We have developed TargetMine, a data warehouse that can integrate in-house experimental data into public databases and enables efficient target prioritization. An objective computational method for selecting relevant genes from a large list of initial candidates has been developed and applied to the analysis of new experimental data. Our prediction about hepatitis C virus (HCV) pathogenesis has been confirmed experimentally and further experimental validation is under way.
For a better understanding of how specific target proteins work, we have developed a range of software programmes, including the FUGUE software for protein structure prediction, PSIVER for predicting protein-protein interaction sites and other methods for predicting detailed protein functions and for predicting protein-DNA interaction sites from sequence information alone. Using these methods, we are anlalyzing potential targets relevant to chronic inflammatory diseases, cancer, infectious diseases and other disease areas, in collaboration with experimental groups.
The Industrial Technology Research Grant Program in 2007 from New Energy and Industrial Technology Development Organization (NEDO) of Japan (2007-2011) “Target discovery through network-based function prediction” (Principal Investigator: Kenji Mizuguchi)
Chen YA., Tripathi LP., Mizuguchi K., TargetMine, an integrated data warehouse for candidate gene prioritisation and target discovery, 2011, PLoS One
Tripathi LP., Kataoka C., Taguwa S., Moriishi K., Mori Y., Matsuura Y., Mizuguchi K., Network based analysis of hepatitis C virus Core and NS4B protein interactions, Molecular BioSystems 6(12): 2539-2553, 2010 PubMed
Takayuki Abe, Kohji Moriishi and Yoshiharu Matsuura (Osaka University)
Teppei Nishikawa and Kazuyuki Yoshizaki (Osaka University)