Insights into an alternative benzofuran binding mode and novel scaffolds of polyketide synthase 13 inhibitors
Small molecules targeting biosynthesis of mycolic acids in the tuberculosis causing bacterium carry high potential for anti-tuberculosis drug discovery. Hitherto, benzofuran containing molecules were identified to target the thioesterase domain of polyketide synthase 13 (Pks13), one of the crucial constituents of this pathway. Among these, TAM16 was also reported to be highly potent in vivo. Here we performed a multi-stage virtual screening methodology recruiting both ligand- and structure-based screening tools for identification of novel Pks13 inhibitors. The large ZINC15 database comprising more than 1 billion ligands was reduced to 21,277 ligands with benzofuran rings and similarity to TAM16. This collection was screened by docking and the 20 top scoring ligands were further analyzed by molecular dynamics simulations. Molecular mechanics/generalized Born surface area (MM-GBSA) based binding free energy predictions confirmed five molecules in the ZINC15 database lead to remarkable increases in the binding free energy of TAM16. Essentially, the most potent ligand ZINC840169872, which carries a distinct scaffold with a cyclohexane group fused to the furan ring, produced a twofold change in the ligand efficiency of TAM16. Further, assessments of the structure-based tools on five different Pks13-TAM complex structures suggested a high-level agreement with the experiments, substantiating the validity of our methodology for screening Pks13 inhibitors. Overall, these in silico insights into a low energy benzofuran-based scaffold and an alternative binding mode for the benzofuran ring unravel viable strategies to generate potent anti-tuberculosis drugs, accentuating the applications of virtual screening approaches for exploring large compound libraries that cannot be easily accessed by experimentation.
KeywordsTuberculosis Polyketide synthase 13 thioesterase domain Benzofuran ring Virtual screening ZINC15
The authors thankfully acknowledge the support from TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources) for the computational resources.
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