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13 results for “hERG”
Molecular Dynamics simulations suggest possible activation and deactivation pathways in hERG channel
<ol> <li>equil_gating_4_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 4 equilibration trajectory</li> <li>equil_gating_6_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 6 equilibration trajectory</li> <li>equil_gating_8_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 8 equilibration trajectory</li> <li>equil_gating_4.dcd: 100 ns NPT trajectory of the hERG closed state with gating charge 4</li> <li>equil_gating_6.dcd: 100 ns NPT trajectory of the hERG closed state with gating charge 6</li> <li>equil_gating_8.dcd: 100 ns NPT trajectory of the hERG closed state with gating charge 8</li> <li>equil_open_assembly_xleap.prmtop: file topology of the hERG open state equilibration trajectory</li> <li>equil_open.dcd: 100 ns NPT trajectory of the hERG open state</li> <li>herg_closed_gating_4.pdb: PDB file of hERG closed state with gating charge 4 after Steered MD simulations</li> <li>herg_closed_gating_6.pdb: PDB file of hERG closed state with gating charge 6 after Steered MD simulations</li> <li>herg_closed_gating_8.pdb: PDB file of hERG closed state with gating charge 8 after Steered MD simulations</li> <li>TMD_O-C_closed_gating_8_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 8 TMD trajectory</li> <li>TMD_O-C_closed_gating_6_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 6 TMD trajectory</li> <li>TMD_O-C_closed_gating_4_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 4 TMD trajectory</li> <li>TMD_O-C_closed_gating_8.dcd: TMD trajectory of the hERG closed state with gating charge 8</li> <li>TMD_O-C_closed_gating_6.dcd: TMD trajectory of the hERG closed state with gating charge 6</li> <li>TMD_O-C_closed_gating_4.dcd: TMD trajectory of the hERG closed state with gating charge 4</li> </ol> <p>MD trajectories (equilibration and Targeted MD trajectories) in dcd format can be visualized using visualization tools such as VMD or PyMol after uploading the topology file.</p> <p>The directory data_supplementary-note-4.tar.bz2 contains the files related to the Supplementary Notes 4: "A practical example of pathway calculation".</p>
Noncanonical electromechanical coupling paths in cardiac hERG potassium channel (semi-binary contact maps)
<p>Matrices of the semi-binary contact maps of the following open and closed systems: WT, A527L, A614G, L524R, L529H, L532H, T425L, T618L, W563L.</p> <p>The residue numbering is not the official one because the first residues (397) of hERG (PAS domain) were not included in our simulations so that each subunit comprizes 466 residues. Moreover, the four subunits were numbered consecutively. The official numbering of a residue can be easily recovered. The general rule is:</p> <p>official residue - 397 = our residue</p> <p>For example, the official T425 corresponds to T28 in the first subunit (425-397), T494 in the second subunit (425-397+466), T960 in the third subunit (425-397+466+466), and T1426 in the fourth subunit (425-397+466+466+466).</p>
An integrated approach including docking, MD simulations, and network analysis highlights the action mechanism of the cardiac hERG activator RPR260243
<p>500 ns MD trajectories of the hERG bound states (protein and ligand) used for the analyses discussed in the paper. There are three replica for each system. The trajectories can be visualized using molecular visualization programs such as Pymol or VMD uploading the PDB and the DCD file.</p> <p>The PDB and the topology files of the hERG bound state (protein, membrane, ions and ligand) are also included.</p> <p>An example of input file used for the production step of the dynamics has been provided (production_1.conf). </p>
Structural modeling of hERG channel: Drug interactions using Rosetta
<p>Human Ether-a-go-go-Related Gene (hERG) encodes a potassium-selective voltage-gated ion channel essential for normal electrical activity in the heart but is also a major drug anti-target. Genetic hERG mutations and blockage of the channel pore by drugs can cause long QT syndrome (LQTS), which predisposes individuals to potentially deadly arrhythmias. However, not all hERG blocking drugs are pro-arrhythmic, and their differential affinities to discrete channel conformational states have been suggested to contribute to arrhythmogenicity. We used Rosetta electron density refinement and homology modeling to build structural models of open-state hERG channel wild-type (WT) and mutant variants (Y652A, F656A, and Y652A/F656A), and a closed state WT channel based on cryo-electron microscopy structures of hERG and EAG1 channels. These models were used as protein targets for molecular docking of charged and neutral forms of amiodarone, nifekalant, dofetilide, d/l-sotalol, flecainide, and moxifloxacin. We selected these drugs based on their different arrhythmogenic potentials and abilities to facilitate hERG current. Our docking studies and clustering provided atomistic structural insights into state-dependent drug–channel interactions l that play a key role in differentiating safe and harmful hERG blockers and can explain hERG channel facilitation through drug interactions with its open-state hydrophobic pockets.</p>
