DeepRank-GNN: a graph neural network framework to learn patterns in protein–protein interfaces

Author:

Réau Manon1,Renaud Nicolas2,Xue Li C3,Bonvin Alexandre M J J1ORCID

Affiliation:

1. Computational Structural Biology Group, Department of Chemistry, Bijvoet Centre, Faculty of Science, Utrecht University , Utrecht 3584CH, The Netherlands

2. Netherlands eScience Center , Amsterdam 1098 XG, The Netherlands

3. Center for Molecular and Biomolecular Informatics, Radboudumc , Nijmegen 6525 GA, The Netherlands

Abstract

Abstract Motivation Gaining structural insights into the protein–protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning from protein–protein interfaces using convolutional neural network (CNN) approaches. However, CNN is not rotation invariant and data augmentation is required to desensitize the network to the input data orientation which dramatically impairs the computation performance. Representing protein–protein complexes as atomic- or residue-scale rotation invariant graphs instead enables using graph neural networks (GNN) approaches, bypassing those limitations. Results We have developed DeepRank-GNN, a framework that converts protein–protein interfaces from PDB 3D coordinates files into graphs that are further provided to a pre-defined or user-defined GNN architecture to learn problem-specific interaction patterns. DeepRank-GNN is designed to be highly modularizable, easily customized and is wrapped into a user-friendly python3 package. Here, we showcase DeepRank-GNN’s performance on two applications using a dedicated graph interaction neural network: (i) the scoring of docking poses and (ii) the discriminating of biological and crystal interfaces. In addition to the highly competitive performance obtained in those tasks as compared to state-of-the-art methods, we show a significant improvement in speed and storage requirement using DeepRank-GNN as compared to DeepRank. Availability and implementation DeepRank-GNN is freely available from https://github.com/DeepRank/DeepRank-GNN. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Netherlands eScience Center

SURF Open Lab ‘Machine

Computing Time on National Computer Facilities

Netherlands Organization for Scientific Research

European Union Horizon 2020 project BioExcel

Hypatia Fellowship from Radboudumc

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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