Deep graph learning of inter-protein contacts

Abstract

Motivation: Inter-protein (interfacial) contact prediction is very useful for in silico structural characterization of protein–protein interactions. Although deep learning has been applied to this problem, its accuracy is not as good as intra-protein contact prediction. Results: We propose a new deep learning method GLINTER (Graph Learning of INTER-protein contacts) for interfacial contact prediction of dimers, leveraging a rotational invariant representation of protein tertiary structures and a pretrained language model of multiple sequence alignments. Tested on the 13th and 14th CASP-CAPRI datasets, the average top L/10 precision achieved by GLINTER is 54% on the homodimers and 52% on all the dimers, much higher than 30% obtained by the latest deep learning method DeepHomo on the homodimers and 15% obtained by BIPSPI on all the dimers. Our experiments show that GLINTER-predicted contacts help improve selection of docking decoys. Availability and implementation: The software is available at https://github.com/zw2x/glinter. The datasets are available at https://github.com/zw2x/glinter/data. Supplementary information: Supplementary data are available at Bioinformatics online.