The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
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.
Overview
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
Verification
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?
In the citing paper
“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.
Citations
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
Flagged first, then by citations
Cited by
Sorted by citations
Items 401–500 of 1,002
Interdomain Contacts and the Stability of Serralysin Protease from Serratia marcescens
Zhang, L. N., Morrison, A. J., & Thibodeau, P. H. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138419 · Cited by 29
Coevolutionary Landscape of Kinase Family Proteins: Sequence Probabilities and Functional Motifs
Haldane, A., Flynn, W. F., He, P., & Levy, R. M. (2018) · Biophysical Journal · doi:10.1016/j.bpj.2017.10.028 · Cited by 28
Shah, N. H., & Kuriyan, J. (2018) · Nature Structural & Molecular Biology · doi:10.1038/s41594-018-0175-9 · Cited by 27
Recent advances in automated protein design and its future challenges
Setiawan, D., Brender, J., & Zhang, Y. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1465922 · Cited by 26
Lee, J.-W., Won, J.-H., Jeon, S., Choo, Y., Yeon, Y., Oh, J.-S., Kim, M., Kim, S., Joung, I., Jang, C., et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad712 · Cited by 29
Capturing coevolutionary signals inrepeat proteins
Espada, R., Parra, R. G., Mora, T., Walczak, A. M., & Ferreiro, D. U. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0648-3 · Cited by 27
eSBMTools 1.0: enhanced native structure-based modeling tools
Lutz, B., Sinner, C., Heuermann, G., Verma, A., & Schug, A. H. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt478 · Cited by 27
Co-evolution techniques are reshaping the way we do structural bioinformatics
de Oliveira, S. H. P., & Deane, C. M. (2017) · F1000Research · doi:10.12688/f1000research.11543.1 · Cited by 28
A protein coevolution method uncovers critical features of the Hepatitis C Virus fusion mechanism
Douam, F., Fusil, F., Enguehard, M., Dib, L., Nadalin, F., Schwaller, L., Hrebikova, G., Mancip, J., Mailly, L., Montserret, R., et al. (2018) · PLoS Pathogens · doi:10.1371/journal.ppat.1006908 · Cited by 27
DeepConPred2: An Improved Method for the Prediction of Protein Residue Contacts
Ding, W., Mao, W., Shao, D., Zhang, W., & Gong, H. (2018) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2018.10.009 · Cited by 29
Parsimonious evolutionary scenario for the origin of allostery and coevolution patterns in proteins
Rivoire, O. (2019) · Physical review. E · doi:10.1103/physreve.100.032411 · Cited by 25
The intrinsic dimension of protein sequence evolution
Facco, E., Pagnani, A., Russo, E. T., & Laio, A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006767 · Cited by 28
Self-citationECF σ factors with regulatory extensions: the one‐component systems of the σ universe
Pinto, D., Liu, Q., & Mascher, T. (2019) · Molecular Microbiology · doi:10.1111/mmi.14323 · Cited by 27
Inference of compressed Potts graphical models
Rizzato, F., Coucke, A., Leonardis, E. D., Barton, J. P., Tubiana, J., Monasson, R., & Cocco, S. (2020) · Physical review. E · doi:10.1103/physreve.101.012309 · Cited by 27
Liu, C., Lupala, C. S., Lyu, C., Zhu, W.-L., Wang, H., Jiang, C., Mei, L., Liu, H., & Huang, J. (2021) · Biochemical Engineering Journal · doi:10.1016/j.bej.2021.107926 · Cited by 27
Singh, J., Paliwal, K. K., Litfin, T., Singh, J., & Zhou, Y. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac421 · Cited by 25
Enhancing luciferase activity and stability through generative modeling of natural enzyme sequences
Xie, W.-J., Liu, D., Wang, X.-Y., Zhang, A., Wei, Q., Nandi, A., Dong, S.-W., & Warshel, A. (2023) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2312848120 · Cited by 3
Global Quantitative Modeling of Chromatin Factor Interactions
Zhou, J., & Troyanskaya, O. G. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003525 · Cited by 27
Protein structure prediction: making AWSEM AWSEM‐ER by adding evolutionary restraints
Sirovetz, B. J., Schafer, N. P., & Wolynes, P. G. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25367 · Cited by 27
A global machine learning based scoring function for protein structure prediction
Faraggi, E., & Kloczkowski, A. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24454 · Cited by 28
From traditional to AI-driven: The evolution of intelligent enzyme engineering for biocatalysis
