In Silico Design of a Peptide Receptor for Dopamine Recognition

Abstract: Dopamine (DA) is an important neurotransmitter with a fundamental role in regulatory functions related to the central, peripheral, renal, and hormonal nervous systems. Dopaminergic neurotransmission dysfunctions are commonly associated with several diseases; thus, in situ quantification of...

Full description

Autores:
Rodriguez Salazar, Luna
Guevara Pulido, James
Cifuentes, Andrés
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Universidad El Bosque
Repositorio:
Repositorio U. El Bosque
Idioma:
eng
OAI Identifier:
oai:repositorio.unbosque.edu.co:20.500.12495/5496
Acceso en línea:
http://hdl.handle.net/20.500.12495/5496
https://doi.org/10.3390/molecules25235509
Palabra clave:
In silico
Dopamine
Molecular docking
Molecular dynamics
Bioreceptor
Rights
openAccess
License
Attribution 4.0 International
Description
Summary:Abstract: Dopamine (DA) is an important neurotransmitter with a fundamental role in regulatory functions related to the central, peripheral, renal, and hormonal nervous systems. Dopaminergic neurotransmission dysfunctions are commonly associated with several diseases; thus, in situ quantification of DA is a major challenge. To achieve this goal, enzyme-based biosensors have been employed for substrate recognition in the past. However, due to their sensitivity to changes in temperature and pH levels, new peptide bioreceptors have been developed. Therefore, in this study, four bioreceptors were designed in silico to exhibit a higher a_nity for DA than the DA transporters (DATs). The design was based on the hot spots of the active sites of crystallized enzyme structures that are physiologically related to DA. The a_nities between the chosen targets and designed bioreceptors were calculated using AutoDock Vina. Additionally, the binding free energy, DG, of the dopamine-4xp1 complex was calculated by molecular dynamics (MD). This value presented a direct relationship with the E_refine value obtained from molecular docking based on the DG functions obtained from MOE of the promising bioreceptors. The control variables in the design were amino acids, bond type, steric volume, stereochemistry, a_nity, and interaction distances. As part of the results, three out of the four bioreceptor candidates presented promising values in terms of DA a_nity and distance.