earticle

영어

Identification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development. Experimental methods for the identification of essential proteins are always costly, time-consuming, and laborious. High throughput technologies have resulted in a large number of protein-protein interaction data, which provided a stepping stone for predicting essential proteins using computational approaches. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. However, the network topology-based centrality measures are very sensitive to noise of network. In this paper, we propose a naive essential protein discovery method, named PMN, based on the integration of weighted interactome network and functional modules. The performance of PMN is validated based on the PPI network of Saccharomyces cerevisiae. Experimental results show that PMN significantly outperforms the classical centrality measures. The results also uncover relationship between the modularity and essentiality of proteins.