Lv, F., Zhang, J., You, S., & Qi, W. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108788 · Cited by 31
Consistent inference of a general model using the pseudolikelihood method
Mozeika, A., Dikmen, O., & Piili, J. (2014) · Physical Review E · doi:10.1103/physreve.90.010101 · Cited by 26
Assessing the accuracy of direct-coupling analysis for RNA contact prediction
Cuturello, F., Tiana, G., & Bussi, G. (2020) · RNA · doi:10.1261/rna.074179.119 · Cited by 28
De novo protein structure prediction using ultra-fast molecular dynamics simulation
Cheung, N. J., & Yu, W. (2018) · PLoS ONE · doi:10.1371/journal.pone.0205819 · Cited by 26
Exploring the sequence fitness landscape of a bridge between protein folds
Tian, P., & Best, R. B. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008285 · Cited by 26
Quantifying information of intracellular signaling: progress with machine learning
Tang, Y., & Hoffmann, A. (2022) · Reports on Progress in Physics · doi:10.1088/1361-6633/ac7a4a · Cited by 24
Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons
Ferrari, U., Deny, S., Chalk, M., Tkačik, G., Marre, O., & Mora, T. (2018) · Physical review. E · doi:10.1103/physreve.98.042410 · Cited by 26
The landscape of coadaptation in Vibrio parahaemolyticus
Cui, Y., Yang, C., Qiu, H., Wang, H., Yang, R., & Falush, D. (2020) · eLife · doi:10.7554/elife.54136 · Cited by 26
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
Consensus designs and thermal stability determinants of a human glutamate transporter
Cirri, E., Brier, S., Assal, R., Canul-Tec, J. C., Chamot‐Rooke, J., & Reyes, N. (2018) · eLife · doi:10.7554/elife.40110 · Cited by 25
Deciphering the structure of the condensin protein complex
Krepel, D., Cheng, R. R., Pierro, M. D., & Onuchic, J. N. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1812770115 · Cited by 26
Self-citationA multi-scale coevolutionary approach to predict interactions between protein domains
Croce, G., Gueudré, T., Cuevas, M. V. R., Keidel, V., Figliuzzi, M., Szurmant, H., & Weigt, M. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006891 · Cited by 25
Self-citationRevealing evolutionary constraints on proteins through sequence analysis
Wang, S., Bitbol, A., & Wingreen, N. S. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007010 · Cited by 25
Suh, D., Lee, J. W., Choi, S., & Lee, Y. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22116032 · Cited by 25
adabmDCA: adaptive Boltzmann machine learning for biological sequences
Muntoni, A. P., Pagnani, A., Weigt, M., & Zamponi, F. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04441-9 · Cited by 25
Self-citationUncovering the statistical physics of 3D chromosomal organization using data-driven modeling
Contessoto, V. G., Cheng, R. R., & Onuchic, J. N. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102418 · Cited by 23
Self-citationUnsupervised hierarchical clustering using the learning dynamics of restricted Boltzmann machines
Decelle, A., Seoane, B., & Rosset, L. (2023) · Physical review. E · doi:10.1103/physreve.108.014110 · Cited by 29
Improving accuracy of protein contact prediction using balanced network deconvolution
Sun, H.-P., Huang, Y., Wang, X.-F., Zhang, Y., & Shen, H.-B. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24744 · Cited by 25
Mao, W., Kaya, C., Dutta, A., Horovitz, A., & Bahar, İ. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv103 · Cited by 25
Observation selection bias in contact prediction and its implications for structural bioinformatics
Orlando, G., Raimondi, D., & Vranken, W. (2016) · Scientific Reports · doi:10.1038/srep36679 · Cited by 25
Exploring protein sequence–function landscapes
Starr, T. N., & Thornton, J. W. (2017) · Nature Biotechnology · doi:10.1038/nbt.3786 · Cited by 25
Protein structure modeling and refinement by global optimization in CASP12
Hong, S. H., Joung, I., Flores‐Canales, J. C., Manavalan, B., Cheng, Q., Heo, S., Kim, J. Y., Lee, S., Nam, M., Joo, K., et al. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25426 · Cited by 25
Zhang, Z., Xiong, P., Zhang, T., Wang, J., Zhan, J., & Zhou, Y. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1192 · Cited by 26
Data-driven enzyme engineering to identify function-enhancing enzymes
Jiang, Y., Ran, X., & Yang, Z. J. (2022) · Protein Engineering Design and Selection · doi:10.1093/protein/gzac009 · Cited by 27
The Maximum Entropy Fallacy Redux?
