Causes of evolutionary rate variation among protein sites

Echave, Julián; Spielman, Scott R.; Wilke, Claus O.

doi:10.1038/nrg.2015.18

Cited by 267 publications

(320 citation statements)

References 150 publications

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“…In the last decade, a series of investigations tackled this issue (e.g. [29][30][31][32][33][34] and references therein), but the answers remained elusive and too frequently model dependent. For example, it is still debated whether or not the distribution of the effects of amino acid substitutions on protein stability is universal and whether or not they are conserved during evolution.…”

Section: Analysis At the Structuromic Scalementioning

confidence: 99%

Improved insights into protein thermal stability: from the molecular to the structurome scale

Pucci

Rooman

2016

Phil. Trans. R. Soc. A.

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One contribution of 17 to a theme issue 'Multiscale modelling at the physics-chemistry-biology interface' . Despite the intense efforts of the last decades to understand the thermal stability of proteins, the mechanisms responsible for its modulation still remain debated. In this investigation, we tackle this issue by showing how a multiscale perspective can yield new insights. With the help of temperaturedependent statistical potentials, we analysed some amino acid interactions at the molecular level, which are suggested to be relevant for the enhancement of thermal resistance. We then investigated the thermal stability at the protein level by quantifying its modification upon amino acid substitutions. Finally, a large scale analysis of protein stability-at the structurome level-contributed to the clarification of the relation between stability and natural evolution, thereby showing that the mutational profile of proteins differs according to their thermal properties. Some considerations on how the multiscale approach could help in unravelling the protein stability mechanisms are briefly discussed.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'.

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Section: Analysis At the Structuromic Scalementioning

confidence: 99%

Improved insights into protein thermal stability: from the molecular to the structurome scale

Pucci

Rooman

2016

Phil. Trans. R. Soc. A.

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“…As a result of this interplay, functional requirements are reflected in the patterns of change within homologous protein sequences. Amino acid mutations that occur on the surface of a protein's folded structure, or are distant from its active site, tend to have a smaller effect on the fitness of a protein, and are fixed at a higher rate [1]. At the same time, local rates of change in protein sequence are correlated with local rates of change in protein structure [2][3][4].…”

Section: Introductionmentioning

confidence: 73%

Evolution of off-lattice model proteins under ligand binding constraints

Nelson

Grishin

2016

Phys. Rev. E

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We investigate protein evolution using an off-lattice polymer model evolved to imitate the behavior of small enzymes. Model proteins evolve through mutations to nucleotide sequences (including insertions and deletions) and are selected to fold and maintain a specific binding site compatible with a model ligand. We show that this requirement is, in itself, sufficient to maintain an ordered folding domain, and we compare it to the requirement of folding an ordered (but otherwise un-restricted) domain. We measure rates of amino acid change as a function local environment properties such as solvent exposure, packing density, and distance from the active site, as well as overall rates of sequence and structure change, both along and among model lineages in star phylogenies. The model recapitulates essentially all of the behavior found in protein phylogenetic analyses, and predicts that amino acid substitution rates vary linearly with distance from the binding site.3

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“…Increasing the number of taxa in a given analysis may be beneficial only if the new sequences are substantially diverged from the existing sequences. These findings should also inform studies that seek to relate site-specific protein evolutionary rate (i.e., dN=dS) to structural properties, such as relative-solvent accessibility or weighted contact number (Echave et al 2016). Our findings predict that more diverged data sets should provide more meaningful information about long-term evolutionary constraints, which structural quantities reflect.…”

Section: Discussionmentioning

confidence: 94%

A Comparison of One-Rate and Two-Rate Inference Frameworks for Site-Specific dN/ dS Estimation

2016

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Two broad paradigms exist for inferring dN=dS; the ratio of nonsynonymous to synonymous substitution rates, from coding sequences: (i) a one-rate approach, where dN=dS is represented with a single parameter, or (ii) a two-rate approach, where d N and d S are estimated separately. The performances of these two approaches have been well studied in the specific context of proper model specification, i.e., when the inference model matches the simulation model. By contrast, the relative performances of one-rate vs. two-rate parameterizations when applied to data generated according to a different mechanism remain unclear. Here, we compare the relative merits of one-rate and two-rate approaches in the specific context of model misspecification by simulating alignments with mutation-selection models rather than with dN=dS-based models. We find that one-rate frameworks generally infer more accurate dN=dS point estimates, even when d S varies among sites. In other words, modeling d S variation may substantially reduce accuracy of dN=dS point estimates. These results appear to depend on the selective constraint operating at a given site. For sites under strong purifying selection (dN=dS ≲ 0:3), one-rate and two-rate models show comparable performances. However, one-rate models significantly outperform two-rate models for sites under moderate-to-weak purifying selection. We attribute this distinction to the fact that, for these more quickly evolving sites, a given substitution is more likely to be nonsynonymous than synonymous. The data will therefore be relatively enriched for nonsynonymous changes, and modeling d S contributes excessive noise to dN=dS estimates. We additionally find that high levels of divergence among sequences, rather than the number of sequences in the alignment, are more critical for obtaining precise point estimates.KEYWORDS dN/dS; mutation-selection models; evolutionary rate; sequence simulation; molecular evolution A variety of computational approaches have been developed to infer selection pressure from protein-coding sequences in a phylogenetically aware context. Among the most commonly used methods are those that compute the evolutionary rate ratio dN=dS; which represents the ratio of nonsynonymous to synonymous substitution rates. Beginning in the mid-1990s, this value has been calculated using maximum-likelihood (ML) approaches (Goldman and Yang 1994;Muse and Gaut 1994), and since then, a wide variety of inference frameworks have been developed to infer dN=dS at individual sites in protein-coding sequences (Nielsen and Yang 1998;Yang et al. 2000;Yang and Nielsen 2002;Yang and Swanson 2002;Lemey et al. 2012;Murrell et al. 2012bMurrell et al. , 2013.Typically, the performance of evolutionary models is examined using simulation-based approaches wherein sequences are simulated according to the model being examined, and inferences are subsequently performed on the simulated data. This approach ensures that simulated and inferred parameters correspond. Although useful, this strategy fundam...

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Causes of evolutionary rate variation among protein sites

Cited by 267 publications

References 150 publications

Improved insights into protein thermal stability: from the molecular to the structurome scale

Improved insights into protein thermal stability: from the molecular to the structurome scale

Evolution of off-lattice model proteins under ligand binding constraints

A Comparison of One-Rate and Two-Rate Inference Frameworks for Site-Specific dN/ dS Estimation

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