Literature DB >> 17400572

An empirical codon model for protein sequence evolution.

Carolin Kosiol1, Ian Holmes, Nick Goldman.   

Abstract

In the past, 2 kinds of Markov models have been considered to describe protein sequence evolution. Codon-level models have been mechanistic with a small number of parameters designed to take into account features, such as transition-transversion bias, codon frequency bias, and synonymous-nonsynonymous amino acid substitution bias. Amino acid models have been empirical, attempting to summarize the replacement patterns observed in large quantities of data and not explicitly considering the distinct factors that shape protein evolution. We have estimated the first empirical codon model (ECM). Previous codon models assume that protein evolution proceeds only by successive single nucleotide substitutions, but our results indicate that model accuracy is significantly improved by incorporating instantaneous doublet and triplet changes. We also find that the affiliations between codons, the amino acid each encodes and the physicochemical properties of the amino acids are main factors driving the process of codon evolution. Neither multiple nucleotide changes nor the strong influence of the genetic code nor amino acids' physicochemical properties form a part of standard mechanistic models and their views of how codon evolution proceeds. We have implemented the ECM for likelihood-based phylogenetic analysis, and an assessment of its ability to describe protein evolution shows that it consistently outperforms comparable mechanistic codon models. We point out the biological interpretation of our ECM and possible consequences for studies of selection.

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Year:  2007        PMID: 17400572     DOI: 10.1093/molbev/msm064

Source DB:  PubMed          Journal:  Mol Biol Evol        ISSN: 0737-4038            Impact factor:   16.240


  80 in total

1.  Empirical analysis of the most relevant parameters of codon substitution models.

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2.  FAVITES: simultaneous simulation of transmission networks, phylogenetic trees and sequences.

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3.  Bayesian analysis of amino acid substitution models.

Authors:  John P Huelsenbeck; Paul Joyce; Clemens Lakner; Fredrik Ronquist
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2008-12-27       Impact factor: 6.237

4.  Evaluating the robustness of phylogenetic methods to among-site variability in substitution processes.

Authors:  Mark T Holder; Derrick J Zwickl; Christophe Dessimoz
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2008-12-27       Impact factor: 6.237

Review 5.  Models of coding sequence evolution.

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Journal:  Brief Bioinform       Date:  2008-10-29       Impact factor: 11.622

6.  Bayesian comparisons of codon substitution models.

Authors:  Nicolas Rodrigue; Nicolas Lartillot; Hervé Philippe
Journal:  Genetics       Date:  2008-09-14       Impact factor: 4.562

7.  Mutation-selection models of coding sequence evolution with site-heterogeneous amino acid fitness profiles.

Authors:  Nicolas Rodrigue; Hervé Philippe; Nicolas Lartillot
Journal:  Proc Natl Acad Sci U S A       Date:  2010-02-22       Impact factor: 11.205

8.  An effective model for natural selection in promoters.

Authors:  Michael M Hoffman; Ewan Birney
Journal:  Genome Res       Date:  2010-03-01       Impact factor: 9.043

9.  The universal trend of amino acid gain-loss is caused by CpG hypermutability.

Authors:  Kazuharu Misawa; Naoyuki Kamatani; Reiko F Kikuno
Journal:  J Mol Evol       Date:  2008-09-23       Impact factor: 2.395

10.  INDELible: a flexible simulator of biological sequence evolution.

Authors:  William Fletcher; Ziheng Yang
Journal:  Mol Biol Evol       Date:  2009-05-07       Impact factor: 16.240

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