title: "HIV tutorial with ScipionTomo" author: [Scipion Team] date: "2024-10-13" subject: "Markdown" keywords: [Markdown, Example] subtitle: "From movies to averages" lang: "en"
titlepage: true titlepage-text-color: "7137C8" titlepage-rule-color: "7137C8" titlepage-rule-height: 2 titlepage-logo: "logo.pdf" logo-width: 30mm toc: true toc-own-page: true ...
This tutorial can be carried out with:
- Small dataset: Quick tutorial with a single tilt series (recommended), it obtains a low resolution map.
- Large dataset: Long tutorial with 5 tilt series, it obtains a high resolution structure.
Both datasets are subsets of the EMPIAR entry EMPIAR-10164, see the reference B. Turonova et.al 2020
It only contains a single tilt series. This workflow is ideal to be executed in a normal laptop with GPU. The data set can be downloaded using the next command line
scipion3 testdata --download reliontomo_STA_HIV_smalldataset
Alternatively, the dataset can be found in the next link https://scipion.cnb.csic.es/downloads/scipion/data/tests/reliontomo_STA_HIV_smalldataset/
This tutorial makes use of 5 tilt series, with the next identifiers: TS_01, TS_03, TS_43, TS_45, TS_54. These tilt series are a subset of the EMPIAR entry EMPIAR-10164.
The tilt series and the mdoc files can be downloaded using the next shell script.
for i in 01 03 43 45 54;
do
echo "======================================================"
echo "================= Downloading TS_${i} ================"
# download the mdoc file
wget \
--timestamping \
--no-directories \
--directory-prefix ./mdoc \
ftp://ftp.ebi.ac.uk/empiar/world_availability/10164/data/mdoc-files/TS_${i}.mrc.mdoc;
# download the frames
wget \
--timestamping \
--no-directories \
--directory-prefix ./frames \
ftp://ftp.ebi.ac.uk/empiar/world_availability/10164/data/frames/TS_${i}_*.mrc;
done
The workflow of this tutorial can be summarized in the next figures. Use the this workflow to follow the different steps of the next sections
Reference: J. Jimenez de la Morena et.at. 2022
Plugin: scipion-em-tomo
In this step the acquired data will be imported into the Scipion framework. The protocol tomo - import tilt series movies allows to import different kinds of raw data. In this tutorial the raw data is a set of .mrc and .mdoc files.
Note: It is mandatory that the mdoc files will be located in the same folder of the mrc files.
The .mrc files contain the acquired images, while the .mdoc files store detailed acquisition information such as sampling rate, dose per frame, and tilt axis angle. The protocol will parse the .mdoc files and store this information in the Scipion database.
Protocol parameters: Microscope voltage, spherical aberration, amplitude contrast, magnification, pixel size, tilt axis angle or dose are empty. An empty entry means that the parameter will be read from the mdoc. However, if the user introduces a parameter, Scipion will take the introduced value from the user instead of the mdoc parameter. This allows to correct possible errors in the mdoc file.
The used parameters are shown in the Figure. The critical ones are:
- Files directory: Set the path of the movies. Note that the mdoc files must be in the same folder.
- Pattern: This tutorial uses mdoc. Set as
*.mdocto import all mdoc files. - Tomo5 mdoc: Set as No.
- Microscope Voltage: 300kV
- Spherical Aberration: 2.7 mm
- Amplitude contrast: 0.1
- Pixel size: 0.675 A/px. By setting this parameter the pixel size from the mdoc will be overwritten.
Reference: X. Li et. al. 2013, S. Q Zhenget et.al. 2017
Plugin: scipion-em-motioncorr
Once the tilt series movies were imported, they must be aligned to obtain a tilt series. The protocol motioncor - ts movie alignment will find and correct the relative movement between the frames of each tilt image.
This protocol can be executed with default values. It will align all frames that conform each “tilt stack" (movie) and without binning the output images.
Note: The input tilt series movies of this dataset where acquired in superresolution mode. For this reason a binning 2 was introduced.
The most important parameters are:
- Input Tilt-Series (movies): The imported set of tilt series movies from the previous step.
