ArticleNature methods2022
ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction.
Article in Nature methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 131 papers, 1 of them a synthesis that pooled it.
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Who cites it
131 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
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- ProRB: a structure-free unified framework for joint prediction and design of protein-RNA interactions.Nucleic acids research · 2026Article
- Surf2Spot: a surface-informed geometry-aware model for predicting partner-independent binder and nanobody design hotspots.Briefings in bioinformatics · 2026Article
- PatchEpi: patch-aware equivariant learning improves structure-based epitope prediction.Bioinformatics (Oxford, England) · 2026Article
- Hierarchical Graph Representation Learning for Protein-Protein Interaction Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Strategic Template Filtering Accelerates Fragment-Based Peptide Docking.Journal of chemical information and modeling · 2026Article
- Edge-aware GAT-based protein binding sites prediction.PLoS computational biology · 2026Article
- GeoPep: A Geometry-Aware Masked Language Model for Protein-Peptide Binding Site Prediction.Journal of chemical information and modeling · 2026Article
- xBind: an integrated webserver for large language model-enabled cross-molecular protein binding site prediction.Nucleic acids research · 2026Article
- The critical role of artificial intelligence and bioinformatics in accelerating peptide-based vaccine discovery for tackling global infectious diseases.Briefings in bioinformatics · 2026Review
- BiMba: using Vision Mamba to predict protein sites that bind other proteins.Bioinformatics (Oxford, England) · 2026Article
- MD-Transformer: Multimodal Integration of ProtBERT Embeddings and Physicochemical Descriptors for Protein-Protein Interface Residue Prediction.International journal of molecular sciences · 2026Article
- The evolutionary dynamics between viral mimics and host proteins.Molecular systems biology · 2026Article
- Cryo-EM Structure Guided Engineering of Botulinum Neurotoxin A With Advanced Receptor Binding Affinity and Therapeutical Benefits.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Novel Mechanism for Autoantigenicity: Condensate Conformational Change.Biomolecules · 2026Article
- Interpretable deep learning framework for mapping E3-substrate binding interfaces.Nature communications · 2026Article
- VUStruct: A compute pipeline for high throughput and personalized structural biology.PLoS computational biology · 2026Article
- Repertoire-scale antibody structural prediction informs therapeutic design.Science advances · 2026Article
- Investigating Escherichia coli Colicin E9 immunity protein interactions with DNA gyrase of Pseudomonas aeruginosa: advanced computational approach for developing novel antimicrobial strategies.Scientific reports · 2026Article
71 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.