Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information
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Do the amino acid sequence identities of residues that make contact across protein interfaces covary during evolution? If so, such covariance could be used to predict contacts across interfaces and assemble models of biological complexes. We find that residue pairs identified using a pseudo-likelihood-based method to covary across protein–protein interfaces in the 50S ribosomal unit and 28 additional bacterial protein complexes with known structure are almost always in contact in the complex, provided that the number of aligned sequences is greater than the average length of the two proteins. We use this method to make subunit contact predictions for an additional 36 protein complexes with unknown structures, and present models based on these predictions for the tripartite ATP-independent periplasmic (TRAP) transporter, the tripartite efflux system, the pyruvate formate lyase-activating enzyme complex, and the methionine ABC transporter.
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The Direct Coupling Analysis (DCA) method could not effectively distinguish a gold standard set of 768 yeast protein pairs known to interact from a much larger set of 768,000 primarily non-interacting pairs (area under the curve: 0.016).
“In a first set of calculations, we found that even with the advantages of S. cerevisiae and improved ortholog identification, the statistical method (Direct Coupling Analysis, DCA) we had used in our previous coevolution-guided protein-protein interaction (PPI) screen in Prokaryotes (9) (the more accurate GREMLIN (11) method is too slow for this) could not effectively distinguish a “gold standard””
From Computed structures of core eukaryotic protein complexes (2021) · cites this paper as [11]
Amino acid coevolution has been used to guide modeling of complexes for cases in which the structures of the partners are known, and to systematically identify pairs of interacting proteins in Prokaryotes with accuracy higher than experimental screens.
“Such amino acid coevolution has been used to guide modeling of complexes for cases in which the structures of the partners are known (11,12), and to systematically identify pairs of interacting proteins in Prokaryotes with accuracy higher than experimental screens (9).”
From Computed structures of core eukaryotic protein complexes (2021) · cites this paper as [11]
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Peter, M. F., Depping, P., Schneberger, N., Severi, E., Gatterdam, K., Tindall, S., Durand, A., Heinz, V., Koenig, P.-A., Geyer, M., et al. (2021) · doi:10.1101/2021.12.03.471092 · Cited by 38 · see citing works
Genetic interaction mapping informs integrative structure determination of protein complexes
Braberg, H., Echeverria, I., Bohn, S., Cimermancic, P., Shiver, A., Alexander, R., Xu, J., Shales, M., Dronamraju, R., Jiang, S., et al. (2020) · Science · doi:10.1126/science.aaz4910 · Cited by 37 · see citing works
Deep learning methods for 3D structural proteome and interactome modeling
Lee, D., Xiong, D., Wierbowski, S., Li, L., Liang, S., & Yu, H. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102329 · Cited by 26 · see citing works
Limits and potential of combined folding and docking
Pozzati, G., Zhu, W., Bassot, C., Lamb, J., Kundrotas, P., & Elofsson, A. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab760 · Cited by 25 · see citing works
Flexible backbone assembly and refinement of symmetrical homomeric complexes
Roy Burman, S. S., Yovanno, R. A., & Gray, J. J. (2019) · Structure · doi:10.1016/j.str.2019.03.014 · Cited by 18 · see citing works
Protein oligomer modeling guided by predicted interchain contacts in <scp>CASP14</scp>
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Self-citationDeep learning reveals many more inter-protein residue-residue contacts than direct coupling analysis
Zhou, T.-M., Wang, S., & Xu, J. (2017) · doi:10.1101/240754 · Cited by 25 · see citing works
Refinement of protein-protein complexes in contact map space with metadynamics simulations
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- Authors
- Ovchinnikov, S., Kamisetty, H., & Baker, D.
- Affiliations
- Howard Hughes Medical Institute (US)
- Corresponding author
- Baker, David
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2014-05-01
- Volume
- 3
- Pages
- e02030-e02030
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC4034769
- OpenAlex
- W2107867854
- Cited by
- 629
- References
- 64
- Citations by year
- 2026: 22 · 2025: 25 · 2024: 36 · 2023: 69 · 2022: 60 · 2021: 60 · 2020: 66 · 2019: 76 · 2018: 53 · 2017: 67 · 2016: 44 · 2015: 45 · 2014: 6
- FWCI
- 22.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (1R01GM092802-04, National Institute of General Medical Studies, P41 GM103533)
- Defense Threat Reduction Agency (DTRA) (N00024-10-D-6318/0024-02)
- 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
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- residue-residue interactions, protein-protein interfaces, contact prediction, coevolution, covariance, pseudo-likelihood, protein complexes, TRAP transporter, protein structure prediction
- MeSH terms
- Biological Evolution, Models, Molecular, Protein Binding, Protein Conformation, Proteins
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