- Frames to Align: This is the range of frames per tilt image to be aligned. If the parameter
tois empty, then all frames will be aligned - Use align frames range to sum: Yes.
- Binning factor: 2. Because this movies were acquired in superresolution mode
- Split & sum odd/even frames: No
The other tabs of the protocol (motioncor params, gain and defects, EER, Mag corr) can be executed with the default parameters.
Reference: J. Jimenez de la Morena et.at. 2022
Plugin: scipion-em-tomo
Views or tilt images can be excluded with the TomoViewer. The main reasons to exclude a tilt image are:
- Bad quality image: The tilt image is dark, presents stripes or was exposed to a high dosee.
- Misaligned image: The tilt image is misaligned. The tilt series sequence presents a jump at that tilt angle, making a non smooth transition between tilt images.
To exclude a tilt images just select the corresponding image and press the space, alternatively it can be marked by clicking on the exclude box. The excluded images will be highlighted in red. Finally, it will be neccesary to generate a new set of tilt series by clicking on the botton Save. This botton opens a new window asking if the user wants to re-stack the tilt series, or if the user preffers to mark the tilt images as excluded. The differences are the next ones:
- Re-stack: A new stack of tilt series will be created without the excluded views. From this point the later protocols will not have access to the excluded views.
- Marked as excluded: The excluded views are marked but not removed from the stack. The later protocols will process these images (if it is possible, this depends on the protocol), but they will be kept marked as excluded. This allows to rescue the views.
The views to exclude for the small dataset are the ones at -60.0º, -57.0º, -54.0º and 60.0º. In this tutorial we will use the re-stacked tilt series.
Tip: We remove the dark images but also those views that present a significant displacement.
Reference: J.R. Kremer 1996
Plugin: scipion-em-imod
This protocol is completely optional and is not included in most of the workflows, however, it is a good practice to remove bright points in the images. The interaction of electrons with the sample can generate X-rays. They can be detected by the camera, and identified in the images as very bright pixels. Therefore, the X-ray peaks are an unwanted effect that should be corrected. The protocol imod - Xray eraser allows to remove these bright points. The input will be a tilt series after excluding the views (output of the movie alignment). This protocol also can be executed with default parameters.
Reference: J.R. Kremer 1996
Plugin: scipion-em-imod
The effect of the radiation damage should be taken into account. This is not a mandatory step, but it is also a good practice. During the image acquisition the sample is exposed to the radiation. Each tilt image has associated a different accumulated dose. The protocol imod - dose filter applies a low pass filter based on the accumulated dose per tilt image. The result will be a set of tilt series, but with a dose correction that makes the images look like a low pass filtered version of the original ones (with a soft graining).
The protocol can be executed with the default parameters.
Tip: The dose filter helps in the tilt series alignment step.
Warning: As a consequence of the dose filter, the output tilt series will present a zero dose values (see output with tomoViewer). If the dose filtered tilt series are later aligned, then it will be neccesary to associate the alignment to the unfiltered tilt series (using
tomo - assign alignment). Otherwise the subtomogram averaging algorithms that make use of the dose will work in a suboptimal manner, this can be specially import for the STA with reliontomo.
There are many methods to align tilt series in the ScipionTomo framework, as they are:
- IMOD
- Aretomo
- EmanTomo
In this tutorial IMOD will be used, however, it is absolutely possible to do this tutorial with any other of the list.
Reference: J.R. Kremer 1996
Plugin: scipion-em-imod
The imod alignment is composed by three independent protocols in the next order:
imod - Coarse alignment: It estimates the shifts between the tilt imagesimod - generate fiducial model: It find the gold beads position (fiducial alignment) or patches position (patch tracking or fiducialess alignment) to be used as markers for aligning.imod - fiducial alignment: It estimates rotations and carries out the final alignment of the tilt series.
The first step of the IMOD alignment is the protocol imod - coarse prealignment. In this step only the translational alignment (shifts) is solved (no angle correction). This calculation is performed by cross correlating the successive images from the tilt-series and stretching the images with the larger tilt angle perpendicular to the tilt axis (cosine stretching).
