Origins of coevolution between residues distant in protein 3D structures
Open accessAbstract
Residue pairs that directly coevolve in protein families are generally close in protein 3D structures. Here we study the exceptions to this general trend-directly coevolving residue pairs that are distant in protein structures-to determine the origins of evolutionary pressure on spatially distant residues and to understand the sources of error in contact-based structure prediction. Over a set of 4,000 protein families, we find that 25% of directly coevolving residue pairs are separated by more than 5 Å in protein structures and 3% by more than 15 Å. The majority (91%) of directly coevolving residue pairs in the 5-15 Å range are found to be in contact in at least one homologous structure-these exceptions arise from structural variation in the family in the region containing the residues. Thirty-five percent of the exceptions greater than 15 Å are at homo-oligomeric interfaces, 19% arise from family structural variation, and 27% are in repeat proteins likely reflecting alignment errors. Of the remaining long-range exceptions (<1% of the total number of coupled pairs), many can be attributed to close interactions in an oligomeric state. Overall, the results suggest that directly coevolving residue pairs not in repeat proteins are spatially proximal in at least one biologically relevant protein conformation within the family; we find little evidence for direct coupling between residues at spatially separated allosteric and functional sites or for increased direct coupling between residue pairs on putative allosteric pathways connecting them.
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In recent years, there has been major progress in utilizing residue co-evolution information for predicting structures of individual proteins, based on rapid growth of experimental data and new ideas, combined with deep learning.
“In recent years, there has been major progress in utilizing the residue co-evolution information for predicting structures of individual proteins, based on the rapid growth of experimental data on proteins and new ideas on how to use it, combined with spectacular advances in computer science (deep learning) [30–33].”
From Challenges in protein docking (2020) · cites this paper as [32]
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Cites
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
Remmert, M., Biegert, A., Hauser, A., & Söding, J. (2012) · Nature Methods · doi:10.1038/nmeth.1818 · Cited by 2,288 · see citing works
UniProt Knowledgebase: a hub of integrated protein data
Magrane, M. (2011) · Database · doi:10.1093/database/bar009 · Cited by 1,455 · see citing works
Morcos, F., Pagnani, A., Lunt, B., Bertolino, A., Marks, D. S., Sander, C., Zecchina, R., Onuchic, J. N., Hwa, T., & Weigt, M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1111471108 · Cited by 1,434 · 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
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
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
Evolutionarily conserved networks of residues mediate allosteric communication in proteins
Süel, G. M., Lockless, S. W., Wall, M. A., & Ranganathan, R. (2002) · Nature Structural Biology · doi:10.1038/nsb881 · Cited by 816 · see citing works
Jones, D. T., Buchan, D. W. A., Cozzetto, D., & Pontil, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr638 · Cited by 760 · see citing works
Kamisetty, 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
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
Ovchinnikov, S., Kamisetty, H., & Baker, D. (2014) · eLife · doi:10.7554/elife.02030 · Cited by 629 · see citing works
Self-citationThe RCSB Protein Data Bank: views of structural biology for basic and applied research and education
Rose, P. W., Prlić, A., Bi, C., Bluhm, W. F., Christie, C. H., Dutta, S., Green, R. K., Goodsell, D. S., Westbrook, J., Woo, J., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1214 · Cited by 519 · 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
Three-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
Dunn, S. D., Wahl, L. M., & Gloor, G. B. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm604 · Cited by 490 · see citing works
PISCES: recent improvements to a PDB sequence culling server
Wang, G., & Dunbrack, R. L. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki402 · Cited by 355 · see citing works
Hot Spots for Allosteric Regulation on Protein Surfaces
Reynolds, K. A., McLaughlin, R. N., & Ranganathan, R. (2011) · Cell · doi:10.1016/j.cell.2011.10.049 · Cited by 350 · see citing works
Learning generative models for protein fold families
Balakrishnan, S., Kamisetty, H., Carbonell, J. G., Lee, S., & Langmead, C. J. (2011) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22934 · Cited by 316 · see citing works
Self-citationGenomics-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
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
Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method
Burger, L., & van Nimwegen, E. (2008) · Molecular Systems Biology · doi:10.1038/msb4100203 · Cited by 191 · see citing works
An antibiotic factory caught in action
Keatinge‐Clay, A. T., Maltby, D. A., Medzihradszky, K. F., et al. (2004) · Nature Structural & Molecular Biology · doi:10.1038/nsmb808 · Cited by 182 · 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
Inferring interaction partners from protein sequences
Bitbol, A., Dwyer, R. S., Colwell, L. J., & Wingreen, N. S. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1606762113 · Cited by 137 · see citing works
Dago, A. E., Schug, A. H., Procaccini, A., Hoch, J. A., Weigt, M., & Szurmant, H. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1201301109 · Cited by 136 · see citing works
From residue coevolution to protein conformational ensembles and functional dynamics
