ArticleMolecular biology and evolution2018
How Pairwise Coevolutionary Models Capture the Collective Residue Variability in Proteins?
Article in Molecular biology and evolution, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 72 papers.
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72 citing papers in PubMed.
- A structure-aware generative AI framework for revealing functional relationships in protein families.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Hierarchical Organization of Evolutionary Constraint in Duplicated EF-Hand Motifs.Journal of molecular evolution · 2026Article
- A spectral framework for measuring diversity in multiple sequence alignments.PLoS computational biology · 2026Article
- Beyond native sequence recovery: Improved modeling of the sequence-energy landscape of protein structures.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Mind the gap: An embedding guide to safely travel in sequence space.PLoS computational biology · 2026Article
- Epistasis plays a limited role in driving entrenchment during neutral protein evolution.Genome biology · 2026Article
- Searching for sequence features that control DNA cyclizability.PNAS nexus · 2026Article
- Dual role of glycosylation in resistance to CD4-binding site broadly neutralizing antibodies.Journal of virology · 2026Article
- Functional bottlenecks can emerge from non-epistatic underlying traits.PLoS computational biology · 2026Article
- Controllable protein design via autoregressive direct coupling analysis conditioned on principal components.PLoS computational biology · 2026Article
- Beyond native sequence recovery: Improved modeling of the sequence-energy landscape of protein structures.bioRxiv : the preprint server for biology · 2026Article
- Computational design of class II MHC binding peptide with sequence-based evolution information.Bioinformatics advances · 2026Article
- adabmDCA 2.0-A Flexible but Easy-to-Use Package for Direct Coupling Analysis.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Predicting Protein Folding Dynamics Using Sequence Information.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Exploring evolutionary trajectories of drug resistance.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Integrating experimental feedback improves generative models for biological sequences.Nucleic acids research · 2025Article
- Direct coupling analysis and the attention mechanism.BMC bioinformatics · 2025Article
- Generating Artificial Ribozymes Using Sparse Coevolutionary Models.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Article
- Emergent time scales of epistasis in protein evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
12 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Global coevolutionary models of homologous protein families, as constructed by direct coupling analysis (DCA), have recently gained popularity in particular due to their capacity to accurately predict residue-residue contacts from sequence information alone, and thereby to facilitate tertiary and quaternary protein structure prediction. More recently, they have also been used to predict fitness effects of amino-acid substitutions in proteins, and to predict evolutionary conserved protein-protein interactions. These models are based on two currently unjustified hypotheses: 1) correlations in the amino-acid usage of different positions are resulting collectively from networks of direct couplings; and 2) pairwise couplings are sufficient to capture the amino-acid variability. Here, we propose a highly precise inference scheme based on Boltzmann-machine learning, which allows us to systematically address these hypotheses. We show how correlations are built up in a highly collective way by a large number of coupling paths, which are based on the proteins three-dimensional structure. We further find that pairwise coevolutionary models capture the collective residue variability across homologous proteins even for quantities which are not imposed by the inference procedure, like three-residue correlations, the clustered structure of protein families in sequence space or the sequence distances between homologs. These findings strongly suggest that pairwise coevolutionary models are actually sufficient to accurately capture the residue variability in homologous protein families.
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