Errors in protein translation can lead to non-genetic, phenotypic mutations, including amino acid misincorporations. While phenotypic mutations can increase protein diversity, the systematic characterization of their proteome-wide frequencies and their evolutionary impact has been lacking. Here, we developed a mechanistic model of translation errors to investigate how selection acts on protein populations produced by amino acid misincorporations. We fitted the model to empirical observations of misincorporations obtained from over a hundred mass spectrometry datasets of E. coli and S. cerevisiae. We found that on average 20-23% of proteins synthesized in the cell are expected to harbour at least one amino acid misincorporation, and that deleterious misincorporations are less likely to occur. Combining misincorporation probabilities and the estimated fitness effects of amino acid substitutions in a population genetics framework, we found 74% of mistranslation events in E. coli and 94% in S. cerevisiae to be neutral. We further show that the set of available synonymous tRNAs is subject to evolutionary pressure, as the presence of missing tRNAs would increase codon-anticodon cross-reactivity and misincorporation error rates. Overall, we find that the translation machinery is likely optimal in E. coli and S. cerevisiae and that both local solutions at the level of codons and a global solution such as the tRNA pool can mitigate the impact of translation errors. We provide a framework to study the evolutionary impact of codon specific translation errors and a method for their proteome-wide detection across organisms and conditions.

中文翻译：

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蛋白质组规模表型突变的适应性效应揭示了翻译机制的最优性

蛋白质翻译错误可能导致非遗传表型突变，包括氨基酸错误掺入。虽然表型突变可以增加蛋白质多样性，但仍缺乏对其蛋白质组频率及其进化影响的系统表征。在这里，我们开发了翻译错误的机制模型，以研究选择如何作用于氨基酸错误掺入产生的蛋白质群体。我们将该模型与从一百多个大肠杆菌和酿酒酵母质谱数据集中获得的错误掺入的经验观察结果进行拟合。我们发现，细胞中合成的蛋白质中平均有 20-23% 预计至少存在一种氨基酸错误掺入，并且发生有害的错误掺入的可能性较小。结合群体遗传学框架中的错误掺入概率和氨基酸取代的估计适应性效应，我们发现大肠杆菌中 74% 的错误翻译事件和酿酒酵母中 94% 的错误翻译事件是中性的。我们进一步表明，可用的同义 tRNA 集受到进化压力的影响，因为缺失 tRNA 的存在会增加密码子-反密码子交叉反应性和错误掺入错误率。总体而言，我们发现大肠杆菌和酿酒酵母中的翻译机制可能是最佳的，并且密码子水平的局部解决方案和 tRNA 池等全局解决方案都可以减轻翻译错误的影响。我们提供了一个框架来研究密码子特异性翻译错误的进化影响，以及一种跨生物体和条件进行蛋白质组范围检测的方法。