Authors: Yun-Yuan Dong
Addresses: Basic Course Department, Military Economics Academy, Wuhan, Hubei, 430035, China
Abstract: Essential proteins are crucial for the survival of cellular life and they are also important for many applications, such as drug design and the defense against human pathogens. Existing experimental approaches to identify essential proteins are time-consuming and expensive. Therefore, many computational methods to predict essential proteins are employed to address this issue. However, most of those approaches focus on topological features of protein-protein interaction (PPI) networks and they often fail to consider the low-connectivity essential proteins. In this paper, we present three indexes to measure biological features of low-connectivity essential proteins. And we also propose a centrality measure interaction-complex-function centrality (ICFC) to predict low-connectivity essential proteins, which combines topological structure information of the PPI network with the distinguishable biological properties of essential proteins. The experimental results demonstrate that the predicted precision of ICFC outperforms ten exiting centrality measures. The improvements of ICFC over the ten exiting centrality measures are up to 1.08∼3.31 times.
Keywords: low-connectivity essential proteins; high-connectivity essential proteins; centrality measure; protein-protein interaction; PPI networks; bioinformatics.
International Journal of High Performance Systems Architecture, 2016 Vol.6 No.3, pp.171 - 178
Received: 04 Mar 2016
Accepted: 22 Jul 2016
Published online: 27 Nov 2016 *