Development and implementation of an algorithm for detection of protein complexes in large interaction networks
Development and implementation of an algorithm for detection of protein complexes in large interaction networks
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DOI：
10.1186/1471-2105-7-207
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发表时间：
2006-04-14
影响因子：
3
通讯作者：
Kanaya, Shigehiko
中科院分区：
文献类型：
--
作者：
Altaf-Ul-Amin, Md;Shinbo, Yoko;Kanaya, Shigehiko
背景：在对许多基因组进行完整测序之后，焦点现在转向蛋白质组学。先进的蛋白质组学技术，例如两种杂交测定法，质谱等，正在生产大量的蛋白质 - 蛋白质相互作用数据集，这些相互作用可以描绘成网络，而燃烧的问题之一是在此类网络中找到蛋白质复合物。蛋白质 - 蛋白质相互作用（PPI）网络的巨大尺寸值得开发有效的计算方法来提取重要的复合物。回报：本文提出了一种用于检测大相互作用网络中蛋白质复合物的算法。在PPI网络中，节点代表蛋白质，边缘表示相互作用。算法的输入是相互作用网络的相关矩阵，输出是蛋白质复合物。这些复合物是通过查找簇的方式来确定的，即网络中的密集连接区域。我们还展示和分析了来自大肠杆菌和酿酒酵母的典型PPI网络所提出的算法产生的一些蛋白质复合物。 PPI和随机网络之间的比较也是在提出的算法的背景下进行的：构成的判决：拟议的算法使得可以检测PPI网络中蛋白质簇，这主要代表分子生物学功能单元。因此，仅基于相互作用数据确定的蛋白质复合物可以帮助我们预测蛋白质的功能，并且它们也可用于理解和解释某些生物学过程。
Background: After complete sequencing of a number of genomes the focus has now turned to proteomics. Advanced proteomics technologies such as two-hybrid assay, mass spectrometry etc. are producing huge data sets of protein-protein interactions which can be portrayed as networks, and one of the burning issues is to find protein complexes in such networks. The enormous size of protein-protein interaction (PPI) networks warrants development of efficient computational methods for extraction of significant complexes.Results: This paper presents an algorithm for detection of protein complexes in large interaction networks. In a PPI network, a node represents a protein and an edge represents an interaction. The input to the algorithm is the associated matrix of an interaction network and the outputs are protein complexes. The complexes are determined by way of finding clusters, i.e. the densely connected regions in the network. We also show and analyze some protein complexes generated by the proposed algorithm from typical PPI networks of Escherichia coli and Saccharomyces cerevisiae. A comparison between a PPI and a random network is also performed in the context of the proposed algorithm.Conclusion: The proposed algorithm makes it possible to detect clusters of proteins in PPI networks which mostly represent molecular biological functional units. Therefore, protein complexes determined solely based on interaction data can help us to predict the functions of proteins, and they are also useful to understand and explain certain biological processes.