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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Covarying residues in proteins are frequently found to be close together in the tertiary structure.
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
The Pfam Protein Families Database
Bateman, A. G. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.276 · Cited by 14,062 · see citing works
Information theory and statistical mechanics
Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.106.620 · Cited by 12,931 · see citing works
Eddy, S. R. (1998) · Bioinformatics · doi:10.1093/bioinformatics/14.9.755 · Cited by 5,515 · see citing works
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 · see citing works
Solvable Model of a Spin-Glass
Sherrington, D., & Kirkpatrick, S. (1975) · Physical Review Letters · doi:10.1103/physrevlett.35.1792 · Cited by 3,577 · see citing works
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 · see citing works
Durbin, R. M., Eddy, S. R., Krogh, A., & Mitchison, G. (1998) · Cambridge University Press eBooks · doi:10.1017/cbo9780511790492 · Cited by 3,255 · see citing works
Information Theory and Statistical Mechanics. II
Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.108.171 · Cited by 3,253 · see citing works
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 · see citing works
Solution of 'Solvable model of a spin glass'
Thouless, D. J., Anderson, P. W., & Palmer, R. G. (1977) · Philosophical magazine · doi:10.1080/14786437708235992 · Cited by 1,305 · see citing works
Evolutionarily Conserved Pathways of Energetic Connectivity in Protein Families
Lockless, S. W., & Ranganathan, R. (1999) · Science · doi:10.1126/science.286.5438.295 · Cited by 1,288 · see citing works
PAI, E. F., Krengel, U., Petsko, G. A., Goody, R. S., Kabsch, W., & Wittinghofer, A. (1990) · The EMBO Journal · doi:10.1002/j.1460-2075.1990.tb07409.x · Cited by 1,074 · see citing works
Pagani, I., Liolios, K., Jansson, J., Chen, I.-M. A., Smirnova, T., Nosrat, B., Markowitz, V. M., & Kyrpides, N. C. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr1100 · Cited by 1,061 · see citing works
Identification of direct residue contacts in protein–protein interaction by message passing
Weigt, M., White, R. A., Szurmant, H., Hoch, J. A., & Hwa, T. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0805923106 · Cited by 1,025 · see citing works
Self-citationCorrelated mutations and residue contacts in proteins
Göbel, U., Sander, C., Schneider, R., & Valencia, A. (1994) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340180402 · Cited by 863 · see citing works
Self-citationTwo-component and phosphorelay signal transduction
Hoch, J. A. (2000) · Current Opinion in Microbiology · doi:10.1016/s1369-5274(00)00070-9 · Cited by 794 · see citing works
Protein Sectors: Evolutionary Units of Three-Dimensional Structure
Halabi, N. M., Rivoire, O., Leibler, S., & Ranganathan, R. (2009) · Cell · doi:10.1016/j.cell.2009.07.038 · Cited by 697 · see citing works
Liolios, K., Mavromatis, K., Tavernarakis, N., & Kyrpides, N. C. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm884 · Cited by 661 · see citing works
Dunn, S. D., Wahl, L. M., & Gloor, G. B. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm604 · Cited by 490 · see citing works
Rewiring the Specificity of Two-Component Signal Transduction Systems
Skerker, J. M., Perchuk, B. S., Siryaporn, A., Lubin, E. A., Ashenberg, O., Goulian, M., & Laub, M. T. (2008) · Cell · doi:10.1016/j.cell.2008.04.040 · Cited by 464 · see citing works
Tanaka, S., & Scheraga, H. A. (1976) · Macromolecules · doi:10.1021/ma60054a013 · Cited by 437 · see citing works
Convergence condition of the TAP equation for the infinite-ranged Ising spin glass model
Plefka, T. (1982) · Journal of Physics A Mathematical and General · doi:10.1088/0305-4470/15/6/035 · Cited by 336 · see citing works
Shindyalov, I. N., Kolchanov, N. A., & Sander, C. (1994) · "Protein Engineering, Design and Selection" · doi:10.1093/protein/7.3.349 · Cited by 297 · see citing works
Self-citationAltschuh, D., Lesk, A. M., Bloomer, A. C., & Klug, A. (1987) · Journal of Molecular Biology · doi:10.1016/0022-2836(87)90352-4 · Cited by 284 · see citing works
The MiST2 database: a comprehensive genomics resource on microbial signal transduction
Ulrich, L. E., & Zhulin, I. B. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp940 · Cited by 262 · see citing works
Correlations Among Amino Acid Sites in bHLH Protein Domains: An Information Theoretic Analysis
Atchley, W. R., Wollenberg, K. R., Fitch, W. M., Terhalle, W. F., & Dress, A. (2000) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a026229 · Cited by 256 · see citing works
Akama, H., Matsuura, T., Kashiwagi, S., Hiroshi Yoneyama, C., Narita, S., Tomitake Tsukihara, A., Nakagawa, A., & Nakae, T. (2004) · Journal of Biological Chemistry · doi:10.1074/jbc.c400164200 · Cited by 255 · see citing works
Crystal Structure of Escherichia coli σE with the Cytoplasmic Domain of Its Anti-σ RseA
Campbell, E. A., Tupy, J. L., Gruber, T. M., Wang, S., Sharp, M. M., Gross, C. A., & Darst, S. A. (2003) · Molecular Cell · doi:10.1016/s1097-2765(03)00148-5 · Cited by 241 · see citing works
How frequent are correlated changes in families of protein sequences?
Neher, E. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.1.98 · Cited by 240 · see citing works
Influence of conservation on calculations of amino acid covariance in multiple sequence alignments
Fodor, A. A., & Aldrich, R. W. (2004) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20098 · Cited by 238 · see citing works
Respective roles of short- and long-range interactions in protein folding.
Gō, N., & Taketomi, H. (1978) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.75.2.559 · Cited by 235 · see citing works
Lee, S., Torre, A. D. L., Yan, D., Kustu, S. G., Nixon, B. T., & Wemmer, D. E. (2003) · Genes & Development · doi:10.1101/gad.1125603 · Cited by 200 · see citing works
Disentangling Direct from Indirect Co-Evolution of Residues in Protein Alignments
Burger, L., & van Nimwegen, E. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000633 · Cited by 193 · see citing works
Dimerization allows DNA target site recognition by the NarL response regulator
Maris, A. E., Sawaya, M. R., Kaczor-Grzeskowiak, M., Jarvis, M. R., Bearson, S. M. D., Kopka, M. L., Schröder, I., Gunsalus, R. P., & Dickerson, R. E. (2002) · Nature Structural Biology · doi:10.1038/nsb845 · Cited by 159 · see citing works
High-resolution protein complexes from integrating genomic information with molecular simulation
Schug, A. H., Weigt, M., Onuchic, J. N., Hwa, T., & Szurmant, H. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0912100106 · Cited by 157 · see citing works
Self-citationHow to expand around mean-field theory using high-temperature expansions
Georges, A., & Yedidia, J. S. (1991) · Journal of Physics A Mathematical and General · doi:10.1088/0305-4470/24/9/024 · Cited by 154 · see citing works
Tillier, E. R. M., & Lui, T. W. H. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg072 · Cited by 127 · see citing works
The role of water in sequence-independent ligand binding by an oligopeptide transporter protein
Tame, J. R. H., Sleigh, S. H., Wilkinson, A. J., & Ladbury, J. E. (1996) · Nature Structural Biology · doi:10.1038/nsb1296-998 · Cited by 125 · see citing works
Wollenberg, K. R., & Atchley, W. R. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.7.3288 · Cited by 120 · see citing works
Dima, R. I., & Thirumalai, D. (2006) · Protein Science · doi:10.1110/ps.051767306 · Cited by 87 · see citing works
Procaccini, A., Lunt, B., Szurmant, H., Terence Hwa, B., & Weigt, M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019729 · Cited by 85 · see citing works
Self-citationWisedchaisri, G., Wu, M., Sherman, D. R., & Hol, W. G. J. (2008) · Journal of Molecular Biology · doi:10.1016/j.jmb.2008.02.029 · Cited by 63 · see citing works
Gouveia‐Oliveira, R., & Pedersen, A. G. (2007) · Algorithms for Molecular Biology · doi:10.1186/1748-7188-2-12 · Cited by 56 · see citing works
