Consensus sequences improve PSI-BLAST through mimicking profile-profile alignments.
Consensus sequences improve PSI-BLAST through mimicking profile-profile alignments.
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DOI：
10.1093/nar/gkm107
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发表时间：
2007
影响因子：
14.9
通讯作者：
Rost B
中科院分区：
文献类型：
--
作者：
Przybylski D;Rost B
序列比对可能是分子生物学最基本的计算资源。通过谱-谱比较鉴定序列相关性的最佳方法比序列-序列和序列-谱比较（例如分别为BLAST和PSI-BLAST）慢得多且更复杂。相关基因和基因产物（蛋白质）的家族可以由共有序列表示，所述共有序列列出在该家族中的每个序列位置处最常见的核酸/氨基酸。在这里，我们提出了一种新的方法，共识序列为基础的比较。这种方法改进了搜索和比对，作为PSI-BLAST的标准附加项，而无需任何代码更改。对于更困难的任务，例如识别蛋白质之间的远距离结构关系及其相应的比对，改进尤其重要。尽管事实上，改善是更高的分歧关系，他们是一致的，即使在高精度/低错误率的非平凡相关的蛋白质。这些改进非常容易实现; PSI-BLAST使用的参数没有改变，代码也没有改变。此外，共有序列添加需要相对较少的额外CPU时间。我们讨论了PSI-BLAST的高级用户如何立即从在本地计算机上使用共识序列中受益。我们还通过因特网（http：//www.rostlab.org/services/consensus/）提供这种方法。
Sequence alignments may be the most fundamental computational resource for molecular biology. The best methods that identify sequence relatedness through profile–profile comparisons are much slower and more complex than sequence–sequence and sequence–profile comparisons such as, respectively, BLAST and PSI-BLAST. Families of related genes and gene products (proteins) can be represented by consensus sequences that list the nucleic/amino acid most frequent at each sequence position in that family. Here, we propose a novel approach for consensus-sequence-based comparisons. This approach improved searches and alignments as a standard add-on to PSI-BLAST without any changes of code. Improvements were particularly significant for more difficult tasks such as the identification of distant structural relations between proteins and their corresponding alignments. Despite the fact that the improvements were higher for more divergent relations, they were consistent even at high accuracy/low error rates for non-trivially related proteins. The improvements were very easy to achieve; no parameter used by PSI-BLAST was altered and no single line of code changed. Furthermore, the consensus sequence add-on required relatively little additional CPU time. We discuss how advanced users of PSI-BLAST can immediately benefit from using consensus sequences on their local computers. We have also made the method available through the Internet (http://www.rostlab.org/services/consensus/).