Heger Andreas, Mallick Swapan, Wilton Christopher, Holm Liisa
Institute of Biotechnology, P.O. Box 56 (Viikinkaari 5), FI-00014 University of Helsinki, Finland.
Bioinformatics. 2007 Sep 15;23(18):2361-7. doi: 10.1093/bioinformatics/btm358. Epub 2007 Sep 6.
Propagating functional annotations to sequence-similar, presumably homologous proteins lies at the heart of the bioinformatics industry. Correct propagation is crucially dependent on the accurate identification of subtle sequence motifs that are conserved in evolution. The evolutionary signal can be difficult to detect because functional sites may consist of non-contiguous residues while segments in-between may be mutated without affecting fold or function.
Here, we report a novel graph clustering algorithm in which all known protein sequences simultaneously self-organize into hypothetical multiple sequence alignments. This eliminates noise so that non-contiguous sequence motifs can be tracked down between extremely distant homologues. The novel data structure enables fast sequence database searching methods which are superior to profile-profile comparison at recognizing distant homologues. This study will boost the leverage of structural and functional genomics and opens up new avenues for data mining a complete set of functional signature motifs.
http://www.bioinfo.biocenter.helsinki.fi/gtg.
Supplementary data are available at Bioinformatics online.
将功能注释传播到序列相似、可能同源的蛋白质是生物信息学行业的核心。正确的传播关键取决于对进化中保守的细微序列基序的准确识别。进化信号可能难以检测，因为功能位点可能由不连续的残基组成，而其间的片段可能发生突变而不影响折叠或功能。
在此，我们报告了一种新颖的图聚类算法，其中所有已知蛋白质序列同时自组织成假设的多序列比对。这消除了噪声，从而可以在极其遥远的同源物之间追踪不连续的序列基序。这种新颖的数据结构实现了快速的序列数据库搜索方法，在识别遥远的同源物方面优于轮廓-轮廓比较。本研究将提高结构和功能基因组学的影响力，并为挖掘完整的功能特征基序集开辟新途径。
http://www.bioinfo.biocenter.helsinki.fi/gtg。
补充数据可在《生物信息学》在线获取。
