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Investigation of D(2) Receptor–Agonist Interactions Using a Combination of Pharmacophore and Receptor Homology Modeling

A combined modeling approach was used to identify structural factors that underlie the structure–activity relationships (SARs) of full dopamine D(2) receptor agonists and structurally similar inactive compounds. A 3D structural model of the dopamine D(2) receptor was constructed, with the agonist (−...

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Detalles Bibliográficos
Autores principales: Malo, Marcus, Brive, Lars, Luthman, Kristina, Svensson, Peder
Formato: Online Artículo Texto
Lenguaje:English
Publicado: WILEY-VCH Verlag 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3382189/
https://www.ncbi.nlm.nih.gov/pubmed/22315215
http://dx.doi.org/10.1002/cmdc.201100545
Descripción
Sumario:A combined modeling approach was used to identify structural factors that underlie the structure–activity relationships (SARs) of full dopamine D(2) receptor agonists and structurally similar inactive compounds. A 3D structural model of the dopamine D(2) receptor was constructed, with the agonist (−)-(R)-2-OH-NPA present in the binding site during the modeling procedure. The 3D model was evaluated and compared with our previously published D(2) agonist pharmacophore model. The comparison revealed an inconsistency between the projected hydrogen bonding feature (Ser-TM5) in the pharmacophore model and the TM5 region in the structure model. A new refined pharmacophore model was developed, guided by the shape of the binding site in the receptor model and with less emphasis on TM5 interactions. The combination of receptor and pharmacophore modeling also identified the importance of His393(6.55) for agonist binding. This convergent 3D pharmacophore and protein structure modeling strategy is considered to be general and can be highly useful in less well-characterized systems to explore ligand–receptor interactions. The strategy has the potential to identify weaknesses in the individual models and thereby provides an opportunity to improve the discriminating predictivity of both pharmacophore searches and structure-based virtual screens.