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Identifying Protein Complexes from Interaction Networks Based on Clique Percolation and Distance Restriction

机译:基于群体渗透和距离限制的相互作用网络中蛋白质复合物的鉴定

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摘要

Background: Identification of protein complexes in large interaction networks is crucial to understand principles of cellular organization and predict protein functions, which is one of the most important issues in the post-genomic era. Each protein might be subordinate multiple protein complexes in the real protein-protein interaction networks.Identifying overlapping protein complexes from protein-protein interaction networks is a considerable research topic.Result: As an effective algorithm in identifying overlapping module structures, clique percolation method (CPM) has a wide range of application in social networks and biological networks. However, the recognition accuracy of algorithm CPM is lowly. Furthermore, algorithm CPM is unfit to identifying protein complexes with meso-scale when it applied in protein-protein interaction networks. In this paper, we propose a new topological model by extending the definition of k-clique community of algorithm CPM and introduced distance restriction, and develop a novel algorithm called CP-DR based on the new topological model for identifying protein complexes. In this new algorithm, the protein complex size is restricted by distance constraint to conquer the shortcomings of algorithm CPM. The algorithm CP-DR is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes.Conclusion: The proposed algorithm CP-DR based on clique percolation and distance restriction makes it possible to identify dense subgraphs in protein interaction networks, a large number of which correspond to known protein complexes. Compared to algorithm CPM, algorithm CP-DR has more outstanding performance.
机译:背景:在大型相互作用网络中鉴定蛋白质复合物对于理解细胞组织原理和预测蛋白质功能至关重要,这是后基因组时代最重要的问题之一。在真实的蛋白质-蛋白质相互作用网络中,每种蛋白质可能是多个蛋白质复合物的下属。从蛋白质-蛋白质相互作用网络中识别重叠的蛋白质复合物是一个重要的研究课题。结果:作为一种有效的算法,识别重叠模块结构的方法是集团渗透法(CPM) )在社交网络和生物网络中具有广泛的应用。然而,算法CPM的识别精度较低。此外,算法CPM当应用于蛋白质-蛋白质相互作用网络时,不适合以中等规模鉴定蛋白质复合物。在本文中,我们通过扩展算法CPM的k-clique社区的定义并引入距离限制,提出了一种新的拓扑模型,并基于新的拓扑模型识别蛋白质复合物,开发了一种称为CP-DR的新算法。在这种新算法中,蛋白质复合物的大小受到距离约束的限制,以克服算法CPM的缺点。 CP-DR算法应用于酿酒酵母的蛋白质相互作用网络中,并鉴定出许多众所周知的复合物。结论:基于群体渗透和距离限制的CP-DR算法使得识别蛋白质相互作用网络中的密集子图成为可能。其中大量对应于已知的蛋白质复合物。与算法CPM相比,算法CP-DR具有更出色的性能。

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