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

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.