ScanNet: A Web Server for Structure-based Prediction of Protein Binding Sites with Geometric Deep Learning

Jérôme Tubiana, Dina Schneidman-Duhovny, Haim J. Wolfson

Research output: Contribution to journalArticlepeer-review

Abstract

Predicting the various binding sites of a protein from its structure sheds light on its function and paves the way towards design of interaction inhibitors. Here, we report ScanNet, a freely available web server for prediction of protein–protein, protein - disordered protein and protein - antibody binding sites from structure. ScanNet (Spatio-Chemical Arrangement of Neighbors Network) is an end-to-end, interpretable geometric deep learning model that learns spatio-chemical patterns directly from 3D structures. ScanNet consistently outperforms Machine Learning models based on handcrafted features and comparative modeling approaches. The web server is linked to both the PDB and AlphaFoldDB, and supports user-provided structure files. Predictions can be readily visualized on the website via the Molstar web app and locally via ChimeraX. ScanNet is available at http://bioinfo3d.cs.tau.ac.il/ScanNet/.

Original languageEnglish
Article number167758
JournalJournal of Molecular Biology
Volume434
Issue number19
DOIs
StatePublished - 15 Oct 2022

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