Reaction Coupling through Interdomain Contacts in Imidazole Glycerol Phosphate Synthase
Myers, R. S., Amaro, R. E., Luthey-Schulten, Z. A., & Davisson, V. J. (2005) · Biochemistry · doi:10.1021/bi050706b · Cited by 50 · see citing works
Features of Protein–Protein Interactions in Two‐Component Signaling Deduced from Genomic Libraries
White, R. A., Szurmant, H., Hoch, J. A., & Terence Hwa, B. (2007) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(06)22004-4 · Cited by 47 · see citing works
Inference of Direct Residue Contacts in Two-Component Signaling
Lunt, B., Szurmant, H., Procaccini, A., Hoch, J. A., Terence Hwa, B., & Weigt, M. (2010) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(10)71002-8 · Cited by 41 · see citing works
Self-citationPasternak, A., Ringe, D., & Hedstrom, L. (1999) · Protein Science · doi:10.1110/ps.8.1.253 · Cited by 39 · see citing works
Rigsby, R. E., Rife, C. L., Fillgrove, K. L., Newcomer, M. E., & Armstrong, R. N. (2004) · Biochemistry · doi:10.1021/bi048767h · Cited by 36 · see citing works
Liu, Z., Chen, J., & Thirumalai, D. (2009) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22498 · Cited by 35 · see citing works
Miyazawa, S., & Jernigan, R. L. (2002) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10242 · Cited by 24 · see citing works
Structure–based Mechanism of Ligand Binding for Periplasmic Solute-binding Protein of the Bug Family
Herrou, J., Bompard, C., Antoine, R., Leroy, A., Rucktooa, P., Hot, D., Huvent, I., Locht, C., Villeret, V., & Jacob‐Dubuisson, F. (2007) · Journal of Molecular Biology · doi:10.1016/j.jmb.2007.08.006 · Cited by 21 · see citing works
Computational Modeling of Phosphotransfer Complexes in Two-Component Signaling
Schug, A. H., Weigt, M., Hoch, J. A., Onuchic, J. N., Terence Hwa, B., & Szurmant, H. (2010) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(10)71003-x · Cited by 20 · see citing works
Self-citationLashuel, H. A., & Pappu, R. V. (2009) · ChemBioChem · doi:10.1002/cbic.200900373 · Cited by 5 · see citing works
3D Protein Structure Predicted from SequencePreprint
Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R., & Sander, C. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1110.5091 · Cited by 4 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,523 · see citing works
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286 · see citing works
Improved protein structure prediction using potentials from deep learning
Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1923-7 · Cited by 3,378 · see citing works
The Protein-Folding Problem, 50 Years On
Dill, K. A., & MacCallum, J. L. (2012) · Science · doi:10.1126/science.1219021 · Cited by 1,699 · see citing works
Large language models generate functional protein sequences across diverse families
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. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01618-2 · Cited by 1,078 · see citing works
Protein 3D Structure Computed from Evolutionary Sequence Variation
Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R., & Sander, C. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028766 · Cited by 1,144 · see citing works
Self-citationAccurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
Wang, S., Sun, S., Li, Z., Zhang, R., & Xu, J. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005324 · Cited by 931 · see citing works
Perspective: Coarse-grained models for biomolecular systems
Noid, W. G. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4818908 · Cited by 885 · see citing works
Advances in protein structure prediction and design
Kuhlman, B., & Bradley, P. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0163-x · Cited by 806 · see citing works
Mutation effects predicted from sequence co-variation
Hopf, T. A., Ingraham, J. B., Poelwijk, F. J., Schärfe, C. P. I., Springer, M., Sander, C., & Marks, D. S. (2017) · Nature Biotechnology · doi:10.1038/nbt.3769 · Cited by 775 · see citing works
