Identification of Essential Proteins by Using Complexes and Biological Information on Dynamic PPI Network

Identifying essential proteins is not only important for understanding cellular activity, but also for detecting human disease genes. A series of centrality measures have been proposed to identify essential proteins based on the protein-protein interaction(PPI) network. However, most of the essential proteins identifying algorithms are based on static PPI networks which cannot reflect the dynamic and transient nature of protein interactions. Meanwhile, studies have shown that essentiality is a product of the protein complex rather than the individual protein. Therefore, we proposed a new method to identify essential proteins method by using protein complexes and biological information on the dynamic protein-protein interaction network(IEP-PCD). Experimental results on four Saccharomyces cerevisiae datasets have shown that IEP-PCD can not only improve prediction accuracy but also outperform the other existing prediction methods, including the most commonly-used centrality measures (DC, SC, BC, NC), topology-based methods (LAC) and biological data integrating methods (PeC, CoEWC, and LBCC).