The protocol can be executed with default parameters:
- Input tilt series: The tilt series to be aligned the dose filtered one is recommended.
- Use cummulative correlation: No.
- Tilt axis angle: Leave it empty. It takes the axis orientation from the scipion database.
Warning: This protocol is quite sensitive to the tilt axis orientation. A wrong tilt axis orientation can be the main suspicious of casting unusual results. If a wrong tilt axis orientation was imported, the tilt axis can be fixed in the tilt axis form of this protocol.
Note: The output of this protocol is a set of tilt series. In the summary, the output tilt series presents a
+aliflag. This flag informs about the alignment, the tilt series present an associated transformation matrix (with the estimated shifts). The +ali indicates that matrix is associated as metadata but not applied to the tilt series.
Once the coarse prealignment process is finished, it is possible to generate the landmark (fiducial) models associated to the tilt series. The protocol imod - generate fiducial model can work with fiducial and fiducialess samples. The objective of the protocol is to define a chains of landmarks to align the tilt series in a later step.
The landmark model generation provides information of the position of each gold bead in the image. Specifically, it defines the 2D coordinates of one fiducial along all the images in which it has been detected (not necessarily the whole tilt-series). Thus, it is possible to track the position of several landmarks along the whole tilt series, a posterior step will align the tilt series based on these landmarks. If the sample is fiducialless, then the landmarks will be centers of some patches defined in the tilt images.
We will use as input the Tilt Series from coarse prealignment with the next parameters:
- Model generation: Make seed and Track. This is the fiducial based alignment
- Tilt series: The output of the imod - coarse prealignment
- Fiducial diameter (nm): 10 nm
- Nuber of fiducials: 10
- Find on two surfaces: Yes
- Track with fiducial model as seed: Yes
Tip: In samples with gold beads (fiducials), it is important to properly set the fiducial radius (in nanometers) since, if the indicated size is significantly different from the real one, the algorithm will fail in the fiducial location and posterior tracking. Also, it is possible to set the algorithm to differentiate between those gold beads that are in front (or over) the sample and the ones that are in the rear part (or under it), using the Find on two surfaces option.
The output of this protocol is a SetOfLandmarkModels. This object is able to store the position information of each gold bead through the tilt-series for every tilt series belonging to the set. It is possible to visualize this object with the imod viewer. Once the landmarks are openned with imod-3dmod, then with the key v the trajectories can be observed.
Once the landmark (fiducial) models are generated it is possible to calculate the final alignment of the tilt-series. To do that the protocol imod - fiducial alignment will be used. In this final stage not only the translational movements (shifts) are corrected, basically inherited from the prealignment correction, but also the rotation of the images (angle), aligning the tilt axis with the vertical (Y) axis of the image.
Note: Scipion uses the imod convention putting the tilt axis in the vertical (Y-axis)
The protocol imod -fiducial alignment will make use of the SetOfLandmarkModels as result of the previous protocol and will try to solve the geometry to find the transformation matrix per tilt image that explains the landmark model as rotations of the sample around the Y axis.
To execute the protocol the next paramaters are used:
- Input:
-- Fiducial model: The set of landmarks obtained in the previous protocol,
imod - generate fiducial model. -- Assume beads on two surfaces: Yes -- Generate interpolated tilt series: Yes at binning 4 -- Assume beads on two surfaces: Yes - Global variables: -- Rotation solution type: Group tilt angles, size 5 -- Tilt angle solution type: Yes at binning 2 -- Distorsion solution type: Disable
Tip: To validate the tilt series alignment, the output interpolated tilt series should be visualized.
This protocol also generates a refined SetOfLandmarkModels with no gaps, meaning that the position of the landmark lost for some images in the previous steps are now interpolated from the transformation matrices calculated. The protocol also generates a setOfCoordinates3D. These are the coordinates of the fiducials (gold beads) in the third-dimensional space, being possible to calculate their positions because we already know the final alignment.
The output tilt series as result of the alignment process can be visualized with the TomoViewer. In the figure, it is shown the output tilt series with the alignment (+ali). If the mouse is place on a tilt image, the associated transforation matrix to that tilt image appears. The shown rot, shiftX and shiftY are the rotation and shift extracted from the tilt image following the imod convention.