Highly curated hERG dataset of 8879 unique molecular compounds with corresponding potency values
<p>This dataset was built during a research project, in the field of Computer-Aided Drug Discovery (CADD), funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grant. The aim of the project was to build descriptor-based machine learning models for hERG cardiotoxicity liability predictions. The dataset includes a total of 8879 unique molecular compounds gathered from ChEMBL and PubChem publicly available bioactivity databases, as well as from literature mining. The list is split into 2 sets, 8380 for training and 499 for testing. All molecular compounds are represented in their SMILE format with their corresponding PIC50 potency values.</p> <p>To access the full original work, please visit the following link: <a href="https://arxiv.org/abs/2112.13467">Manuscript</a></p> <p><strong>Note:</strong> Upon usage of this data, kindly cite the original manuscript describing the curation process:<br><br>Arab, Issar, and Khaled Barakat. "ToxTree: descriptor-based machine learning models for both hERG and Nav1. 5 cardiotoxicity liability predictions." <em>arXiv preprint arXiv:2112.13467</em> (2021).</p> <p> </p> <p><strong>Refer to our latest manually curated and a much larger dataset here: <a href="../record/8359714">link</a> </strong></p>
Ambient temperature CiPA dynamic hERG protocol dataset recorded on the SyncroPatch 384SE
<p><strong>Abstract </strong></p> <p>The Comprehensive in vitro Proarrhythmic Assay (CiPA) has promoted use of <em>in silico</em> models of drug effects on cardiac repolarization to improve proarrhythmic risk prediction. These models contain a pharmacodynamic component describing drug binding to hERG channels that required <em>in vitro</em> data for kinetics of block, in addition to potency, to constrain them. To date, development and validation has been undertaken using data from manual patch-clamp. The application of this approach at scale requires the development of a high-throughput, automated patch-clamp (APC) implementation. Here, we present a comprehensive analysis of the implementation of the Milnes, or CiPA dynamic protocol, on an APC platform, including quality control and data analysis. Kinetics and potency of block were assessed for bepridil, cisapride, terfenadine and verapamil with data retention/QC pass rate of 21.8% overall, or as high as 50.4% when only appropriate sweep lengths were considered for drugs with faster kinetics. The variability in IC<sub>50</sub> and kinetics between manual and APC was comparable to that seen between sites/platforms in previous APC studies of potency. Whilst the experimental success is less than observed in screens of potency alone, it is still significantly greater than manual patch. With the modifications to protocol design, including sweep length, number of repetitions, and leak correction recommended in this study, this protocol can be applied on APC to acquire data comparable to manual patch clamp.</p> <p><strong>Description</strong></p> <p>High-throughput patch clamp electrophysiology dataset acquired on Nanion SyncroPatch 384PE platform describing the potency and kinetics of drug block of hERG channels stably expressed in CHO cells. The voltage protocols used were 10 or 40 s 0mV steps with a 15s, -80mV inter-pulse intervals, repeated 5 or 10 times in the absence (control) and presence of drug. Drug was added and cells clamped at -80 mV for 4 minutes prior to recording to ensure drug equilibration prior to recording the onset (kinetics) of drug block. This dataset accompanies the paper titled '<strong>High throughput measurement of hERG drug block kinetics using the CiPA dynamic protocol' </strong>by Monique J. Windley, Jessica Farr, Clifford TeBay, Jamie I. Vandenberg and Adam P. Hill<strong>. </strong></p> <p><strong>File information</strong></p> <p>Raw and leak corrected files for individual plates run on the SyncroPatch 384PE</p> <p><strong>Folder naming convention</strong>: Plate identifier (6 digit date_user initials_plate number)_type of data (leak uncorrected raw / leak corrected).