Deep learning reveals many more inter-protein residue-residue contacts than direct coupling analysis
Open accessAbstract
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.”
From AF2Complex predicts direct physical interactions in multimeric proteins with deep learning (2022) · cites this paper as [18]
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AF2Complex predicts direct physical interactions in multimeric proteins with deep learning
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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
- ORCID
- 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
- green
- doi.org
- 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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