Protein interaction network analysis--approach for potential drug target identification in Mycobacterium tuberculosis.
In host-parasite diseases like tuberculosis, non-homologous proteins (enzymes) as drug target are first preference. Most potent drug target can be identified among large number of non-homologous protein through protein interaction network analysis. In this study, the entire promising dimension has been explored for identification of potential drug target. A comparative metabolic pathway analysis of the host Homo sapiens and the pathogen M. tuberculosis H37Rv has been performed with three level of analysis. In first level, the unique metabolic pathways of M. tuberculosis have been identified through its comparative study with H. sapiens and identification of non-homologous proteins has been done through BLAST similarity search. In second level, choke-point analysis has been performed with identified non-homologous proteins of metabolic pathways. In third level, two type of analysis have been performed through protein interaction network. First analysis has been done to find out the most potential metabolic functional associations among all identified choke point proteins whereas second analysis has been performed to find out the functional association of high metabolic interacting proteins to pathogenesis causing proteins. Most interactive metabolic proteins which have highest number of functional association with pathogenesis causing proteins have been considered as potential drug target. A list of 18 potential drug targets has been proposed which are various stages of progress at the TBSGC and proposed drug targets are also studied for other pathogenic strains. As a case study, we have built a homology model of identified drug targets histidinol-phosphate aminotransferase (HisC1) using MODELLER software and various information have been generated through molecular dynamics which will be useful in wetlab structure determination. The generated model could be further explored for insilico docking studies with suitable inhibitors.
Kushwaha
Sandeep K
SK
Department of Bioinformatics, MANIT, Bhopal 462051, India. sandeepkushwaha09@gmail.com
Shakya
Madhvi
M
D000995:Antitubercular Agents / Q000494:pharmacology*; D001426:Bacterial Proteins / Q000737:chemistry / Q000378:metabolism*; D030562:Databases, Protein; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000187:drug effects; D056004:Molecular Dynamics Simulation; D009169:Mycobacterium tuberculosis / Q000187:drug effects* / Q000201:enzymology / Q000378:metabolism*; D011485:Protein Binding / Q000187:drug effects; D017433:Protein Structure, Secondary; D012984:Software
D000995:Antitubercular Agents; D001426:Bacterial Proteins