Sutto, L., Marsili, S., Valencia, A., & Gervasio, F. L. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508584112 · Cited by 130 · see citing works
Tyrrell, R., Verschueren, K. H. G., Dodson, E. J., Murshudov, G. N., Addy, C., & Wilkinson, A. J. (1997) · Structure · doi:10.1016/s0969-2126(97)00254-2 · Cited by 127 · see citing works
Tillier, E. R. M., & Lui, T. W. H. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg072 · Cited by 127 · see citing works
Structured States of Disordered Proteins from Genomic Sequences
Tóth-Petróczy, Á., Palmedo, P., Ingraham, J., Hopf, T. A., Berger, B., Sander, C., & Marks, D. S. (2016) · Cell · doi:10.1016/j.cell.2016.09.010 · Cited by 124 · 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
Novinec, M., Korenč, M., Caflisch, A., Ranganathan, R., Lenar≷cic̆, B., & Baici, A. (2014) · Nature Communications · doi:10.1038/ncomms4287 · Cited by 91 · see citing works
Dimeric interactions and complex formation using direct coevolutionary couplings
dos Santos, R. N., Morcos, F., Jana, B., Andricopulo, A. D., & Onuchic, J. N. (2015) · Scientific Reports · doi:10.1038/srep13652 · Cited by 81 · see citing works
Protein structure determination by combining sparse NMR data with evolutionary couplings
Tang, Y., Huang, Y. J., Hopf, T. A., Sander, C., Marks, D. S., & Montelione, G. T. (2015) · Nature Methods · doi:10.1038/nmeth.3455 · Cited by 75 · see citing works
Yang, M., Lu, R., Guja, K. E., Wipperman, M. F., Clair, J. R. S., Bonds, A. C., García–Díaz, M., & Sampson, N. S. (2015) · ACS Infectious Diseases · doi:10.1021/id500033m · Cited by 68 · see citing works
All-atom 3D structure prediction of transmembrane β-barrel proteins from sequences
Hayat, S., Sander, C., Marks, D. S., & Elofsson, A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419956112 · Cited by 50 · see citing works
Noivirt‐Brik, O., Horovitz, A., & Unger, R. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000592 · Cited by 48 · see citing works
Structure, Regulation, and Inhibition of the Quorum-Sensing Signal Integrator LuxO
Boyaci, H., Shah, T., Hurley, A. I., et al. (2016) · PLoS Biology · doi:10.1371/journal.pbio.1002464 · Cited by 44 · see citing works
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 · see citing works
Jacob, E., Unger, R., & Horovitz, A. (2015) · eLife · doi:10.7554/elife.08932 · Cited by 12 · see citing works
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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
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
Self-citationRives, 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
“Contacts can be inferred through evolutionary correlations between residues in a sequence family (Marks et al., 2011; Anishchenko et al., 2017).”
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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
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
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
Deep‐learning contact‐map guided protein structure prediction in CASP13
Zheng, W., Li, Y., Zhang, C., Pearce, R., Mortuza, S. M., & Zhang, Y. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25792 · Cited by 208 · see citing works
Protein language model embeddings for fast, accurate, alignment-free protein structure prediction
Weissenow, K., Heinzinger, M., & Rost, B. (2021) · doi:10.1101/2021.07.31.454572 · Cited by 139 · see citing works
Structure-based prediction of protein allostery
Greener, J. G., & Sternberg, M. J. (2018) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.10.002 · Cited by 91 · see citing works
Predicting functional effect of missense variants using graph attention neural networks
Zhang, H., Xu, M. S., Fan, X., Chung, W. K., & Shen, Y. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00561-w · Cited by 98 · see citing works
Inter-residue, inter-protein and inter-family coevolution: bridging the scales
Szurmant, H., & Weigt, M. (2018) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.10.014 · Cited by 81 · see citing works
Novel machine learning approaches revolutionize protein knowledge
Bordin, N., Dallago, C., Heinzinger, M., Kim, S., Littmann, M., Rauer, C., Steinegger, M., Rost, B., & Orengo, C. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2022.11.001 · Cited by 72 · see citing works
Vakser, I. A. (2020) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2020.07.001 · Cited by 62 · see citing works
Structural evolution of the ancient enzyme, dissimilatory sulfite reductase
Colman, D. R., Labesse, G., Swapna, G. V. T., Stefanakis, J., Montelione, G. T., Boyd, E. S., & Royer, C. A. (2022) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26315 · Cited by 10 · see citing works
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Danielsson, S. N., & Elofsson, A. (2025) · doi:10.1101/2025.11.27.691011
- Authors
- Anishchenko, I., Ovchinnikov, S., Kamisetty, H., & Baker, D.
- Affiliations
- University of Washington (US)
- Meta (United States) (US)
- Howard Hughes Medical Institute (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2017-08-07
- Volume
- 114
- Issue
- 34
- Pages
- 9122-9127
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC5576787
- OpenAlex
- W2742834898
- Cited by
- 185
- References
- 40
- Citations by year
- 2026: 5 · 2025: 18 · 2024: 15 · 2023: 30 · 2022: 21 · 2021: 26 · 2020: 15 · 2019: 31 · 2018: 17 · 2017: 7
- FWCI
- 7.82
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NIH (R01 GM 092802-06)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- protein coevolution, protein families, repeat proteins, structural variants, allosteric pathways, homologous structures
- MeSH terms
- Protein Domains, Amino Acids, Binding Sites, Models, Molecular, Protein Binding, Protein Conformation, Proteins, Crystallography, X-Ray, Evolution, Molecular, Databases, Protein, Protein Multimerization
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