Cited by 276Protein Structure and DynamicsMachine Learning in BioinformaticsComputational Drug Discovery Methods

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ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction

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Tubiana, Jérôme, Schneidman-Duhovny, Dina, Wolfson, Haim J.·2022·Nature Methods

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Abstract

Predicting the functional sites of a protein from its structure, such as the binding sites of small molecules, other proteins or antibodies, sheds light on its function in vivo. Currently, two classes of methods prevail: machine learning models built on top of handcrafted features and comparative modeling. They are, respectively, limited by the expressivity of the handcrafted features and the availability of similar proteins. Here, we introduce ScanNet, an end-to-end, interpretable geometric deep learning model that learns features directly from 3D structures. ScanNet builds representations of atoms and amino acids based on the spatio-chemical arrangement of their neighbors. We train ScanNet for detecting protein-protein and protein-antibody binding sites, demonstrate its accuracy-including for unseen protein folds-and interpret the filters learned. Finally, we predict epitopes of the SARS-CoV-2 spike protein, validating known antigenic regions and predicting previously uncharacterized ones. Overall, ScanNet is a versatile, powerful and interpretable model suitable for functional site prediction tasks. A webserver for ScanNet is available from http://bioinfo3d.cs.tau.ac.il/ScanNet/ .

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Authors: Tubiana, J., Schneidman-Duhovny, D., & Wolfson, H. J.
Affiliations: Tel Aviv University (IL)
Corresponding author: Tubiana, Jérôme
Wolfson, Haim J.
ORCID: Tubiana, Jérôme
Schneidman-Duhovny, Dina
Wolfson, Haim J.
Venue: Nature Methods
Publisher: Springer Science and Business Media LLC
Published: 2022-05-30
Volume: 19
Issue: 6
Pages: 730-739
ISSN: 1548-7091 (ISSN-L)
1548-7105
DOI: 10.1038/s41592-022-01490-7
OpenAlex: W4229031603
Cited by: 274
References: 104
Citations by year: 2026: 70 · 2025: 75 · 2024: 69 · 2023: 48 · 2022: 11
FWCI: 16.82
Type: journal article
Language: en
Open access: green
doi.org
Licence: cc-by
www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Methods (publishedVersion)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
Research Square (acceptedVersion, cc-by)
PubMed (publishedVersion)
Funders: Human Frontier Science Program (LT001058/2019-C)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Keywords: protein binding site prediction, geometric deep learning, model interpretability, protein structure, protein-protein binding, epitope prediction, SARS-CoV-2 spike protein
MeSH terms: Deep Learning, COVID-19, SARS-CoV-2, Binding Sites, Humans, Protein Binding, Proteins, Spike Glycoprotein, Coronavirus

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ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction

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