Cited by 124Genomics and Phylogenetic StudiesProtein Structure and DynamicsRNA and protein synthesis mechanisms

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Correlated mutations in models of protein sequences: phylogenetic and structural effects

Lapedes, Alan S., Giraud, Bertrand, Liu, LonChang, Stormo, Gary D.·1999·Institute of Mathematical Statistics Lecture Notes - Monograph Series

DOI 10.1214/lnms/1215455556

Open this paper in CoChat — free account

Abstract

Covariation analysis of sets of aligned sequences for RNA molecules is relatively successful in elucidating RNA secondary structure, as well as some aspects of tertiary structure [Gutell et al. (1992)].Covariation analysis of sets of aligned sequences for protein molecules is successful in certain instances in elucidating certain structural and functional links [Korber et al. (1993)], but in general, pairs of sites displaying highly covarying mutations in protein sequences do not necessarily correspond to sites that are spatially close in the protein structure [Gobel et al. (1994), Clarke (1995), Shindyalov et al. (1994), Thomas et al. (1996), Taylor &;Hatrick (1994), Neher (1994)].In this paper we identify two reasons why naive use of covariation analysis for protein sequences fails to reliably indicate sequence positions that are spatially proximate.The first reason involves the bias introduced in calculation of covariation measures due to the fact that biological sequences are generally related by a non-trivial phylogenetic tree.We present a null-model approach to solve this problem.The second reason involves linked chains of covariation which can result in pairs of sites displaying significant covariation even though they are not spatially proximate.We present a maximum entropy solution to this classic problem of "causation versus correlation".The methodologies are validated in simulation.

Integrity signals

Registry

Crossref · 10.1214/lnms/1215455556

Cites

30 of 30 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 37,139
Evolution of Protein Molecules
JUKES, T. H., & CANTOR, C. R. (1969) · Mammalian Protein Metabolism · doi:10.1016/b978-1-4832-3211-9.50009-7 · Cited by 11,032
Introduction to Phase Transitions and Critical Phenomena
Stanley, H. E., & Wong, V. K. (1972) · American Journal of Physics · doi:10.1119/1.1986710 · Cited by 4,108
Introduction to Phase Transitions and Critical Phenomena
Stanley, E., & Ahlers, G. (1973) · Physics Today · doi:10.1063/1.3127900 · Cited by 3,690
Time-Dependent Statistics of the Ising Model
Glauber, R. J. (1963) · Journal of Mathematical Physics · doi:10.1063/1.1703954 · Cited by 3,649
Elements of Information Theory
Jacobs, K. (1992) · Birkhäuser Basel eBooks · doi:10.1007/978-3-0348-8645-1_7 · Cited by 2,957
Hidden Markov Models in Computational Biology
Krogh, A., Brown, M., Mian, S. I., Sjölander, K. V., & Haussler, D. H. (1994) · Journal of Molecular Biology · doi:10.1006/jmbi.1994.1104 · Cited by 1,948
Detecting Subtle Sequence Signals: a Gibbs Sampling Strategy for Multiple Alignment
Lawrence, C. E., Altschul, S. F., Boguski, M. S., Liu, J. S., Neuwald, A. F., & Wootton, J. C. (1993) · Science · doi:10.1126/science.8211139 · Cited by 1,802
Profile analysis: detection of distantly related proteins.
Gribskov, M., McLachlan, A. D., & Eisenberg, D. (1987) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.84.13.4355 · Cited by 1,325
The Theory of Critical Phenomena: An Introduction to the Renormalization Group
Binney, J. J., Dowrick, N. J., Fisher, A. J., & Newman, M. E. J. (1992) · doi:10.1093/oso/9780198513940.001.0001 · Cited by 1,243

