Application of mechanistic models to separate the effects of mutation, selection, and drift on protein sequence evolution

Mathematical and statistical models are useful for describing and understanding observations in genetics and genomics. These models have to constantly be updated to reflect current biological understanding. As opposed to descriptive and phenomenological models, mechanistic models allow for the extraction of more biologically relevant information based on underlying principles. Mutation, selection, and genetic drift are the three forces guiding evolution. Mechanistic models rooted in population genetics principles allow us to determine how these forces shape observed data. I demonstrate the usage of mechanistic models to relate protein coding sequences to their fitness landscapes and the evolutionary forces shaping them. Using the yeast L. kluyveri, I show the increased cost of protein synthesis due to a large scale introgression with mismatched codon usage. Furthermore, I analyze site- specific selection on amino acids in the beta-lactamase protein TEM, which confers antibiotic resistance in E. coli and related species

Homology-based inference sets the bar high for protein function prediction

Background: Any method that de novo predicts protein function should do better than random. More challenging, it also ought to outperform simple homology-based inference. Methods: Here, we describe a few methods that predict protein function exclusively through homology. Together, they set the bar or lower limit for future improvements. Results and conclusions: During the development of these methods, we faced two surprises. Firstly, our most successful implementation for the baseline ranked very high at CAFA1. In fact, our best combination of homology-based methods fared only slightly worse than the top-of-the-line prediction method from the Jones group. Secondly, although the concept of homology-based inference is simple, this work revealed that the precise details of the implementation are crucial: not only did the methods span from top to bottom performers at CAFA, but also the reasons for these differences were unexpected. In this work, we also propose a new rigorous measure to compare predicted and experimental annotations. It puts more emphasis on the details of protein function than the other measures employed by CAFA and may best reflect the expectations of users. Clearly, the definition of proper goals remains one major objective for CAFA

Unlocking a signal of introgression from codons in Lachancea kluyveri using a mutation-selection model

Abstract Background For decades, codon usage has been used as a measure of adaptation for translational efficiency and translation accuracy of a gene’s coding sequence. These patterns of codon usage reflect both the selective and mutational environment in which the coding sequences evolved. Over this same period, gene transfer between lineages has become widely recognized as an important biological phenomenon. Nevertheless, most studies of codon usage implicitly assume that all genes within a genome evolved under the same selective and mutational environment, an assumption violated when introgression occurs. In order to better understand the effects of introgression on codon usage patterns and vice versa, we examine the patterns of codon usage in Lachancea kluyveri, a yeast which has experienced a large introgression. We quantify the effects of mutation bias and selection for translation efficiency on the codon usage pattern of the endogenous and introgressed exogenous genes using a Bayesian mixture model, ROC SEMPPR, which is built on mechanistic assumptions about protein synthesis and grounded in population genetics. Results We find substantial differences in codon usage between the endogenous and exogenous genes, and show that these differences can be largely attributed to differences in mutation bias favoring A/T ending codons in the endogenous genes while favoring C/G ending codons in the exogenous genes. Recognizing the two different signatures of mutation bias and selection improves our ability to predict protein synthesis rate by 42% and allowed us to accurately assess the decaying signal of endogenous codon mutation and preferences. In addition, using our estimates of mutation bias and selection, we identify Eremothecium gossypii as the closest relative to the exogenous genes, providing an alternative hypothesis about the origin of the exogenous genes, estimate that the introgression occurred ∼6×108 generation ago, and estimate its historic and current selection against mismatched codon usage. Conclusions Our work illustrates how mechanistic, population genetic models like ROC SEMPPR can separate the effects of mutation and selection on codon usage and provide quantitative estimates from sequence data

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Data from: Recommendations for using msBayes to incorporate uncertainty in selecting an ABC model prior: a response to Oaks et al.

Prior specification is an essential component of parameter estimation and model comparison in Approximate Bayesian computation (ABC). Oaks et al. present a simulation-based power analysis of msBayes and conclude that msBayes has low power to detect genuinely random divergence times across taxa, and suggest the cause is Lindley's paradox. Although the predictions are similar, we show that their findings are more fundamentally explained by insufficient prior sampling that arises with poorly chosen wide priors that critically undersample nonsimultaneous divergence histories of high likelihood. In a reanalysis of their data on Philippine Island vertebrates, we show how this problem can be circumvented by expanding upon a previously developed procedure that accommodates uncertainty in prior selection using Bayesian model averaging. When these procedures are used, msBayes supports recent divergences without support for synchronous divergence in the Oaks et al. data and we further present a simulation analysis that demonstrates that msBayes can have high power to detect asynchronous divergence under narrower priors for divergence time. Our findings highlight the need for exploration of plausible parameter space and prior sampling efficiency for ABC samplers in high dimensions. We discus potential improvements to msBayes and conclude that when used appropriately with model averaging, msBayes remains an effective and powerful tool

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Supplemental 2. Genbank accession numbers and species names of data used to test for synchronous divergence with msBayes versions 20120222 and 20120510 given 23 species-pairs. Each pair of species has the same genus name

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R scripts for generating estimates in table S

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