A two-stage computational approach to predict novel ligands for a chemosensory receptor.
APF, Atomic property field
Amber, Assisted model Building with Energy Refinement
Atomic property field
Binding free energy calculation
CSF, Cerebrospinal fluid
ECL, Extracellular loop
GPCR, G protein coupled receptor
HCMV, Human cytomegalovirus
HMDB, Human metabolome database
Hydrophobicity correspondence
LBVS, Ligand based virtual screening
LC, Lung carcinoids
MD, Molecular dynamics
MMGBSA, Molecular mechanics generalized born surface area
MMPBSA, Molecular mechanics Poisson–Boltzmann surface area
Molecular dynamics
NAFLD, Non-alcoholic fatty liver disease
NASH, Nonalcoholic steatohepatitis
OR, olfactory receptor
OR1A2
Olfactory receptor
PMEMD, Particle-Mesh Ewald Molecular Dynamics
POPC, 1-palmitoyl-2-oleoyl-sn-glycero- 3-phosphatidylcholine
RMSD, Root mean square deviation
RMSF, Root mean square fluctuation
SBVS, Structure based virtual screening
SSD, Sum of squared difference
TM, Transmembrane
Virtual ligand screening
Journal
Current research in structural biology
ISSN: 2665-928X
Titre abrégé: Curr Res Struct Biol
Pays: Netherlands
ID NLM: 101767537
Informations de publication
Date de publication:
2020
2020
Historique:
received:
17
03
2020
revised:
29
09
2020
accepted:
03
10
2020
entrez:
8
7
2021
pubmed:
9
7
2021
medline:
9
7
2021
Statut:
epublish
Résumé
Olfactory receptor (OR) 1A2 is the member of largest superfamily of G protein-coupled receptors (GPCRs). OR1A2 is an ectopically expressed receptor with only 13 known ligands, implicated in reducing hepatocellular carcinoma progression, with enormous therapeutic potential. We have developed a two-stage screening approach to identify novel putative ligands of OR1A2. We first used a pharmacophore model based on atomic property field (APF) to virtually screen a library of 5942 human metabolites. We then carried out structure-based virtual screening (SBVS) for predicting the potential agonists, based on a 3D homology model of OR1A2. This model was developed using a biophysical approach for template selection, based on multiple parameters including hydrophobicity correspondence, applied to the complete set of available GPCR structures to pick the most appropriate template. Finally, the membrane-embedded 3D model was refined by molecular dynamics (MD) simulations in both the
Identifiants
pubmed: 34235481
doi: 10.1016/j.crstbi.2020.10.001
pii: S2665-928X(20)30021-0
pmc: PMC8244491
doi:
Types de publication
Journal Article
Langues
eng
Pagination
213-221Informations de copyright
© 2020 The Authors.
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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