Aurell, E. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004777 · Cited by 25
Chakraborty, A. K., & Barton, J. P. (2017) · Reports on Progress in Physics · doi:10.1088/1361-6633/aa574a · Cited by 23
Sánchez-Ibarra, H. E., Reyes, L. M., Jiang, X., Luna-Aguirre, C. M., Aguirre-Treviño, D., Morales-Alvarado, I. A., León-Cachón, R. B. R., Lavalle-Gonzalez, F. J., Morcos, F., & Barrera-Saldaña, H. A. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.00320 · Cited by 24
Self-citationProtein Complexes Form a Basis for Complex Hybrid Incompatibility
Swamy, K. B. S., Schuyler, S. C., & Leu, J. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.609766 · Cited by 26
Abnormal Behavior Detection Based on Traffic Pattern Categorization in Mobile Networks
DeAlmeida, J. M., Pontes, C. F. T., DaSilva, L. A., Both, C. B., Gondim, J. J. C., Ralha, C. G., & Marotta, M. A. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3125019 · Cited by 24
Quantifying relevance in learning and inference
Matteo Marsili, A., & Roudi, Y. (2022) · Physics Reports · doi:10.1016/j.physrep.2022.03.001 · Cited by 25
Natural Evolution Provides Strong Hints about Laboratory Evolution of Designer Enzymes
Xie, W. J., & Warshel, A. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2207904119 · Cited by 23
Thirteen dubious ways to detect conserved structural RNAs
Gao, W. N. D., Yang, A., & Rivas, E. (2022) · IUBMB Life · doi:10.1002/iub.2694 · Cited by 21
Savojardo, C., Fariselli, P., Martelli, P. L., & Casadio, R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt555 · Cited by 24
On the Entropy of Protein Families
Barton, J. P., Chakraborty, A. K., Cocco, S., Jacquin, H., & Monasson, R. (2016) · Journal of Statistical Physics · doi:10.1007/s10955-015-1441-4 · Cited by 22
Can Protein Structure Prediction Methods Capture Alternative Conformations of Membrane Transporters?
