A recent paper discusses the use of site-specific analysis of amino acid changes in the context of viruses, but it can be equally well applied to any other organism.
Modeling site-specific amino-acid preferences deepens phylogenetic estimates of viral sequence divergence, Hilton SK, Bloom JD. Virus Evol. 2018 Nov 6;4(2):vey033. doi: 10.1093/ve/vey033. eCollection 2018 Jul. PubMed PMID: 30425841; PubMed Central PMCID: PMC6220371.
Abstract: Molecular phylogenetics is often used to estimate the time since the divergence of modern gene sequences. For highly diverged sequences, such phylogenetic techniques sometimes estimate surprisingly recent divergence times. In the case of viruses, independent evidence indicates that the estimates of deep divergence times from molecular phylogenetics are sometimes too recent. This discrepancy is caused in part by inadequate models of purifying selection leading to branch-length underestimation. Here we examine the effect on branch-length estimation of using models that incorporate experimental measurements of purifying selection. We find that models informed by experimentally measured site-specific amino-acid preferences estimate longer deep branches on phylogenies of influenza virus hemagglutinin. This lengthening of branches is due to more realistic stationary states of the models, and is mostly independent of the branch-length extension from modeling site-to-site variation in amino-acid substitution rate. The branch-length extension from experimentally informed site-specific models is similar to that achieved by other approaches that allow the stationary state to vary across sites. However, the improvements from all of these site-specific but time homogeneous and site independent models are limited by the fact that a protein’s amino-acid preferences gradually shift as it evolves. Overall, our work underscores the importance of modeling site-specific amino-acid preferences when estimating deep divergence times-but also shows the inherent limitations of approaches that fail to account for how these preferences shift over time.