Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed.
Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed.
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DOI：
10.1002/humu.21490
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发表时间：
2011-06
期刊：
影响因子：
3.9
通讯作者：
Kimmel, Marek
中科院分区：
文献类型：
--
作者：
Hicks, Stephanie;Wheeler, David A.;Plon, Sharon E.;Kimmel, Marek
多种算法利用算法生成的序列比对或人工整理的比对来预测错义突变对蛋白质结构和功能的影响。我们比较了SIFT、Align - GVGD、PolyPhen - 2和Xvar在对BRCA1、MSH2、MLH1和TP53基因中特征明确的错义突变（n = 267）进行功能预测时与原始比对的准确性。我们还评估了在提供相同的四种比对（包括由(1)SIFT、(2)PolyPhen - 2、(3)Uniprot自动生成的比对以及(4)为Align - GVGD调整的人工整理的比对）时，所采用的比对对这些算法（Xvar除外）预测结果的影响。比对在序列组成和进化深度上存在差异。基于数据的接受者操作特征曲线采用每种算法的原始比对，所有四种算法的曲线下面积为78 - 79%。PolyPhen - 2算法的预测对所采用的比对依赖性最小。相比之下，当提供具有大量序列的比对时，Align - GVGD预测所有变异为中性。值得注意的是，即使提供相同的比对，算法对变异的预测也不同，而且使用自身比对时不一定表现最佳。因此，研究人员应考虑优化错义预测中所采用的算法和序列比对。
Multiple algorithms are used to predict the impact of missense mutations on protein structure and function using algorithm-generated sequence alignments or manually curated alignments. We compared the accuracy with native alignment of SIFT, Align-GVGD, PolyPhen-2 and Xvar when generating functionality predictions of well characterized missense mutations (n = 267) within the BRCA1, MSH2, MLH1 and TP53 genes. We also evaluated the impact of the alignment employed on predictions from these algorithms (except Xvar) when supplied the same four alignments including alignments automatically generated by (1) SIFT, (2) Polyphen-2, (3) Uniprot, and (4) a manually curated alignment tuned for Align-GVGD. Alignments differ in sequence composition and evolutionary depth. Data-based receiver operating characteristic curves employing the native alignment for each algorithm result in area under the curve of 78-79% for all four algorithms. Predictions from the PolyPhen-2 algorithm were least dependent on the alignment employed. In contrast, Align-GVGD predicts all variants neutral when provided alignments with a large number of sequences. Of note, algorithms make different predictions of variants even when provided the same alignment and do not necessarily perform best using their own alignment. Thus, researchers should consider optimizing both the algorithm and sequence alignment employed in missense prediction.
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