Achievement

IGERT Trainee Eric Franzosa in E. Franzosa, Y. Xia, & M. Gerstein, Integrated Assessment of Genomic Correlates of Protein Evolutionary Rate, PLoS Computational Biology, in press.

We use an integrated probabilistic modeling approach to study genomic correlates of protein evolutionary rate in S. cerevisiae. We measure and rank degrees of association between (i) an approximate measure of protein evolutionary rate with high genome coverage, and (ii) a diverse list of protein properties (sequence, structural, functional, network, and phenotypic). We observe that slowly evolving proteins tend to be regulated by more transcription factors, deficient in predicted structural disorder, involved in characteristic biological functions, biased in amino acid composition, and tend to be more abundant, essential, and enriched for interaction partners. However, secondary structure content and transmembrane helix content are only weakly related to evolutionary rate.