Identifying functionally important mutations from phenotypically diverse sequence data
Identifying functionally important mutations from phenotypically diverse sequence data
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DOI：
10.1128/aem.72.5.3696-3701.2006
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发表时间：
2006-05-01
影响因子：
4.4
通讯作者：
Stephanopoulos, Gregory
中科院分区：
文献类型：
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作者：
Jensen, Kyle;Alper, Hal;Stephanopoulos, Gregory
在这里，我们提出了一个简单的统计方法来确定一个单一的突变的表型贡献的突变体库中的不同表型，其中每个突变体包含大量的突变。该方法的中心前提是，给定M个表型类别，不影响表型的突变应根据多项分布在M个类别之间分配。偏离该分布表明特定突变和表型之间存在联系。我们认为，这种方法将有助于工程的功能性核酸，蛋白质和其他生物分子，通过发现合理的诱变的靶位点。作为一个证明的原则，我们展示了如何可以使用该方法来推断一组69 P-L-λ启动子变体的突变的个体效应。这些启动子中的每一个都是通过易错PCR产生的，并掺入了许多突变。使用流式细胞术测定启动子的活性以测量绿色荧光蛋白报告基因的荧光。我们对这些突变体的序列的分析揭示了与启动子活性具有统计学显著相关性的七个位置。使用定点诱变，我们构建了几个位点的点突变，统计学上显着的和不显着的，以及这些位点的组合。我们的研究结果表明，统计方法正确地阐明了这些突变的表型表现。我们建议，这种方法可能是有用的，通过允许所需的和不需要的突变被识别和纳入轮之间的诱变加速定向进化实验。
Here we present a simple statistical method to determine the phenotypic contribution of a single mutation from libraries of mutants with diverse phenotypes in which each mutant contains a multitude of mutations. The central premise of this method is that, given M phenotypic classes, mutations that do not affect the phenotype should partition among the M classes according to a multinomial distribution. Deviations from this distribution are indicative of a link between specific mutations and phenotypes. We suggest that this method will aid the engineering of functional nucleic acids, proteins, and other biomolecules by uncovering target sites for rational mutagenesis. As a proof of the principle, we show how the method can be used to deduce the individual effects of mutations in a set of 69 P-L-lambda promoter variants. Each of these promoters was generated by error-prone PCR and incorporated numerous mutations. The activity of the promoters was assayed using flow cytometry to measure the fluorescence of a green fluorescent protein reporter gene. Our analysis of the sequences of these mutants revealed seven positions having a statistically significant correlation with promoter activity. Using site-directed mutagenesis, we constructed point mutations for several sites, both statistically significant and insignificant, and combinations of these sites. Our results show that the statistical method correctly elucidated the phenotypic manifestations of these mutations. We suggest that this method may be useful for expediting directed evolution experiments by allowing both desired and undesired mutations to be identified and incorporated between rounds of mutagenesis.