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Methods. 2015 Jul 15;83:51-62. doi: 10.1016/j.ymeth.2015.04.013. Epub 2015 Apr 16.

Essential protein identification based on essential protein-protein interaction prediction by Integrated Edge Weights.

Author information

1
Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun 130012, China; Department of Computer Science, University of Missouri, Columbia, MO, United States.
2
Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun 130012, China; Department of Information Engineering and Computer Science, University of Trento, Povo, Italy. Electronic address: wy6868@hotmail.com.
3
School of Natural and Computing Sciences, University of Aberdeen, Aberdeen, UK.
4
Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun 130012, China.
5
Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, Changchun 130012, China; Department of Computer Science and Technology, Zhuhai College of Jilin University, Zhuhai 519041, China. Electronic address: ycliang@jlu.edu.cn.
6
Department of Information Engineering and Computer Science, University of Trento, Povo, Italy. Electronic address: blanzier@disi.unitn.it.

Abstract

Essential proteins play a crucial role in cellular survival and development process. Experimentally, essential proteins are identified by gene knockouts or RNA interference, which are expensive and often fatal to the target organisms. Regarding this, an alternative yet important approach to essential protein identification is through computational prediction. Existing computational methods predict essential proteins based on their relative densities in a protein-protein interaction (PPI) network. Degree, betweenness, and other appropriate criteria are often used to measure the relative density. However, no matter what criterion is used, a protein is actually ordered by the attributes of this protein per se. In this research, we presented a novel computational method, Integrated Edge Weights (IEW), to first rank protein-protein interactions by integrating their edge weights, and then identified sub PPI networks consisting of those highly-ranked edges, and finally regarded the nodes in these sub networks as essential proteins. We evaluated IEW on three model organisms: Saccharomyces cerevisiae (S. cerevisiae), Escherichia coli (E. coli), and Caenorhabditis elegans (C. elegans). The experimental results showed that IEW achieved better performance than the state-of-the-art methods in terms of precision-recall and Jackknife measures. We had also demonstrated that IEW is a robust and effective method, which can retrieve biologically significant modules by its highly-ranked protein-protein interactions for S. cerevisiae, E. coli, and C. elegans. We believe that, with sufficient data provided, IEW can be used to any other organisms' essential protein identification. A website about IEW can be accessed from http://digbio.missouri.edu/IEW/index.html.

KEYWORDS:

Essential protein; Essential protein–protein interaction; Integrated Edge Weights

PMID:
25892709
DOI:
10.1016/j.ymeth.2015.04.013
[Indexed for MEDLINE]

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