Scott, Oliver B;
Gu, Jing;
Chan, AW Edith;
(2022)
Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network.
Journal of Chemical Information and Modeling
10.1021/acs.jcim.2c00832.
(In press).
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Abstract
The analysis and comparison of protein-binding sites aid various applications in the drug discovery process, e.g., hit finding, drug repurposing, and polypharmacology. Classification of binding sites has been a hot topic for the past 30 years, and many different methods have been published. The rapid development of machine learning computational algorithms, coupled with the large volume of publicly available protein–ligand 3D structures, makes it possible to apply deep learning techniques in binding site comparison. Our method uses a cutting-edge spherical convolutional neural network based on the DeepSphere architecture to learn global representations of protein-binding sites. The model was trained on TOUGH-C1 and TOUGH-M1 data and validated with the ProSPECCTs datasets. Our results show that our model can (1) perform well in protein-binding site similarity and classification tasks and (2) learn and separate the physicochemical properties of binding sites. Lastly, we tested the model on a set of kinases, where the results show that it is able to cluster the different kinase subfamilies effectively. This example demonstrates the method’s promise for lead hopping within or outside a protein target, directly based on binding site information.
| Type: | Article |
|---|---|
| Title: | Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1021/acs.jcim.2c00832 |
| Publisher version: | https://doi.org/10.1021/acs.jcim.2c00832 |
| Language: | English |
| Additional information: | This is an Open Access article published under a Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Hydrophobicity, Ligands, Peptides and proteins, Protein structure, Screening assays |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159317 |
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