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bioRxiv · 10.1101/2021.08.14.456342

Deep graph learning of inter-protein contacts

Xie, Z.·Xu, J.
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Abstract

MotivationInter-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. ResultsWe 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 (MSAs). Tested on the 13th and 14th CASP-CAPRI datasets, the average top L/10 precision achieved by GLINTER is 54.35% on the homodimers and 51.56% on all the dimers, much higher than 30.43% obtained by the latest deep learning method DeepHomo on the homodimers and 14.69% obtained by BIPSPI on all the dimers. Our experiments show that GLINTER-predicted contacts help improve selection of docking decoys. Contactjinboxu@gmail.com

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Xie, Z., Xu, J.. 2021-08-15. Deep graph learning of inter-protein contacts. https://doi.org/10.1101/2021.08.14.456342

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