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PreprintCited by 45Protein Structure and DynamicsMachine Learning in BioinformaticsEnzyme Structure and Function

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

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Roy, Raj S., Quadir, Farhan, Soltanikazemi, Elham, Cheng, Jianlin·2021·Bioinformatics
DOI 10.1101/2021.09.19.460941PMC PMC8963319

Preprint — published as 10.1093/bioinformatics/btac063 · Paper page

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Abstract

Motivation: 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. Results: Tested on three homodimer test datasets (Homo_std dataset, DeepHomo dataset, and CASP14-CAPRI dataset), the precision of DRCon for top L/5 interchain contact predictions (L: length of monomer in a homodimer) is 43.46%, 47.15%, and 24.81% respectively, which is substantially better than two existing deep learning interchain contact prediction methods. Moreover, our experiments demonstrate that using predicted tertiary structure or intrachain contacts of monomers in the unbound state as input, DRCon still performs reasonably well, even though its accuracy is lower than when 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. Availability: The source code of DRCon is available at https://github.com/jianlin-cheng/DRCon . Contact: chengji@missouri.edu

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Metadata

Authors
Roy, R. S., Quadir, F., Soltanikazemi, E., & Cheng, J.
Affiliations
University of Missouri (US)
Corresponding author
Cheng, Jianlin
ORCID
Venue
Bioinformatics
Publisher
openRxiv
Published
2021-09-22
Volume
38
Issue
7
Pages
1904-1910
ISSN
1367-4803 (ISSN-L)
DOI
10.1101/2021.09.19.460941
PMC
PMC8963319
OpenAlex
W4210646117
Cited by
45
References
1
Citations by year
2026: 6 · 2025: 4 · 2024: 7 · 2023: 17 · 2022: 10 · 2021: 1
FWCI
3.10
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Bioinformatics (publishedVersion, cc-by-nc)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
Funders
  • National Science Foundation (DEAC05- 00OR22725, 1763246, 00OR22725, 1759934, R01GM093123)
  • U.S. Department of Energy (DEAR0001213, AC05-00OR22725, DESC0021303, DE--AC05--00OR22725, DE-AC05?, under Contract DE-AC05-00OR22725, DESC0020400, 00OR22725)
  • National Institutes of Health (DE-AC05-00OR22725)
  • Office of Science (DEAC05- 00OR22725, AC05-00OR22725)
  • National Institute of General Medical Sciences (R01GM093123)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
Keywords
protein homodimer, quaternary structure prediction, multiple sequence alignment, protein complexes
MeSH terms
Proteins, Software, Sequence Alignment, Neural Networks, Computer, Computational Biology

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