Predicting essential proteins based on subcellular localization, orthology and PPI networks. Issue 8 (August 2016)

• Record Type: Journal Article
• Authors: Li, Gaoshi; Li, Min; Wang, Jianxin; Wu, Jingli; Wu, Fang-Xiang; Pan, Yi
• Is Part Of: BMC bioinformatics. Volume 17:Issue 8(2016)
• Page Start: 571
• Page End: 581
• Publication Date: 2016-08
• Subjects: Essential proteins -- Protein-protein interaction network -- Subcellular localization -- Orthology; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-016-1115-5
• Languages: English
• ISSNs: 1471-2105

Abstract

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 ofs ubcellular localization, o rthologous proteins and PPIn etworks, 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.