Structure-guided identification of histone deacetylase 11 inhibitors for targeted chemotherapy through long-timescale molecular dynamics simulation.

contextHistone deacetylase 11 (HDAC11), the sole member of class IV histone deacetylases, is an emerging epigenetic target in cancer therapy. In this study, an integrated computational approach involving ADMET screening, molecular docking, and molecular dynamics simulations was employed to identify potential HDAC11 inhibitors from 35 known HDAC inhibitors. Based on combined docking and pharmacokinetic analyses, five compounds were shortlisted for detailed evaluation. Mocetinostat exhibited the highest binding affinity toward HDAC11 (-8.70 kcal/mol) with acceptable pharmacokinetic properties (LogP: 2.25; TPSA: 99.11 Å

methodsThe HDAC11 protein structure was prepared and optimized along with ligand structures prior to docking. Pharmacokinetic properties and drug-likeness of all 35 selected HDAC inhibitors were predicted using the SwissADME web server, and toxicity profiles were assessed using the Protox-II server. Molecular docking was performed using PyRx with the AutoDock Vina scoring function. Protein-ligand interactions were analyzed using PyMOL and Discovery Studio visualizers. Molecular dynamics simulations were conducted using GROMACS with the CHARMM27 force field and TIP3P water model. A total of 3.5 µs of molecular dynamics simulations were performed, including a 500 ns production run and triplicate 100 ns validation runs for each complex. MD trajectory analyses, including RMSD, RMSF, radius of gyration, solvent-accessible surface area, hydrogen bond analysis, and kernel density estimation, were used to evaluate structural stability and interaction dynamics.