Discovery of Monoacylglycerol Lipase (MAGL) Inhibitors Based on a Pharmacophore-Guided Virtual Screening Study.
MAGL
hit identification
pharmacophore model
virtual screening
Journal
Molecules (Basel, Switzerland)
ISSN: 1420-3049
Titre abrégé: Molecules
Pays: Switzerland
ID NLM: 100964009
Informations de publication
Date de publication:
26 Dec 2020
26 Dec 2020
Historique:
received:
07
12
2020
revised:
24
12
2020
accepted:
25
12
2020
entrez:
30
12
2020
pubmed:
31
12
2020
medline:
11
9
2021
Statut:
epublish
Résumé
Monoacylglycerol lipase (MAGL) is an important enzyme of the endocannabinoid system that catalyzes the degradation of the major endocannabinoid 2-arachidonoylglycerol (2-AG). MAGL is associated with pathological conditions such as pain, inflammation and neurodegenerative diseases like Parkinson's and Alzheimer's disease. Furthermore, elevated levels of MAGL have been found in aggressive breast, ovarian and melanoma cancer cells. Due to its different potential therapeutic implications, MAGL is considered as a promising target for drug design and the discovery of novel small-molecule MAGL inhibitors is of great interest in the medicinal chemistry field. In this context, we developed a pharmacophore-based virtual screening protocol combined with molecular docking and molecular dynamics simulations, which showed a final hit rate of 50% validating the reliability of the in silico workflow and led to the identification of two promising and structurally different reversible MAGL inhibitors, VS1 and VS2. These ligands represent a valuable starting point for structure-based hit-optimization studies aimed at identifying new potent MAGL inhibitors.
Identifiants
pubmed: 33375358
pii: molecules26010078
doi: 10.3390/molecules26010078
pmc: PMC7794939
pii:
doi:
Substances chimiques
Enzyme Inhibitors
0
Monoacylglycerol Lipases
EC 3.1.1.23
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : MIUR
ID : PRIN 2017, project 2017SA5837
Organisme : Italian Ministry of Health
ID : Ricerca Finalizzata 2016 - NET-2016-02363765
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