Identifying Essential Proteins Based On Protein Interaction Networks

Essential proteins are those proteins which are indispensable to the viability and reproduction of an organism. They play an important role in cell activities. Identification of essential proteins is significant not only for the research of life science, but for practical purposes, such as diagnosis and treatment for diseases and drug design. With the development of high-throughput technology in the post-genomic era, a wealth of protein-protein interaction data have been produced. Consequently, identifying essential proteins based on protein interaction networks becomes a hot topic.This paper proceeds from network topology, explores the characteristics of protein interaction networks on the basis of analysis of topological characteristics of nodes, and designs efficient methods for identifying essential proteins. The main original works include:The current methods for identifying essential proteins based on topology, such as centrality measures, only indicate the features of nodes in the network but can not characterize the importance of edges. In view of this, we propose a novel method based on edge clustering coefficient, named as SoECC, which binds characteristics of edges and nodes effectively. The experimental results on yeast protein interaction network show that, both accuracy and efficiency of SoECC are universally higher than that of the six centrality measures. Besides, we find that essential proteins identified by SoECC show obvious cluster effect. It is a significant phenomenon which agreed with previous researches.The existing methods for identifying essential proteins mostly ignore the biological significance and function of proteins. Aiming at this drawback, we introduce protein complexes into our research and construct a new measure SoID for identifying essential proteins. The experimental results indicate that, comparing with the six conventional centrality measures, SoID has a certain advantage in sensitivity and specificity. The essential proteins detected by SoID are also universally more than that detected by the six centrality measures. Besides, SoID can effectively discover the low-connectivity essential proteins.In consideration of the fact that there exist a lot of false positives in currently available protein interaction datasets, we propose a new method for weighting the interactions and predict essential proteins using the six classic centrality measures in the weighted protein interaction network. The experimental results show that, the accuracy and efficiency of any centrality measure in weighted protein interaction network are universally higher than that in the corresponding unweighted protein interaction network. The accuracy of identification methods based on network topology is heavily affected by reliability of networks and reality of datasets. Weighting the protein interaction networks can improve the performance of identification of essential proteins.The several 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.