Language models enable zero-shot prediction of the effects of mutations on protein function
Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., & Rives, A. (2021) · doi:10.1101/2021.07.09.450648 · Cited by 635 · see citing works
AlphaFold and Implications for Intrinsically Disordered Proteins
Ruff, K. M., & Pappu, R. V. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167208 · Cited by 635 · see citing works
Protein structure prediction from sequence variation
Marks, D. S., Hopf, T. A., & Sander, C. (2012) · Nature Biotechnology · doi:10.1038/nbt.2419 · Cited by 622 · see citing works
Self-citationImproved contact prediction in proteins: using pseudolikelihoods to infer Potts models
Ekeberg, M., Lövkvist, C., Lan, Y., Weigt, M., & Aurell, E. (2013) · Physical Review E · doi:10.1103/physreve.87.012707 · Cited by 661 · see citing works
Self-citationKamisetty, H., Ovchinnikov, S., & Baker, D. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1314045110 · Cited by 672 · see citing works
Deep generative models of genetic variation capture the effects of mutations
Riesselman, A. J., Ingraham, J. B., & Marks, D. S. (2018) · Nature Methods · doi:10.1038/s41592-018-0138-4 · Cited by 601 · see citing works
Self-citationModeling aspects of the language of life through transfer-learning protein sequences
Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., & Rost, B. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3220-8 · Cited by 606 · see citing works
Ovchinnikov, S., Kamisetty, H., & Baker, D. (2014) · eLife · doi:10.7554/elife.02030 · Cited by 629 · see citing works
Emerging methods in protein co-evolution
de Juan, D., Pazos, F., & Valencia, A. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3414 · Cited by 607 · see citing works
Modeling the language of life – Deep Learning Protein Sequences
Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., & Rost, B. (2019) · doi:10.1101/614313 · Cited by 606 · see citing works
Epistasis in protein evolution
Starr, T. N., & Thornton, J. W. (2016) · Protein Science · doi:10.1002/pro.2897 · Cited by 575 · see citing works
Predicting multiple conformations via sequence clustering and AlphaFold2
Wayment-Steele, H. K., Ojoawo, A., Otten, R., Apitz, J. M., Pitsawong, W., Hömberger, M., Ovchinnikov, S., Colwell, L., & Kern, D. (2024) · Nature · doi:10.1038/s41586-023-06832-9 · Cited by 545 · see citing works
Computed structures of core eukaryotic protein complexes
Humphreys, I. R., Pei, J., Baek, M., Krishnakumar, A., Anishchenko, I., Ovchinnikov, S., Zhang, J., Ness, T. J., Banjade, S., Bagde, S. R., et al. (2021) · Science · doi:10.1126/science.abm4805 · Cited by 542 · see citing works
Protein structure determination using metagenome sequence data
Ovchinnikov, S., Park, H., Varghese, N., Huang, P.-S., Pavlopoulos, G. A., Kim, D. E., Kamisetty, H., Kyrpides, N. C., & Baker, D. (2017) · Science · doi:10.1126/science.aah4043 · Cited by 520 · see citing works
Sequence co-evolution gives 3D contacts and structures of protein complexes
Hopf, T. A., Schärfe, C. P. I., Rodrigues, J. P. G. L. M., Green, A. G., Kohlbacher, O., Sander, C., Bonvin, A. M. J. J., & Marks, D. S. (2014) · eLife · doi:10.7554/elife.03430 · Cited by 515 · see citing works
Self-citationThree-Dimensional Structures of Membrane Proteins from Genomic Sequencing
Hopf, T. A., Colwell, L. J., Sheridan, R., Rost, B., Sander, C., & Marks, D. S. (2012) · Cell · doi:10.1016/j.cell.2012.04.012 · Cited by 501 · see citing works
Self-citationDistance-based protein folding powered by deep learning
Xu, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1821309116 · Cited by 401 · see citing works
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280 · see citing works
“Maximum entropy methods disentangle direct interactions from indirect interactions by inferring parameters of a posited generating distribution for the sequence family (Weigt et al., 2009; Marks et al., 2011; Morcos et al., 2011; Jones et al., 2011; Balakrishnan et al., 2011; Ekeberg et al., 2013b).”