Note: The output tilt series present a dose equal to zero. This is because the tilt series were dose filtered in a previous step.
Tip: The
TomoViewercan be used to check the transformation matrix, the refined tilt angles.
The fiducials trajectories can also be observed opening the output SetOfLandmarks with imod-3dmod, and pressing the key v.
Reference: TeamTomo
Plugin: scipion-em-fidder
Fiducials markers were used to tilt series, due to their high contrast. However, the strong signal of the gold beads can introduce artifacts in the reconstruction. Specially, the artifacts can complicate the tomogram interpretation and introduce errors in the use of image processing algorithms as: Picking and sutomogram averaging. These effects can be avoided by erasingthe fiducial markers in the images. To do that the protocol fidder - detect and erase fiducials can be used. Fidder uses a U-net (deep learning) trained at 8A/px to segment the fiducials. In a second step, the segmented fiducial markers are substituted with white noise matching the local mean and global standard deviation of the image. Fidder only presents a free parameter, the threshold, which aims to determine probability threshold for deteting the gold beads. An strict value of 0.9 should work with this data set. The figures
To execute the protocol the next paramaters are used:
- Input:
-- Tilt series: The raw tilt series as result of the xray eraser,
imod - xray eraser. -- Threshold: 0.5
The result of the protocol can be observed in the next figure
When the dose filter was applied to align the tilt series, the dose of the aligned tilt series was set to zero. The does filter helped for aligning tilt series. Unfortunately, some subtomogram averaging protocols requires unfiltered tilt series, it means without dose filtering. For this reason, it would be usefull to assign the alignment information to the tilt non-dose filtered tilt series. This task can be carried out with the protocol tomo - tilt-series assign alignment. The protocol involves two tilt series: One to take the alignment and other to set the alignment. In this tutorial the transformation matrix from the alignment will be set to the output of the x-ray eraser tilt series (see the workflow).
Note that the output of tomo - tilt-series assign alignment presents a non-zero dose. This can be checked by by visualizing the tilt series with TomoViewer, see figure.
There are many methods to estimate the CTF in ScipionTomo, as they are:
- CTFfind
- gCTF
- AreTomo
- IMOD CTFPlotter
- emantomo
In this tutorial CTFfind will be used.
Reference: J. Elferich et. al. 2024
Plugin: scipion-em-cistem
The CTF estimation is a critical step to achieve high resolution in Subtomogram Averaging, or even to visualize fine details in cellular environments. The CTF correction attempts to compensate for the loss of information that the microscope introduces as a consequence of aberrations and defocus.
Tip: The CTF should be estimated with the raw tilt series without any kind of preprocessing. The use of dose filters, or alignment can change the spectral properties of the tilt series, and throw inaccuracies in the CTF estimation.
The CTF will be estimated with the protocol cistem - tilt-series ctffind. The critical parameters are:
- FFT box size: 512 px as default value.
- Resolution (A): Min 50A and max 5A
- Defocus range: In this case from 10000 to 50000 A.
- Defocus Step: 500 A
- Phase shift: No
Tip: A narrow defocus range ensure a better defocus estimation. A good approach is to provide a range centered in the nominal defocus of the tilt series acquisition. This information is contained in the mdoc files.
The result of any CTF estimation that can be visualized by clicking on Analyze results or alternatively by right-clicking on the output, in the Summary panel (low area of the main Scipion window). The viewer will open a window with the list of SetOfCTFTomoSeries corresponding to each tilt series. In this window the defocus, astigmatism and resolution associated to each tilt image can be visualized. Also, the window presents a summary plot with the defocus and resolution per tilt image. This plot will be of special interest to validate the estimation of the CTF.
CTFs can be excluded with the CTFTomoViewer. The main reasons to exclude a CTF are: a bad estimation of the defocus value, or a high astigmatism. To exclude a CTF just select the corresponding CTF and press the space, alternatively it can be marked by clicking on the exclude box. The excluded CTFs will be highlighted in red. Finally, it is neccesary to generate a new set of CTF by clicking on the botton Generate subsets. Despite some resolution values are out of range, the defocus estimations are in range, for this reason only the out of range views are excluded.
