2018/08/31 by Shou‐Wen Wang, Shou-Wen Wang, Anne‐Florence Bitbol +2 · 32 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · Physics and Astronomy · #Artificial intelligence #Bioinformatics and Genomic Networks #Biological system #Biology #Computational biology #Computer science #Covariance #Eigenvalues and eigenvectors #Evolution and Genetic Dynamics #Gene #Genetics #Mathematics #Peptide sequence #Physics #Protein Structure and Dynamics #Protein sequencing #Robustness (evolution) #Selection (genetic algorithm) #Sequence (biology) #Sequence alignment #Statistics #Trait #physics.bio-ph #q-bio.BM #q-bio.PE
paper · pdf · doi:10.1371/journal.pcbi.1007010
published in PLoS Computational Biology 15(4), e1007010 (Public Library of Science) · 37 pages, 28 figures
arxiv created 2019/03/11 · openalex publication_date 2019/04/24 · arxiv updated 2019/04/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.