Identification of polyketide synthase 13 inhibitor: Pharmacophore-based virtual screening and molecular dynamics simulation

Polyketide synthase 13 (Pks13) is one of prominent targets to treat Mycobacterium tuberculosis (Mtb). In the presentstudy, pharmacophore features for Pks13, including two hydrogen bond donors, one hydrogen bond acceptor, and onehydrophobic feature, were built using a novel Pks13 inhibitor, TAM16. The pharmacophore features were then usedto perform virtual screening on ZINC database to identify small molecules of Pks13 inhibitors. The obtained virtualhits of 107 small molecules were subjected to molecular docking studies employing iDock software to reveal theirbinding orientation to Pks13. Furthermore, four best hits, each bound to Pks13, were submitted to 40-ns moleculardynamics simulation to explore their conformational changes throughout simulation. The result showed that all hitcompounds, i.e., Lig79/ZINC09281113, Lig94/ZINC09584070, Lig95/ZINC09209668, and Lig97/ZINC09216165,have better stabilities than that of TAM16 as indicated by their lower values of root-mean-square-deviation and rootmean-square-fluctuation. In a similar way, prediction of binding free energy using molecular mechanics Poisson–Boltzmann Surface Area method showed that all hit compounds have lower binding free energies than that of TAM16,indicating their potential as novel compounds of Pks13 inhibitors.

3D-QSAR, molecular docking and dynamics simulation of difluorophenol pyridine derivatives as RSK2 inhibitor

Given the increasing role of P90 Ribosomal S6 Kinase 2 (RSK2) as an anticancer drug target, we performed3D-Quantitative structure–activity relationship, including comparative molecular field analysis (CoMFA) andcomparative molecular similarity indices analysis (CoMSIA) on difluorophenol pyridine derivatives as the inhibitorof RSK2. CoMFA model with q2 of 0.597 and R2 of 0.993, while CoMSIA model with q2 of 0.563 and R2 of 0.993,were obtained. The predictive ability of both models was assured using a test set compound with R2pred values of 0.996each. Using the validated models, novel compound was proposed and its interaction with RSK2 was investigatedemploying molecular docking and molecular dynamics simulation of 50 ns. Furthermore, molecular-mechanicsPoisson–Boltzmann surface area calculation was performed. The result showed that the newly designed compoundhas a comparable binding free energy with the known RSK2 inhibitor, indicating its potential as a new RSK2 inhibitor.

3D-QSAR, molecular docking, and dynamics simulation of quinazoline–phosphoramidate mustard conjugates as EGFR inhibitor

To develop novel and more potent quinazoline–phosphoramidate mustard conjugates as epidermal growth factorreceptor (EGFR) inhibitor, three-dimensional quantitative structure-activity relationship [comparative molecularfield analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA)] combined with moleculardocking were performed. A series of 13 compounds in the training set gave q2 values of 0.577 and 0.537, as well as r2values of 0.926 and 0.921 for CoMFA and CoMSIA models, respectively. The contour maps that were produced by theCoMFA and CoMSIA models revealed that steric, electrostatic, and hydrophobic fields were crucial in the inhibitoryactivity of quinazoline–phosphoramidate derivatives. Based on the CoMFA and CoMSIA models, several novel EGFRinhibitors were designed, which established crucial interactions at the ligand binding domain of EGFR. Nearly, 100ns MD simulation indicated the stability of the designed compounds at 100 ns, while molecular mechanics-PoissonBoltzmann surface area calculation showed that the designed compound had a higher affinity than that of the parentcompound.