TY - JOUR
T1 - Ligand recognition properties of the vasopressin V2 receptor studied under QSAR and molecular modeling strategies
AU - Martínez-Archundia, Marlet
AU - Colín-Astudillo, Brenda
AU - Moreno-Vargas, Liliana M.
AU - Ramírez-Galicia, Guillermo
AU - Garduño-Juárez, Ramón
AU - Deeb, Omar
AU - Contreras-Romo, Martha Citlalli
AU - Quintanar-Stephano, Andres
AU - Abarca-Rojano, Edgar
AU - Correa-Basurto, José
N1 - Publisher Copyright:
© 2017 John Wiley & Sons A/S.
PY - 2017/11
Y1 - 2017/11
N2 - The design of new drugs that target vasopressin 2 receptor (V2R) is of vital importance to develop new therapeutic alternatives to treat diseases such as heart failure, polycystic kidney disease. To get structural insights related to V2R-ligand recognition, we have used a combined approach of docking, molecular dynamics simulations (MD) and quantitative structure–activity relationship (QSAR) to elucidate the detailed interaction of the V2R with 119 of its antagonists. The three-dimensional model of V2R was built by threading methods refining its structure through MD simulations upon which the 119 ligands were subjected to docking studies. The theoretical results show that binding recognition of these ligands on V2R is diverse, but the main pharmacophore (electronic and π–π interactions) is maintained; thus, this information was validated under QSAR results. QSAR studies were performed using MLR analysis followed by ANN analysis to increase the model quality. The final equation was developed by choosing the optimal combination of descriptors after removing the outliers. The applicability domains of the constructed QSAR models were defined using the leverage and standardization approaches. The results suggest that the proposed QSAR models can reliably predict the reproductive toxicity potential of diverse chemicals, and they can be useful tools for screening new chemicals for safety assessment.
AB - The design of new drugs that target vasopressin 2 receptor (V2R) is of vital importance to develop new therapeutic alternatives to treat diseases such as heart failure, polycystic kidney disease. To get structural insights related to V2R-ligand recognition, we have used a combined approach of docking, molecular dynamics simulations (MD) and quantitative structure–activity relationship (QSAR) to elucidate the detailed interaction of the V2R with 119 of its antagonists. The three-dimensional model of V2R was built by threading methods refining its structure through MD simulations upon which the 119 ligands were subjected to docking studies. The theoretical results show that binding recognition of these ligands on V2R is diverse, but the main pharmacophore (electronic and π–π interactions) is maintained; thus, this information was validated under QSAR results. QSAR studies were performed using MLR analysis followed by ANN analysis to increase the model quality. The final equation was developed by choosing the optimal combination of descriptors after removing the outliers. The applicability domains of the constructed QSAR models were defined using the leverage and standardization approaches. The results suggest that the proposed QSAR models can reliably predict the reproductive toxicity potential of diverse chemicals, and they can be useful tools for screening new chemicals for safety assessment.
KW - QSAR
KW - applicability domain
KW - arginine vasopressin
KW - molecular dynamics simulations
KW - vaptans
UR - http://www.scopus.com/inward/record.url?scp=85019763766&partnerID=8YFLogxK
U2 - 10.1111/cbdd.13005
DO - 10.1111/cbdd.13005
M3 - Artículo
C2 - 28419717
SN - 1747-0277
VL - 90
SP - 840
EP - 853
JO - Chemical Biology and Drug Design
JF - Chemical Biology and Drug Design
IS - 5
ER -