Enhanced statistics for local alignment of multiple alignments improves prediction of protein function and structure
Enhanced statistics for local alignment of multiple alignments improves prediction of protein function and structure
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DOI：
10.1093/bioinformatics/bti462
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发表时间：
2005-07-01
期刊：
影响因子：
5.8
通讯作者：
Pietrokovski, S
中科院分区：
文献类型：
--
作者：
Frenkel-Morgenstern, M;Voet, H;Pietrokovski, S
动机：多序列比对（配置文件）与其他配置文件的改进比较，可以识别蛋白质家族和基序之间的微妙关系显着超出了基于序列的comparisons.Results的分辨率：局部比对的多重比对（LAMA）方法进行了修改，估计比对得分的显着性，通过应用一个新的措施，基于Fisher的组合方法。为了验证新的程序，我们使用了已知的蛋白质结构，序列注释和循环关系一致性分析（CYRCA）集一致对齐块。使用新的显著性度量提高了LAMA的灵敏度，而不改变其选择性。该程序的性能优于其他配置文件到配置文件的方法（COMPASS和Prof-sim）和序列到配置文件的方法（PSI-BLAST）。测试是大规模的，并使用了几个参数，包括伪计数剖面计算和局部无间隙区块或更多扩展的有间隙剖面。这种比较为不同情况下每种方法的相对优势提供了指导。我们展示和讨论的独特优势，使用块多比对的蛋白质基序。
Motivation: Improved comparisons of multiple sequence alignments (profiles) with other profiles can identify subtle relationships between protein families and motifs significantly beyond the resolution of sequence-based comparisons.Results: The local alignment of multiple alignments (LAMA) method was modified to estimate alignment score significance by applying a new measure based on Fisher's combining method. To verify the new procedure, we used known protein structures, sequence annotations and cyclical relations consistency analysis (CYRCA) sets of consistently aligned blocks. Using the new significance measure improved the sensitivity of LAMA without altering its selectivity. The program performed better than other profile-to-profile methods (COMPASS and Prof-sim) and a sequence-to-profile method (PSI-BLAST). The testing was large scale and used several parameters, including pseudo-counts profile calculations and local ungapped blocks or more extended gapped profiles. This comparison provides guidelines to the relative advantages of each method for different cases. We demonstrate and discuss the unique advantages of using block multiple alignments of protein motifs.