Prediction of essential proteins based on subcellular localization and gene expression correlation

BMC Bioinformatics

Research

Xiaohua Hu¹  Qing Ping¹  Xiwei Tang²  Wei Wu³  Yetian Fan⁴ 

[1] College of Computing and Informatics, Drexel University, 19104, Philadelphia, USA;Department of Information Science and Engineering, Hunan First Normal University, 410205, Changsha, China;College of Computer, National University of Defense Technology, 410073, Changsha, China;School of Mathematical Sciences, Dalian University of Technology, 116023, Dalian, China;School of Mathematics, Liaoning University, 110036, Shenyang, China;

关键词: Essential proteins;    Subcellular localization information;    Modified PageRank algorithm;    Protein-protein interaction networks;   

DOI  :  10.1186/s12859-017-1876-5

来源: Springer

【 摘 要 】

BackgroundEssential proteins are indispensable to the survival and development process of living organisms. To understand the functional mechanisms of essential proteins, which can be applied to the analysis of disease and design of drugs, it is important to identify essential proteins from a set of proteins first. As traditional experimental methods designed to test out essential proteins are usually expensive and laborious, computational methods, which utilize biological and topological features of proteins, have attracted more attention in recent years. Protein-protein interaction networks, together with other biological data, have been explored to improve the performance of essential protein prediction.ResultsThe proposed method SCP is evaluated on Saccharomyces cerevisiae datasets and compared with five other methods. The results show that our method SCP outperforms the other five methods in terms of accuracy of essential protein prediction.ConclusionsIn this paper, we propose a novel algorithm named SCP, which combines the ranking by a modified PageRank algorithm based on subcellular compartments information, with the ranking by Pearson correlation coefficient (PCC) calculated from gene expression data. Experiments show that subcellular localization information is promising in boosting essential protein prediction.

【 授权许可】

CC BY   
© The Author(s) 2017

【 图 表 】

Fig. 5

Fig. 14

Fig. 11

12936_2017_1882_Article_IEq9.gif

Fig. 7

Fig. 4