title: Double-layer clustering method to predict protein complexes based on power-law distribution and protein sublocalization.
abstract: Identifying protein complexes from Protein-protein Interaction Networks (PINs) is fundamental for understanding protein functions and activities in cell. Based on the assumption that protein complexes are highly connected areas in PINs, many algorithms were proposed to identify protein complexes from PINs. However, most of these approaches neglected that not all proteins in complexes are highly connected, and proteins in PINs with different topological properties may form protein complexes in different ways and should be treated differently. In this paper, we proposed a double-layer clustering method based on the power-law distribution (PLCluster). To calculate the centrality scores of nodes, we proposed a Dense-Spread Centrality method. The centrality scores calculated by Dense-Spread Centrality method follow a power-law distribution. Based on the power-law distribution of the centrality scores, PLCluster divides the nodes into two categories: the nodes with very high centrality scores and the nodes with lower centrality scores. Then different strategies are applied to nodes in different categories for detecting protein complexes from the PIN, respectively. Furthermore, the predicted protein complexes, which are inconsistent with the fact that all proteins in a protein complex should be in the same subcellular compartment, are filtered out. Compared with other nine existing methods on a high reliable yeast PIN, PLCluster shows great advantages in terms of the number of known complexes that are identified, Sensitivity, Specificity, f-measure and the number of perfect matches.
authors: School of Information Science and Engineering, Central South University, 410083 Changsha, Hunan, China. Electronic address: xqpeng@csu.edu.cn.
authors: Xiaoqing
authors: X
authors: Peng
authors: School of Information Science and Engineering, Central South University, 410083 Changsha, Hunan, China. Electronic address: jxwang@csu.edu.cn.
authors: Jianxin
authors: J
authors: Wang
authors: Department of Electrical Engineering and Computer Sciences, University of Kansas, Lawrence, KS 66047-7621, USA. Electronic address: jhuan@ittc.ku.edu.
authors: Jun
authors: J
authors: Huan
authors: Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada. Electronic address: faw341@mail.usask.ca.
authors: Fang-Xiang
authors: FX
authors: Wu
mesh_terms: D008954:Models, Biological; D046912:Multiprotein Complexes / Q000235:genetics* / Q000378:metabolism*; D021381:Protein Transport
chemical_list: D046912:Multiprotein Complexes
keywords: Dense-spread centrality; Protein complex identification; Protein–protein Interactions Networks