Modeling site-specific amino-acid preferences deepens phylogenetic estimates of viral sequence divergence
Author(s) -
Sarah K. Hilton,
Jesse D. Bloom
Publication year - 2018
Publication title -
virus evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.231
H-Index - 23
ISSN - 2057-1577
DOI - 10.1093/ve/vey033
Subject(s) - divergence (linguistics) , phylogenetic tree , negative selection , phylogenetics , evolutionary biology , biology , sequence (biology) , positive selection , selection (genetic algorithm) , amino acid , molecular evolution , computational biology , genetics , gene , computer science , artificial intelligence , philosophy , linguistics , genome
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.
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