Direct coevolutionary couplings reflect biophysical residue interactions in proteins.
Coevolution of residues in contact imposes strong statistical constraints on the sequence variability between homologous proteins. Direct-Coupling Analysis (DCA), a global statistical inference method, successfully models this variability across homologous protein families to infer structural information about proteins. For each residue pair, DCA infers 21 × 21 matrices describing the coevolutionary coupling for each pair of amino acids (or gaps). To achieve the residue-residue contact prediction, these matrices are mapped onto simple scalar parameters; the full information they contain gets lost. Here, we perform a detailed spectral analysis of the coupling matrices resulting from 70 protein families, to show that they contain quantitative information about the physico-chemical properties of amino-acid interactions. Results for protein families are corroborated by the analysis of synthetic data from lattice-protein models, which emphasizes the critical effect of sampling quality and regularization on the biochemical features of the statistical coupling matrices.
Laboratoire de Physique Théorique, Ecole Normale Supérieure and CNRS-UMR8549, PSL Research University, Sorbonne Universités UPMC, 24 Rue Lhomond, 75005 Paris, France.
Alice
A
Coucke
Sorbonne Universités, UPMC, Institut de Biologie Paris-Seine, CNRS, Laboratoire de Biologie Computationnelle et Quantitative UMR 7238, 75005 Paris, France.
Guido
G
Uguzzoni
Sorbonne Universités, UPMC, Institut de Biologie Paris-Seine, CNRS, Laboratoire de Biologie Computationnelle et Quantitative UMR 7238, 75005 Paris, France.
Francesco
F
Oteri
Laboratoire de Physique Statistique, Ecole Normale Supérieure and CNRS-UMR8550, PSL Research University, Sorbonne Universités UPMC, 24 Rue Lhomond, 75005 Paris, France.
Simona
S
Cocco
Laboratoire de Physique Théorique, Ecole Normale Supérieure and CNRS-UMR8549, PSL Research University, Sorbonne Universités UPMC, 24 Rue Lhomond, 75005 Paris, France.
Remi
R
Monasson
Sorbonne Universités, UPMC, Institut de Biologie Paris-Seine, CNRS, Laboratoire de Biologie Computationnelle et Quantitative UMR 7238, 75005 Paris, France.
Martin
M
Weigt
D055592:Biophysical Phenomena; D019277:Entropy; D019143:Evolution, Molecular; D008958:Models, Molecular; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D012997:Solvents / Q000737:chemistry
D011506:Proteins; D012997:Solvents