The hERG Potassium Channel and Drug Trapping: Insight from Docking Studies with Propafenone Derivatives

Thai, Khac-Minh; Windisch, Andreas; Stork, Daniela; Stary-Weinzinger, Anna; Schiesaro, Andrea; Guy, Robert; Timin, E. N.; Hering, Steffen; Ecker, Gerhard F.

doi:10.1002/cmdc.200900374

Cited by 21 publications

(22 citation statements)

References 42 publications

(57 reference statements)

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“…Based on sequence homology to potassium and cyclic nucleotide-gated channels with known structures, numerous homology models of the Kv11.1 channel have been constructed (304,343,591,603,628,646,705). The homology models have usually included only the pore domain regions, with the focus being on structural basis of ion permeation or drug binding.…”

Section: B Herg Proteinmentioning

confidence: 99%

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

Vandenberg

Perry

Perrin

et al. 2012

Physiological Reviews

608

773

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The human ether-a-go-go related gene (hERG) encodes the pore-forming subunit of the rapid component of the delayed rectifier K(+) channel, Kv11.1, which are expressed in the heart, various brain regions, smooth muscle cells, endocrine cells, and a wide range of tumor cell lines. However, it is the role that Kv11.1 channels play in the heart that has been best characterized, for two main reasons. First, it is the gene product involved in chromosome 7-associated long QT syndrome (LQTS), an inherited disorder associated with a markedly increased risk of ventricular arrhythmias and sudden cardiac death. Second, blockade of Kv11.1, by a wide range of prescription medications, causes drug-induced QT prolongation with an increase in risk of sudden cardiac arrest. In the first part of this review, the properties of Kv11.1 channels, including biogenesis, trafficking, gating, and pharmacology are discussed, while the second part focuses on the pathophysiology of Kv11.1 channels.

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Section: B Herg Proteinmentioning

confidence: 99%

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

Vandenberg

Perry

Perrin

et al. 2012

Physiological Reviews

608

773

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“…[55] The exact nature of the interactions, hence the chemical significance of molecular substructures, may be different for blockers binding to different states of/ sites within the ion channel. [55][56][57] We present detailed assessments of the important features for our Winnow models in Table S8 in the Supporting Information. …”

Section: Features Emphasised By Our Modelsmentioning

confidence: 99%

Development and Comparison of hERG Blocker Classifiers: Assessment on Different Datasets Yields Markedly Different Results

Robinson

Glen

Mitchell

2011

Molecular Informatics

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In recent years, considerable effort has been invested in the development of classification models for prospective hERG inhibitors, due to the implications of hERG blockade for cardiotoxicity and the low throughput of functional hERG assays. We present novel approaches for binary classification which seek to separate strong inhibitors (IC50 <1 µM) from 'non-blockers' exhibiting moderate (1-10 µM) or weak (IC50 ≥10 µM) inhibition, as required by the pharmaceutical industry. Our approaches are based on (discretized) 2D descriptors, selected using Winnow, with additional models generated using Random Forest (RF) and Support Vector Machines (SVMs). We compare our models to those previously developed by Thai and Ecker and by Dubus et al. The purpose of this paper is twofold: 1. To propose that our approaches (with Matthews Correlation Coefficients from 0.40 to 0.87 on truly external test sets, when extrapolation beyond the applicability domain was not evident and sufficient quantities of data were available for training) are competitive with those currently proposed in the literature. 2. To highlight key issues associated with building and assessing truly predictive models, in particular the considerable variation in model performance when training and testing on different datasets.

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“…Further progress in hERG channel modelling concerns the elucidation of channel blocker trapping. Open- and closed-state homology models in combination with docking of propafenone derivatives have been reported [89]. In another study, 12 blockers with known activity along with mutagenesis data were used for validating open- and closed-state models, also using docking [90].…”

Section: Hergmentioning

confidence: 99%

Modelling three-dimensional protein structures for applications in drug design

Schmidt

Bergner

Schwede

2014

Drug Discovery Today

139

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A structural perspective of drug target and anti-target proteins, and their molecular interactions with biologically active molecules, largely advances many areas of drug discovery, including target validation, hit and lead finding and lead optimisation. In the absence of experimental 3D structures, protein structure prediction often offers a suitable alternative to facilitate structure-based studies. This review outlines recent methodical advances in homology modelling, with a focus on those techniques that necessitate consideration of ligand binding. In this context, model quality estimation deserves special attention because the accuracy and reliability of different structure prediction techniques vary considerably, and the quality of a model ultimately determines its usefulness for structure-based drug discovery. Examples of G-protein-coupled receptors and ADMET-related proteins were selected to illustrate recent progress and current limitations of protein structure prediction. Basic guidelines for good modelling practice are also provided.

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The hERG Potassium Channel and Drug Trapping: Insight from Docking Studies with Propafenone Derivatives

Cited by 21 publications

References 42 publications

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

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

Development and Comparison of hERG Blocker Classifiers: Assessment on Different Datasets Yields Markedly Different Results

Modelling three-dimensional protein structures for applications in drug design

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The hERG Potassium Channel and Drug Trapping: Insight from Docking Studies with Propafenone Derivatives

Cited by 21 publications

References 42 publications

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

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

Development and Comparison of hERG Blocker Classifiers: Assessment on Different Datasets Yields Markedly Different Results

Modelling three-dimensional protein structures for applications in drug design

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Product

Resources

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

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