Motif-based protein ranking by network propagation
Motif-based protein ranking by network propagation
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DOI：
10.1093/bioinformatics/bti608
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发表时间：
2005-10-01
期刊：
影响因子：
5.8
通讯作者：
Leslie, C
中科院分区：
文献类型：
--
作者：
Kuang, R;Weston, J;Leslie, C
动机：序列相似性通常表明蛋白质序列之间的进化关系，这对于推断结构或功能的相似性很重要。最广泛使用的成对序列比较算法的同源性检测，如BLAST和PSI-BLAST，往往无法检测到保守性较低的远程相关targets.Results：在本文中，我们提出了一种新的通用的基于图形的传播算法称为MotifProp检测更微妙的相似性关系比成对比较方法。MotifProp基于蛋白质基序网络，其中边缘连接蛋白质及其包含的基于k聚体的基序特征。我们表明，我们的新的图案为基础的传播算法可以提高排名的结果比一个基本的算法，如PSI-BLAST，这是用来初始化排名。尽管蛋白质基序网络的结构复杂，但MotifProp可以很容易地使用排名靠前的基序和由传播诱导的富含基序的区域来解释，这两者都有助于发现远程同源性中的保守结构组分。
Motivation: Sequence similarity often suggests evolutionary relationships between protein sequences that can be important for inferring similarity of structure or function. The most widely-used pairwise sequence comparison algorithms for homology detection, such as BLAST and PSI-BLAST, often fail to detect less conserved remotely-related targets.Results: In this paper, we propose a new general graph-based propagation algorithm called MotifProp to detect more subtle similarity relationships than pairwise comparison methods. MotifProp is based on a protein-motif network, in which edges connect proteins and the k-mer based motif features that they contain. We show that our new motif-based propagation algorithm can improve the ranking results over a base algorithm, such as PSI-BLAST, that is used to initialize the ranking. Despite the complex structure of the protein-motif network, MotifProp can be easily interpreted using the top-ranked motifs and motif-rich regions induced by the propagation, both of which are helpful for discovering conserved structural components in remote homologies.