Xie, T., & Huang, J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01936 · Cited by 23
Zhang, C., Wang, Q., Li, Y., Teng, A., Hu, G., Wuyun, Q., & Zheng, W. H. (2024) · Biomolecules · doi:10.3390/biom14121531 · Cited by 29
Emerging frontiers in protein structure prediction following the AlphaFold revolution
Rennie, M. L., & Oliver, M. R. (2025) · Journal of The Royal Society Interface · doi:10.1098/rsif.2024.0886 · Cited by 34
Population-specific design of de-immunized protein biotherapeutics
Schubert, B., Schärfe, C., Dönnes, P., Hopf, T. A., Marks, D. S., & Kohlbacher, O. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005983 · Cited by 24
Self-citationStatistical physics and representations in real and artificial neural networks
Cocco, S., Monasson, R., Posani, L., Rosay, S., & Tubiana, J. (2017) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2017.11.153 · Cited by 25
Residue co-evolution helps predict interaction sites in α-helical membrane proteins
Zeng, B., Hönigschmid, P., & Frishman, D. (2019) · Journal of Structural Biology · doi:10.1016/j.jsb.2019.02.009 · Cited by 23
Li, Y., Zhang, C., Zheng, W. H., Zhou, X., Bell, E. W., Yu, D., & Zhang, Y. (2021) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26211 · Cited by 24
Evolution and folding of repeat proteins
Galpern, E. A., Marchi, J., Mora, T., Walczak, A. M., & Ferreiro, D. U. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2204131119 · Cited by 23
Direct-acting antiviral resistance of Hepatitis C virus is promoted by epistasis
Zhang, H., Quadeer, A. A., & McKay, M. R. (2023) · Nature Communications · doi:10.1038/s41467-023-42550-6 · Cited by 23
Identifying relevant positions in proteins by Critical Variable Selection
Grigolon, S., Franz, S., & Matteo Marsili, A. (2016) · Molecular BioSystems · doi:10.1039/c6mb00047a · Cited by 23
Prediction of the optimal set of contacts to fold the smallest knotted protein
Dąbrowski-Tumański, P., Jarmolinska, A. I., & Sulkowska, J. I. (2015) · Journal of Physics Condensed Matter · doi:10.1088/0953-8984/27/35/354109 · Cited by 23
Suplatov, D. A., Sharapova, Y., Timonina, D., Kopylov, K., & Švedas, V. K. (2017) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s021972001840005x · Cited by 22
Using molecular simulation to explore the nanoscale dynamics of the plant kinome
Moffett, A. S., & Shukla, D. (2018) · Biochemical Journal · doi:10.1042/bcj20170299 · Cited by 23
ConDo: protein domain boundary prediction using coevolutionary information
Hong, S. H., Joo, K., & Lee, J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty973 · Cited by 22
DIRECT: RNA contact predictions by integrating structural patterns
Jian, Y., Wang, X., Qiu, J., Wang, H., Liu, Z., Zhao, Y., & Zeng, C. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3099-4 · Cited by 24
Empirical fitness models for hepatitis C virus immunogen design
Hart, G. R., & Ferguson, A. L. (2015) · Physical Biology · doi:10.1088/1478-3975/12/6/066006 · Cited by 23
Pucci, F., & Schug, A. H. (2019) · Methods · doi:10.1016/j.ymeth.2019.04.012 · Cited by 23
Deep generative models of genetic variation capture mutation effectsPreprint
Riesselman, A. J., Ingraham, J., & Marks, D. S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/235655 · Cited by 29
Self-citationCoevolving residues inform protein dynamics profiles and disease susceptibility of nSNVs
Butler, B. M., Kazan, I. C., Kumar, A., & Ozkan, S. B. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006626 · Cited by 19
Limits in accuracy and a strategy of RNA structure prediction using experimental information
Wang, J., Williams, B., Chirasani, V. R., Krokhotin, A., Das, R., & Dokholyan, N. V. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz427 · Cited by 22
Astl, L., & Verkhivker, G. M. (2019) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2019.07.008 · Cited by 21
Si, Y., & Yan, C. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab341 · Cited by 21
Wang, L., Li, F., Ma, X., Cang, Y., & Bai, F. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01033 · Cited by 22
In vivo functional phenotypes from a computational epistatic model of evolution
Alvarez, S., Nartey, C. M., Mercado, N., de la Paz, J. A., Huseinbegovic, T., & Morcos, F. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2308895121 · Cited by 20
Self-citationPredicting protein β-sheet contacts using a maximum entropy-based correlated mutation measure
