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Identification of drug-target modules in the human protein–protein interaction network


The human protein–protein interaction network (PIN) has a modular structure, in which interactions between proteins are much denser within the same module than between different modules. Proteins within the same module tend to have closely related functions with each other. Therefore, if a module is composed of relatively small number of proteins (e.g., modules composed of less than 5 % of all proteins in the PIN) and significantly enriched with target proteins for a disease, proteins and interactions in the module are likely to play an important role in disease mechanisms and may be potential candidate targets for the disease. We defined such modules as “drug-target modules.” In order to find drug-target modules in the human PIN, we developed a novel computational approach that decomposes the network into small modules and maps drug targets on the modules. The approach successfully identified drug-target modules that contain more than 40 % of targets of cancer molecular-targeted drugs (e.g., kinase inhibitors and monoclonal antibodies). Furthermore, proteins in the modules are significantly involved in cancer-related signaling pathways (e.g., vascular endothelial growth factor signaling pathway). These results indicate that the listing of proteins and interactions in the drug-target modules may help us to search efficiently for drug action mechanisms and novel candidate targets for cancerous diseases. It may be pertinent to note here that, among proteins in the drug-target modules, proteins with a small number of interactions may be potential candidate anti-cancer targets with less severe side effects.

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  1. Barabási AL, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12:56–68

    Article  Google Scholar 

  2. Hase T, Niimura Y (2012) Protein– Protein interaction networks: structures, evolution, and application to drug design. In: Cai W (ed) Protein–Protein interactions—computational and experimental tools, InTech, pp 405-426

  3. Jeong H et al (2001) Lethality and centrality in protein networks. Nature 411:41–42

    Article  Google Scholar 

  4. Hase T et al (2008) Non-uniform survival rate of heterodimerization links in the evolution of the yeast protein–protein interaction network. PLoS One 3:e1667

    Article  Google Scholar 

  5. Hase T, Niimura Y, Tanaka H (2010) Difference in gene duplicability may explain the difference in overall structure of protein–protein interaction networks among eukaryotes. BMC Evol Biol 10:358

    Article  Google Scholar 

  6. Hase T et al (2009) Structure of protein interaction networks and their implications on drug design. PLoS Comput Biol 5:e1000550

    Article  MathSciNet  Google Scholar 

  7. Yao L, Rzhetsky A (2008) Quantitative systems-level determinants of human genestargeted by successful drugs. Genome Res 18:213–216

    Article  Google Scholar 

  8. Wang Z, Zhang J (2007) In search of the biological significance of modular structures in protein networks. PLoS Comput Biol 3:e107

    Article  Google Scholar 

  9. Guimerá R, Amaral LAN (2005) Functional cartography of complex metabolic networks. Nature 433:895–900

    Article  Google Scholar 

  10. Ciriello G et al (2013) Emerging landscape of oncogenic signatures across human cancers. Nat Genet 45:1127–1133

    Article  Google Scholar 

  11. Stark C et al (2006) Biogrid: a general repository for interaction datasets. Nucleic Acids Res 34:D535–D539

    Article  Google Scholar 

  12. Prasad TSK et al (2009) Human Protein Reference Database - 2009 Update. Nucleic Acids Res 37:D767–D772

    Article  Google Scholar 

  13. Knox C et al (2011) DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res 39:D1035–D1041

    Article  Google Scholar 

  14. Eran E (2009) GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinform 10:48

    Article  Google Scholar 

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The authors thank Matsuoka Y, Kang H, Fujita K, Lopes T, Kikuchi M, and Shoemaker J for their useful comments and discussion. This study was supported by JSPS KAKENHI Grant Number 25870197 [Grant-in-Aid for Young Scientists (B)] to TH. For analyses in this study, we used the super-computing resource that was provided by Human Genome Center, the Institute of Medical Science, the University of Tokyo (

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Correspondence to Takeshi Hase, Hiroaki Kitano or Hiroshi Tanaka.

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This work was presented in part at the 19th International Symposium on Artificial Life and Robotics, Beppu, Oita, January 22–24, 2014.

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Hase, T., Kikuchi, K., Ghosh, S. et al. Identification of drug-target modules in the human protein–protein interaction network. Artif Life Robotics 19, 406–413 (2014).

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  • Protein–Protein interaction
  • Module
  • Drug target
  • Cancer
  • Network analysis