Identification of Essential Proteins by Using Complexes and Interaction Network

Essential proteins are indispensable in maintaining the cellular life. Identification of essential proteins can provide basis for drug target design, disease treatment as well as synthetic biology minimal genome. However, it is still time-consuming and expensive to identify essential protein based on experimental approaches. With the development of high-throughput experimental techniques in the post-genome era, a large number of PPI data and gene expression data can be obtained, which provide an unprecedented opportunity to study essential proteins at the network level. So far, many network topological methods have been proposed to identify the essential proteins. In this paper, we propose a new method, United complex Centrality(UC), to identify essential proteins by integrating protein complexes information and topological features of PPI network. By analysis of the relationship between protein complexes and essential proteins, we find that proteins appeared in multiple complexes are more inclined to be essential compared to these only appeared in a single complex. The experiment results show that protein complex information can help identify the essential proteins more accurate. Our method UC is obviously better than traditional centrality methods(DC, IC, EC, SC, BC, CC, NC) for identifying essential proteins. In addition, even compared with Harmonic Centricity which also used protein complexes information, it still has a great advantage.