Mycobacterium tuberculosis (Mtb) is the causative agent of Tuberculosis and accounts for approximately two million deaths and nine million new cases per year. Despite a variety of anti-tuberculous drugs, treatment options are often limited, due to the emergence of multiple and extensively drug resistant TB strains. To combat these drug-resistant forms of bacteria new methods are needed to identify promising TB drug targets. We have developed a computational pipeline that allowed the successful identification of nine Mtb putative drug targets. In addition to this, we exploited these targets using molecular modeling, simulation and docking studies to identify potential lead compounds for treatment of resistant strains of Mycobacterium tuberculosis. The approaches employed in this study yielded compounds that were able to inhibit the growth of Mtb in vitro. This study provides proof of concept for the use of structural bioinformatic techniques in drug discovery.
Research objectives of our group:
1) Our group is involved in understanding structure function relationship by elucidating the 3D structures of Mtb proteins involved in essential metabolic pathways associated with mycobacterium tuberculosis survival.
2) We use a combination of computational chemistry tools in rationale drug design to identify potential lead compounds for further investigation.
3) Putative drug target candidates and drugs have been identified and further exploited in whole cell assays.
4) Methods used in our lab assist us in understanding the physiology of Mtb pathogenesis and the mode of action between several novel drugs and their drug targets.