Burkoff, N. S., Várnai, C., & Wild, D. L. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt005 · Cited by 22
A Comparison of the Maximum Entropy Principle Across Biological Spatial Scales
Cofré, R., Herzog, R. A., Corcoran, D., & Rosas, F. E. (2019) · Entropy · doi:10.3390/e21101009 · Cited by 22
Toward Inferring Potts Models for Phylogenetically Correlated Sequence Data
Rodríguez-Horta, E., Barrat-Charlaix, P., & Weigt, M. (2019) · Entropy · doi:10.3390/e21111090 · Cited by 24
Self-citationStructural prediction of protein interactions and docking using conservation and coevolution
Andréani, J., Quignot, C., & Guérois, R. (2020) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1470 · Cited by 23
Mesdaghi, S., Murphy, D., Rodríguez, F. S., Burgos-Mármol, J. J., & Rigden, D. J. (2020) · F1000Research · doi:10.12688/f1000research.27676.1 · Cited by 23
Barrat-Charlaix, P., Muntoni, A. P., Shimagaki, K. S., Weigt, M., & Zamponi, F. (2021) · Physical review. E · doi:10.1103/physreve.104.024407 · Cited by 26
Self-citationEfficient generative modeling of protein sequences using simple autoregressive modelsPreprint
Trinquier, J., Uguzzoni, G., Pagnani, A., Zamponi, F., & Weigt, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.04.433959 · Cited by 22
Self-citationThe role of local versus nonlocal physicochemical restraints in determining protein native structure
Skolnick, J., & Gao, M. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2020.10.008 · Cited by 21
Siwek, J. C., Omelchenko, A. A., Chhibbar, P., Arshad, S., Rosengart, A., Nazarali, I., Patel, A., Nazarali, K., Rahimikollu, J., Tilstra, J. S., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02723-1 · Cited by 27
Baltoumas, F. A., Karatzas, E., Páez-Espino, D., Venetsianou, N. K., Aplakidou, E., Oulas, A., Finn, R. D., Ovchinnikov, S., Pafilis, E., Kyrpides, N. C., et al. (2023) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2023.1157956 · Cited by 21
Inferring Contacting Residues within and between Proteins: What Do the Probabilities Mean?
van Nimwegen, E. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004726 · Cited by 21
On the effect of phylogenetic correlations in coevolution-based contact prediction in proteins
Rodríguez-Horta, E., & Weigt, M. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008957 · Cited by 26
Self-citationProbing allosteric regulations with coevolution-driven molecular simulations
Colizzi, F., & Orozco, M. (2021) · Science Advances · doi:10.1126/sciadv.abj0786 · Cited by 21
Modlin, S. J., Elghraoui, A., Gunasekaran, D., Zlotnicki, A. M., Dillon, N. A., Dhillon, N., Kuo, N., Robinhold, C., Chan, C. K., Baughn, A. D., et al. (2021) · mSystems · doi:10.1128/msystems.00673-21 · Cited by 27
Design of novel cyanovirin-N variants by modulation of binding dynamics through distal mutations
Kazan, I. C., Sharma, P., Rahman, M. I., Bobkov, A., Fromme, R., Ghirlanda, G., & Ozkan, S. B. (2022) · eLife · doi:10.7554/elife.67474 · Cited by 21
A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity
Bravi, B., Gioacchino, A. D., Fernández-de-Cossio-Díaz, J., Walczak, A. M., Mora, T., Cocco, S., & Monasson, R. (2023) · eLife · doi:10.7554/elife.85126 · Cited by 20
Neuwald, A. F. (2016) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.04.006 · Cited by 21
Random versus maximum entropy models of neural population activity
Ferrari, U., Obuchi, T., & Mora, T. (2017) · Physical review. E · doi:10.1103/physreve.95.042321 · Cited by 21
Protein structure prediction assisted with sparse NMR data in CASP13
Sala, D., Huang, Y. J., Cole, C. A., Snyder, D. A., Liu, G., Ishida, Y., Swapna, G. V. T., Brock, K. P., Sander, C., Fidelis, K. A., et al. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25837 · Cited by 23
Self-citationSparse Epistatic Patterns in the Evolution of Terpene Synthases
Ballal, A., Laurendon, C., Salmon, M., Vardakou, M., Cheema, J., Defernez, M., O’Maille, P. E., & Morozov, A. V. (2020) · Molecular Biology and Evolution · doi:10.1093/molbev/msaa052 · Cited by 20
Guo, Y., Wu, J., Ma, H., Wang, S., & Huang, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45257-5_6 · Cited by 21
State-of-the-art web services for de novo protein structure prediction
Abriata, L. A., & Peraro, M. D. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa139 · Cited by 21
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
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
