Miguel Arenas1, Helena G Dos Santos, David Posada, Ugo Bastolla. 1. Centre for Molecular Biology 'Severo Ochoa', Consejo Superior de Investigaciones Científicas (CSIC), Madrid, Spain and Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo, Spain.
Abstract
MOTIVATION: Models of molecular evolution aim at describing the evolutionary processes at the molecular level. However, current models rarely incorporate information from protein structure. Conversely, structure-based models of protein evolution have not been commonly applied to simulate sequence evolution in a phylogenetic framework, and they often ignore relevant evolutionary processes such as recombination. A simulation evolutionary framework that integrates substitution models that account for protein structure stability should be able to generate more realistic in silico evolved proteins for a variety of purposes. RESULTS: We developed a method to simulate protein evolution that combines models of protein folding stability, such that the fitness depends on the stability of the native state both with respect to unfolding and misfolding, with phylogenetic histories that can be either specified by the user or simulated with the coalescent under complex evolutionary scenarios, including recombination, demographics and migration. We have implemented this framework in a computer program called ProteinEvolver. Remarkably, comparing these models with empirical amino acid replacement models, we found that the former produce amino acid distributions closer to distributions observed in real protein families, and proteins that are predicted to be more stable. Therefore, we conclude that evolutionary models that consider protein stability and realistic evolutionary histories constitute a better approximation of the real evolutionary process.
MOTIVATION: Models of molecular evolution aim at describing the evolutionary processes at the molecular level. However, current models rarely incorporate information from protein structure. Conversely, structure-based models of protein evolution have not been commonly applied to simulate sequence evolution in a phylogenetic framework, and they often ignore relevant evolutionary processes such as recombination. A simulation evolutionary framework that integrates substitution models that account for protein structure stability should be able to generate more realistic in silico evolved proteins for a variety of purposes. RESULTS: We developed a method to simulate protein evolution that combines models of protein folding stability, such that the fitness depends on the stability of the native state both with respect to unfolding and misfolding, with phylogenetic histories that can be either specified by the user or simulated with the coalescent under complex evolutionary scenarios, including recombination, demographics and migration. We have implemented this framework in a computer program called ProteinEvolver. Remarkably, comparing these models with empirical amino acid replacement models, we found that the former produce amino acid distributions closer to distributions observed in real protein families, and proteins that are predicted to be more stable. Therefore, we conclude that evolutionary models that consider protein stability and realistic evolutionary histories constitute a better approximation of the real evolutionary process.
Authors: David A Liberles; Sarah A Teichmann; Ivet Bahar; Ugo Bastolla; Jesse Bloom; Erich Bornberg-Bauer; Lucy J Colwell; A P Jason de Koning; Nikolay V Dokholyan; Julian Echave; Arne Elofsson; Dietlind L Gerloff; Richard A Goldstein; Johan A Grahnen; Mark T Holder; Clemens Lakner; Nicholas Lartillot; Simon C Lovell; Gavin Naylor; Tina Perica; David D Pollock; Tal Pupko; Lynne Regan; Andrew Roger; Nimrod Rubinstein; Eugene Shakhnovich; Kimmen Sjölander; Shamil Sunyaev; Ashley I Teufel; Jeffrey L Thorne; Joseph W Thornton; Daniel M Weinreich; Simon Whelan Journal: Protein Sci Date: 2012-04-23 Impact factor: 6.725
Authors: Etienne Simon-Loriere; Roman Galetto; Meriem Hamoudi; John Archer; Pierre Lefeuvre; Darren P Martin; David L Robertson; Matteo Negroni Journal: PLoS Pathog Date: 2009-05-08 Impact factor: 6.823