DeepRank: a deep learning framework for data mining 3D protein-protein interfaces

Nicolas Renaud, Cunliang Geng, Sonja Georgievska, Francesco Ambrosetti, Lars Ridder, Dario F. Marzella, Manon F. Réau, Alexandre M. J. J. Bonvin () and Li C. Xue ()
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Nicolas Renaud: Netherlands eScience Center
Cunliang Geng: Netherlands eScience Center
Sonja Georgievska: Netherlands eScience Center
Francesco Ambrosetti: Utrecht University
Lars Ridder: Netherlands eScience Center
Dario F. Marzella: Center for Molecular and Biomolecular Informatics, Radboudumc
Manon F. Réau: Utrecht University
Alexandre M. J. J. Bonvin: Utrecht University
Li C. Xue: Utrecht University

Nature Communications, 2021, vol. 12, issue 1, 1-8

Abstract: Abstract Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance. We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression. We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive with, or outperforms, state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology.

Date: 2021
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DOI: 10.1038/s41467-021-27396-0

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