Computational discovery of potent drugs to improve the treatment of pyrazinamide resistant Mycobacterium tuberculosis mutants.

Emergence of multi-drug resistance tuberculosis has become a serious health problem globally. Accumulation of mutations in the drug target led to the development of multi-drug resistant mycobacterial strains that have made most of the conventional drugs ineffective. Hence, there is desperate need for the development of new therapeutic strategies. Here, we focused on the analysis of mutations in Mycobacterium tuberculosis (Mtb) PncA (pyrazinamidase) that is responsible for resistance against first-line anti-tuberculosis pyrazinamide (PZA) drug. First, PZA and its two isoforms were analyzed for their binding affinity toward ligand binding cavity of Mtb wild-type and mutant PncA proteins. The observations suggested that some drug resistant mutations cause strong binding of PncA with the active form of PZA and impair its release, which is required to inhibit the growth of Mtb. To improve the treatment of PZA resistant Mtb, high throughput virtual drug screening was performed to identify potent drug molecules from a library of compounds derived from ChEMBL database. From this library, we predicted a lead molecule (terta-butyl(2S,4S)-4-amino-2-cyclopropyl-6-(trifluoromethyl)-3,4-dihydro-2H-quinoline-1-carboxylate) to be more effective against PZA resistant Mtb strains in comparison to PZA. The lead molecule showed better drug-like properties such as high affinity and atomic interactions with wild-type and drug-resistant mutations in Mtb PncA proteins. Further, molecular dynamic simulation studies showed that this lead molecule has better conformational stability and compatibility with drug-resistant PncA proteins in comparison to PZA drug. We hypothesized that the predicted lead compound could be more effective, and thus may improve the treatment of PZA resistant tuberculosis.