Pharmacophore modeling, virtual screening and docking studies to identify novel HNMT inhibitors

Abstract

Histamine N-methyltransferase (HNMT) is the key enzyme responsible for inactivating histamine in bronchus, kidney, and the central nervous system of mammals. The inhibition of HNMT has therapeutically potential roles in neurodegenerative disease, memory and learning deficits and attention-deficit hyperactivity disorder. For better understanding the essential chemical features for HNMT inhibition and identifying novel inhibitors, a three-dimensional (3D) chemical-feature-based QSAR pharmacophore model for HNMT inhibitors was first time developed using Discovery Studio 2.5. The best model (Hypo1), which has the highest correlation coefficient (0.96), the highest cost difference (74.51) and the lowest RMS (0.73Å), consists two hydrophobic, one hydrophobic aromatic, one hydrogen bond acceptor and one hydrogen bond acceptor lipid. The reliability of Hypo1 was further validated using external test set, cost analysis, Fischer's randomization method and decoy data set. The validated Hypo1 was then used as a 3D search query for virtual screening to retrieve potential inhibitors from NCI database. Subsequently, the hit compounds were subjected to molecular docking studies with the crystal structure of HNMT. Finally, 10 hits were suggested as potential leads, which exhibited good estimated activities, favorable binding interactions, and high consensus scores. The obtained novel chemotype from this study may facilitate to discover a new scaffold for developing novel HNMT inhibitors.