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Direct-coupling analysis of residue coevolution captures native contacts across many protein families
Open accessAbstract
The similarity in the three-dimensional structures of homologous proteins imposes strong constraints on their sequence variability. It has long been suggested that the resulting correlations among amino acid compositions at different sequence positions can be exploited to infer spatial contacts within the tertiary protein structure. Crucial to this inference is the ability to disentangle direct and indirect correlations, as accomplished by the recently introduced direct-coupling analysis (DCA). Here we develop a computationally efficient implementation of DCA, which allows us to evaluate the accuracy of contact prediction by DCA for a large number of protein domains, based purely on sequence information. DCA is shown to yield a large number of correctly predicted contacts, recapitulating the global structure of the contact map for the majority of the protein domains examined. Furthermore, our analysis captures clear signals beyond intradomain residue contacts, arising, e.g., from alternative protein conformations, ligand-mediated residue couplings, and interdomain interactions in protein oligomers. Our findings suggest that contacts predicted by DCA can be used as a reliable guide to facilitate computational predictions of alternative protein conformations, protein complex formation, and even the de novo prediction of protein domain structures, contingent on the existence of a large number of homologous sequences which are being rapidly made available due to advances in genome sequencing.
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“Analysis of amino acid residue covariation in deep multiple sequence alignments (MSAs) has revealed that covarying residues in proteins are frequently found to be close together in the tertiary structure.”
From Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterised proteins (2020) · cites this paper as [1]
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Self-citationDeep neural language modeling enables functional protein generation across familiesPreprint
Madani, A., Krause, B., Greene, E. R., Subramanian, S., Mohr, B. P., Holton, J. M., Olmos, J. L., Xiong, C., Sun, Z. Z., Socher, R., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.18.452833 · Cited by 47
Di Gioacchino, A., Procyk, J., Molari, M., Schreck, J. S., Zhou, Y., Liu, Y., Monasson, R., Cocco, S., & Šulc, P. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010561 · Cited by 45
Lin, P., Yan, Y., & Huang, S. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac499 · Cited by 40
Metadata
- Authors
- Morcos, F., Pagnani, A., Lunt, B., Bertolino, A., Marks, D. S., Sander, C., Zecchina, R., Onuchic, J. N., Hwa, T., & Weigt, M.
- Affiliations
- University of California San Diego (US)
- Politecnico di Torino (IT)
- Institute for Scientific Interchange (IT)
- Harvard University (US)
- Memorial Sloan Kettering Cancer Center (US)
- Center for Theoretical Biological Physics, University of California at San Diego, La Jolla, CA 92093-0374;
- Biologie Computationnelle, Quantitative et Synthétique (FR)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2011-11-21
- Volume
- 108
- Issue
- 49
- Pages
- E1293-301
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3241805
- OpenAlex
- W2008545402
- Cited by
- 1,434
- References
- 55
- Citations by year
- 2026: 93 · 2025: 99 · 2024: 102 · 2023: 112 · 2022: 145 · 2021: 142 · 2020: 107 · 2019: 134 · 2018: 105 · 2017: 101 · 2016: 82 · 2015: 73 · 2014: 65 · 2013: 48 · 2012: 23
- FWCI
- 18.07
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion)
- PORTO@Iris (submittedVersion)
- PORTO Publications Open Repository TOrino (Politecnico di Torino) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institute of General Medical Sciences (R01GM106303)
- 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
- direct coupling analysis, residue coevolution, protein contact prediction, native contacts, protein domains, homologous sequences, domain-domain interactions, protein oligomers, protein complex formation, de novo protein structure prediction, contact maps
- MeSH terms
- Algorithms, Amino Acids, Binding Sites, Models, Molecular, Protein Binding, Protein Conformation, Proteins, Reproducibility of Results, Computational Biology, Protein Interaction Mapping, Protein Multimerization
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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