Show all 30 references

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 5,990
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
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
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
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
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
PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments
Jones, D. T., Buchan, D. W. A., Cozzetto, D., & Pontil, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr638 · Cited by 787
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
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
Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information
Ovchinnikov, S., Kamisetty, H., & Baker, D. (2014) · eLife · doi:10.7554/elife.02030 · Cited by 629
Emerging methods in protein co-evolution
de Juan, D., Pazos, F., & Valencia, A. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3414 · Cited by 607
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 509
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
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
MSA Transformer
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
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
AlphaFold at CASP13
AlQuraishi, M. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz422 · Cited by 333
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
Quantification of the effect of mutations using a global probability model of natural sequence variationPreprint
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 249
Combining 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
Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences
Ekeberg, M., Hartonen, T., & Aurell, E. M. (2014) · Journal of Computational Physics · doi:10.1016/j.jcp.2014.07.024 · Cited by 184
Accurate de novo structure prediction of large transmembrane protein domains using fragment-assembly and correlated mutation analysis
Nugent, T., & Jones, D. T. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1120036109 · Cited by 177
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 174
Improved 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
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 152
Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners
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 150
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
From Principal Component to Direct Coupling Analysis of Coevolution in Proteins: Low-Eigenvalue Modes are Needed for Structure Prediction
Cocco, S., Monasson, R., & Weigt, M. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003176 · Cited by 134
Analysis of distance‐based protein structure prediction by deep learning in CASP13
Xu, J., & Wang, S. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25810 · Cited by 132
Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models
Stein, R. R., Marks, D. S., & Sander, C. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004182 · Cited by 121
De Novo Structure Prediction of Globular Proteins Aided by Sequence Variation-Derived Contacts
Kościółek, T., & Jones, D. T. (2014) · PLoS ONE · doi:10.1371/journal.pone.0092197 · Cited by 108
Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning
Ma, J., Wang, S., Wang, Z., & Xu, J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv472 · Cited by 107
Structure of a Functional Amyloid Protein Subunit Computed Using Sequence Variation
Tian, P., Boomsma, W., Wang, Y., Otzen, D. E., Jensen, M. H., & Lindorff‐Larsen, K. (2014) · Journal of the American Chemical Society · doi:10.1021/ja5093634 · Cited by 105
PconsFold: improved contact predictions improve protein models
Michel, M., Hayat, S., Skwark, M. J., Sander, C., Marks, D. S., & Elofsson, A. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu458 · Cited by 101
Improving Contact Prediction along Three Dimensions
Feinauer, C., Skwark, M. J., Pagnani, A., & Aurell, E. M. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003847 · Cited by 88
The emergence of protein complexes: quaternary structure, dynamics and allostery
Perica, T., Marsh, J. A., Sousa, F. L., Natan, E., Colwell, L. J., Ahnert, S. E., & Teichmann, S. A. (2012) · Biochemical Society Transactions · doi:10.1042/bst20120056 · Cited by 87
The simultaneous occurrence of both hypercoagulability and hypofibrinolysis in blood and serum during systemic inflammation, and the roles of iron and fibrin(ogen)
Kell, D. B., & Pretorius, E. (2014) · Integrative Biology · doi:10.1039/c4ib00173g · Cited by 81
Protein language models trained on multiple sequence alignments learn phylogenetic relationships
Lupo, U., Sgarbossa, D., & Bitbol, A.-F. (2022) · Nature Communications · doi:10.1038/s41467-022-34032-y · Cited by 67
Evolution of protein interactions: From interactomes to interfaces
Andréani, J., & Guérois, R. (2014) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2014.05.010 · Cited by 70
Power law tails in phylogenetic systems
Qin, C., & Colwell, L. J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1711913115 · Cited by 63
Influence of multiple-sequence-alignment depth on Potts statistical models of protein covariation
Haldane, A., & Levy, R. M. (2019) · Physical review. E · doi:10.1103/physreve.99.032405 · Cited by 58
The generative capacity of probabilistic protein sequence models
McGee, F. A., Hauri, S., Novinger, Q., Vučetić, S., Levy, R. M., Carnevale, V., & Haldane, A. (2021) · Nature Communications · doi:10.1038/s41467-021-26529-9 · Cited by 53
Inferring interaction partners from protein sequences using mutual information
Bitbol, A. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006401 · Cited by 53
