Ligand and Structure-Based Pharmacophore Modeling for the Discovery of Potential Human HNMT Inhibitors

Pavadai      Elumalai; Hsuan-Liang      Liu; Zheng-Li      Zhou; Jian-Hua      Zhao; Wilson      Chen; Chih-Kuang      Chuang; Wei-Bor      Tsai; Yih      Ho

Abstract

The inhibition of Histamine N-methyltransferase (HNMT) has been recently shown to play potential roles in the treatment of neurodegenerative diseases, allergic vasoconstriction and anaphylactic manifestation. For designing and discovering new potential human HNMT inhibitors, the ligand (Hypo1) and structure-based (SB_Hypo1) pharmacophore models were developed based on the most active inhibitors and the highest resolution crystal structure of HNMT, respectively. After validating the reliability of both models with decoy dataset, they were separately used as 3D-query for virtual screening to retrieve potential hits from Maybridge and Chembridge databases. Subsequently, the hit compounds were subjected to filter by applying the ADMET, molecular docking and consensus score. Finally, 10 hits (five compounds from each model) were suggested as potential leads based on the structural diversity, good fit value, favorable binding interactions and high docking consensus score. The obtained novel hits from this study may facilitate to identify and optimize new leads for HNMT inhibition.

Keywords: Consensus scoring, Histamine N-methyltransferase, HipHop, Molecular docking, Structurebased, Virtual screening, Ligand, pharmacophore modeling, Histamine, Hypo1, HNMT, 3D-query, Chembridge databases, ADMET, GPCR, HDC