Dataset Information
Deep graph learning of inter-protein contacts.
Ontology highlight
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.
SUBMITTER: Xie Z
PROVIDER: S-EPMC8796373 | biostudies-literature | 2022 Jan
REPOSITORIES: biostudies-literature
Publications
Deep graph learning of inter-protein contacts.
Bioinformatics (Oxford, England) 20220101 4
<h4>Motivation</h4>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.<h4>Results</h4>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 str ...[more]