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Nat Methods. 2019 Dec 9. doi: 10.1038/s41592-019-0666-6. [Epub ahead of print]

Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning.

Author information

1
Institute of Bioengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland.
2
Institute of Computational Science, Faculty of Informatics, USI, Lugano, Switzerland.
3
Twitter, London, UK.
4
Department of Computer Science, Sapienza University of Rome, Rome, Italy.
5
Technologies of Vision Unit, Fondazione Bruno Kessler, Trento, Italy.
6
Department of Computing, Imperial College London, London, UK.
7
Institute of Bioengineering, École Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, Lausanne, Switzerland. bruno.correia@epfl.ch.

Abstract

Predicting interactions between proteins and other biomolecules solely based on structure remains a challenge in biology. A high-level representation of protein structure, the molecular surface, displays patterns of chemical and geometric features that fingerprint a protein's modes of interactions with other biomolecules. We hypothesize that proteins participating in similar interactions may share common fingerprints, independent of their evolutionary history. Fingerprints may be difficult to grasp by visual analysis but could be learned from large-scale datasets. We present MaSIF (molecular surface interaction fingerprinting), a conceptual framework based on a geometric deep learning method to capture fingerprints that are important for specific biomolecular interactions. We showcase MaSIF with three prediction challenges: protein pocket-ligand prediction, protein-protein interaction site prediction and ultrafast scanning of protein surfaces for prediction of protein-protein complexes. We anticipate that our conceptual framework will lead to improvements in our understanding of protein function and design.

PMID:
31819266
DOI:
10.1038/s41592-019-0666-6

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