Identification of novel inhibitors for mycobacterial polyketide synthase 13 via in silico drug screening assisted by the parallel compound screening with genetic algorithm-based programs.

Identifying small compounds capable of inhibiting Mycobacterium tuberculosis polyketide synthase 13 (Pks13), in charge of final step of mycolic acid biosynthesis, could lead to the development of a novel antituberculosis drug. This study screened for lead compounds capable of targeting M. tuberculosis Pks13 from a chemical library comprising 154,118 compounds through multiple in silico docking simulations. The parallel compound screening (PCS), conducted via two genetic algorithm-based programs was applied in the screening strategy. Out of seven experimentally validated compounds, four compounds showed inhibitory effects on the growth of the model mycobacteria (Mycobacterium smegmatis). Subsequent docking simulation of analogs of the promising leads with the assistance of PCS resulted in the identification of three additional compounds with potent antimycobacterial effects (compounds A1, A2, and A5). Further, molecular dynamics simulation predicted stable interaction between M. tuberculosis Pks13 active site and compound A2, which showed potent antimycobacterial activity comparable to that of isoniazid. The present study demonstrated the efficacy of in silico structure-based drug screening through PCS in antituberculosis drug discovery.

Computer-assisted screening of mycobacterial growth inhibitors: Exclusion of frequent hitters with the assistance of the multiple target screening method.

Background: The emergence of frequent hitters (FHs) remains a challenge in drug discovery. We have previously used in silico structure-based drug screening (SBDS) to identify antimycobacterial candidates. However, excluding FHs has not been integrated into the SBDS system. Methods: A dataset comprising 15,000 docking score (protein-compound affinity matrix) was constructed by multiple target screening (MTS): DOCK-GOLD two-step docking simulations with 154,118 compounds versus the 30 target proteins essential for mycobacterial survival. After extraction of 141 compounds from the protein-compound affinity matrix, compounds determined to be FHs or false positives were excluded. Antimycobacterial properties of the top nine compounds selected through SBDS were experimentally evaluated. Results: Nine compounds designated KS1-KS9 were selected for experimental evaluation. Among the selected compounds, KS3, identified as adenosylhomocysteinase inhibitor, showed a potent inhibitory effect on antimycobacterial growth (inhibitory concentration [IC]50 = 1.2 M). However, the compound also showed potent cytotoxicity. Conclusion: The MTS method is applicable in SBDS for the identification of enzyme-specific inhibitors.