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

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 access

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

Open this paper in CoChat — free account

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.

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

Crossref · 10.1073/pnas.1111471108

Cites

55 of 55 references verified0 citation errors · 0 not checkable

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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

Flagged first, then by citations

Show all 55 references

Research works printed but not linked: 6

10.1093/nar/gkp848 · DOI
10.1093/nar/gkp848 · DOI
10.1073/pnas.0805923106 · DOI · Paper page
10.1016/S1097-2765(03)00148-5 · DOI
10.1101/gad.1125603 · DOI
10.1002/j.1460-2075.1990.tb07409.x · DOI

Sorted by citations

Items 801–900 of 1,002

Biomolecular coevolution and its applications: Going from structure prediction toward signaling, epistasis, and function
Zerihun, M. B., & Schug, A. H. (2017) · Biochemical Society Transactions · doi:10.1042/bst20170063 · Cited by 10
Identification of residue pairing in interacting β-strands from a predicted residue contact map
Mao, W., Wang, T., Zhang, W., & Gong, H. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2150-1 · Cited by 7
New Techniques for Ancient Proteins: Direct Coupling Analysis Applied on Proteins Involved in Iron Sulfur Cluster Biogenesis
Fantini, M., Malinverni, D., Rios, P. D. L., & Pastore, A. (2017) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2017.00040 · Cited by 9
Sibe: a computation tool to apply protein sequence statistics to predict folding and design in silico
Cheung, N. J., & Yu, W. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2984-1 · Cited by 9
Statistical physics of interacting proteins: Impact of dataset size and quality assessed in synthetic sequences
Gandarilla-Pérez, C. A., Mergny, P., Weigt, M., & Bitbol, A. (2020) · Physical review. E · doi:10.1103/physreve.101.032413 · Cited by 8
Self-citation
CoeViz 2: Protein Graphs Derived From Amino Acid Covariance
Corcoran, D., Maltbie, N., Sudalairaj, S., Baker, F. N., Hirschfeld, J., & Porollo, A. (2021) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.653681 · Cited by 8
Effect on intrinsic peroxidase activity of substituting coevolved residues from Ω-loop C of human cytochrome c into yeast iso-1-cytochrome c
Frederick, A. K., Thompson, S. L., Vakharia, Z. M., Cherney, M. M., Lei, H., Evenson, G. E., & Bowler, B. E. (2022) · Journal of Inorganic Biochemistry · doi:10.1016/j.jinorgbio.2022.111819 · Cited by 8
Conformational spread drives the evolution of the calcium–calmodulin protein kinase II
Khan, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-12090-y · Cited by 8
iBIS2Analyzer: a web server for a phylogeny-driven coevolution analysis of protein families
Oteri, F., Sarti, E., Nadalin, F., & Carbone, A. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac481 · Cited by 9
Key aspects of the past 30 years of protein design
Meconi, G. M., Sasselli, I. R., Bianco, V., Onuchic, J. N., & Coluzza, I. (2022) · Reports on Progress in Physics · doi:10.1088/1361-6633/ac78ef · Cited by 8
Self-citation
Functional assays for transcription mechanisms in high-throughput
Qiu, C., & Kaplan, C. D. (2019) · Methods · doi:10.1016/j.ymeth.2019.02.017 · Cited by 9
Will Cryo-Electron Microscopy Shift the Current Paradigm in Protein Structure Prediction?
Abriata, L. A., & Peraro, M. D. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00177 · Cited by 8
Long-range correlation and critical fluctuations in coevolution networks of protein sequences
Xu, X.-L., Shi, J.-X., Wang, J., & Li, W. (2020) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2020.125339 · Cited by 7
PPalign: optimal alignment of Potts models representing proteins with direct coupling information
Talibart, H., & Coste, F. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04222-4 · Cited by 8
