +- RNA/DNA-aware contigs (`R` / `D` suffix) +- Ligand-conditioned aptamer design +- Secondary structure (SS) conditioning +- Base-pair constraints (region- and position-level) +- Partial structure fixing and unindexing + +--- + +## 1. Contig Syntax for RNA/DNA + +Contigs now support nucleic acid specification: + +- `R` → RNA segment +- `D` → DNA segment +- No suffix → protein (default) + +### Example + +```json +{ + "contig": "40-50R,/0,10-20D,/0,80-110" +} +``` +This corresponds to: 40–50 nt RNA, chain break, 10–20 nt DNA, chain break, 80–110 aa protein + +Multipolymer Design + +```json + +{ + "multipolymer": { + "contig": "40-50R,/0,10-20D,/0,80-110", + "length": "130-180", + "input": "../input_pdbs/AMP.pdb" + } +} +``` + +## 2. Secondary Structure Conditioning +### 2.1 Dot-Bracket Notation (Global) +```json +{ + "W05": { + "ss_dbn": ".(((((((((((((((((((..[[[[[[.)))))(((....)))(((....)))))))))))))))))((((((..]]]]]].)))))).", + "select_fixed_atoms": false, + "contig": "90-90R", + "length": "90-90", + "input": "../input_pdbs/AMP.pdb" + } +} +``` +`ss_dbn` specifies full RNA secondary structure + +Will be applied to the first L tokens, where L is the length of `ss_dbn`. + +### 2.2 Dictionary-Based SS Input + +Specify secondary structure for subsections: +``` json +{ + "ss_dbn_dict": { + "A6-25": "(((..)))....(((..)))", + "B1-20": "((((..))))...((...))" + } +} +``` +Used in: +``` json +{ + "dict_input_ss": { + "ss_dbn_dict": { + "A6-25": "(((..)))....(((..)))", + "B1-20": "((((..))))...((...))" + }, + "contig": "30-30R,/0,30-30R", + "length": "60-60", + "input": "../input_pdbs/AMP.pdb" + } +} +``` +## 3. Base Pair region Conditioning +### 3.1 Paired Regions + +Define paired and loop regions: +```json +{ + "paired_region_list": ["A20-25,B10-15"], + "loop_region_list": ["A10-19","B20-30"] +} +``` +Enforces pairing and loop propensity between residue ranges during sampling + +Used in: +```json +{ + "paired_region_input_ss": { + "paired_region_list": ["A20-25,B10-15"], + "loop_region_list": ["A10-19","B20-30"], + "contig": "50-50R,/0,50-50R", + "length": "100-100", + "input": "../input_pdbs/AMP.pdb" + } +} +``` + +### 3.2 Explicit Base Pair Positions + +Fine-grained base pairing control: + +```json +{ + "paired_position_list": [ + "A3,B3","A5,B5","A7,B7","A9,B9","A11,B11", + "A13,B13","A15,B15","A17,B17","A19,B19" + ] +} +``` +Used in: +```json +{ + "paired_position_input_ss": { + "paired_position_list": [ + "A3,B3","A5,B5","A7,B7","A9,B9","A11,B11", + "A13,B13","A15,B15","A17,B17","A19,B19" + ], + "contig": "20-20R,/0,20-20R", + "length": "40-40", + "input": "../input_pdbs/AMP.pdb" + } +} +``` +### Note: Most of the above jsons is not actually reading the `input` field. Kept as a dummy for the `inference3_engine`. + +## 4. Ligand-Conditioned Aptamer Design + +Supports small molecule binding RNA design. + +AMP Aptamer Example +```json +{ + "AMP_aptamer": { + "input": "../input_pdbs/AMP.pdb", + "ligand": "AMP", + "contig": "40-50R", + "length": "40-50", + "ori_jitter": 1, + "select_buried": {"AMP": "ALL"}, + "select_hbond_acceptor": { + "AMP": "N7,O4',O1P,O2P,O3P,N3,N1" + }, + "select_hbond_donor": { + "AMP": "N6,O3',O2'" + } + } +} +``` +Key Options + +`ligand`: ligand name in the input PDB + +`select_buried`: enforce burial of ligand atoms + +`select_hbond_acceptor` / `select_hbond_donor`: suggest Hbond interaction atoms + +`ori_jitter`: small random perturbation of ori token (from ligand COM) + + +## 5. Hybrid RNA–Protein Design with Constraints +### RNase P Active Site Example + +```json +{ + "unindexed_rnasep": { + "input": "../input_pdbs/rnase_p_3q1q_active_site_small.pdb", + "contig": "50-80R,/0,100-120,/0,C1-4,C79-86", + "length": "162-212", + "ligand": "MG,PO4", + "unindex": "B49,B50,B51,B52,B321,/0,A56-58,/0", + "select_fixed_atoms": { + "B49": "ALL", + "B50": "ALL", + "B51": "ALL", + "B52": "ALL", + "B321": "ALL", + "A56-58": "ALL", + "C1-4": "ALL", + "C79-86": "ALL" + } + } +} +``` +Key Features + +Mixed RNA + protein + fixed fragments + +`unindex`: removes residues from positional indexing + +`select_fixed_atoms`: freezes specified atoms + +Ligands (MG, PO4) included in design context + +Useful for catalytic residues or structural motifs + +## 7. Summary of New Features + +R / D suffix → RNA / DNA specification in contigs + +`ss_dbn` → global secondary structure constraint (optional) + +`ss_dbn_dict` → local secondary structure constraints (optional) + +`paired_region_list` → helix-level pairing constraints (optional) + +`paired_position_list` → base-level pairing constraints (optional) + +ligand + selection options → aptamer design + +`unindex` → remove residues from indexing + +`select_fixed_atoms` → freeze structural elements + + +--- + From 24e92c0b4333c136ec6aafda2d1718c243fd04cb Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Sat, 28 Mar 2026 11:57:37 -0700 Subject: [PATCH 48/49] cleanup --- models/rfd3/docs/examples/atom23_design.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/models/rfd3/docs/examples/atom23_design.md b/models/rfd3/docs/examples/atom23_design.md index ce6b1789..f0f3f53a 100644 --- a/models/rfd3/docs/examples/atom23_design.md +++ b/models/rfd3/docs/examples/atom23_design.md @@ -205,19 +205,19 @@ Ligands (MG, PO4) included in design context Useful for catalytic residues or structural motifs -## 7. Summary of New Features +## 7. Summary of Features Used R / D suffix → RNA / DNA specification in contigs -`ss_dbn` → global secondary structure constraint (optional) +`ss_dbn` → global secondary structure constraint -`ss_dbn_dict` → local secondary structure constraints (optional) +`ss_dbn_dict` → local secondary structure constraints -`paired_region_list` → helix-level pairing constraints (optional) +`paired_region_list` → helix-level pairing constraints -`paired_position_list` → base-level pairing constraints (optional) +`paired_position_list` → base-level pairing constraints -ligand + selection options → aptamer design +ligand + selection options → aptamer design `unindex` → remove residues from indexing From 6462d22ecf784e00984abb4448baf27708537ace Mon Sep 17 00:00:00 2001 From: Raktim Mitra Date: Sat, 28 Mar 2026 12:04:48 -0700 Subject: [PATCH 49/49] main readme updates --- models/rfd3/README.md | 16 +++++++++------- models/rfd3/docs/examples/atom23_design.md | 2 +- 2 files changed, 10 insertions(+), 8 deletions(-) diff --git a/models/rfd3/README.md b/models/rfd3/README.md index 0886afc0..bdbf0dd0 100644 --- a/models/rfd3/README.md +++ b/models/rfd3/README.md @@ -1,6 +1,6 @@ # De novo Design of Biomolecular Interactions with RFdiffusion3 -RFdiffusion3 (RFD3) is a diffusion method that can design protein structures +RFdiffusion3 (RFD3) is a diffusion method that can design biopolymer structures under complex constraints. This repository contains both the training and inference code, and @@ -62,6 +62,8 @@ For example, you can fix sequence and not structure (prediction-type task), fix For full details on how to specify inputs, see the [input specification documentation](./docs/input.md). You can also see `foundry/models/rfd3/configs/inference_engine/rfdiffusion3.yaml` for even more options. +Nucleic acid design, along with proteins, is also possible using RFD3 using the atom23 checkpoints. For full details see the [atom23 design documentation](./docs/examples/atom23_design.md) + ## Further example JSONs for different applications Additional examples are broken up by use case. If you have cloned the repository, matching `.json` files are in `foundry/models/rfd3/docs/examples` @@ -75,30 +77,30 @@ you will need to change the path in the `.json` file(s) before running. diff --git a/models/rfd3/docs/examples/atom23_design.md b/models/rfd3/docs/examples/atom23_design.md index f0f3f53a..91f54ebc 100644 --- a/models/rfd3/docs/examples/atom23_design.md +++ b/models/rfd3/docs/examples/atom23_design.md @@ -133,7 +133,7 @@ Used in: } } ``` -### Note: Most of the above jsons is not actually reading the `input` field. Kept as a dummy for the `inference3_engine`. +### Note: Most of the above jsons is not actually reading the `input` field. Kept as a dummy for the `inference_engine`. ## 4. Ligand-Conditioned Aptamer Design
-

Small molecule binder design

+

Small molecule binder design

 -

Protein binder design

+

Protein binder design

 -

Nucleic acid binder design

+

Nucleic acid binder design
-

Enzyme design

+

Enzyme design

 -

Symmetric design

+

Symmetric design

 -

Multipolymer design

+

Multipolymer design

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