Search bioRxivSearch

Biology subjects

Hilton, S. K.

Publications and source records attributed to Hilton, S. K..

3 recordsLinked to original sources

Modeling site-specific amino-acid preferences deepens phylogenetic estimates of viral divergence

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 proteins 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.

evolutionary biology

Mapping mutational effects along the evolutionary landscape of HIV envelope

The immediate evolutionary space accessible to HIV is largely determined by how single amino-acid mutations affect fitness. These mutational effects can shift as the virus evolves. However, the prevalence of such shifts in mutational effects remains unclear. Here we quantify the effects on viral growth of all amino-acid mutations to two HIV envelope (Env) proteins that differ at >100 residues. Most mutations similarly affect both Envs, but the amino-acid preferences of a minority of sites have clearly shifted. These shifted sites usually prefer a specific amino acid in one Env, but tolerate many amino acids in the other. Surprisingly, shifts are only slightly enriched at sites that have substituted between the Envs -- and many occur at residues that do not even contact substitutions. Therefore, long-range epistasis can unpredictably shift Env's mutational tolerance during HIV evolution, although the amino-acid preferences of most sites are conserved between moderately diverged viral strains.

microbiology

phydms: Software for phylogenetic analyses informed by deep mutational scanning

BackgroundThe evolution of protein-coding genes can be quantitatively modeled using phylogenetic methods. Recently, it has been shown that high-throughput experimental measurements of mutational effects made via deep mutational scanning can inform site-specific phylogenetic substitution models of gene evolution. However, there is currently no software tailored for such analyses.\n\nResultsWe describe software that efficiently performs phylogenetic analyses with substitution models informed by deep mutational scanning. This software, phydms, is [~]100-fold faster than existing programs that accommodate such substitution models. It can be used to compare the results of deep mutational scanning experiments to the selection on genes in nature. For instance, phydms enables rigorous comparison of how well different experiments on the same gene describe natural selection. It also enables the re-scaling of deep mutational scanning data to account for differences in the stringency of selection in the lab and nature. Finally, phydms can identify sites that are evolving differently in nature than expected from experiments in the lab.\n\nConclusionsThe phydms software makes it easy to use phylogenetic substitution models informed by deep mutational scanning experiments. As data from such experiments becomes increasingly widespread, phydms will facilitate quantitative comparison of the experimental results to the actual selection pressures shaping evolution in nature.

evolutionary biology