Estimating amino acid substitution models:: A comparison of Dayhoff's estimator, the resolvent approach and a maximum likelihood method
Estimating amino acid substitution models:: A comparison of Dayhoff's estimator, the resolvent approach and a maximum likelihood method
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DOI：
10.1093/oxfordjournals.molbev.a003985
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发表时间：
2002-01-01
影响因子：
10.7
通讯作者：
Vingron, M
中科院分区：
文献类型：
--
作者：
Müller, T;Spang, R;Vingron, M
蛋白质的进化通常被建模为作用于序列的每个位点的马尔可夫过程。替换频率需要根据序列比对来估计。在这里，我们比较了三种方法：第一，Dayhoff，Schwartz和Orcutt（1978）Atlas Protein Seq. Struc. 5：345-352，第二，Muller和Vingron（2000）J. Comput. 7（6）：761-776，以及最后本文开发的最大似然方法（ML）。我们评估的方法，使用一个高度发散和不均匀的序列比对作为输入的估计过程。ML是小的输入数据集的首选方法。虽然RV方法在计算上要求低得多，但它的性能仅略差于ML。因此，它非常适合大规模应用。
Evolution of proteins is generally modeled as a Markov process acting on each site of the sequence. Replacement frequencies need to be estimated based on sequence alignments. Here we compare three approaches: First, the original method by Dayhoff, Schwartz, and Orcutt (1978) Atlas Protein Seq. Struc. 5:345-352, secondly, the resolvent method (RV) by Muller and Vingron (2000) J. Comput. Biol. 7(6):761-776, and finally a maximum likelihood approach (ML) developed in this paper. We evaluate the methods using a highly divergent and inhomogeneous set of sequence alignments as an input to the estimation procedure. ML is the method of choice for small sets of input data. Although the RV method is computationally much less demanding it performs only slightly worse than ML. Therefore, it is perfectly appropriate for large-scale applications.