Literature DB >> 15140949

A universal evolutionary index for amino acid changes.

Hua Tang1, Gerald J Wyckoff, Jian Lu, Chung-I Wu.   

Abstract

Different nonsynonymous changes may be under different selective pressure during evolution. Of the 190 possible interchanges among the 20 amino acids, only 75 can be attained by a single-base substitution. An evolutionary index (EI) can be empirically computed for each of the 75 elementary changes as the likelihood of substitutions, relative to that of synonymous changes. We used 280, 1,306, 2,488, and 309 orthologous genes from primates (human versus Old World monkey), rodents (mouse versus rat), yeast (S. cerevisiae versus S. paradoxus), and Drosophila (D. melanogaster versus D. simulans), respectively, to estimate the EIs. In each data set, EI varies more than 10-fold, and the correlation coefficients of EIs from the pairwise comparisons are high (e.g., r = 0.91 between rodent and yeast). The high correlations suggest that the amino acid properties are strong determinants of protein evolution, irrespective of the identities of the proteins or the taxa of interest. However, these properties are not well captured in conventional measures of amino acid exchangeability. We, therefore, propose a universal index of exchange (U): for any large data set, its EI can be expressed as U*R, where R is the average Ka/Ks for that data set. The codon-based, empirically determined EI (i.e., U*R) makes much better predictions on protein evolution than do previous methods.

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Year:  2004        PMID: 15140949     DOI: 10.1093/molbev/msh158

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


  16 in total

1.  Codon usage and selection on proteins.

Authors:  Joshua B Plotkin; Jonathan Dushoff; Michael M Desai; Hunter B Fraser
Journal:  J Mol Evol       Date:  2006-10-14       Impact factor: 2.395

2.  A burst of protein sequence evolution and a prolonged period of asymmetric evolution follow gene duplication in yeast.

Authors:  Devin R Scannell; Kenneth H Wolfe
Journal:  Genome Res       Date:  2007-11-19       Impact factor: 9.043

Review 3.  Comparative genetic approaches to the evolution of human brain and behavior.

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Journal:  Am J Hum Biol       Date:  2011 Jan-Feb       Impact factor: 1.937

4.  Two decades of suspect evidence for adaptive molecular evolution-negative selection confounding positive-selection signals.

Authors:  Qipian Chen; Hao Yang; Xiao Feng; Qingjian Chen; Suhua Shi; Chung-I Wu; Ziwen He
Journal:  Natl Sci Rev       Date:  2021-12-03       Impact factor: 23.178

5.  A history of recurrent positive selection at the toll-like receptor 5 in primates.

Authors:  Gabriela Wlasiuk; Soofia Khan; William M Switzer; Michael W Nachman
Journal:  Mol Biol Evol       Date:  2009-01-29       Impact factor: 16.240

6.  Modeling compositional dynamics based on GC and purine contents of protein-coding sequences.

Authors:  Zhang Zhang; Jun Yu
Journal:  Biol Direct       Date:  2010-11-08       Impact factor: 4.540

7.  Adaptive genic evolution in the Drosophila genomes.

Authors:  Joshua A Shapiro; Wei Huang; Chenhui Zhang; Melissa J Hubisz; Jian Lu; David A Turissini; Shu Fang; Hurng-Yi Wang; Richard R Hudson; Rasmus Nielsen; Zhu Chen; Chung-I Wu
Journal:  Proc Natl Acad Sci U S A       Date:  2007-02-06       Impact factor: 11.205

8.  Highly conserved regimes of neighbor-base-dependent mutation generated the background primary-structural heterogeneities along vertebrate chromosomes.

Authors:  Marcos A Antezana; I King Jordan
Journal:  PLoS One       Date:  2008-05-14       Impact factor: 3.240

9.  Adaptive evolution in humans revealed by the negative correlation between the polymorphism and fixation phases of evolution.

Authors:  Jun Gojobori; Hua Tang; Joshua M Akey; Chung-I Wu
Journal:  Proc Natl Acad Sci U S A       Date:  2007-02-26       Impact factor: 11.205

10.  Inferring selection on amino acid preference in protein domains.

Authors:  Alan M Moses; Richard Durbin
Journal:  Mol Biol Evol       Date:  2008-12-18       Impact factor: 16.240

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