title: Predicting essential proteins based on subcellular localization, orthology and PPI networks.
abstract: BACKGROUND
  Essential proteins play an indispensable role in the cellular survival and development. There have been a series of biological experimental methods for finding essential proteins; however they are time-consuming, expensive and inefficient. In order to overcome the shortcomings of biological experimental methods, many computational methods have been proposed to predict essential proteins. The computational methods can be roughly divided into two categories, the topology-based methods and the sequence-based ones. The former use the topological features of protein-protein interaction (PPI) networks while the latter use the sequence features of proteins to predict essential proteins. Nevertheless, it is still challenging to improve the prediction accuracy of the computational methods.
  
  
  RESULTS
  Comparing with nonessential proteins, essential proteins appear more frequently in certain subcellular locations and their evolution more conservative. By integrating the information of subcellular localization, orthologous proteins and PPI networks, we propose a novel essential protein prediction method, named SON, in this study. The experimental results on S.cerevisiae data show that the prediction accuracy of SON clearly exceeds that of nine competing methods: DC, BC, IC, CC, SC, EC, NC, PeC and ION.
  
  
  CONCLUSIONS
  We demonstrate that, by integrating the information of subcellular localization, orthologous proteins with PPI networks, the accuracy of predicting essential proteins can be improved. Our proposed method SON is effective for predicting essential proteins.
authors: School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, People's Republic of China.|Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, 541004, Guangxi, People's Republic of China.
authors: Gaoshi
authors: G
authors: Li
authors: School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, People's Republic of China. limin@mail.csu.edu.cn.
authors: Min
authors: M
authors: Li
authors: School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, People's Republic of China. jxwang@mail.csu.edu.cn.
authors: Jianxin
authors: J
authors: Wang
authors: Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, 541004, Guangxi, People's Republic of China.
authors: Jingli
authors: J
authors: Wu
authors: School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, People's Republic of China.|Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, S7N 5A9, SK, Canada.
authors: Fang-Xiang
authors: FX
authors: Wu
authors: School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, People's Republic of China.|Department of Computer Science, Georgia State University, Atlanta, 30302-4110, GA, USA.
authors: Yi
authors: Y
authors: Pan
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D025941:Protein Interaction Mapping / Q000379:methods*; D060066:Protein Interaction Maps; D011506:Proteins / Q000378:metabolism*; D017386:Sequence Homology, Amino Acid; D013347:Subcellular Fractions / Q000378:metabolism*
chemical_list: D011506:Proteins
keywords: Essential proteins; Orthology; Protein-protein interaction network; Subcellular localization