Cited by 90Protein Structure and DynamicsComputational Drug Discovery MethodsMachine Learning in Bioinformatics

Spatiotemporal identification of druggable binding sites using deep learning

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Kozlovskii, Igor, Popov, Petr·2020·Communications Biology

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Abstract

Identification of novel protein binding sites expands druggable genome and opens new opportunities for drug discovery. Generally, presence or absence of a binding site depends on the three-dimensional conformation of a protein, making binding site identification resemble the object detection problem in computer vision. Here we introduce a computational approach for the large-scale detection of protein binding sites, that considers protein conformations as 3D-images, binding sites as objects on these images to detect, and conformational ensembles of proteins as 3D-videos to analyze. BiteNet is suitable for spatiotemporal detection of hard-to-spot allosteric binding sites, as we showed for conformation-specific binding site of the epidermal growth factor receptor, oligomer-specific binding site of the ion channel, and binding site in G protein-coupled receptor. BiteNet outperforms state-of-the-art methods both in terms of accuracy and speed, taking about 1.5 minutes to analyze 1000 conformations of a protein with ~2000 atoms.

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  1. [51]Berman, H. M., Bourne, P. E., Westbrook, J. & Zardecki, C. Protein Structure 394–410 (CRC Press, Boca Raton, 2003).

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Authors
Kozlovskii, I., & Popov, P.
Affiliations
Skolkovo Institute of Science and Technology (RU)
Corresponding author
Popov, Petr
Venue
Communications Biology
Published
2020-10-27
Volume
3
Issue
1
Pages
618-618
ISSN
2399-3642 (ISSN-L)
OpenAlex
W3097054638
Cited by
89
References
64
Citations by year
2026: 12 · 2025: 6 · 2024: 15 · 2023: 17 · 2022: 19 · 2021: 17 · 2020: 3
FWCI
4.52
Type
journal article
Language
en
Open access
Locations
  • Communications Biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Skolkovo Institute of Science and Technology
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
conformational ensembles, allosteric site, epidermal growth factor receptor, G protein-coupled receptors, druggable genome, drug discovery
MeSH terms
Deep Learning, Adenosine Triphosphate, Binding Sites, Models, Biological, Models, Molecular, Protein Binding, Protein Conformation, Software, Ion Channel Gating, Drug Delivery Systems, Receptors, G-Protein-Coupled, Receptors, Purinergic P2X3

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