MethodHigh-resolution <i>de novo</i> structure prediction from primary sequence
Wu, R., Ding, F., Wang, R., Shen, R., Zhang, X., Luo, S., Su, C., Wu, Z., Xie, Q., Berger, B., et al. (2022) · doi:10.1101/2022.07.21.500999 · Cited by 392 · see citing works
Currin, A., Swainston, N., Day, P. J., & Kell, D. B. (2014) · Chemical Society Reviews · doi:10.1039/c4cs00351a · Cited by 384 · see citing works
DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment
Wright, E. S. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0749-z · Cited by 388 · see citing works
Rao, R., Liu, J., Verkuil, R., Meier, J., Canny, J. F., Abbeel, P., Sercu, T., & Rives, A. (2021) · doi:10.1101/2021.02.12.430858 · Cited by 350 · see citing works
An evolution-based model for designing chorismate mutase enzymes
Russ, W. P., Figliuzzi, M., Stocker, C., Barrat-Charlaix, P., Socolich, M., Kast, P., Hilvert, D., Monasson, R., Cocco, S., Weigt, M., et al. (2020) · Science · doi:10.1126/science.aba3304 · Cited by 313 · see citing works
Self-citationSenior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25834 · Cited by 347 · see citing works
Transformer protein language models are unsupervised structure learners
Rao, R., Meier, J., Sercu, T., Ovchinnikov, S., & Rives, A. (2020) · doi:10.1101/2020.12.15.422761 · Cited by 341 · see citing works
Causes of evolutionary rate variation among protein sites
Echave, J., Spielman, S. J., & Wilke, C. O. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2015.18 · Cited by 323 · see citing works
Protein Design: From the Aspect of Water Solubility and Stability
Qing, R., Hao, S., Smorodina, E., Jin, D., Zalevsky, A. O., & Zhang, S. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00757 · Cited by 321 · see citing works
Should evolutionary geneticists worry about higher-order epistasis?
Weinreich, D. M., Lan, Y., Wylie, C. S., & Heckendorn, R. B. (2013) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2013.10.007 · Cited by 277 · see citing works
Protein interaction networks revealed by proteome coevolution
Cong, Q., Anishchenko, I., Ovchinnikov, S., & Baker, D. (2019) · Science · doi:10.1126/science.aaw6718 · Cited by 241 · see citing works
Network deconvolution as a general method to distinguish direct dependencies in networks
Feizi, S., Marbach, D., Médard, M., & Kellis, M. (2013) · Nature Biotechnology · doi:10.1038/nbt.2635 · Cited by 287 · see citing works
The EVcouplings Python framework for coevolutionary sequence analysis
Hopf, T. A., Green, A. G., Schubert, B., Mersmann, S., Schärfe, C., Ingraham, J., Tóth-Petróczy, Á., Brock, K. P., Riesselman, A. J., Palmedo, P., et al. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty862 · Cited by 286 · see citing works
Self-citationDeep learning methods in protein structure prediction
Torrisi, M., Pollastri, G., & Le, Q. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.12.011 · Cited by 289 · see citing works
Inverse statistical problems: from the inverse Ising problem to data science
Nguyen, H. C., Zecchina, R., & Berg, J. (2017) · Advances In Physics · doi:10.1080/00018732.2017.1341604 · Cited by 284 · see citing works
Self-citationBoosting the Full Potential of PyMOL with Structural Biology Plugins
Rosignoli, S., & Paiardini, A. (2022) · Biomolecules · doi:10.3390/biom12121764 · Cited by 281 · see citing works
State-of-the-Art Estimation of Protein Model Accuracy Using AlphaFold
Roney, J. P., & Ovchinnikov, S. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.238101 · Cited by 281 · see citing works
The Causes and Consequences of Genetic Interactions (Epistasis)
Domingo, J., Baeza-Centurion, P., & Lehner, B. (2019) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083118-014857 · Cited by 242 · see citing works
Machine learning for functional protein design
