title: Rechecking the Centrality-Lethality Rule in the Scope of Protein Subcellular Localization Interaction Networks.
abstract: Essential proteins are indispensable for living organisms to maintain life activities and play important roles in the studies of pathology, synthetic biology, and drug design. Therefore, besides experiment methods, many computational methods are proposed to identify essential proteins. Based on the centrality-lethality rule, various centrality methods are employed to predict essential proteins in a Protein-protein Interaction Network (PIN). However, neglecting the temporal and spatial features of protein-protein interactions, the centrality scores calculated by centrality methods are not effective enough for measuring the essentiality of proteins in a PIN. Moreover, many methods, which overfit with the features of essential proteins for one species, may perform poor for other species. In this paper, we demonstrate that the centrality-lethality rule also exists in Protein Subcellular Localization Interaction Networks (PSLINs). To do this, a method based on Localization Specificity for Essential protein Detection (LSED), was proposed, which can be combined with any centrality method for calculating the improved centrality scores by taking into consideration PSLINs in which proteins play their roles. In this study, LSED was combined with eight centrality methods separately to calculate Localization-specific Centrality Scores (LCSs) for proteins based on the PSLINs of four species (Saccharomyces cerevisiae, Homo sapiens, Mus musculus and Drosophila melanogaster). Compared to the proteins with high centrality scores measured from the global PINs, more proteins with high LCSs measured from PSLINs are essential. It indicates that proteins with high LCSs measured from PSLINs are more likely to be essential and the performance of centrality methods can be improved by LSED. Furthermore, LSED provides a wide applicable prediction model to identify essential proteins for different species.
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha, Hunan, 410083, China.
authors.forename: Xiaoqing
authors.initials: X
authors.lastname: Peng
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha, Hunan, 410083, China.
authors.forename: Jianxin
authors.initials: J
authors.lastname: Wang
authors.affiliation: Department of Molecular Physiology & Biophysics, Baylor College of Medicine, Houston, Texas 77030, USA.
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
authors.affiliation: Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada.
authors.forename: Fang-Xiang
authors.initials: FX
authors.lastname: Wu
authors.affiliation: School of Information Science and Engineering, Central South University, Changsha, Hunan, 410083, China; Department of Computer Science, Georgia State University, Atlanta, GA 30302-4110, USA.
authors.forename: Yi
authors.initials: Y
authors.lastname: Pan
mesh_terms: D000818:Animals; D004331:Drosophila melanogaster; D006801:Humans; D051379:Mice; D025941:Protein Interaction Mapping; D060066:Protein Interaction Maps; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism
chemical_list: D029701:Saccharomyces cerevisiae Proteins