Predicting evolutionary site variability from structure in viral proteins: buriedness, packing, flexibility, and design

TL;DR

This study evaluates how various structural properties of viral proteins, such as buriedness and packing density, can predict site-specific evolutionary variability, finding simple measures outperform more complex dynamic and design-based predictors.

Contribution

It provides a comprehensive comparison of multiple structural predictors of evolutionary variation, highlighting the superior predictive power of buriedness and packing density.

Findings

Buriedness and packing density are better predictors than flexibility.

Correlations between structure and variation are generally weak.

Designed structure variability is a weaker predictor than buriedness or packing density.

Abstract

Several recent works have shown that protein structure can predict site-specific evolutionary sequence variation. In particular, sites that are buried and/or have many contacts with other sites in a structure have been shown to evolve more slowly, on average, than surface sites with few contacts. Here, we present a comprehensive study of the extent to which numerous structural properties can predict sequence variation. The quantities we considered include buriedness (as measured by relative solvent accessibility), packing density (as measured by contact number), structural flexibility (as measured by B factors, root-mean-square fluctuations, and variation in dihedral angles), and variability in designed structures. We obtained structural flexibility measures both from molecular dynamics simulations performed on 9 non-homologous viral protein structures and from variation in homologous variants of those proteins, where available. We obtained measures of variability in designed structures from flexible-backbone design in the Rosetta software. We found that most of the structural properties correlate with site variation in the majority of structures, though the correlations are generally weak (correlation coefficients of 0.1 to 0.4). Moreover, we found that buriedness and packing density were better predictors of evolutionary variation than was structural flexibility. Finally, variability in designed structures was a weaker predictor of evolutionary variability than was buriedness or packing density, but it was comparable in its predictive power to the best structural flexibility measures. We conclude that simple measures of buriedness and packing density are better predictors of evolutionary variation than are more complicated predictors obtained from dynamic simulations, ensembles of homologous structures, or computational protein design.