Notin, P., Rollins, N. J., Gal, Y., Sander, C., & Marks, D. S. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02127-0 · Cited by 278 · see citing works
Self-citationTan, Z., Xiao, L., Tang, M., Bai, F., Li, J., Li, L., Shi, F., Li, N., Li, Y., Du, Q., et al. (2018) · Theranostics · doi:10.7150/thno.21451 · Cited by 272 · see citing works
Self-citationCoevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1
Figliuzzi, M., Jacquier, H., Schug, A. H., Tenaillon, O., & Weigt, M. (2015) · Molecular Biology and Evolution · doi:10.1093/molbev/msv211 · Cited by 263 · see citing works
Self-citationGenerative models for graph-based protein design
Ingraham, J., Garg, V., Barzilay, R., & Jaakkola, T. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 258 · see citing works
Machine learning in protein structure prediction
AlQuraishi, M. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.04.005 · Cited by 251 · see citing works
Dynamic architecture of a protein kinase
McClendon, C. L., Kornev, A. P., Gilson, M. K., & Taylor, S. S. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418402111 · Cited by 252 · see citing works
Inverse statistical physics of protein sequences: a key issues review
Cocco, S., Feinauer, C., Figliuzzi, M., Monasson, R., & Weigt, M. (2017) · Reports on Progress in Physics · doi:10.1088/1361-6633/aa9965 · Cited by 235 · see citing works
Self-citationSystematic identification of conditionally folded intrinsically disordered regions by AlphaFold2
Alderson, T. R., Pritišanac, I., Kolarić, Đ., Moses, A. M., & Forman-Kay, J. D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2304302120 · Cited by 240 · see citing works
Moult, J., Fidelis, K. A., Kryshtafovych, A., Schwede, T. F., & Tramontano, A. L. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25064 · Cited by 226 · see citing works
Genomics-aided structure prediction
Sulkowska, J. I., Morcos, F., Weigt, M., Terence Hwa, B., & Onuchic, J. N. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1207864109 · Cited by 226 · see citing works
Self-citationEmerging Computational Methods for the Rational Discovery of Allosteric Drugs
Wagner, J., Lee, C. T., Durrant, J. D., Malmstrom, R. D., Feher, V. A., & Amaro, R. E. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00631 · Cited by 230 · see citing works
Tcf1 and Lef1 transcription factors establish CD8+ T cell identity through intrinsic HDAC activity
Xing, S., Li, F., Zeng, Z., Zhao, Y., Yu, S., Shan, Q., Li, Y., Phillips, F. C., Maina, P. K., Qi, H. H., et al. (2016) · Nature Immunology · doi:10.1038/ni.3456 · Cited by 226 · see citing works
Modeling protein–protein and protein–peptide complexes: CAPRI 6th edition
Lensink, M. F., Velankar, S., & Wodak, S. J. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25215 · Cited by 222 · see citing works
Hopf, T. A., Ingraham, J., Poelwijk, F. J., Springer, M., Sander, C., & Marks, D. S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.04612 · Cited by 224 · see citing works
Self-citationBiophysics of protein evolution and evolutionary protein biophysics
Sikosek, T., & Chan, H. S. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2014.0419 · Cited by 220 · see citing works
Large-scale determination of previously unsolved protein structures using evolutionary information
Ovchinnikov, S., Kinch, L., Park, H., Liao, Y., Pei, J., Kim, D. E., Kamisetty, H., Grishin, N. V., & Baker, D. (2015) · eLife · doi:10.7554/elife.09248 · Cited by 222 · see citing works
A sparse covarying unit that describes healthy and impaired human gut microbiota development
Raman, A. S., Gehrig, J. L., Venkatesh, S., Chang, H.-W., Hibberd, M. C., Subramanian, S., Kang, G., Bessong, P. O., Lima, A. Â. M., Kosek, M. N., et al. (2019) · Science · doi:10.1126/science.aau4735 · Cited by 215 · see citing works
Protein–protein interaction prediction with deep learning: A comprehensive review