Protein-protein interaction prediction by combined analysis of genomic and conservation information
Emamjomeh, A., Goliaei, B., Torkamani, A., Ebrahimpour, R., Mohammadi, N., & Parsian, A. (2014) · Genes & Genetic Systems · doi:10.1266/ggs.89.259 · Cited by 7
Prediction of residue-residue contacts from protein families using similarity kernels and least squares regularizationPreprint
Andreatta, M., Laplagne, S., Li, S. C., & Smale, S. T. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1311.1301 · Cited by 8
Reduced antigenicity of Omicron lowers host serologic responsePreprint
Tubiana, J., Xiang, Y., Fan, L., Wolfson, H. J., Chen, K., Schneidman‐Duhovny, D., & Shi, Y. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.15.480546 · Cited by 8
Co‐evolution at protein–protein interfaces guides inference of stoichiometry of oligomeric protein complexes by de novo structure prediction
Kilian, M., & Bischofs, I. B. (2023) · Molecular Microbiology · doi:10.1111/mmi.15169 · Cited by 8
Detecting co-selection through excess linkage disequilibrium in bacterial genomes
Mallawaarachchi, S., Tonkin‐Hill, G. Q., Pöntinen, A. K., Calland, J. K., Gladstone, R. A., Arredondo-Alonso, S., MacAlasdair, N., Thorpe, H. A., Top, J., Sheppard, S. K., et al. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae061 · Cited by 7
Generative AI techniques for conformational diversity and evolutionary adaptation of proteins
Brownless, A.-L. R., Yehorova, D., Welsh, C. L., & Kamerlin, S. C. L. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103135 · Cited by 9
Evolutionary Constraints Guide AlphaFold2 in Predicting Alternative Conformations and Inform Rational Mutation Design
Piomponi, V., Cazzaniga, A., & Cuturello, F. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01090 · Cited by 7
Probing the dark energy in the functional protein universe
Galpern, E. A., Bueno, C., Sanchez, I. E., Wolynes, P. G., & Ferreiro, D. U. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2531111123 · Cited by 7
Ising-like model replicating time-averaged spiking behaviour of in vitro neuronal networks
Filho, C. I. N. S., de Arcangelis, L., Herrmann, H. J., Plenz, D., Kells, P. A., Ribeiro, T. L., & Andrade, J. S. (2024) · Scientific Reports · doi:10.1038/s41598-024-55922-9 · Cited by 9
A Small Set of Succinct Signature Patterns Distinguishes Chinese and Non-Chinese HIV-1 Genomes
Wang, Y., Rawi, R., Wilms, C., Heider, D., Yang, R., & Hoffmann, D. E. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058804 · Cited by 7
Polymorphic sites preferentially avoid co-evolving residues in MHC class I proteins
Dib, L., Salamin, N., & Gfeller, D. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006188 · Cited by 7
A Hybrid Approach for Protein Structure Determination Combining Sparse NMR with Evolutionary Coupling Sequence Data
Huang, Y. J., Brock, K. P., Sander, C., Marks, D. S., & Montelione, G. T. (2018) · Advances in experimental medicine and biology · doi:10.1007/978-981-13-2200-6_10 · Cited by 7
Self-citation
Inferring epistasis from genomic data with comparable mutation and outcrossing rate
Zeng, H.-L., Mauri, E., Dichio, V., Cocco, S., Monasson, R., & Aurell, E. (2021) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ac0f64 · Cited by 8
FALCON2: a web server for high-quality prediction of protein tertiary structures
Kong, L., Ju, F., Zhang, H., Sun, S., & Bu, D. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04353-8 · Cited by 8
A Minimal yet Flexible Likelihood Framework to Assess Correlated Evolution
Behdenna, A., Godfroid, M., Petot, P., Pothier, J. F., Noûs, C., Lambert, A., & Achaz, G. (2021) · Systematic Biology · doi:10.1093/sysbio/syab092 · Cited by 7
Evolutionary modeling reveals enhanced mutational flexibility of HCV subtype 1b compared with 1a
Zhang, H., Quadeer, A. A., & McKay, M. R. (2021) · iScience · doi:10.1016/j.isci.2021.103569 · Cited by 7
Multiscale molecular simulations to investigate adenylyl cyclase‐based signaling in the brain
