PreprintCited by 25Protein Structure and DynamicsMachine Learning in BioinformaticsGenomics and Phylogenetic Studies

Deep learning reveals many more inter-protein residue-residue contacts than direct coupling analysis

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Zhou, Tian-ming, Wang, Sheng, Xu, Jinbo·2017·bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Intra-protein residue-level contact prediction has drawn a lot of attentions in recent years and made very good progress, but much fewer methods are dedicated to inter-protein contact prediction, which are important for understanding how proteins interact at structure and residue level. Direct coupling analysis (DCA) is popular for intra-protein contact prediction, but extending it to inter-protein contact prediction is challenging since it requires too many interlogs (i.e., interacting homologs) to be effective, which cannot be easily fulfilled especially for a putative interacting protein pair in eukaryotes. We show that deep learning, even trained by only intra-protein contact maps, works much better than DCA for inter-protein contact prediction. We also show that a phylogeny-based method can generate a better multiple sequence alignment for eukaryotes than existing genome-based methods and thus, lead to better inter-protein contact prediction. Our method shall be useful for protein docking, protein interaction prediction and protein interaction network construction.

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  • More recent methods incorporate inter-protein residue-residue contact predictions obtained via co-evolutionary analysis and a deep learning algorithm.

    “More recent methods incorporate inter-protein residue-residue contact predictions obtained via co-evolutionary analysis17 and a deep learning algorithm18.”

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  1. Colak, R., et al. Dense graphlet statistics of protein interaction and random networks. in Pacific Symposium on Biocomputing. 2009.

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Authors
Zhou, T.-M., Wang, S., & Xu, J.
Affiliations
  • Toyota Technological Institute at Chicago (US)
  • King Abdullah University of Science and Technology (SA)
Corresponding author
  • Wang, Sheng
  • Xu, Jinbo
Venue
bioRxiv (Cold Spring Harbor Laboratory)
Publisher
openRxiv
Published
2017-12-29
OpenAlex
W2785722441
Cited by
25
References
50
Citations by year
2026: 1 · 2025: 3 · 2023: 1 · 2022: 8 · 2021: 7 · 2020: 1 · 2019: 3 · 2018: 1
Type
posted content
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion)
Funders
Harvard University
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 › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
protein-protein interaction prediction, direct coupling analysis, protein docking, protein-protein interaction network

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