Integrating sequence variation and protein structure to identify sites under selection.

ABSTRACT: We present a novel method to identify sites under selection in protein-coding genes. Our method combines the traditional Goldman-Yang model of coding-sequence evolution with the information obtained from the 3D structure of the evolving protein, specifically the relative solvent accessibility (RSA) of individual residues. We develop a random-effects likelihood sites model in which rate classes are RSA dependent. The RSA dependence is modeled with linear functions. We demonstrate that our RSA-dependent model provides a significantly better fit to molecular sequence data than does a traditional, RSA-independent model. We further show that our model provides a natural, RSA-dependent neutral baseline for the evolutionary rate ratio ω = dN/dS Sites that deviate from this neutral baseline likely

SUBMITTER: Meyer AG 

PROVIDER: S-EPMC3525147 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature