ArticlePLoS computational biology2019
Revealing evolutionary constraints on proteins through sequence analysis.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing.Journal of mathematical biology · 2026Article
- Soft Modes as a Predictive Framework for Low-Dimensional Biological Systems Across Scales.Annual review of biophysics · 2025Review
- An interpretable deep geometric learning model to predict the effects of mutations on protein-protein interactions using large-scale protein language model.Journal of cheminformatics · 2025Article
- Soft Modes as a Predictive Framework for Low Dimensional Biological Systems across Scales.ArXiv · 2024Article
- Structural Phylogenetics with Protein Mass Spectrometry: A Proof-of-Concept.The protein journal · 2024Article
- Impact of phylogeny on the inference of functional sectors from protein sequence data.PLoS computational biology · 2024Article
- Natural diversifying evolution of nonribosomal peptide synthetases in a defensive symbiont reveals nonmodular functional constraints.PNAS nexus · 2024Article
- The role of promiscuous molecular recognition in the evolution of RNase-based self-incompatibility in plants.Nature communications · 2024Article
- Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences.PLoS computational biology · 2022Article
- Extracting phylogenetic dimensions of coevolution reveals hidden functional signals.Scientific reports · 2022Article
- Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Episodic evolution of coadapted sets of amino acid sites in mitochondrial proteins.PLoS genetics · 2021Article
- Direct coupling analysis of epistasis in allosteric materials.PLoS computational biology · 2020Article
- Deep Analysis of Residue Constraints (DARC): identifying determinants of protein functional specificity.Scientific reports · 2020Article
- Mechanics of Allostery: Contrasting the Induced Fit and Population Shift Scenarios.Biophysical journal · 2019Article
- Inferring interaction partners from protein sequences using mutual information.PLoS computational biology · 2018Article
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3 authors.
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Abstract
Statistical analysis of alignments of large numbers of protein sequences has revealed "sectors" of collectively coevolving amino acids in several protein families. Here, we show that selection acting on any functional property of a protein, represented by an additive trait, can give rise to such a sector. As an illustration of a selected trait, we consider the elastic energy of an important conformational change within an elastic network model, and we show that selection acting on this energy leads to correlations among residues. For this concrete example and more generally, we demonstrate that the main signature of functional sectors lies in the small-eigenvalue modes of the covariance matrix of the selected sequences. However, secondary signatures of these functional sectors also exist in the extensively-studied large-eigenvalue modes. Our simple, general model leads us to propose a principled method to identify functional sectors, along with the magnitudes of mutational effects, from sequence data. We further demonstrate the robustness of these functional sectors to various forms of selection, and the robustness of our approach to the identification of multiple selected traits.
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