A Topology Potential-based Method For Identifying Essential Proteins

Abstract:Essential genes and their products (essential proteins) are the most important materials in a variety of life process. The research of essential proteins can help us lay a solid foundation on the basic requirements to sustain a life form, rational drug design and the identification of disease genes.This paper proceeds from protein-protein interaction(PPI) network and protein complex, explores the topology potential characteristics of protein interaction networks on the basis of analysis of inherent attribute and function of essential proteins, and designs efficient methods for identifying essential proteins. The main original works and innovation points include:We propose a new method to identify essential proteins based on topology potential from a completely new perspective of network topology. To our knowledge it is the first time that topology potential is used to identify essential proteins from protein-protein interaction network. The basic idea is that each protein in the network can be viewed as a material particle which creates a potential field around itself and the interaction of all proteins forms a topological field over the network. By defining and computing the value of each protein’s topology potential, we can obtain a more precise ranking which reflects the importance of proteins from the protein-protein interaction network. The experiment results show that topology potential outperforms traditional topology measures:Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality(SC), Eigenvector Centrality(EC), Information Centrality(IC), and Sum of ECC (NC) for predicting essential proteins. In addition, these centrality measures are improved on their performance for identifying essential proteins in biological network when controlled by topology potential.We propose a new method, United complex Centrality(UC), to identify essential proteins by integrating protein complexes information and topological features of PPI network. We analyze the relationship between protein complexes and essential proteins, and find that proteins appeared in multiple complexes are more inclined to be essential proteins compared to the proteins only appeared in a single complex. The experiment results show that protein complex information can help improve the precision to identify essential proteins. Our method is obviously better than traditional centrality methods(DC, IC, EC, SC, BC, CC, NC) for identifying essential proteins. Specially, even compared with HC which also use protein complex information, it still has a great advantage.Further, when UC is used as the inherent attribute of proteins based on topology potential method (TP-UC), its result is still better than those topology potential methods (TP-*) which centrality methods are used as the inherent attribute of proteins.From the above, two methods proposed in this paper improve the accuracy of identification of essential proteins effectively. Moreover, by means of employing various information, this paper provides a new idea for identification of essential proteins.