2018/01/12 by Matteo Figliuzzi, Pierre Barrat-Charlaix, Martin Weigt · 5 citations
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Artificial intelligence #Biochemistry #Bioinformatics and Genomic Networks #Biology #Coevolution #Computational biology #Computer science #Evolution and Genetic Dynamics #Evolutionary biology #Gene #Genetics #Inference #Multiple sequence alignment #Pairwise comparison #Peptide sequence #Phylogenetic tree #Protein Structure and Dynamics #Protein evolution #Protein structure #Protein superfamily #Residue (chemistry) #Sequence alignment #cond-mat.stat-mech #q-bio.BM #q-bio.QM
paper · pdf · doi:10.1093/molbev/msy007
published as Molecular Biology and Evolution 35, 1018 (2018) · 17 pages, 3 figures and one table
arxiv created 2018/01/12 · openalex publication_date 2018/01/16 · arxiv updated 2019/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.