TY - JOUR
T1 - Exploring the ligand recognition properties of the human vasopressin V1a receptor using QSAR and molecular modeling studies
AU - Contreras-Romo, Martha C.
AU - Martínez-Archundia, Marlet
AU - Deeb, Omar
AU - Ślusarz, Magdalena J.
AU - Ramírez-Salinas, Gema
AU - Garduño-Juárez, Ramõn
AU - Quintanar-Stephano, Andrés
AU - Ramírez-Galicia, Guillermo
AU - Correa-Basurto, José
PY - 2014/2
Y1 - 2014/2
N2 - Vaptans are compounds that act as non-peptide vasopressin receptor antagonists. These compounds have diverse chemical structures. In this study, we used a combined approach of protein folding, molecular dynamics simulations, docking, and quantitative structure-activity relationship (QSAR) to elucidate the detailed interaction of the vasopressin receptor V1a (V1aR) with some of its blockers (134). QSAR studies were performed using MLR analysis and were gathered into one group to perform an artificial neural network (ANN) analysis. For each molecule, 1481 molecular descriptors were calculated. Additionally, 15 quantum chemical descriptors were calculated. The final equation was developed by choosing the optimal combination of descriptors after removing the outliers. Molecular modeling enabled us to obtain a reliable tridimensional model of V1aR. The docking results indicated that the great majority of ligands reach the binding site under π-π, π-cation, and hydrophobic interactions. The QSAR studies demonstrated that the heteroatoms N and O are important for ligand recognition, which could explain the structural diversity of ligands that reach V1aR. Exploration of V1aR under theoretical studies (MD simulations, QSAR and docking studies) to depict the principal recognition properties which could be use for drug design.
AB - Vaptans are compounds that act as non-peptide vasopressin receptor antagonists. These compounds have diverse chemical structures. In this study, we used a combined approach of protein folding, molecular dynamics simulations, docking, and quantitative structure-activity relationship (QSAR) to elucidate the detailed interaction of the vasopressin receptor V1a (V1aR) with some of its blockers (134). QSAR studies were performed using MLR analysis and were gathered into one group to perform an artificial neural network (ANN) analysis. For each molecule, 1481 molecular descriptors were calculated. Additionally, 15 quantum chemical descriptors were calculated. The final equation was developed by choosing the optimal combination of descriptors after removing the outliers. Molecular modeling enabled us to obtain a reliable tridimensional model of V1aR. The docking results indicated that the great majority of ligands reach the binding site under π-π, π-cation, and hydrophobic interactions. The QSAR studies demonstrated that the heteroatoms N and O are important for ligand recognition, which could explain the structural diversity of ligands that reach V1aR. Exploration of V1aR under theoretical studies (MD simulations, QSAR and docking studies) to depict the principal recognition properties which could be use for drug design.
KW - MD simulations
KW - QSAR studies
KW - V1aR
KW - docking
KW - vaptans
UR - http://www.scopus.com/inward/record.url?scp=84892519037&partnerID=8YFLogxK
U2 - 10.1111/cbdd.12229
DO - 10.1111/cbdd.12229
M3 - Artículo
C2 - 24010681
SN - 1747-0277
VL - 83
SP - 207
EP - 223
JO - Chemical Biology and Drug Design
JF - Chemical Biology and Drug Design
IS - 2
ER -