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BioMed Research International
Volume 2017 (2017), Article ID 1289259, 13 pages
https://doi.org/10.1155/2017/1289259
Research Article

Drug Target Protein-Protein Interaction Networks: A Systematic Perspective

1Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
2Department of Bioinformatics, Second Military Medical University, Shanghai, China

Correspondence should be addressed to Tengjiao Wang; nc.ude.umms@gnawjt

Received 15 January 2017; Revised 9 March 2017; Accepted 10 May 2017; Published 11 June 2017

Academic Editor: Yudong Cai

Copyright © 2017 Yanghe Feng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The identification and validation of drug targets are crucial in biomedical research and many studies have been conducted on analyzing drug target features for getting a better understanding on principles of their mechanisms. But most of them are based on either strong biological hypotheses or the chemical and physical properties of those targets separately. In this paper, we investigated three main ways to understand the functional biomolecules based on the topological features of drug targets. There are no significant differences between targets and common proteins in the protein-protein interactions network, indicating the drug targets are neither hub proteins which are dominant nor the bridge proteins. According to some special topological structures of the drug targets, there are significant differences between known targets and other proteins. Furthermore, the drug targets mainly belong to three typical communities based on their modularity. These topological features are helpful to understand how the drug targets work in the PPI network. Particularly, it is an alternative way to predict potential targets or extract nontargets to test a new drug target efficiently and economically. By this way, a drug target’s homologue set containing 102 potential target proteins is predicted in the paper.