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蛋白质序列比对中氨基酸交换矩阵的评估:重温模糊区域

An assessment of amino acid exchange matrices in aligning protein sequences: the twilight zone revisited.

作者信息

Vogt G, Etzold T, Argos P

机构信息

European Molecular Biology Laboratory, Heidelberg, Germany.

出版信息

J Mol Biol. 1995 Jun 16;249(4):816-31. doi: 10.1006/jmbi.1995.0340.

Abstract

The sensitivity of most protein sequence alignment methods depends strongly on the quality of the comparison matrices used. These matrices, which assign weights or similarity scores to every possible amino acid substitution pair, are utilized to differentiate amongst the various possible alignments of two or more sequences. There are many ways to generate these exchange weights and new matrices are constantly published. There has been no overall assessment of these various matrices when applied in different alignment techniques and over many protein folds and families, both close and distant and with the use of several gap penalty values. In this work, a set of amino acid sequences matched by superposition of known protein tertiary topologies is used to test the alignment accuracy of the different method/matrix/penalty combinations. The comparisons show relatively similar results for the top scoring matrices, a preference for the global alignment method of Needleman and Wunsch, and the importance of matrix modification and optimized gap penalties. The relationship between the percentage identity in a resulting alignment and the level of correctness to be expected are given for the top-performing matrix, resulting in a better definition of the so-called "twilight zone". Estimates are made for the probability that two sequences, aligned at a certain level of residue percentage identity, are in fact unrelated.

摘要

大多数蛋白质序列比对方法的灵敏度在很大程度上取决于所使用的比对矩阵的质量。这些矩阵为每一对可能的氨基酸替换赋予权重或相似性得分,用于区分两条或多条序列的各种可能比对。生成这些交换权重有很多方法,并且不断有新的矩阵被公布。当应用于不同的比对技术以及许多蛋白质折叠和家族(包括亲缘关系近和远的)并使用多种空位罚分参数时,尚未对这些不同的矩阵进行全面评估。在这项工作中,一组通过已知蛋白质三级拓扑结构叠加匹配的氨基酸序列被用于测试不同方法/矩阵/罚分组合的比对准确性。比较结果显示,得分最高的矩阵的结果相对相似,偏好Needleman和Wunsch的全局比对方法,以及矩阵修正和优化空位罚分的重要性。给出了所得比对中序列一致性百分比与预期正确水平之间的关系,针对表现最佳的矩阵,从而更好地定义了所谓的“模糊区”。还对在一定残基百分比一致性水平下比对的两条序列实际上不相关的概率进行了估计。

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