Is there a standard way that I can compare the "evolution age" of two proteins, such as I want to compare SRSF1 and U2AF1?

Question

[li487]

I want to compare two proteins' "evolution age", such as, SRSF1 come out early than U2AF1 in the evolution time. I know it is plausible, but I don't know if there are standard way to do this kind of work? 

Thanks

Answer 1

[natasha.sernova]

See these links, for example:

A couple of recent papers:

DisCons: a novel tool to quantify and classify evolutionary conservation of intrinsic protein disorder

http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0592-2

"DisCons is a novel and freely available online and downloadable tool that combines the quantitative description of the position-specific evolutionary conservation of the amino acid sequence with predictions of the conservation of its disordered/flexible state, providing meaningful information on the evolutionary context of a disordered protein segment. Furthermore, DisCons uses this combined information to classify each disordered position into one of three categories, namely: constrained, flexible and non-conserved. These classes have been suggested to correlate with distinct functions that arise from the disordered state; therefore DisCons may provide information orthogonal to those obtained by other methods, which potentially enhances the reliability of the identification of functionally relevant disordered segments within proteins. We demonstrated that DisCons can be used to investigate both sequence- and disorder conservation in a functionally meaningful manner by bench-marking our procedure on MoRF and SLiM datasets, which are known to be conserved functional units enriched in structural disorder. It is important to emphasize that the success of calculation with DisCons strongly depends on the quality of the underlying multiple sequence alignment; therefore it is advised to review and optimize each MSA to maximize the information of the output. "

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4577149/

Comparison of predicted and actual consequences of missense mutations

"At first sight, it appears paradoxical that nearly neutral mutations in essential immune genes will be efficiently removed from the species’ gene pool despite having no easily discernible impact on the immune system of individual homozygotes. One possible explanation is that these subtle variants are individually sufficient to cause a serious problem in conjunction with particular environmental stressors such as malnutrition and pathogenic microbes. Another explanation comes from decades of research into the evolution of protein molecules and the nearly neutral theory of molecular evolution (39, 45). Mathematical models predict that slightly disadvantageous nearly neutral alleles will be lost over many generations through random drift in large populations, even when these alleles reduce fecundity by as little as 1% (39). "

Classical theory: https://www.ncbi.nlm.nih.gov/books/NBK20261/

"Several solutions to these problems have been proposed, each resulting in a different set of substitution scores. The first substitution matrix, constructed by Dayhoff and Eck in 1968 [172], was based on an alignment of closely related proteins, so that the ancestral sequence could be deduced and all the amino acid replacements could be considered occurring just once. This model was then extrapolated to account for more distant relationships (we will not discuss here the mathematics of this extrapolation and the underlying evolutionary model [174]), which resulted in the PAM series of substitution matrices (Figure 4.4). PAM (Accepted Point Mutation) is a unit of evolutionary divergence of protein sequences, corresponding to one amino acid change per 100 residues. Thus, for example, the PAM30 matrix is supposed to apply to proteins that differ, on average, by 0.3 change per aligned residue, whereas PAM250 should reflect evolution of sequences with an average of 2.5 substitutions per position. Accordingly, the former matrix should be employed for constructing alignments of closely related sequences, whereas the latter is useful in database searches aimed at detection of distant relationships. Using an approach similar to that of Dayhoff, combined with rapid algorithms for protein sequence clustering and alignment, Jones, Taylor, and Thornton produced the series of the so-called JTT matrices [403], which are essentially an update of the PAMs."

[li487]

That's helpful, thank you!