Soleymani, F., Paquet, E., Viktor, H. L., Michalowski, W., & Spinello, D. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.08.070 · Cited by 195 · see citing works
Tamir, S., Paddock, M. L., Darash-Yahana-Baram, M., Holt, S. H., Sohn, Y. S., Agranat-Tamir, L., Michaeli, D., Stofleth, J. T., Lipper, C. H., Morcos, F., et al. (2014) · Biochimica et Biophysica Acta (BBA) - Molecular Cell Research · doi:10.1016/j.bbamcr.2014.10.014 · Cited by 194 · see citing works
Self-citationCombining Experiments and Simulations Using the Maximum Entropy Principle
Boomsma, W., Ferkinghoff‐Borg, J., & Lindorff‐Larsen, K. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003406 · Cited by 192 · see citing works
Origins of coevolution between residues distant in protein 3D structures
Anishchenko, I., Ovchinnikov, S., Kamisetty, H., & Baker, D. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1702664114 · Cited by 185 · see citing works
Miao, Z.-C., Adamiak, R., Blanchet, M.-F., Boniecki, M. J., Bujnicki, J., Chen, S.-J., Cheng, C., Chojnowski, G., Chou, F.-C., Cordero, P., et al. (2015) · RNA: A publication of the RNA Society · doi:10.1261/rna.049502.114 · Cited by 197 · see citing works
Li, Y., Hu, J., Zhang, C., Yu, D.-J., & Zhang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz291 · Cited by 158 · see citing works
Ekeberg, M., Hartonen, T., & Aurell, E. M. (2014) · Journal of Computational Physics · doi:10.1016/j.jcp.2014.07.024 · Cited by 184 · see citing works
Jones, D. T., & Kandathil, S. M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty341 · Cited by 180 · see citing works
Hanson, J., Paliwal, K. K., Litfin, T., Yang, Y., & Zhou, Y. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty481 · Cited by 178 · see citing works
Evolution, Energy Landscapes and the Paradoxes of Protein Folding
Wolynes, P. (2014) · Biochimie · doi:10.1016/j.biochi.2014.12.007 · Cited by 176 · see citing works
Assessment of contact predictions in CASP12: Co‐evolution and deep learning coming of age
Schaarschmidt, J., Monastyrskyy, B., Kryshtafovych, A., & Bonvin, A. M. J. J. (2018) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25407 · Cited by 177 · see citing works
Nugent, T., & Jones, D. T. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1120036109 · Cited by 174 · see citing works
Machine learning and data science in soft materials engineering
Ferguson, A. L. (2018) · Journal of Physics: Condensed Matter · doi:10.1088/1361-648x/aa98bd · Cited by 137 · see citing works
Machine learning for protein folding and dynamics
Noé, F., Fabritiis, G. D., & Clementi, C. (2019) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2019.12.005 · Cited by 172 · see citing works
Coevolutionary signals across protein lineages help capture multiple protein conformations
Morcos, F., Jana, B., Terence Hwa, B., & Onuchic, J. N. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1315625110 · Cited by 170 · see citing works
Self-citationBuschiazzo, A., & Trajtenberg, F. (2019) · Annual Review of Microbiology · doi:10.1146/annurev-micro-091018-054627 · Cited by 169 · see citing works
Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning
Stahl, K., Graziadei, A., Dau, T., Brock, O., & Rappsilber, J. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01704-z · Cited by 145 · see citing works
Learning the pattern of epistasis linking genotype and phenotype in a protein
Poelwijk, F. J., Socolich, M., & Ranganathan, R. (2019) · Nature Communications · doi:10.1038/s41467-019-12130-8 · Cited by 165 · see citing works
Hou, J., Wu, T., Cao, R., & Cheng, J. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25697 · Cited by 160 · see citing works
Inverse Ising Inference Using All the Data
Aurell, E., & Ekeberg, M. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.090201 · Cited by 162 · see citing works
Interactome INSIDER: a structural interactome browser for genomic studies
