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Publication Detail
Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network
  • Publication Type:
    Journal article
  • Authors:
    Scott OB, Gu J, Chan AWE
  • Publisher:
    American Chemical Society (ACS)
  • Publication date:
  • Journal:
    Journal of Chemical Information and Modeling
  • Status:
  • Print ISSN:
  • Language:
  • Keywords:
    Hydrophobicity, Ligands, Peptides and proteins, Protein structure, Screening assays
  • Notes:
    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/).
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.
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