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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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Cites
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
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
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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Progress and challenges in predicting protein interfaces
Esmaielbeiki, R., Krawczyk, K., Knapp, B., Nebel, J., & Deane, C. M. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv027 · Cited by 130
Predicting protein contact map using evolutionary and physical constraints by integer programming
Wang, Z., & Xu, J. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt211 · Cited by 138
ComplexContact: a web server for inter-protein contact prediction using deep learning
Zeng, H., Wang, S., Zhou, T., Zhao, F., Li, X., Wu, Q., & Xu, J. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky420 · Cited by 137
Inferring interaction partners from protein sequences
Bitbol, A., Dwyer, R. S., Colwell, L. J., & Wingreen, N. S. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1606762113 · Cited by 137
Dago, A. E., Schug, A. H., Procaccini, A., Hoch, J. A., Weigt, M., & Szurmant, H. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1201301109 · Cited by 136
Self-citationPotts Hamiltonian models of protein co-variation, free energy landscapes, and evolutionary fitness
Levy, R. M., Haldane, A., & Flynn, W. F. (2016) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.11.004 · Cited by 134
Coevolution-based inference of amino acid interactions underlying protein function
Salinas, V. H., & Ranganathan, R. (2018) · eLife · doi:10.7554/elife.34300 · Cited by 132
From residue coevolution to protein conformational ensembles and functional dynamics
Sutto, L., Marsili, S., Valencia, A., & Gervasio, F. L. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508584112 · Cited by 130
Cocco, S., Monasson, R., & Weigt, M. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003176 · Cited by 134
Self-citationHow Pairwise Coevolutionary Models Capture the Collective Residue Variability in Proteins?
Figliuzzi, M., Barrat-Charlaix, P., & Weigt, M. (2018) · Molecular Biology and Evolution · doi:10.1093/molbev/msy007 · Cited by 141
Self-citationThe Context-Dependence of Mutations: A Linkage of Formalisms
Poelwijk, F. J., Krishna, V., & Ranganathan, R. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004771 · Cited by 140
Role of distal sites in enzyme engineering
Gu, J., Xu, Y., & Nie, Y. (2023) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108094 · Cited by 128
Structured States of Disordered Proteins from Genomic Sequences
Tóth-Petróczy, Á., Palmedo, P., Ingraham, J., Hopf, T. A., Berger, B., Sander, C., & Marks, D. S. (2016) · Cell · doi:10.1016/j.cell.2016.09.010 · Cited by 124
Self-citationStructures of a dimodular nonribosomal peptide synthetase reveal conformational flexibility
Reimer, J. M., Eivaskhani, M., Harb, I., Guarné, A., Weigt, M., & Schmeing, T. M. (2019) · Science · doi:10.1126/science.aaw4388 · Cited by 131
Self-citationFLIP: Benchmark tasks in fitness landscape inference for proteinsPreprint
Dallago, C., Mou, J., Johnston, K. E., Wittmann, B. J., Bhattacharya, N., Goldman, S., Madani, A., & Yang, K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.09.467890 · Cited by 142
Adaptability of protein structures to enable functional interactions and evolutionary implications
Haliloǧlu, T., & Bahar, İ. (2015) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2015.07.007 · Cited by 128
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Pearce, R., & Zhang, Y. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.01.007 · Cited by 127
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Schwede, T. F. (2013) · Structure · doi:10.1016/j.str.2013.08.007 · Cited by 128
Wang, J., Mao, K., Zhao, Y., Zeng, C., Xiang, J., Zhang, Y., & Xiao, Y. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx386 · Cited by 125
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Cheng, R. R., Morcos, F., Levine, H., & Onuchic, J. N. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1323734111 · Cited by 121
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Self-citationLees, J. A., Mai, T., Galardini, M., Wheeler, N. E., Horsfield, S., Parkhill, J., & Corander, J. (2020) · mBio · doi:10.1128/mbio.01344-20 · Cited by 120
Gauto, D. F., Estrozi, L. F., Schwieters, C. D., Effantin, G., Macek, P., Sounier, R., Sivertsen, A. C., Schmidt, E. Y., Kerfah, R., Mas, G., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10490-9 · Cited by 119
Gueudré, T., Baldassi, C., Zamparo, M., Weigt, M., & Pagnani, A. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1607570113 · Cited by 116
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Liu, Y., Palmedo, P., Ye, Q., Berger, B., & Peng, J. (2018) · Cell Systems · doi:10.1016/j.cels.2017.11.014 · Cited by 114
Li, Y., Zhang, C., Bell, E. W., Yu, D., & Zhang, Y. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25798 · Cited by 94
Louie, R. H. Y., Kaczorowski, K. J., Barton, J. P., Chakraborty, A. K., & McKay, M. R. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717765115 · Cited by 111
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Yu, H., Ma, S., Li, Y., & Dalby, P. A. (2022) · Biotechnology Advances · doi:10.1016/j.biotechadv.2022.107926 · Cited by 114
A new ensemble coevolution system for detecting HIV-1 protein coevolution
Li, G., Theys, K., Verheyen, J., Pineda-Peña, A.-C., Khouri, R., Piampongsant, S., Eusébio, M., Ramon, J., & Vandamme, A. (2015) · Biology Direct · doi:10.1186/s13062-014-0031-8 · Cited by 114
An introduction to the maximum entropy approach and its application to inference problems in biology
Martino, A. D., & Martino, D. D. (2018) · Heliyon · doi:10.1016/j.heliyon.2018.e00596 · Cited by 124
Uguzzoni, G., Lovis, S. J., Oteri, F., Schug, A. H., Szurmant, H., & Weigt, M. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1615068114 · Cited by 108
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Dallago, C., Schütze, K., Heinzinger, M., Olenyi, T., Littmann, M., Lu, A. X., Yang, K., Min, S., Yoon, S., Morton, J. T., et al. (2021) · Current Protocols · doi:10.1002/cpz1.113 · Cited by 109
Leonardis, E. D., Lutz, B., Ratz, S., Cocco, S., Monasson, R., Schug, A., & Weigt, M. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv932 · Cited by 103
Self-citationEvaluation of residue–residue contact predictions in CASP9
Monastyrskyy, B., Fidelis, K. A., Tramontano, A. L., & Kryshtafovych, A. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.23160 · Cited by 102
PconsFold: improved contact predictions improve protein models
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Barton, J. P., Leonardis, E. D., Coucke, A., & Cocco, S. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw328 · Cited by 103
Stetz, G., & Verkhivker, G. M. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005299 · Cited by 95
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PconsC4: fast, accurate and hassle-free contact predictions
Michel, M., Hurtado, D. M., & Elofsson, A. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty1036 · Cited by 101
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Sohoni, S. S., Ghosh, P., Wang, T., Kalainayakan, S. P., Vidal, C., Dey, S., Konduri, P. C., & Li, Z. (2019) · Cancer Research · doi:10.1158/0008-5472.can-18-2156 · Cited by 95
Skwark, M. J., Croucher, N. J., Puranen, S., Chewapreecha, C., Pesonen, M., Xu, Y., Turner, P., Harris, S. R., Beres, S. B., Musser, J. M., et al. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006508 · Cited by 95
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Mosca, R., Pons, T., Ceol, A. G. M., Valencia, A., & Aloy, P. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2013.07.005 · Cited by 91
Efficient generative modeling of protein sequences using simple autoregressive models
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Leonardis, E. D., Lutz, B., Ratz, S., Cocco, S., Monasson, R., & Martin, A. S. (2016) · Cited by 92
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Kim, D. E., DiMaio, F., Wang, R. Y., Song, Y., & Baker, D. A. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24374 · Cited by 88
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He, B., Mortuza, S. M., Wang, Y., Shen, H., & Zhang, Y. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx164 · Cited by 72
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Marsh, J. A., & Teichmann, S. A. (2013) · BioEssays · doi:10.1002/bies.201300134 · Cited by 72
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Seffernick, J. T., & Lindert, S. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0026025 · Cited by 70
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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