</p> <ul> </ul> <p><strong>Raw data </strong></p> <ul> <li><strong>File naming convention: </strong>Plate identifier (6 digit date_user initials_plate number)_well number</li> <li>Each raw data folder also contains a file named QC_plate identifier no. which contains information used for cell/patch and hERG current quality controls including seal resistance, series resistance, capacitance, baseline (average current at -80mV), average (average leak uncorrected current during last 100 ms of 0mV step). QC details are reported for each sweep along with the drug and concentration, with external and 0 M reported for control sweeps.</li> <li>Full SyncroPatch plates were not used for all runs, only applicable wells were included where partial plates were run (see plates 240719_MW1-3, 170719_MW1-3, 180719_MW1 and 291119_MW1)</li> </ul> <p><strong>Leak corrected data</strong></p> <ul> <li><strong>File naming convention:</strong> well number_drug_concentration in M_leak_correction</li> <li>Refer to folder for plate identifier number.</li> <li>Each folder containing leak corrected data also contains a file labelled 'percent block' which contains percentage block measurements for each leak correction file in the same folder as identified by the well number.</li> <li>Only files passing the first pass QC were leak corrected (>125 MOhm seal, <80 MOhm series, 10-200 pA capacitance, >-300 pA baseline, >50 pA hERG current size and <20% change in peak control current)</li> </ul> <p> </p>
A large comprehensive curated dataset of small molecules and their activities covering three cardiac ion channels: hERG, Cav1.2, and Nav1.5
<p>The compressed data folder (dataset.rar) represents a data framework for researchers in the field of drug discovery to perform in depth analyses on a very large open-access unique and comprehensive hERG, Nav1.5, and Cav1.2 cardiotoxicity integrated database of small molecules and their activities. The database is organized as follows:</p> <ul> <li>Each sub-folder represents a cardiac ion channel target: hERG, Nav1.5, and Cav1.2</li> <li>Each target sub-folder consists of 3 files in CSV format: One file containing the development set (split into training and validation sets using an 80/20 ratio for hyperparameter tuning). The other 2 files contain external evaluation sets. The first test dataset consists of compounds with a structural similarity of no more than 60% (Tanimoto similarity ≤ 0.6) to the remaining development set, while the second test dataset comprises compounds with a structural similarity of no more than 70% (Tanimoto similarity ≤ 0.7) to the remaining development set.</li> <li>Each file contains data with 7 columns: "InChl Key" as a unique identifier of the chemical structure, "SMILES" as the string format of storage and exchange of the chemical structure, "Source" as the upstream data source from which the data was retrieved, "ChEMBL ID" as the ChEMBL identifier if the compound comes from ChEMBL database, "PubChem CID" as the PubChem compound identifier if the compound comes from PubChem database, "pIC50" as the negative logarithm of the half-maximal inhibitory concentration (IC50) to describe the potency of the compound, and "USED_AS" column specifying whether the compound was used for training or validation.</li> </ul> <p><strong>Upon usage, please cite this publication:</strong></p> <ul> <li>Issar Arab, Kristof Egghe, Kris Laukens, Ke Chen, Khaled Barakat, Wout Bittremieux, <strong>Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction</strong>, <em>Journal of Chemical Information and Modeling</em>, (2023). doi:<a href="https://doi.org/10.1021/acs.jcim.3c01301">10.1021/acs.jcim.3c01301</a></li> </ul> <p> </p>
Structural modeling of hERG channel: Drug interactions using Rosetta
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Data from: Harnessing AlphaFold to reveal hERG channel conformational state secrets
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herg
<p>herg_chembl_torchdrug</p>
hERG Kv11.1 and Janus Kinase 2 interplay in retinoblastoma cell migration
<p>Docking poses, Molecular Dynamics simulation starting structures and MD trajectories</p>
Co-expression of calcium and hERG potassium channels reduces the incidence of proarrhythmic events
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The Physiological Effects of Human Ether-a-go-go-Related Gene (hERG)Blockade on Metabolism
ClinicalTrials.gov study NCT03868657. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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