A new formulation of protein evolutionary models that account for structural constraints

TL;DR

This paper introduces a probabilistic framework based on factor graphs for modeling protein evolution that accounts for structural constraints and site-site correlations, improving data fit over traditional models.

Contribution

The authors develop a novel, computationally tractable model for protein evolution incorporating site interdependence using factor graphs and approximate inference algorithms.

Findings

The new model better fits sequence data than traditional rate matrix models.

Site-site correlations significantly influence amino acid substitution probabilities.

Evolution within homohexameric enzymes shows contributions from contacting subunits.

Abstract

Despite the importance of a thermodynamically stable structure with a conserved fold for protein function, almost all evolutionary models neglect site-site correlations that arise from physical interactions between neighboring amino acid sites. This is mainly due to the difficulty in formulating a computationally tractable model since rate matrices can no longer be used. Here we introduce a general framework, based on factor graphs, for constructing probabilistic models of protein evolution with site interdependence. Conveniently, efficient approximate inference algorithms, like Belief Propagation, can be used to calculate likelihoods for these models. We fit an amino acid substitution model of this type that accounts for both solvent accessibility and site-site correlations. Comparisons of the new model with rate matrix models and a model accounting only for solvent accessibility demonstrate that it better fits the sequence data. We also examine evolution within a family of homohexameric enzymes and find that site-site correlations between most contacting subunits contribute to a higher likelihood. In addition, we show that the new substitution model has a similar mathematical form to the one introduced in (Rodrigue et al. 2005), although with different parameter interpretations and values. We also perform a statistical analysis of the effects of amino acids at neighboring sites on substitution probabilities and find a significant perturbation of most probabilities, further supporting the significant role of site-site interactions in protein evolution and motivating the development of new evolutionary models like the one described here. Finally, we discuss possible extensions and applications of the new substitution models.