Welcome to the official documentation for GlmUpred, a specialized computational resource developed to identify and design inhibitors against the bacterial GlmU protein. The GlmU protein is a bifunctional enzyme essential for the synthesis of peptidoglycan and lipopolysaccharide in bacteria, making it an attractive target for developing new antibacterial agents, particularly against drug-resistant strains like Mycobacterium tuberculosis.
Web Server: https://webs.iiitd.edu.in/raghava/gdoq
Singla, D., Anurag, M., Dash, D., & Raghava, G. P. S. (2011). A web server for predicting inhibitors against bacterial target GlmU protein. BMC Pharmacology, 11, 5. https://doi.org/10.1186/1471-2210-11-5
Zenodo:-(https://doi.org/10.5281/zenodo.20140080)
The emergence of drug-resistant bacteria poses a global health threat. GlmUpred addresses this by providing a platform to predict the inhibitory activity (
- QSAR Modeling: Predicts inhibitory activity based on molecular descriptors calculated from the chemical structure.
- Docking Integration: Incorporates docking energies into the prediction models to account for the structural fit within the GlmU active site.
- Diverse Training Data: Models were trained on 84 diverse GlmU inhibitors retrieved from PubChem BioAssay.
The performance of GlmUpred is based on sophisticated machine learning models that link chemical features to biological activity.
| Model Type | Descriptors | Correlation ( |
|---|---|---|
| Docking-based | AutoDock Energies | 0.35 |
| Descriptor-based | CDK & PaDEL Descriptors | 0.81 |
| Hybrid | Mixed Descriptors | 0.81 |
-
Activity Prediction: Users can submit the structure of a chemical compound (in SMILES or SDF format) to predict its potential
$pIC_{50}$ against GlmU. - Molecular Descriptors: The server calculates a wide range of descriptors including constitutional, topological, and geometric properties.
- Target Specificity: Specifically optimized for the C-terminal uridylyltransferase domain of the GlmU protein.
- Antibiotic Discovery: Screening large chemical libraries to find novel leads against tuberculosis and other bacterial infections.
- Lead Optimization: Assisting medicinal chemists in modifying compounds to increase their potency against the GlmU target.
- Computational Biology: Providing a specialized tool for studying the structure-activity relationships of bacterial enzyme inhibitors.
Prof. Gajendra P. S. Raghava Bioinformatics Centre, Institute of Microbial Technology, Sector 39A, Chandigarh, India. Email: raghava@imtech.res.in
This resource is open-access and distributed under the terms of the Creative Commons Attribution License, permitting unrestricted use and distribution provided the original work is properly credited.