ArticleBMC evolutionary biology2012
Modeling coding-sequence evolution within the context of residue solvent accessibility.
Article in BMC evolutionary biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
- Evidence for Non-optimal Codon Choice in Highly Transcribed Sex-Biased Genes of Drosophila melanogaster.Genome biology and evolution · 2026Article
- Improved prediction of site-rates from structure with averaging across homologs.Protein science : a publication of the Protein Society · 2024Article
- Insertion-Deletion Events Are Depleted in Protein Regions with Predicted Secondary Structure.Genome biology and evolution · 2024Article
- Large-scale in silico mutagenesis experiments reveal optimization of genetic code and codon usage for protein mutational robustness.BMC biology · 2020Article
- Differential strengths of molecular determinants guide environment specific mutational fates.PLoS genetics · 2018Article
- Article
- Biophysical Models of Protein Evolution: Understanding the Patterns of Evolutionary Sequence Divergence.Annual review of biophysics · 2017Review
- Measuring evolutionary rates of proteins in a structural context.F1000Research · 2017Article
- Calculating site-specific evolutionary rates at the amino-acid or codon level yields similar rate estimates.PeerJ · 2017Article
- Intermediate divergence levels maximize the strength of structure-sequence correlations in enzymes and viral proteins.Protein science : a publication of the Protein Society · 2016Article
- Dissecting the roles of local packing density and longer-range effects in protein sequence evolution.Proteins · 2016Article
- Functional Sites Induce Long-Range Evolutionary Constraints in Enzymes.PLoS biology · 2016Article
- Causes of evolutionary rate variation among protein sites.Nature reviews. Genetics · 2016Review
- The utility of protein structure as a predictor of site-wise dN/dS varies widely among HIV-1 proteins.Journal of the Royal Society, Interface · 2015Article
- Universal distribution of mutational effects on protein stability, uncoupling of protein robustness from sequence evolution and distinct evolutionary modes of prokaryotic and eukaryotic proteins.Physical biology · 2015Article
- The relationship between dN/dS and scaled selection coefficients.Molecular biology and evolution · 2015Article
- Roles of solvent accessibility and gene expression in modeling protein sequence evolution.Evolutionary bioinformatics online · 2015Article
- Biophysics of protein evolution and evolutionary protein biophysics.Journal of the Royal Society, Interface · 2014Article
- Predicting evolutionary site variability from structure in viral proteins: buriedness, packing, flexibility, and design.Journal of molecular evolution · 2014Article
- An experimentally informed evolutionary model improves phylogenetic fit to divergent lactamase homologs.Molecular biology and evolution · 2014Article
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3 authors.
Funding
Abstract
backgroundProtein structure mediates site-specific patterns of sequence divergence. In particular, residues in the core of a protein (solvent-inaccessible residues) tend to be more evolutionarily conserved than residues on the surface (solvent-accessible residues).
resultsHere, we present a model of sequence evolution that explicitly accounts for the relative solvent accessibility of each residue in a protein. Our model is a variant of the Goldman-Yang 1994 (GY94) model in which all model parameters can be functions of the relative solvent accessibility (RSA) of a residue. We apply this model to a data set comprised of nearly 600 yeast genes, and find that an evolutionary-rate ratio ω that varies linearly with RSA provides a better model fit than an RSA-independent ω or an ω that is estimated separately in individual RSA bins. We further show that the branch length t and the transition-transverion ratio κ also vary with RSA. The RSA-dependent GY94 model performs better than an RSA-dependent Muse-Gaut 1994 (MG94) model in which the synonymous and non-synonymous rates individually are linear functions of RSA. Finally, protein core size affects the slope of the linear relationship between ω and RSA, and gene expression level affects both the intercept and the slope.
conclusionsStructure-aware models of sequence evolution provide a significantly better fit than traditional models that neglect structure. The linear relationship between ω and RSA implies that genes are better characterized by their ω slope and intercept than by just their mean ω.
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