Combined Receptor and Ligand-Based Approach to the Universal Pharmacophore Model Development for Studies of Drug Blockade to the hERG1 Pore Domain

Abstract

Long QT syndrome, LQTS, results in serious cardiovascular disorders, such as tachyarrhythmia and sudden cardiac death. A promiscuous binding of different drugs to the intracavitary binding site in the pore domain (PD) of human ether-a-go-go related gene (hERG) 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 hERG1 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 hERG1 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 hERG 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 hERG blockers with diverse hERG 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 hERG1 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 hERG1 channel. To cross-validate consistency between the constructed hERG1 pore domain and the pharmacophore models, we performed docking studies using the homology model of hERG1. To understand how polar or nonpolar moieties of inhibitors stimulate channel inhibition, critical amino acid replacement (i.e., T623, S624, S649, Y652 and F656) at the hERG cavity was examined by in silico mutagenesis. The average docking score differences between wild type and mutated hERG channels was found to have the following order: F656A > Y652A > S624A > T623A > S649A. These results are in agreement with experimental data.

Modeling of Open, Closed, and Open-Inactivated States of the hERG1 Channel: Structural Mechanisms of the State-Dependent Drug Binding

“…There is an apparent correlation between the binding affinity for many hERG1 blockers and the conformational state of the channel suggesting stabilization of the blocker in the channel's binding site by conformational rearrangements during inactivation. 39 This suggests a possibility for state-dependent drug binding and hERG-related current modulation, which necessitates development of drugs for cardiac diseases. The mechanism by which state-dependent drug amplifies blockade by C-type inactivation process remains to be poorly understood.…”

hERG K+Channels: Structure, Function, and Clinical Significance

“…A further complication is the inclusion of lipid membranes into these models using molecular dynamics simulations, which may be important for a stable Kv11.1 channel model (399,595). Despite these significant problems in obtaining an accurate receptor model of Kv11.1, several sophisticated in silico models of drug binding exist (74,160,168,282,399,591,595,705). The different docking strategies and diverse test structures used in these studies makes a comparison of binding conformations difficult.…”

A Novel Approach for Overcoming Drug Resistance in Breast Cancer Chemotherapy by Targeting new Synthetic Curcumin Analogues Against Aldehyde Dehydrogenase 1 (ALDH1A1) and Glycogen Synthase Kinase-3 β (GSK-3β)

“…Some side chain and hydrogen atoms are missing in the crystallized structures of proteins that were added using prime module. For optimization and energy minimization, OPLS-2005 force field has been used and the final energy-minimized receptors were saved for further use [32].…”

“…1a, b) for study were drawn using ChemSketch [33] and 3D coordinates were generated using LigPrep module of Schrodinger and optimized by generating different possible structural conformations, and for energy minimization, it uses OPLS-2005 force field. To correct Lewis structure, tautomeric state and ionization state (pH 7.0 ± 2.0) default settings were used [32,34].…”