van Keulen, S. C., Martin, J., Colizzi, F., Frezza, E., Trpevski, D., Cirauqui, N., Vidossich, P., Röthlisberger, U., Kotaleski, J. H., Wade, R. C., et al. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1623 · Cited by 7
Singular value decomposition of protein sequences as a method to visualize sequence and residue space
Baxter‐Koenigs, A. R., Nesr, G. E., & Barrick, D. (2022) · Protein Science · doi:10.1002/pro.4422 · Cited by 7
The maximum entropy principle for compositional data
Weistuch, C., Zhu, J., Deasy, J. O., & Tannenbaum, A. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-05007-z · Cited by 7
Evolutionary Couplings and Molecular Dynamic Simulations Highlight Details of GPCRs Heterodimers’ Interfaces
Nchourupouo, K. W. T., Nde, J., Wadop, Y. N., Zekeng, S. S., & Fongang, B. (2023) · Molecules · doi:10.3390/molecules28041838 · Cited by 7
Enzyme Substrate Prediction from Three-Dimensional Feature Representations Using Space-Filling Curves
Rappoport, D., & Jinich, A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00005 · Cited by 9
Impact of population size on early adaptation in rugged fitness landscapes
Servajean, R., & Bitbol, A. (2023) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2022.0045 · Cited by 6
What geometrically constrained models can tell us about real-world protein contact maps
Güven, J. J., Molkenthin, N., Mühle, S., & Mey, A. S. J. S. (2023) · Physical Biology · doi:10.1088/1478-3975/acd543 · Cited by 8
Empowering Protein Engineering through Recombination of Beneficial Substitutions
Wang, X., Li, A., Li, X., & Cui, H. (2024) · Chemistry - A European Journal · doi:10.1002/chem.202303889 · Cited by 7
Evolutionary Conservation of Mechanical Strain Distributions in Functional Transitions of Protein Structures
Sartori, P., & Leibler, S. (2024) · Physical Review X · doi:10.1103/physrevx.14.011042 · Cited by 7
RECENT ADVANCES IN FUNCTIONAL REGION PREDICTION BY USING STRUCTURAL AND EVOLUTIONARY INFORMATION – REMAINING PROBLEMS AND FUTURE EXTENSIONS
Nemoto, W., Saito, A., & Oikawa, H. (2013) · Computational and Structural Biotechnology Journal · doi:10.5936/csbj.201308007 · Cited by 7
Proteins: From sequence to structure
Zheng, W. (2014) · Chinese Physics B · doi:10.1088/1674-1056/23/7/078705 · Cited by 7
Horizontal gene transfer from human host to HIV-1 reverse transcriptase confers drug resistance and partly compensates for replication deficits
Megens, S., Vaira, D., Baets, G. D., Dekeersmaeker, N., Schrooten, Y., Li, G., Schymkowitz, J. W. H., Rousseau, F., Vandamme, A., Moutschen, M. P., et al. (2014) · Virology · doi:10.1016/j.virol.2014.03.023 · Cited by 7
A unified statistical model of protein multiple sequence alignment integrating direct coupling and insertions
Kinjo, A. R. (2016) · Biophysics and Physicobiology · doi:10.2142/biophysico.13.0_45 · Cited by 7
Learning Sequence Determinants of Protein:Protein Interaction Specificity with Sparse Graphical Models
Kamisetty, H., Ghosh, B., LANGMEAD, C. J., & Bailey‐Kellogg, C. (2015) · Journal of Computational Biology · doi:10.1089/cmb.2014.0289 · Cited by 7
Coevolutionary constraints in the sequence‐space of macromolecular complexes reflect their self‐assembly pathways
Mallik, S., & Kundu, S. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25292 · Cited by 7
Pareto Optimization of Combinatorial Mutagenesis Libraries
Verma, D., Grigoryan, G., & Bailey‐Kellogg, C. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2858794 · Cited by 7
hoDCA: higher order direct-coupling analysis
Schmidt, M., & Hamacher, K. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2583-6 · Cited by 7
DCA-MOL: A PyMOL Plugin To Analyze Direct Evolutionary Couplings
Jarmolinska, A. I., Zhou, Q., Sulkowska, J. I., & Morcos, F. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00690 · Cited by 7
Self-citation
Direct coupling analysis improves the identification of beneficial amino acid mutations for the functional thermostabilization of a delicate decarboxylase
