Abstract

Essential proteins are necessary for the survival and development of organism. Many computational approaches have been proposed for predicting essential proteins based on protein-protein interaction (PPI) network. In this paper, we propose a new centrality algorithm for identifying essential proteins, named CSC algorithm. CSC algorithm integrates topology character of PPI network and in-degree of proteins in complexes. We use CSC algorithm to identify the essential proteins in PPI network of Saccharomyces cerevisiae. The results show that the ratio of identified essential proteins on CSC algorithm is higher than other ten centrality methods: Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), Bottle Neck (BN), Local Average Connectivity-based method (LAC), Sum of ECC (SoECC) and PeC. Particularly, the identification accuracy of CSC algorithm is more than 40% over the six classic centrality measures (DC, BC, CC, SC, EC, IC).

Keywords: Centrality measures, clustering coefficient, essential proteins, protein-protein interaction, protein complex, topology.