Functional genomic analysis of the rates of protein evolution

Abstract

The evolutionary rates of proteins vary over several orders of magnitude. Recent work suggests that analysis of large data sets of evolutionary rates in conjunction with the results from high-throughput functional genomic experiments can identify the factors that cause proteins to evolve at such dramatically different rates. To this end, we estimated the evolutionary rates of >3,000 proteins in four species of the yeast genus Saccharomyces and investigated their relationship with levels of expression and protein dispensability. Each protein's dispensability was estimated by the growth rate of mutants deficient for the protein. Our analyses of these improved evolutionary and functional genomic data sets yield three main results. First, dispensability and expression have independent, significant effects on the rate of protein evolution. Second, measurements of expression levels in the laboratory can be used to filter data sets of dispensability estimates, removing variates that are unlikely to reflect real biological effects. Third, structural equation models show that although we may reasonably infer that dispensability and expression have significant effects on protein evolutionary rate, we cannot yet accurately estimate the relative strengths of these effects

RETRACTED: Why proteins evolve at different rates: The functional hypothesis versus the mistranslation‐induced protein misfolding hypothesis

“…Protein evolutionary rates have been also reported to correlate with other genome variables ( Table 2) including expression level (or breadth) [10,15,[17][18][19][20][21][22][23] and a gene's propensity to be lost (computed based on the pattern of presence and absence of genes across multiple genomes) [24]. The functional hypothesis posits that each protein molecule by performing its function contributes a small amount to organism fitness, so mutations that reduce two proteins' functional output (e.g., catalytic rate) equally will have fitness effects weighted by the number of molecules of each protein in the cell, or their abundances, causing the more abundant protein to evolve slower.…”

The application of statistical physics to evolutionary biology

“…Although population genetics initially concerned itself with the dynamics at one or a few sites of interest, the recent flood of genomic data has allowed measurement of averages over many sites. For instance, it is now common to compare average evolutionary rates or average levels of codon bias across many genes in the genome (35,36). Such studies encounter tremendous noise, and predictions are far from precise (especially by the standards of statistical physics), but averaging across sites within genes and comparing large numbers of genes often allow detection of important trends.…”

Evolution of genes and genomes on the Drosophila phylogeny

“…The sequenced genomes of the melanogaster group provide unprecedented statistical power to identify factors affecting rates of protein evolution. Previous analyses have suggested that although the level of gene expression consistently seems to be a major determinant of variation in rates of evolution among proteins 86,87 , other factors probably play a significant, if perhaps minor, part [88][89][90][91] . In Drosophila, although highly expressed genes do evolve more slowly, breadth of expression across tissues, gene essentiality and intron number all also independently correlate with rates of protein evolution, suggesting that the additional complexities of multicellular organisms are important factors in modulating rates of protein evolution 78 .…”