Peng, M., Maier, M., Esch, J., Schug, A. H., & Rabe, K. S. (2019) · Biological Chemistry · doi:10.1515/hsz-2019-0156 · Cited by 7
Coevolutive, evolutive and stochastic information in protein-protein interactions
Andrade, M. D. S., Pontes, C. F. T., & Treptow, W. L. (2019) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.10.005 · Cited by 7
Atomic-level evolutionary information improves protein–protein interface scoring
Quignot, C., Granger, P., Chacón, P., Guérois, R., & Andréani, J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab254 · Cited by 9
Structure and Sequence-based Computational Approaches to Allosteric Signal Transduction: Application to Electromechanical Coupling in Voltage-gated Ion Channels
Elbahnsi, A., & Delemotte, L. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167095 · Cited by 7
Co-evolutionary landscape at the interface and non-interface regions of protein-protein interaction complexes
Mukherjee, I., & Chakrabarti, S. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.06.039 · Cited by 7
Mechanical couplings of protein backbone and side chains exhibit scale-free network properties and specific hotspots for function
Raj, N., Click, T., Yang, H., & Chu, J. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.09.004 · Cited by 4
Systematic evaluation of CS-Rosetta for membrane protein structure prediction with sparse NOE restraints
Reichel, K., Fisette, O., Braun, T., Lange, O. F., Hummer, G., & Schäfer, L. V. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25224 · Cited by 7
Statistical investigations of protein residue direct couplings
Neuwald, A. F., & Altschul, S. F. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006237 · Cited by 7
Artificial intelligence in bioinformatics
Feng, S., Xu, J., & Shen, H. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816034-3.00007-9 · Cited by 7
MPF–BML: a standalone GUI-based package for maximum entropy model inference
Quadeer, A. A., McKay, M. R., Barton, J. P., & Louie, R. H. Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz925 · Cited by 7
Using graphics processing units to investigate molecular coevolution
Waechter, M., Jaeger, K., Thuerck, D., Weißgraeber, S., Widmer, S., Goesele, M., & Hamacher, K. (2013) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.3074 · Cited by 7
Bayesian Sparsity for Intractable DistributionsPreprint
Ingraham, J., & Marks, D. S. (2016) · arXiv (Cornell University) · Cited by 7
Self-citation
Molecular Structures of Yeast Kinetochore Subcomplexes and Their Roles in Chromosome SegregationPreprint
Jenni, S., Dimitrova, Y. N., Valverde, R., Hinshaw, S. M., & Harrison, S. C. (2017) · Cold Spring Harbor Symposia on Quantitative Biology · doi:10.1101/sqb.2017.82.033738 · Cited by 7
PconsC4: fast, free, easy, and accurate contact predictionsPreprint
Michel, M., Hurtado, D. M., & Elofsson, A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/383133 · Cited by 7
Decoding and reengineering the promoter specificity of T7-like RNA polymerases based on phage genome sequences
Zhu, J., Liu, Z., Lou, C., Chen, Q., & Liu, H. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf140 · Cited by 6
Correlations among Hydrogen Bond Fluctuations in the Apo State Are Enough to Reveal Allosteric Networks in Proteins
Sarkar, S., Dhibar, S., & Jana, B. (2025) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5c03281 · Cited by 6
Capturing intrinsic protein dynamics for explaining beneficial substitutions from protein engineering campaigns
Meng, S., Li, Z., Zhang, P., Ji, Y., & Schwaneberg, U. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108660 · Cited by 6
Distinguishing direct interactions from global epistasis using rank statistics
Carlson, M. O., Andrews, B. L., & Simons, Y. B. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2509444122 · Cited by 6
Trends in substitution models of protein evolution for phylogenetic inference
Ferreiro, D., Pazos, E., & Arenas, M. (2025) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2025.108473 · Cited by 8
A combination of mutational and computational scanning guides the design of an artificial ligand-binding controlled lipase
Kaschner, M., Schillinger, O., Fettweiss, T., Nutschel, C., Krause, F., Fulton, A., Strodel, B., Stadler, A. M., Jaeger, K., & Krauß, U. (2017) · Scientific Reports · doi:10.1038/srep42592 · Cited by 6
Combining cysteine scanning with chemical labeling to map protein-protein interactions and infer bound structure in an intrinsically disordered region
Ahmed, S., Chattopadhyay, G., Manjunath, K., Bhasin, M., Singh, N., Rasool, M., Das, S., Rana, V., Khan, N., Mitra, D., et al. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.997653 · Cited by 6
The physical and evolutionary energy landscapes of devolved protein sequences corresponding to pseudogenes
Jaafari, H., Bueno, C., Schafer, N. P., Martin, J., Morcos, F., & Wolynes, P. G. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2322428121 · Cited by 6
Self-citation
Machine Learning Applications in Drug Discovery
Ugurlu, S. Y., McDonald, D., & He, S. (2025) · French-Ukrainian Journal of Chemistry · doi:10.17721/fujcv13i1p1-66 · Cited by 6
Protein Structures, Interactions and Function from Evolutionary Couplings
Hopf, T. A., & Marks, D. S. (2017) · Springer eBooks · doi:10.1007/978-94-024-1069-3_2 · Cited by 6
Self-citation
Including residual contact information into replica-exchange MD simulations significantly enriches native-like conformations
Voronin, A., Weiel, M., & Schug, A. H. (2020) · PLoS ONE · doi:10.1371/journal.pone.0242072 · Cited by 6
Step‐by‐step design of proteins for small molecule interaction: A review on recent milestones
Pereira, J. M. R., Vieira, M., & Santos, S. M. (2021) · Protein Science · doi:10.1002/pro.4098 · Cited by 7
Divergence in Dimerization and Activity of Primate APOBEC3C
Gaba, A., Hix, M. A., Suhail, S., Flath, B., Boysan, B., Williams, D. R., Pelletier, T., Emerman, M., Morcos, F., Cisneros, G. A., et al. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167306 · Cited by 6
Self-citation
DeepRCI: Predicting ATP-Binding Proteins Using the Residue-Residue Contact Information
Zhang, Z., Zhao, Y., Wang, J., & Guo, M. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3137840 · Cited by 6
Interpretable pairwise distillations for generative protein sequence models
Feinauer, C., Meynard-Piganeau, B., & Lucibello, C. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010219 · Cited by 6
From Statistical Physics to Data-Driven Modelling
Cocco, S., Monasson, R., & Zamponi, F. (2022) · doi:10.1093/oso/9780198864745.001.0001 · Cited by 6
Explaining the effects of non-convergent sampling in the training of energy-based models*
Agoritsas, E., Catania, G., Decelle, A., & Seoane, B. (2025) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/adde41 · Cited by 15
Comprehensive computational analysis of the SRK–SP11 molecular interaction underlying self-incompatibility in Brassicaceae using improved structure prediction for cysteine-rich proteins
Sawa, T., Moriwaki, Y., Jiang, H., Murase, K., Takayama, S., Shimizu, K., & Terada, T. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.10.026 · Cited by 6
Unlearning regularization for Boltzmann machines
Ventura, E., Cocco, S., Monasson, R., & Zamponi, F. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad5a5f · Cited by 8
Inference and design of antibody specificity: From experiments to models and back
Fernández-de-Cossio-Díaz, J., Uguzzoni, G., Ricard, K., Anselmi, F., Nizak, C., Pagnani, A., & Rivoire, O. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012522 · Cited by 7
Self-citation
Protein sector analysis for the clustering of disease-associated mutations
Guevara-Coto, J. A., Schwartz, C. E., & Wang, L. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-s11-s4 · Cited by 6
Using kernelized partial canonical correlation analysis to study directly coupled side chains and allostery in small G proteins
