Abstract
Essential proteins are crucial to living cells. Identification of essential proteins from protein-protein interaction (PPI) networks can be applied to pathway analysis and function prediction, furthermore, it can contribute to disease diagnosis and drug design. There have been some experimental and computational methods designed to identify essential proteins, however, the prediction precision remains to be improved. In this paper, we propose a new method for identifying essential proteins based on Participation degree of a protein in protein Complexes and Subgraph Density, named as PCSD. In order to test the performance of PCSD, four PPI datasets (DIP, Krogan, MIPS and Gavin) are used to conduct experiments. The experiment results have demonstrated that PCSD achieves a better performance for predicting essential proteins compared with some competing methods including DC, SC, EC, IC, LAC, NC, WDC, PeC, UDoNC, and compared with the most recent method LBCC, PCSD can correctly predict more essential proteins from certain numbers of top ranked proteins on the DIP dataset, which indicates that PCSD is very effective in discovering essential proteins in most case.
Highlights
Proteins are the products of genes, and they are the vital material and functional units for living organisms
In order to evaluate the performance of proposed method PCSD, we conduct a group of experiments on Saccharomyces cerevisiae protein data
Essential proteins play a crucial role in the viability and reproduction of living organisms, and the identification of essential proteins contribute to promoting the process of disease study and drug design
Summary
Proteins are the products of genes, and they are the vital material and functional units for living organisms. Essential proteins are those proteins which are indispensable for organisms to normally grow and multiply. Accurately identifying essential proteins makes important contribution to understanding the key biological processes of an organism at molecular level, which is beneficial to guide disease diagnosis and drug design. Both experimental and computational approaches have been exploited to detect essential proteins. The experimental approaches for identifying essential proteins, such as single gene knockout [1], RNA interference [2] and conditional knockout [3], all of which are time consuming and expensive.
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