Identification of positive selection in genes is greatly improved by using experimentally informed site-specific models.
Identification of positive selection in genes is greatly improved by using experimentally informed site-specific models.
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DOI：
10.1186/s13062-016-0172-z
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发表时间：
2017-01-17
期刊：
影响因子：
5.5
通讯作者：
Bloom JD
中科院分区：
文献类型：
--
作者：
Bloom JD
通过比较观察到的进化模式与在没有这种选择的情况下在零进化模型下预期的进化模式来确定正选择的位点。对于蛋白质编码基因，最常见的空模型是非同义突变和同义突变以相同的速率固定;这种不切实际的模型在检测许多有趣的选择形式方面能力有限。我描述了一种新的方法，使用一个空模型的基础上实验测量的基因的位点特异性氨基酸的偏好产生的深度突变扫描在实验室中。这种空模型使得有可能识别重复氨基酸变化的多样化选择和在实验室中进行测量时意外的氨基酸突变的差异选择。我表明，这种方法确定网站的自适应替换在四个基因（内酰胺酶，Gal 4，流感核蛋白，流感血凝素）远远优于一个可比的方法，简单地比较率的非同义和同义替换。随着生物学数据的快速增长，对单个蛋白质位点的限制条件的描述越来越细致入微，像这里这样的方法可以提高我们在自然序列中识别许多有趣的选择形式的能力。本文由塞巴斯蒂安Maurer-Stroh、Olivier Tenaillon和Tal Pupko审阅。所有三位审稿人都是生物学直接编辑委员会的成员。本文的在线版本（doi：10.1186/s13062-016-0172-z）包含补充材料，可供授权用户使用。
Sites of positive selection are identified by comparing observed evolutionary patterns to those expected under a null model for evolution in the absence of such selection. For protein-coding genes, the most common null model is that nonsynonymous and synonymous mutations fix at equal rates; this unrealistic model has limited power to detect many interesting forms of selection. I describe a new approach that uses a null model based on experimental measurements of a gene’s site-specific amino-acid preferences generated by deep mutational scanning in the lab. This null model makes it possible to identify both diversifying selection for repeated amino-acid change and differential selection for mutations to amino acids that are unexpected given the measurements made in the lab. I show that this approach identifies sites of adaptive substitutions in four genes (lactamase, Gal4, influenza nucleoprotein, and influenza hemagglutinin) far better than a comparable method that simply compares the rates of nonsynonymous and synonymous substitutions. As rapid increases in biological data enable increasingly nuanced descriptions of the constraints on individual protein sites, approaches like the one here can improve our ability to identify many interesting forms of selection in natural sequences. This article was reviewed by Sebastian Maurer-Stroh, Olivier Tenaillon, and Tal Pupko. All three reviewers are members of the Biology Direct editorial board. The online version of this article (doi:10.1186/s13062-016-0172-z) contains supplementary material, which is available to authorized users.