Ghoraie, L. S., Burkowski, F. J., & Zhu, M. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv241 · Cited by 6
Sparse networks of directly coupled, polymorphic, and functional side chains in allosteric proteins
Ghoraie, L. S., Burkowski, F. J., & Zhu, M. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24752 · Cited by 6
Improving protein-protein interaction prediction using evolutionary information from low-quality MSAs
Várnai, C., Burkoff, N. S., & Wild, D. L. (2017) · PLoS ONE · doi:10.1371/journal.pone.0169356 · Cited by 6
Assessment of data‐assisted prediction by inclusion of crosslinking/mass‐spectrometry and small angle X‐ray scattering data in the 12th Critical Assessment of protein Structure Prediction experiment
Tamò, G. E., Abriata, L. A., Fonti, G., & Peraro, M. D. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25442 · Cited by 6
From inverse problems to learning: a Statistical Mechanics approach
Baldassi, C., Gerace, F., Saglietti, L., & Zecchina, R. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/955/1/012001 · Cited by 7
Self-citation
Protein Inter-Residue Contacts Prediction: Methods, Performances and Applications
Jing, X., Dong, Q., Lu, R., & Dong, Q. (2018) · Current Bioinformatics · doi:10.2174/1574893613666181109130430 · Cited by 6
Driving Protein Conformational Cycles in Physiology and Disease: “Frustrated” Amino Acid Interaction Networks Define Dynamic Energy Landscapes
D’Amico, R. N., Murray, A. M., & Boehr, D. D. (2020) · BioEssays · doi:10.1002/bies.202000092 · Cited by 6
Genome-wide covariation in SARS-CoV-2
Cresswell-Clay, E., & Periwal, V. (2021) · Mathematical Biosciences · doi:10.1016/j.mbs.2021.108678 · Cited by 6
AMaLa: Analysis of Directed Evolution Experiments via Annealed Mutational Approximated Landscape
Sesta, L., Uguzzoni, G., Fernández-de-Cossio-Díaz, J., & Pagnani, A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222010908 · Cited by 7
Self-citation
Study of transcription factor druggabilty for prostate cancer using structure information, gene regulatory networks and protein moonlighting
Dey, A., Sen, S., & Maulik, U. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab465 · Cited by 6
Structure-mechanics statistical learning uncovers mechanical relay in proteins
Raj, N., Click, T. H., Yang, H., & Chu, J. (2022) · Chemical Science · doi:10.1039/d1sc06184d · Cited by 6
Coevolution and smFRET Enhances Conformation Sampling and FRET Experimental Design in Tandem PDZ1–2 Proteins
Krishnamohan, A., Hamilton, G. L., Goutam, R., Sanabria, H., & Morcos, F. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c06720 · Cited by 6
Self-citation
Information and redundancy in the burial folding code of globular proteins within a wide range of shapes and sizes
Ferreira, D. C., van der Linden, M. G., de Oliveira, L. C., Onuchic, J. N., & de Araújo, A. F. P. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24998 · Cited by 6
Self-citation
Structural facets of POU2F1 in light of the functional annotations and sequence-structure patterns
Dey, A., Sen, S., Uversky, V. N., & Maulik, U. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1733092 · Cited by 6
Methods and applications of RNA contact prediction*
Wang, H., & Zhao, Y. (2020) · Chinese Physics B · doi:10.1088/1674-1056/abb7f3 · Cited by 6
Information-theoretic analysis of molecular (co)evolution using graphics processing units
Waechter, M., Jaeger, K., Weißgraeber, S., Widmer, S., Goesele, M., & Hamacher, K. (2012) · doi:10.1145/2483954.2483963 · Cited by 6
On the effect of phylogenetic correlations in coevolution-based contact prediction in proteinsPreprint
Rodríguez-Horta, E., & Weigt, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.12.247577 · Cited by 6
Self-citation

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

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

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks