Pharmacophore Mapping for Kv1.5 Potassium Channel Blockers

Yang, Qian; Du, Lüpei; Tsai, Keng‐Chang; Wang, Xiaojian; Li, Minyong; You, Qidong

doi:10.1002/qsar.200810050

Cited by 12 publications

(5 citation statements)

References 66 publications

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“…In this study, an evaluation of the 3D quantitative structure−activity relationships (QSAR)/PHASE algorithm has been performed for diverse set of hERG blockers. Several studies report the utilization of QSAR methods for studying drug binding to K-channels, including hERG1 blockers. − The main idea of QSAR methods is to utilize a general pharmacophore model that can combine information of key functional groups of the ligand and available experimental information on binding for a data set of ligands. A major weakness of approaches based on QSAR is in the alignment and low-resolution of the molecules, as well as a lack of information on protein-drug complexes.…”

Section: Introductionmentioning

confidence: 99%

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

Durdağı

Duff

Noskov

2011

J. Chem. Inf. Model.

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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.

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Section: Introductionmentioning

confidence: 99%

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

Durdağı

Duff

Noskov

2011

J. Chem. Inf. Model.

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“…H ypo G en , the most leading algorithm for the automated pharmacophore generation, was used to identify the critical chemical features of CatD inhibitors. The algorithm has shown to be successful in a large number of applications in medicinal chemistry (38–40). Typically, H ypo G en requires an informative training set as minimum number of 16 molecules with bioactivities spans over four orders of magnitude.…”

Section: Methodsmentioning

confidence: 99%

Ligand‐Based Virtual Screening and Molecular Docking Studies to Identify the Critical Chemical Features of Potent Cathepsin D Inhibitors

Sakkiah

Thangapandian

Lee³

2012

Chem Biol Drug Des

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Cathepsin D is a major component of lysosomes and plays a major role in catabolism and degenerative diseases. The quantitative structure-activity relationship study was used to explore the critical chemical features of cathepsin D inhibitors. Top 10 hypotheses were built based on 36 known cathepsin D inhibitors using HYPOGEN ⁄ DISCOVERY STUDIO v2.5. The best hypothesis Hypo1 consists of three hydrophobic, one hydrogen bond acceptor lipid, and one hydrogen bond acceptor features. The selected Hypo1 model was cross-validated using Fischer's randomization method to identify the strong correlation between experimental and predicted activity value as well as the test set and decoy sets used to validate its predictability. Moreover, the best hypothesis was used as a 3D query in virtual screening of Scaffold database. Subsequently, the screened hit molecules were filtered by applying Lipinski's rule of five, absorption, distribution, metabolism, and toxicity, and molecular docking studies. Finally, 49 compounds were obtained as potent cathepsin D inhibitors based on the consensus scoring values, critical interactions with protein active site residues, and predicted activity values. Thus, we suggest that the application of Hypo1 could assist in the selection of potent cathepsin D leads from various databases. Hence, this model was used as a valuable tool to design new candidate for cathepsin D inhibitors.

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“…243 In 2002, Ekins et al 244 constructed a 3D-QSAR pharmacophore model for hERG inhibitors based on 22 compounds containing four hydrophobic features and one positive ionization feature. Subsequently, Yang et al 245 developed a pharmacophore model for Kv1.5 based on the chemical structure of a lot of blockers of Kv1.5 channel using the HypoGen method. This pharmacophore model was validated by test set prediction and Fischer randomization tests, and all validation results indicated that it was a statistically good and reliable pharmacophore model.…”

Section: Ligand-based Drug Designmentioning

confidence: 99%

Computational methods for unlocking the secrets of potassium channels: Structure, mechanism, and drug design

Wang,

Zhang,

Tong

et al. 2024

WIREs Comput Mol Sci

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Potassium (K+) channels play vital roles in various physiological functions, including regulating K+ flow in cell membranes, impacting nervous system signal transduction, neuronal firing, muscle contraction, neurotransmitters, and enzyme secretion. Their activation and switch‐off are directly linked to diseases like arrhythmias, atrial fibrillation, and pain etc. Although the experimental methods play important roles in the studying the structure and function of K+ channels, they are still some limitations to enclose the dynamic molecular processes and the corresponding mechanisms of conformational changes during ion transport, permeation, and gating control. Relatively, computational methods have obvious advantages in studying such problems compared with experimental methods. Recently, more and more three‐dimensional structures of K+ channels have been disclosed based on experimental methods and in silico prediction methods, which provide a good chance to study the molecular mechanism of conformational changes related to the functional regulations of K+ channels. Based on these structural details, molecular dynamics simulations together with related methods such as enhanced sampling and free energy calculations, have been widely used to reveal the conformational dynamics, ion conductance, ion channel gating, and ligand binding mechanisms. Additionally, the accessibility of structures also provides a large space for structure‐based drug design. This review mainly addresses the recent progress of computational methods in the structure, mechanism, and drug design of K+ channels. After summarizing the progress in these fields, we also give our opinion on the future direction in the area of K+ channel research combined with the cutting edge of computational methods.This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Chemoinformatics

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Pharmacophore Mapping for Kv1.5 Potassium Channel Blockers

Cited by 12 publications

References 66 publications

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

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

Ligand‐Based Virtual Screening and Molecular Docking Studies to Identify the Critical Chemical Features of Potent Cathepsin D Inhibitors

Computational methods for unlocking the secrets of potassium channels: Structure, mechanism, and drug design

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