Title: A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers

Abstract

Deep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy tertiary structures for most individual protein chains. However, the accuracy of predicting quaternary structures of protein complexes consisting of multiple chains is still relatively low due to lack of advanced deep learning methods in the field. Because interchain residue–residue contacts can be used as distance restraints to guide quaternary structure modeling, here we develop a deep dilated convolutional residual network method (DRCon) to predict interchain residue–residue contacts in homodimers from residue–residue co-evolutionary signals derived from multiple sequence alignments of monomers, intrachain residue–residue contacts of monomers extracted from true/predicted tertiary structures or predicted by deep learning, and other sequence and structural features. Tested on three homodimer test datasets (Homo_std dataset, DeepHomo dataset and CASP-CAPRI dataset), the precision of DRCon for top L/5 interchain contact predictions (L: length of monomer in a homodimer) is 43.46%, 47.10% and 33.50% respectively at 6 Å contact threshold, which is substantially better than DeepHomo and DNCON2_inter and similar to Glinter. Moreover, our experiments demonstrate that using predicted tertiary structure or intrachain contacts of monomers in the unbound state as input, DRCon still performs well, even thoughmore » its accuracy is lower than using true tertiary structures in the bound state are used as input. Finally, our case study shows that good interchain contact predictions can be used to build high-accuracy quaternary structure models of homodimers.« less

Authors:

Roy, Raj S.; Quadir, Farhan; Soltanikazemi, Elham; Cheng, Jianlin ;

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• Search DOE PAGES for ORCID "0000-0003-0305-2853"

Xu, ed., Jinbo

Publication Date:

Fri Feb 04 00:00:00 EST 2022

Research Org.:

Univ. of Missouri, Columbia, MO (United States); Donald Danforth Plant Science Center, St. Louis, MO (United States)

Sponsoring Org.:

USDOE Advanced Research Projects Agency - Energy (ARPA-E); National Science Foundation (NSF); National Institutes of Health (NIH); Thompson Missouri Distinguished Professorship; USDOE Office of Science (SC), Biological and Environmental Research (BER)

OSTI Identifier:

1859808

Alternate Identifier(s):

OSTI ID: 1904187; OSTI ID: 2278958; OSTI ID: 2318548

Grant/Contract Number:  

AR0001213; SC0020400; SC0021303; AC05-00OR22725; DBI1759934; IIS1763246; R01GM093123

Resource Type:

Published Article

Journal Name:

Bioinformatics

Additional Journal Information:

Journal Name: Bioinformatics Journal Volume: 38 Journal Issue: 7; Journal ID: ISSN 1367-4803

Publisher:

Oxford University Press

Country of Publication:

United Kingdom

Language:

English

Subject:

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING