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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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

Original languageAmerican English
Article number167758
JournalJournal of Molecular Biology
Issue number19
StatePublished - 15 Oct 2022

Bibliographical note

Funding Information:
J.T. acknowledges financial support from the Edmond J. Safra Center for Bioinformatics at Tel Aviv University and from the Human Frontier Science Program (cross-disciplinary postdoctoral fellowship LT001058/2019-C). D.S. was supported by ISF 1466/18, Israel ministry of Science and Technology and HUJI-CIDR. This work was supported by Len Blavatnik and the Blavatnik Family Foundation. We are grateful to Sonia Lichtenzveig Sela and the TAU-CS system team for their technical support.

Publisher Copyright:
© 2022 Elsevier Ltd


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