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Evolutionary Rate Covariation of Domain Families

Evolutionary rate covariation (ERC) is a phylogenetic measure of the evolutionary relationship between pairs of proteins. As proteins evolve over time, their rate of evolution (dN/dS) may vary. ERC measures how closely the evolutionary rates of two proteins match over a phylogeny. Proteins known to interact directly or indirectly tend to have higher ERC, because they typically experience similar evolutionary pressures within each lineage. Much is known about ERC at the whole protein level, but little is known at the domain level. Because individual functions of a protein are often performed by distinct domains, a focus on the domain level is expected to provide a clearer relationship between specific functions and ERC. Here we investigate ERC within and between domain families. In particular, we identify domain families with high ERC and investigate potential biochemical explanations. Introduction As proteins evolve over time, their rate of evolution will change depending on influences from the environment. Proteins that experience the same influences at the same times will show evolutionary rate covariation (ERC) [Clark, N. L., Alani, E., & Aquadro, C. F. (2012)]. This can be represented by comparing the phylogenetic trees of two proteins. The branch lengths represent the evolutionary rate of a protein, and each of the branches represents a time-span in which that protein evolved. Two proteins that share faster or slower than average evolutionary rates within the same branches will have higher ERC than those that do not (Figure 1). Figure 1. Compared to the average species tree across all proteins in the proteome, Proteins A and B have higher than average evolutionary rates (red) in their top branches. Their other branches also match, both showing average rates in the middle branch (black), and slower rates in the bottom branch (blue). This corresponds to a high ERC, shown in the scatter plot at the top right by a high correlation between their evolutionary rates. Proteins B and C show differing evolutionary rates in their corresponding branches, so have a low ERC, shown in the scatter plot in the bottom right [Figure from Clark, N. L., Alani, E., & Aquadro, C. F. (2012)]. Previous work with ERC has shown relationships between whole proteins. ERC can be used to infer which proteins are related to a disease, even when they haven’t been implicated with other methods [Priedigkeit N, Wolfe N, Clark NL (2015)]. It can also be used to find new members of functional biochemical networks [Findlay GD, Sitnik JL, Wang W, Aquadro CF, Clark NL, et al. (2014)]. However, less effort has been gone into exploring ERC at the domain level. In most cases a protein will contain at least one domain which acts as a functional subunit [Bork Peer(1991)]. Since the function of a protein is often dependent upon the domain(s) present, isolating the ERC of the domains should reveal a more distinct relationship between a function and ERC. Because not every domain in any given proteome has been linked to its function, the best currently available way to explore ERC on the domain level is through domain families, which have been identified on the whole proteome level in databases like Pfam [Finn, R. D., Bateman, A., Clements, J., Coggill, P., Eberhardt, R. Y., Eddy, S. R., ... Punta, M. (2014)]. We calculated the ERC of all the domains in the S. cerevisiae proteome using data from 13 yeast species. Yeast was chosen since previous work had been done with yeast ERC on the protein level with the data made available so that we could check our methods [Clark, N. L., Alani, E., & Aquadro, C. F. (2012)]. The domain ERC values were then grouped into their respective domain families and underwent a t-test to determine the significant values. These values were then visualized in a network to see how groups of domain families were interrelated. Groups of domain families with significant high ERC between them were then examined for function, evolutionary rate, and number of representative domains to explore potential explanations for their grouping.