Reference: J.R. Kremer 1996
Plugin: scipion-em-imod
Note: Scipion has a standard CTF model, when the CTF is estimated with any CTF estimator, the output is converted and stored in the Scipion standard. To correct the CTF, Scipion converts the standard into the corresponding package (in this case imod).
The CTF correction can be performed with the protocol imod - correct CTF. The input of this protocol are the tilt series with assigned alignment and a set of CTFs previously estimated. The tilt series will be the ones we assigned the alignment information. The used parameters for this protocol will be left as default parameters.
Warning: The output of the CTF correction is a set of tilt series with the alignment applied. This is because Imod applies the alignment to correct the CTF in strips parallel to the tilt axis
Reference: J.R. Kremer 1996
Plugin: scipion-em-imod
The CTF-corrected tilt series will be the input data for a later tomogram reconstruction. Up to this step we have worked with the full-size tilt series (binning 1). IF the tomograms are reconstructed at bin 1, they will be very heavy. To safe disc and enhance the SNR of the tomogram the tilt series will be binned. The protocol imod - preprocess tilt series allows to perform different preprocessing operation on the tilt series, as binning or adjusting the gray values.
For the tutorial that uses the small dataset, use bin 6 For the tutorial that uses the large dataset, use bin 4
There are many methods to reconstruct tomogram in ScipionTomo framework, as they are:
- Tomo3d
- Imod
- NovaCTF
- AreTomo
- Emantomo
In this tutorial tomo3D will be used.
Reference: J.I. Agulleiro 2011J.I. Agulleiro 2015
Plugin: scipion-em-tomo3d
To reconstruct the tomogram from the tilt series the protocol tomo3d - reconstruct tomogram. Tomo3D provides two different reconstruction algorithm: Weighted Back Projection (WBP) and Simultaneous Iterative reconstruction technique (SIRT). The method of SIRT will be used.
Tip: WBP is faster than the SIRT method, but SIRT provides higher contrast. To visualize cellular enviroments SIRT is recommended, or to pick subtomogram with a template matching approach. If classical subtomogram averaging is going to be carried out, WBP is recommended.
The input of the reconstruction will be the binned CTF corrected tilt series. A SIRT recontruction is recommended in this case to produced tomograms with high contrast. The Tomogram Thickness was set to 300 voxels.
The output can be visualized by clicking on Analyze results or alternatively by choosing the visualization tool by right-clicking on the output in the Summary box. In 3dmod, it is interesting to visualize the tomogram in slices along the 3 axis, this can be done with Ctrl+x
Reference: J.J. Moreno 2018J.J. Moreno 2018
Plugin: scipion-em-tomo3d
To highlight the signal o the HIV it is possible to use a denoising algorithm. In this case, the procol tomo3d - denoise tomogram. Tomo3D provides two different denoising algorithms: EDD and Bflow. In this tutorial the EED mehtod will be used.
The input of the denoising will be the previous reconstructed tomograms with the next parameters:
- Tomograms: The reconstructed ones
- Denoising method: Edge-Enhancing Diffusion (EED).
- Sigma Gaussian Filter: 0.5
- Number of iterations: 10
The output can be visualized by clicking on Analyze results or alternatively by choosing the visualization tool by right-clicking on the output in the Summary box.
Reference: D. Castaño-Diez et.al 2012
Plugin: scipion-em-dynamo
This picker is quite flexible and it is designed to pick broad variety of geometries: vesicles, spheres, or filaments among other. The input of this protocol only requires the set of tomograms to be picked. It is composed by two steps:
- dynamo - vectorial picking: This step aims to pick a set of points that define a mesh. The mesh would represent a first approach to the geometry to be picked.
- dynamo - model workflow: The previous meshed will be fitted to the selected geometrical model. Then, random points will be selected on the mesh as coordinates of the particles.
This section shows how to pick with dynamo, however, to avoid the manual task of picking a set of coordinates is also provided in a file. These coordinates can be imported in ScipionTomo.
The reconstructed present several several HIV virus. In our case we are interested in the reconstruction of structure of the immature capsid lattice. To do that, tt is neccesary to identify the capsid in the tomograms. The protocol dynamo - vectorial picking will be used. The parameter boxsize only has a visualization purpose (size of the plotted points on the tomogram), it does not affect to the picked coordinates.
Note: For the small set tutorial only one or two viruses will be picked. For the large dataset tutorial, all viruses will be picked. To avoid manual picking the coordinates can be imported later in te tutorial.
When the protocol is executed, a new window with the list of tomograms to be picked will appear. By double clicking on a tomogram the dynamo picking interface will be opened. Dynamo hsa different kinds of geometrical pickers. Due to the geometry of the HIV virus an ellipsoidal vesicle model will be used to fit the geometry of the HIV. By means of this model, the virus capsid will be manually picked, as it is shown in the figure. Then, it will be neccesary to select on the capsid contour placing the mouse pointer on the capsid and using the key c to fix a marker. Once the capsid of a virus has been picked, a new model (ellipsoidal vesicle) will be created and the process repeated until end with the picking of all viruses presented the tomogram. Then, the dynamo interface can be closed and the list window with the list of tomogram will be updated with the number of picked coordinates in the already picked tomogram.
It is not neccesary to pick the complete surface of the virus, dynamo just requires some points to complete the virus geometry as it is shown in the figure
Note: Dynamo picker is an oriented picker. This means that the picked coordinates have an orientation (transformation matrix). The orientation will be the normal direction to the picked surface.
Warning: The output of this protocol is a set of meshes, not coordinates. The meshes will be used by the next protocol to fit the geometry (ellipsoid) and and pick on it.
The protocol dynamo - model workflows uses the picked set of meshed as result of the protocol dynamo - vectorial picking and provides a
To avoid the manual picking, a sqlite file with all picked coordinates for this data set is provided. This file can be found in the downloaded dataset. The file contains the result of the protocol dynamo - model workflow. The import aims to sumimulate the result of the model workflow. Thus, the manual picking with dynamo is avoided simplifying the tutorial. To import the coordinates in Scipion, the protocol tomo - import 3D coordinates from Scipion can be used. The protocol requires:
- Scipion sqlite file:
- Tomograms: Set the tomogram that will be associated to the coordinates. The reconstructed tomograms with tomo3D or imod reconstruction.
- Box size (px): 54 px. This only has visualization purpose. It represents the size of the plotted boxed of the picked coordinates on the tomogram.
Note: ScipionTomo has other protocol to import coordinates that do not requires sqlite files, it is called
tomo - import coordinates.
If the imported coordinates are opened with dynamo viewer, the next result should be visualized. This result is identical to the obtained one with the protocol dynamo - model workflow.
Reference: A. Burt 2024
Plugin: scipion-em-reliontomo
Pseudo-subtomograms are 2D cropped images or 3D-reconstructed subtomograms, premultiplied by the CTF. They do not represent physical objects. The premultiplication by the CTF, allows to avoid aliasing and speed up the computational times. The protocol reliontomo - extract subtomos can be used to extract the pseudo-subtomograms. This protocol allows to extract them as 2D or 3D. In this tutorial both kinds of psuedo subtomogram will be extracted. The 3D pseudo-subtomogram will be used for generating an initial volume, and the 2D pseudo-sutomograms will be used in the refinement step.
To extract the pseudo subtomograms the next input data will be required:
- Tilt series: They must contain the alignment information.
- CTF estimation: From the CTFfind estimation with the excluded views
- Coordinates: They are the picked coordinates. The imported coordinates will be used
- Binning factor: 6 This is the scaling factor in relation to the input tilt series
- Box size (px): 128. This box size will be used to correct the CTF in the cropped particles from the tilt series
- Croppped box size (px): 64. This will be the size of the pseudo-subtomograms and therefore of the reconstructed map.
- Write output as 2D stacks: Set Yes for 2D pseudo-subtomograms and No for 3D pseudo-subtomograms.
Note This protocol will be executed twice using the same parameters, but first writing output as 3D and later as 2D pseudo-subtomogram.