Meyer, M. J., Beltrán, J. F., Liang, S., Fragoza, R., Rumack, A., Liang, J. H., Wei, X., & Yu, H. (2018) · Nature Methods · doi:10.1038/nmeth.4540 · Cited by 152 · see citing works
3dRNA v2.0: An Updated Web Server for RNA 3D Structure Prediction
Wang, J., Wang, J., Huang, Y., & Xiao, Y. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20174116 · Cited by 160 · see citing works
3D RNA and Functional Interactions from Evolutionary Couplings
Weinreb, C., Riesselman, A. J., Ingraham, J. B., Gross, T., Sander, C., & Marks, D. S. (2016) · Cell · doi:10.1016/j.cell.2016.03.030 · Cited by 159 · see citing works
Self-citationInferring protein 3D structure from deep mutation scans
Rollins, N. J., Brock, K. P., Poelwijk, F. J., Stiffler, M. A., Gauthier, N. P., Sander, C., & Marks, D. S. (2019) · Nature Genetics · doi:10.1038/s41588-019-0432-9 · Cited by 163 · see citing works
Self-citationImproved Contact Predictions Using the Recognition of Protein Like Contact Patterns
Skwark, M. J., Raimondi, D., Michel, M., & Elofsson, A. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003889 · Cited by 155 · see citing works
Origins of Allostery and Evolvability in Proteins: A Case Study
Raman, A. S., White, K. I., & Ranganathan, R. (2016) · Cell · doi:10.1016/j.cell.2016.05.047 · Cited by 154 · see citing works
FreeContact: fast and free software for protein contact prediction from residue co-evolution
Kaján, L., Hopf, T. A., Kalaš, M., Marks, D. S., & Rost, B. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-85 · Cited by 152 · see citing works
Self-citationMassively Parallel Assays and Quantitative Sequence–Function Relationships
Kinney, J. B., & McCandlish, D. M. (2019) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083118-014845 · Cited by 148 · see citing works
Wen, B., Zeng, W., Liao, Y., Shi, Z., Savage, S. R., Jiang, W., & Zhang, B. (2020) · PROTEOMICS · doi:10.1002/pmic.201900335 · Cited by 152 · see citing works
Evolution-Based Functional Decomposition of Proteins
Rivoire, O., Reynolds, K. A., & Ranganathan, R. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004817 · Cited by 148 · see citing works
Coevolutionary information, protein folding landscapes, and the thermodynamics of natural selection
Morcos, F., Schafer, N. P., Cheng, R. R., Onuchic, J. N., & Wolynes, P. G. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1413575111 · Cited by 147 · see citing works
Self-citationEpistasis and Adaptation on Fitness Landscapes
Bank, C. (2022) · Annual Review of Ecology Evolution and Systematics · doi:10.1146/annurev-ecolsys-102320-112153 · Cited by 156 · see citing works
Baldassi, C., Zamparo, M., Feinauer, C., Procaccini, A., Zecchina, R., Weigt, M., & Pagnani, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0092721 · Cited by 140 · see citing works
Self-citationATPase Subdomain IA Is a Mediator of Interdomain Allostery in Hsp70 Molecular Chaperones
General, I. J., Liu, Y., Blackburn, M. E., Mao, W., Gierasch, L. M., & Bahar, İ. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003624 · Cited by 144 · see citing works
Mann, J. K., Barton, J. P., Ferguson, A. L., Omarjee, S., Walker, B. D., Chakraborty, A. K., & Ndung’u, T. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003776 · Cited by 136 · see citing works
Determining protein structures using deep mutagenesis
Schmiedel, J. M., & Lehner, B. (2019) · Nature Genetics · doi:10.1038/s41588-019-0431-x · Cited by 145 · see citing works
Ultra-large alignments using phylogeny-aware profiles
Nguyen, N., Mirarab, S., Kumar, K., & Warnow, T. J. (2015) · Genome biology · doi:10.1186/s13059-015-0688-z · Cited by 141 · see citing works
- 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
- Physical Sciences › Materials Science › Materials Chemistry › 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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