Genome-wide discovery of epistatic loci affecting antibiotic resistance in Neisseria gonorrhoeae using evolutionary couplings
Schubert, B., Maddamsetti, R., Nyman, J., Farhat, M. R., & Marks, D. S. (2018) · Nature Microbiology · doi:10.1038/s41564-018-0309-1 · Cited by 51
Protein Structure from Experimental Evolution
Stiffler, M. A., Poelwijk, F. J., Brock, K. P., Stein, R. R., Riesselman, A. J., Teyra, J., Sidhu, S. S., Marks, D. S., Gauthier, N. P., & Sander, C. (2019) · Cell Systems · doi:10.1016/j.cels.2019.11.008 · Cited by 45
RNAcmap: a fully automatic pipeline for predicting contact maps of RNAs by evolutionary coupling analysis
Zhang, T., Singh, J., Litfin, T., Zhan, J., Paliwal, K. K., & Zhou, Y. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab391 · Cited by 43
Protein sequence‐to‐structure learning: Is this the end(‐to‐end revolution)?
Laine, E., Eismann, S., Elofsson, A., & Grudinin, S. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26235 · Cited by 44
“While traditional measures of covariations in natural sequences led to a few successes [1, 2, 3], major improvements came from recasting the problem as an inverse Potts model [4, 5].”
Background
Generative power of a protein language model trained on multiple sequence alignments
(2023) · doi:10.7554/elife.79854 · Cited by 43
Applications of contact predictions to structural biology
Šimkovic, F., Ovchinnikov, S., Baker, D. A., & Rigden, D. J. (2017) · IUCrJ · doi:10.1107/s2052252517005115 · Cited by 42
Protein Residue Contacts and Prediction Methods
Adhikari, B., & Cheng, J. J. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3572-7_24 · Cited by 40
Mi3-GPU: MCMC-based inverse Ising inference on GPUs for protein covariation analysis
Haldane, A., & Levy, R. M. (2020) · Computer Physics Communications · doi:10.1016/j.cpc.2020.107312 · Cited by 38
Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone
Rodriguez-Rivas, J., Marsili, S., Juan, D., & Valencia, A. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611861114 · Cited by 36
Prediction of contacts from correlated sequence substitutions
Taylor, W. R., Hamilton, R. S., & Sadowski, M. I. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2013.04.001 · Cited by 34
EPSILON-CP: using deep learning to combine information from multiple sources for protein contact prediction
Stahl, K., Schneider, M., & Brock, O. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1713-x · Cited by 34
Phylogenetic correlations can suffice to infer protein partners from sequences
Marmier, G., Weigt, M., & Bitbol, A. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007179 · Cited by 34
Predicting Functionally Informative Mutations inEscherichia coliBamA Using Evolutionary Covariance Analysis
Dwyer, R. S., Ricci, D. P., Colwell, L. J., Silhavy, T. J., & Wingreen, N. S. (2013) · Genetics · doi:10.1534/genetics.113.155861 · Cited by 32
Applications of sequence coevolution in membrane protein biochemistry
Nicoludis, J. M., & Gaudet, R. (2017) · Biochimica et Biophysica Acta (BBA) - Biomembranes · doi:10.1016/j.bbamem.2017.10.004 · Cited by 30
Pairing interacting protein sequences using masked language modeling
Lupo, U., Sgarbossa, D., & Bitbol, A.-F. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311887121 · Cited by 31
Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity
Asti, L., Uguzzoni, G., Marcatili, P., & Pagnani, A. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004870 · Cited by 30
Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction
Vorberg, S., Seemayer, S., & Söding, J. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006526 · Cited by 36
Single Layers of Attention Suffice to Predict Protein ContactsPreprint
Bhattacharya, N., Thomas, N., Rao, R., Dauparas, J., Koo, P. K., Baker, D. A., Song, Y. S., & Ovchinnikov, S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.21.423882 · Cited by 33
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 watershed moment for protein structure prediction
AlQuraishi, M. (2020) · Nature · doi:10.1038/d41586-019-03951-0 · Cited by 27
Structural Constraints on the Covariance Matrix Derived from Multiple Aligned Protein Sequences
Taylor, W. R., & Sadowski, M. I. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028265 · Cited by 25
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
In silico prediction of structure and function for a large family of transmembrane proteins that includes human Tmem41b
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
Sparse generative modeling via parameter reduction of Boltzmann machines: Application to protein-sequence families
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
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
Non-Identifiability and the Blessings of Misspecification in Models of Molecular Fitness
Weinstein, E. N., Amin, A., Frazer, J., & Marks, D. S. (2022) · neural information processing systems · doi:10.52202/068431-0396 · Cited by 21
Oops I Took A Gradient: Scalable Sampling for Discrete DistributionsPreprint
Grathwohl, W., Swersky, K., Hashemi, M., Duvenaud, D., & Maddison, C. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.04509 · Cited by 19
Evolutionary couplings detect side-chain interactions
Hockenberry, A. J., & Wilke, C. O. (2019) · PeerJ · doi:10.7717/peerj.7280 · Cited by 16
Recent progress and future challenges in structure-based protein-protein interaction prediction
Yuan, R., Zhang, J., Zhou, J., & Cong, Q. (2025) · Molecular Therapy · doi:10.1016/j.ymthe.2025.04.003 · Cited by 25
Phylogenetic Weighting Does Little to Improve the Accuracy of Evolutionary Coupling Analyses
Hockenberry, A. J., & Wilke, C. O. (2019) · Entropy · doi:10.3390/e21101000 · Cited by 18
