Spatiotemporal identification of druggable binding sites using deep learning
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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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Key to discovering allosteric sites is to consider proteins as ensembles of structures, capturing feasible but transient conformational states.
“Key to discovering allosteric sites is to consider proteins as ensembles of structures, capturing feasible, but transient, conformational states.262,263”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [263]
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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)
- PMC
- PMC7591901
- 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
- gold
- doi.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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