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单一决定因素主导酵母蛋白质的进化速率。

A single determinant dominates the rate of yeast protein evolution.

作者信息

Drummond D Allan, Raval Alpan, Wilke Claus O

机构信息

Program in Computation and Neural Systems, California Institute of Technology, Pasadena, USA.

出版信息

Mol Biol Evol. 2006 Feb;23(2):327-37. doi: 10.1093/molbev/msj038. Epub 2005 Oct 19.

DOI:10.1093/molbev/msj038
PMID:16237209
Abstract

A gene's rate of sequence evolution is among the most fundamental evolutionary quantities in common use, but what determines evolutionary rates has remained unclear. Here, we carry out the first combined analysis of seven predictors (gene expression level, dispensability, protein abundance, codon adaptation index, gene length, number of protein-protein interactions, and the gene's centrality in the interaction network) previously reported to have independent influences on protein evolutionary rates. Strikingly, our analysis reveals a single dominant variable linked to the number of translation events which explains 40-fold more variation in evolutionary rate than any other, suggesting that protein evolutionary rate has a single major determinant among the seven predictors. The dominant variable explains nearly half the variation in the rate of synonymous and protein evolution. We show that the two most commonly used methods to disentangle the determinants of evolutionary rate, partial correlation analysis and ordinary multivariate regression, produce misleading or spurious results when applied to noisy biological data. We overcome these difficulties by employing principal component regression, a multivariate regression of evolutionary rate against the principal components of the predictor variables. Our results support the hypothesis that translational selection governs the rate of synonymous and protein sequence evolution in yeast.

摘要

基因的序列进化速率是常用的最基本的进化量之一,但决定进化速率的因素仍不明确。在此,我们首次对先前报道的对蛋白质进化速率有独立影响的七个预测因子(基因表达水平、必要性、蛋白质丰度、密码子适应指数、基因长度、蛋白质-蛋白质相互作用数量以及基因在相互作用网络中的中心性)进行了综合分析。引人注目的是,我们的分析揭示了一个与翻译事件数量相关的单一主导变量,它对进化速率变化的解释力比其他任何变量都高出40倍,这表明在这七个预测因子中,蛋白质进化速率有一个主要的决定因素。这个主导变量解释了同义进化速率和蛋白质进化速率近一半的变化。我们表明,在应用于有噪声的生物学数据时,用于剖析进化速率决定因素的两种最常用方法——偏相关分析和普通多元回归,会产生误导性或虚假的结果。我们通过采用主成分回归克服了这些困难,主成分回归是进化速率对预测变量主成分的多元回归。我们的结果支持了翻译选择控制酵母中同义序列和蛋白质序列进化速率的假说。

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