Learning protein constitutive motifs from sequence data
Tubiana, J., Cocco, S., & Monasson, R. (2019) · eLife · doi:10.7554/elife.39397 · Cited by 129
Sequence specificity between interacting and non-interacting homologs identifies interface residues – a homodimer and monomer use case
Hou, Q., Dutilh, B. E., Huynen, M. A., Heringa, J., & Feenstra, K. A. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0758-y · Cited by 16
Opportunities and limitations in applying coevolution-derived contacts to protein structure prediction
Tetchner, S., Kościółek, T., & Jones, D. T. (2014) · Bio-Algorithms and Med-Systems · doi:10.1515/bams-2014-0013 · Cited by 14
Predicting protein inter-residue contacts using composite likelihood maximization and deep learning
Zhang, H., Zhang, Q., Ju, F., Zhu, J., Gao, Y., Xie, Z., Deng, M., Sun, S., Zheng, W., & Bu, D. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3051-7 · Cited by 13
Scaling down protein language modeling with MSA PairformerPreprint
Akiyama, Y., Zhang, Z., Mirdita, M., Steinegger, M., & Ovchinnikov, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.02.668173 · Cited by 18
Unsupervisedly Prompting AlphaFold2 for Accurate Few-Shot Protein Structure Prediction
Zhang, J., Liu, S., Chen, M., Chu, H., Wang, M., Wang, Z., Yu, J., Ni, N., Fan, Y., Chen, D., et al. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00528 · Cited by 15
Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences
(2022) · doi:10.1371/journal.pcbi.1010147 · Cited by 12
Field-theoretic density estimation for biological sequence space with applications to 5′ splice site diversity and aneuploidy in cancer
Chen, W.-C., Zhou, J., Sheltzer, J. M., Kinney, J. B., & McCandlish, D. M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025782118 · Cited by 10
Coevolutionary Analysis of Protein Sequences for Molecular Modeling
Malinverni, D., & Barducci, A. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9608-7_16 · Cited by 10
Unified framework for modeling multivariate distributions in biological sequences
Dauparas, J., Wang, H., Swartz, A., Koo, P. K., Nitzan, M., & Ovchinnikov, S. (2019) · Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) · doi:10.48550/arxiv.1906.02598 · Cited by 12
Inverse Ising inference with correlated samples
Obermayer, B., & Levine, E. (2014) · MDC Repository (Max-Delbrueck-Center for Molecular Medicine) · Cited by 10
Neural Potts ModelPreprint
Sercu, T., Verkuil, R., Meier, J., Amos, B., Lin, Z., Chen, C., Liu, J. J., LeCun, Y., & Rives, A. W. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.08.439084 · Cited by 10
Contact prediction is hardest for the most informative contacts, but improves with the incorporation of contact potentials
Holland, J. E., Pan, Q., & Grigoryan, G. (2018) · PLoS ONE · doi:10.1371/journal.pone.0199585 · Cited by 8
Impact of phylogeny on structural contact inference from protein sequence data
Dietler, N., Lupo, U., & Bitbol, A.-F. (2023) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0707 · Cited by 6
SeaMoon: From protein language models to continuous structural heterogeneity
Lombard, V., Timsit, D., Grudinin, S., & Laine, E. (2025) · Structure · doi:10.1016/j.str.2025.06.010 · Cited by 8
Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies
Larrea, A., Jebari‐Benslaiman, S., García, U. G., Urteaga, A. S. J., Uribe, K. B., Benito‐Vicente, A., & Martı́n, C. (2023) · Current Atherosclerosis Reports · doi:10.1007/s11883-023-01154-7 · Cited by 12
De Novo Membrane Protein Structure Prediction
Nugent, T. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-1465-4_15 · Cited by 9
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
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
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
The structure-fitness landscape of pairwise relations in generative sequence modelsPreprint
Marshall, D., Wang, H., Stiffler, M. A., Dauparas, J., Koo, P. K., & Ovchinnikov, S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.29.402875 · Cited by 8
Inference of Hopfield-Potts patterns from covariation in protein families: calculation and statistical error bars
Cocco, S., Monasson, R., & Weigt, M. (2013) · Journal of Physics Conference Series · doi:10.1088/1742-6596/473/1/012010 · Cited by 6
A genome-wide scan for correlated mutations detects macromolecular and chromatin interactions in Arabidopsis thaliana
Perlaza-Jiménez, L., & Walther, D. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky576 · Cited by 5
Advances in Computational Methods for Transmembrane Protein Structure Prediction
Nugent, T. C. O., Jones, D. T., & Hayat, S. (2017) · Springer eBooks · doi:10.1007/978-94-024-1069-3_5 · Cited by 4
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
Artificial Intelligence Learns Protein PredictionPreprint
Heinzinger, M., & Rost, B. (2024) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a041458 · Cited by 4
Residue coevolution reveals functionally important intramolecular interactions in formamidopyrimidine-DNA glycosylase
Endutkin, A. V., Koptelov, S. S., Popov, A. V., Torgasheva, N. A., Lomzov, A. A., Tsygankova, A. R., Skiba, T. V., Afonnikov, D. A., & Zharkov, D. O. (2018) · DNA repair · doi:10.1016/j.dnarep.2018.07.004 · Cited by 3
RRCRank: a fusion method using rank strategy for residue-residue contact prediction
Jing, X., Dong, Q., & Lu, R. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1811-9 · Cited by 3
Entrenchment and contingency in neutral protein evolution with epistasisPreprint
Schmelkin, L., Carnevale, V., Haldane, A., Townsend, J. P., Chung, S., Levy, R. M., & Kumar, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.09.632266 · Cited by 4

