A Complete Assessment of Dopamine Receptor- Ligand Interactions through Computational Methods

• Autor(a) principal: Bueschbell, Beatriz
• Data de Publicação: 2019
• Outros Autores: Barreto, Carlos A. V., Preto, Antonio J., Schiedel, Anke C., Moreira, Irina S.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/107170; https://doi.org/10.3390/molecules24071196
• topic: dopamine receptors; molecular docking; molecular dynamics; receptor-ligand interactions; Binding Sites; Drug Design; Humans; Hydrogen Bonding; Hydrophobic and Hydrophilic Interactions; Molecular Conformation; Molecular Docking Simulation; Molecular Dynamics Simulation; Protein Binding; Protein Conformation; Receptors, Dopamine; Reproducibility of Results; Structure-Activity Relationship; Ligands; Models, Molecular

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.