Combined receptor and ligand-based approach to the universal pharmacophore model development for studies of drug blockade to the XXXX pore domain.

@entity1 @entity1164 @entity34690 @entity535 @entity34691 @entity66811 @entity72307 @entity591 @entity9493 @entity72308 @entity1162 @entity9678 @entity65
@entity1162 , LQTS, results in serious @entity65 , such as @entity591 and @entity1164 . A promiscuous binding of different drugs to the intracavitary binding site in the pore domain (PD) of @entity1 ether-a-go-go related gene ( @entity9493 ) channels leads to a similar dysfunction, known as a drug-induced LQTS. Therefore, an assessment of the blocking ability for potent drugs is of great pragmatic value for molecular pharmacology and medicinal chemistry of hERGs. Thus, we attempted to create an in silico model aimed at blinded drug screening for their blocking ability to the @entity9678 PD. Two distinct approaches to the drug blockage, ligand-based QSAR and receptor-based molecular docking methods, are combined for development of a universal pharmacophore model, which provides rapid assessment of drug blocking ability to the @entity9678 channel. The best 3D-QSAR model (AAADR.7) from PHASE modeling was selected from a pool consisting of 44 initial candidates. The constructed model using 31 @entity9493 blockers was validated with 9 test set compounds. The resulting model correctly predicted the pIC(50) values of test set compounds as true unknowns. To further evaluate the pharmacophore model, 14 @entity9493 blockers with diverse @entity9493 blocking potencies were selected from literature and they were used as additional external blind test sets. The resulting average deviation between in vitro and predicted pIC(50) values of external test set blockers is found as 0.29 suggesting that the model is able to accuretely predict the pIC(50) values as true unknowns. These pharmacophore models were merged with a previously developed atomistic receptor model for the @entity9678 PD and exhibited a high consistency between ligand-based and receptor-based models. Therefore, the developed 3D-QSAR model provides a predictive tool for profiling candidate compounds before their synthesis. This model also indicated the key functional groups determining a high-affinity blockade of the @entity9678 channel. To cross-validate consistency between the constructed @entity9678 pore domain and the pharmacophore models, we performed docking studies using the homology model of @entity9678 . To understand how polar or nonpolar moieties of inhibitors stimulate channel inhibition, critical @entity535 replacement (i.e., T623, S624, S649, Y652 and F656) at the @entity9493 cavity was examined by in silico mutagenesis. The average docking score differences between wild type and mutated @entity9493 channels was found to have the following order: @entity34691 > @entity34690 > @entity72307 > @entity66811 > @entity72308 . These results are in agreement with experimental data.
