Assessing site-interdependent phylogenetic models of sequence evolution.
In recent works, methods have been proposed for applying phylogenetic models that allow for a general interdependence between the amino acid positions of a protein. As of yet, such models have focused on site interdependencies resulting from sequence-structure compatibility constraints, using simplified structural representations in combination with a set of statistical potentials. This structural compatibility criterion is meant as a proxy for sequence fitness, and the methods developed thus far can incorporate different site-interdependent fitness proxies based on other measurements. However, no methods have been proposed for comparing and evaluating the adequacy of alternative fitness proxies in this context, or for more general comparisons with canonical models of protein evolution. In the present work, we apply Bayesian methods of model selection-based on numerical calculations of marginal likelihoods and posterior predictive checks-to evaluate models encompassing the site-interdependent framework. Our application of these methods indicates that considering site-interdependencies, as done here, leads to an improved model fit for all data sets studied. Yet, we find that the use of pairwise contact potentials alone does not suitably account for across-site rate heterogeneity or amino acid exchange propensities; for such complexities, site-independent treatments are still called for. The most favored models combine the use of statistical potentials with a suitably rich site-independent model. Altogether, the methodology employed here should allow for a more rigorous and systematic exploration of different ways of modeling explicit structural constraints, or any other site-interdependent criterion, while best exploiting the richness of previously proposed models.
Rodrigue
Nicolas
N
Canadian Institute for Advanced Research, Département de Biochimie, Université de Montréal, Montréal, Québec, Canada. nicolas.rodrigue@umontreal.ca
Philippe
Hervé
H
Lartillot
Nicolas
N
D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D019143:Evolution, Molecular; D005544:Forecasting; D018740:Genetic Heterogeneity; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical; D010802:Phylogeny; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry / Q000235:genetics*; D012641:Selection, Genetic
D011506:Proteins