Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/107170
Title: A Complete Assessment of Dopamine Receptor- Ligand Interactions through Computational Methods
Authors: Bueschbell, Beatriz 
Barreto, Carlos A. V. 
Preto, Antonio J. 
Schiedel, Anke C.
Moreira, Irina S. 
Keywords: dopamine receptors; molecular docking; molecular dynamics; receptor-ligand interactions
Issue Date: 27-Mar-2019
Publisher: MDPI
Project: CENTRO-01-0145-FEDER-000008: BrainHealth 2020 
PTDC/QUI-OUT/32243/2017 
German Federal Ministry of Education and Research (BMBF project) of the Bonn International Graduate School in Drug Sciences (BIGS DrugS) 
FCT -IF/00578/2014 
metadata.degois.publication.title: Molecules
metadata.degois.publication.volume: 24
metadata.degois.publication.issue: 7
Abstract: Background: Selectively targeting dopamine receptors (DRs) has been a persistent challenge in the last years for the development of new treatments to combat the large variety of diseases involving these receptors. Although, several drugs have been successfully brought to market, the subtype-specific binding mode on a molecular basis has not been fully elucidated. Methods: Homology modeling and molecular dynamics were applied to construct robust conformational models of all dopamine receptor subtypes (D₁-like and D₂-like). Fifteen structurally diverse ligands were docked. Contacts at the binding pocket were fully described in order to reveal new structural findings responsible for selective binding to DR subtypes. Results: Residues of the aromatic microdomain were shown to be responsible for the majority of ligand interactions established to all DRs. Hydrophobic contacts involved a huge network of conserved and non-conserved residues between three transmembrane domains (TMs), TM2-TM3-TM7. Hydrogen bonds were mostly mediated by the serine microdomain. TM1 and TM2 residues were main contributors for the coupling of large ligands. Some amino acid groups form electrostatic interactions of particular importance for D₁R-like selective ligands binding. Conclusions: This in silico approach was successful in showing known receptor-ligand interactions as well as in determining unique combinations of interactions, which will support mutagenesis studies to improve the design of subtype-specific ligands.
URI: https://hdl.handle.net/10316/107170
ISSN: 1420-3049
DOI: 10.3390/molecules24071196
Rights: openAccess
Appears in Collections:I&D CNC - Artigos em Revistas Internacionais

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