Decomposition of overlapping protein complexes: a graph theoretical method for analyzing static and dynamic protein associations.
Decomposition of overlapping protein complexes: a graph theoretical method for analyzing static and dynamic protein associations.
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DOI：
10.1186/1748-7188-1-7
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发表时间：
2006-04-26
影响因子：
1
通讯作者：
Przytycka, Teresa M
中科院分区：
文献类型：
--
作者：
Zotenko, Elena;Guimaraes, Katia S;Jothi, Raja;Przytycka, Teresa M
大多数细胞过程是由多蛋白质复合物（即结合在一起执行特定任务的蛋白质组）执行的。一些蛋白质形成稳定的复合物，而其他蛋白质则形成短暂的缔合，并且是细胞过程不同阶段的几种复合物的一部分。更好地理解蛋白质这种高阶组织重叠复合体是揭示生物网络背后的功能和进化机制的重要一步。我们提出了一种新方法，用于识别和表示蛋白质相互作用网络中重叠的蛋白质复合物（或称为功能组的更大单元）。我们开发了一个图论框架，可以自动构建这种表示。我们通过将其应用于 TNFα/NF-κB 和信息素信号通路来说明该方法的有效性。所提出的表示有助于理解功能组之间的转变，并允许跟踪蛋白质通过级联功能组的路径。因此，根据网络的性质，我们的表示能够阐明功能组之间的时间关系。我们的结果表明，所提出的方法为蛋白质相互作用网络的分析开辟了一条新途径。
Most cellular processes are carried out by multi-protein complexes, groups of proteins that bind together to perform a specific task. Some proteins form stable complexes, while other proteins form transient associations and are part of several complexes at different stages of a cellular process. A better understanding of this higher-order organization of proteins into overlapping complexes is an important step towards unveiling functional and evolutionary mechanisms behind biological networks. We propose a new method for identifying and representing overlapping protein complexes (or larger units called functional groups) within a protein interaction network. We develop a graph-theoretical framework that enables automatic construction of such representation. We illustrate the effectiveness of our method by applying it to TNFα/NF-κB and pheromone signaling pathways. The proposed representation helps in understanding the transitions between functional groups and allows for tracking a protein's path through a cascade of functional groups. Therefore, depending on the nature of the network, our representation is capable of elucidating temporal relations between functional groups. Our results show that the proposed method opens a new avenue for the analysis of protein interaction networks.