Tip: The 3D pseudo-subtomograms work better for obtaining a 3D initial model than the 2D pseudo-subtomograms.
The initial model can be estimated with the protocol reliontomo - 3D initial model. The input will be the extracted 3D pseudo-subtomogram at bin 6 from the previous step.
- Number of VDAM mini-batches: 70 large dataset, 40 small dataset. This is the number of iterations to be carried out.
- Regularization parameter: 4. It goes from 0 to 4. Values close to 4 put more strenght on the data.
- Circular Mask diameter: 350 A. A good value is to set the protein diameter
- Symmetry group: C6. In this case the protein has C6 symmetry. For initial volumes a C1 symmetry is recommended.
- Prior width on tilt angle: 10. degrees. It defines the prior on the tilt to be estimated
The result of this protocol should be similar to the one shown in the Figure. To visualize it, the average map can be opened with Scipion or Chimera.
Using the initial model, it is possible to refine it to enhance the map quality pushing the resolution. The objective of this step will be to reach Nyquist resolution, and then in a later step extract the pseudo-subtomogram at a smaler pixel size. To refine the model, the protocol reliontomo - 3D auto-refine can be used. The input will be the extracted 2D pseudo-subtomogram (2D not 3D) at bin 6 and the estimated initial model. The refinement parameter will be.
- Pseudo-subtomograms: The extracted 2D-pseudosubtomograms
- Reference volume: The obtained initial volume
- Is initial 3D map on absolute greyscale?1: Yes
- Resize references if needed?: Yes
- Initial low-pass filter (A): 60A
- Symmetry group: C6. In this case the protein has C6 symmetry
- Do CTF-correction?: Yes
- Ignore CTF until first peak?: No
- Circular Mask diameter: 350A.
- Mask particles with zeros: Yes
- Use blush regularization: No
- Initial angular sampluing interval: 7.5 deg
- Initial offset range (px): 5 px
- Initial offset step (px): 1 px
- Local searches from auto-sampling: 1.8 deg
- Symmetry to be relaxed: Leave empty
- Use finer angulat sampling faster: No
- Prior width on tilt angle: 10 deg
The result of this protocol should be similar to the one shown in the Figure. Fpr the latge dataset the FSC should reach Nyquist, for the small data set with 1-2 picked viruses the FSC should be close to Nyquist, around 20A resolution. If Nyquist resolution is reached, the next step will be to reduce the binning refining again the obtained map
.
This steps shows how to reduce the binning keeping the alignment of already refined pseudo-subtomograms. The protocol reliontomo - extract subtomos allows this task. The parameters :
- Coordinates: They will be the refined pseudo-subtomograms from the 'reliontomo - 3d auto-refine`.
- CTF: The estimated with CTFfind with excluded views.
- Tilt series: The aligned ones with dose and excluded views.
- Binning: 2.
- Box size (px): 256 px. This box size will be used to correct the CTF in the cropped particles from the tilt series
- Croppped box size (px): 128 px. This will be the size of the pseudo-subtomograms and therefore of the reconstructed map.
- Write output as 2D stacks: Set Yes for refinin 2D pseudo-subtomograms are recommended
In this step the refined pseudo-subtomograms from the previous autorefine are used to reconstruct the protein, but keeping their angular assignment. This is only a reconstruction step. The protocol reliontomo - reconstruct particle
The reconstructed protein can be visualized with Scipion (to see the slices) or with Chimera (to see the 3D map). As it can be observed in the figure the map quality enhanced in comparison to the reconstruction at bin 6.
The user can continue refining at higher binning (Bin 1) to reach a high resolution structure. To do that the previous refinement cycle must be reapeted.
We want to hear from you! Any comment, question, or complaints regarding this tutorial, the use of Scipion or xmipp can be sent to these emails: scipion@cnb.csic.es, xmipp@cnb.csic.es.
Also you can follow us on our social media
Twitter: https://twitter.com/instructi2pc
Tutorials about Scipion use, and cryoEM seminars can be found on your YouTube channel
Youtube: https://www.youtube.com/user/BiocompWebs
We also have a slack channel where our most active members keep in touch daily. You can request access on scipion@cnb.csic.es
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