Authors: Lapedes, A. S., Giraud, B., Liu, L., & Stormo, G. D.
Affiliations: Santa Fe Institute (US)
ORCID: Giraud, Bertrand
Stormo, Gary D.
Venue: Institute of Mathematical Statistics Lecture Notes - Monograph Series
Publisher: Institute of Mathematical Statistics
Published: 1999
Pages: 236-256
ISSN: 0749-2170 (ISSN-L)
DOI: 10.1214/lnms/1215455556
OpenAlex: W1514077080
Cited by: 124
References: 30
Citations by year: 2026: 1 · 2025: 6 · 2024: 6 · 2023: 7 · 2022: 4 · 2021: 13 · 2020: 9 · 2019: 16 · 2018: 9 · 2017: 8 · 2016: 9 · 2015: 7 · 2014: 14 · 2013: 7 · 2012: 3
FWCI: 0.00
Type: book chapter
Language: en
Open access: closed
Locations: Lecture notes-monograph series (publishedVersion)
Project Euclid (Cornell University) (submittedVersion)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
Keywords: correlated mutations, covariation analysis, protein sequences, phylogenetic bias, null models, correlation versus causation, maximum entropy, protein structure, protein tertiary structure

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

Correlated mutations in models of protein sequences: phylogenetic and structural effects

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks