Cited by 1,434Protein Structure and DynamicsMachine Learning in BioinformaticsEnzyme Structure and Function

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Direct-coupling analysis of residue coevolution captures native contacts across many protein families

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Morcos, Faruck, Pagnani, Andrea, Lunt, Bryan, Bertolino, Arianna, Marks, Debora S., Sander, Chris, Zecchina, Riccardo, Onuchic, José N., Hwa, Terence, Weigt, Martin·2011·Proceedings of the National Academy of Sciences

DOI 10.1073/pnas.1111471108PMC PMC3241805

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Abstract

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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The Protein Data Bank
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196
The Pfam Protein Families Database
Bateman, A. G. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.276 · Cited by 14,075
Information theory and statistical mechanics
Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.106.620 · Cited by 12,931
Profile hidden Markov models
Eddy, S. R. (1998) · Bioinformatics · doi:10.1093/bioinformatics/14.9.755 · Cited by 5,515
The Pfam protein families database
Punta, M., Coggill, P. C., Eberhardt, R. Y., Mistry, J., Tate, J., Boursnell, C., Pang, N., Forslund, K., Ceric, G., Clements, J., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1065 · Cited by 3,719
Solvable Model of a Spin-Glass
Sherrington, D., & Kirkpatrick, S. (1975) · Physical Review Letters · doi:10.1103/physrevlett.35.1792 · Cited by 3,577
Biological sequence analysis probabilistic models of proteins and nucleic acids
Durbin, R. M., Eddy, S. R., Krogh, A., & Mitchison, G. (1998) · Cited by 3,565
Biological Sequence Analysis
Durbin, R. M., Eddy, S. R., Krogh, A., & Mitchison, G. (1998) · Cambridge University Press eBooks · doi:10.1017/cbo9780511790492 · Cited by 3,255
Information Theory and Statistical Mechanics. II
Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.108.171 · Cited by 3,253
The Pfam protein families database
Finn, R. D., Mistry, J., Tate, J., Coggill, P., Heger, A., Pollington, J. E., Gavin, O. L., Gunasekaran, P., Ceric, G., Forslund, K., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp985 · Cited by 2,694

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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: Morcos, Faruck
Pagnani, Andrea
Lunt, Bryan
Marks, Debora S.
Sander, Chris
Zecchina, Riccardo
Onuchic, José N.
Weigt, Martin
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
DOI: 10.1073/pnas.1111471108
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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Direct-coupling analysis of residue coevolution captures native contacts across many protein families

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