From residue coevolution to protein conformational ensembles and functional dynamics.
The analysis of evolutionary amino acid correlations has recently attracted a surge of renewed interest, also due to their successful use in de novo protein native structure prediction. However, many aspects of protein function, such as substrate binding and product release in enzymatic activity, can be fully understood only in terms of an equilibrium ensemble of alternative structures, rather than a single static structure. In this paper we combine coevolutionary data and molecular dynamics simulations to study protein conformational heterogeneity. To that end, we adapt the Boltzmann-learning algorithm to the analysis of homologous protein sequences and develop a coarse-grained protein model specifically tailored to convert the resulting contact predictions to a protein structural ensemble. By means of exhaustive sampling simulations, we analyze the set of conformations that are consistent with the observed residue correlations for a set of representative protein domains, showing that (i) the most representative structure is consistent with the experimental fold and (ii) the various regions of the sequence display different stability, related to multiple biologically relevant conformations and to the cooperativity of the coevolving pairs. Moreover, we show that the proposed protocol is able to reproduce the essential features of a protein folding mechanism as well as to account for regions involved in conformational transitions through the correct sampling of the involved conformers.
Institute of Structural and Molecular Biology, University College London, London WC1H 0AJ, United Kingdom;
Ludovico
http://orcid.org/0000-0002-4084-8562
L
Sutto
Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain;
Simone
S
Marsili
Structural Biology and Biocomputing Programme, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain; avalencia@cnio.es f.l.gervasio@ucl.ac.uk.
Alfonso
A
Valencia
Institute of Structural and Molecular Biology, University College London, London WC1H 0AJ, United Kingdom; Department of Chemistry, University College London, London WC1H 0AJ, United Kingdom avalencia@cnio.es f.l.gervasio@ucl.ac.uk.
Francesco Luigi
FL
Gervasio
D000465:Algorithms; D001665:Binding Sites; D005075:Biological Evolution; D018360:Crystallography, X-Ray; D056004:Molecular Dynamics Simulation; D017510:Protein Folding; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D013816:Thermodynamics; D018631:ras Proteins / Q000737:chemistry; D018909:src Homology Domains
D011506:Proteins; D018631:ras Proteins
coarse-grained; coevolution; network inference; protein dynamics; protein folding