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

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)17-29
Number of pages13
JournalLetters in Drug Design and Discovery
Volume9
Issue number1
DOIs
Publication statusPublished - Jan 2012

Keywords

  • Consensus scoring
  • HipHop
  • Histamine N-methyltransferase
  • Molecular docking
  • Pharmacophore model
  • Structure-based
  • Virtual screening

ASJC Scopus subject areas

  • Pharmaceutical Science
  • Drug Discovery
  • Molecular Medicine

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