Molecular dynamics of hERG channel: insights into understanding the binding of small molecules for detuning cardiotoxicity

Koulgi, Shruti; Jani, Vinod; Nair, Vinay; Saini, Jagmohan; Phukan, Samiron; Sonavane, Uddhavesh; Joshi, Rajendra; Kamboj, Raj; Palle, Venkata P.

doi:10.1080/07391102.2021.1875883

Cited by 6 publications

(5 citation statements)

References 61 publications

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“…Such a computational technique has been widely employed to shed light on the hERG –drug interactions, often in combination with other computational (e.g., molecular dynamics, MD) − and experimental (mutagenesis studies) approaches, , allowing the identification of a pool or residues responsible for drug binding in the so-called hERG central cavity (CC), namely, F656, Y652, G648, T623, S624, V625, and F557 . As a result, although we cannot exclude the presence of other binding sites (BS) for some hERG binders, as postulated in some papers, , CC is today the recognized pocket for hERG blockers .…”

Section: Introductionmentioning

confidence: 99%

Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study

Creanza

Delre

Ancona

et al. 2021

J. Chem. Inf. Model.

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Drug-induced blockade of the human ether-a-go-gorelated gene (hERG) channel is today considered the main cause of cardiotoxicity in postmarketing surveillance. Hence, several ligandbased approaches were developed in the last years and are currently employed in the early stages of a drug discovery process for in silico cardiac safety assessment of drug candidates. Herein, we present the first structure-based classifiers able to discern hERG binders from nonbinders. LASSO regularized support vector machines were applied to integrate docking scores and protein−ligand interaction fingerprints. A total of 396 models were trained and validated based on: (i) high-quality experimental bioactivity information returned by 8337 curated compounds extracted from ChEMBL (version 25) and (ii) structural predictor data. Molecular docking simulations were performed using GLIDE and GOLD software programs and four different hERG structural models, namely, the recently published structures obtained by cryoelectron microscopy (PDB codes: 5VA1 and 7CN1) and two published homology models selected for comparison. Interestingly, some classifiers return performances comparable to ligand-based models in terms of area under the ROC curve (AUC MAX = 0.86 ± 0.01) and negative predictive values (NPV MAX = 0.81 ± 0.01), thus putting forward the herein proposed computational workflow as a valuable tool for predicting hERG-related cardiotoxicity without the limitations of ligand-based models, typically affected by low interpretability and a limited applicability domain. From a methodological point of view, our study represents the first example of a successful integration of docking scores and protein−ligand interaction fingerprints (IFs) through a support vector machine (SVM) LASSO regularized strategy. Finally, the study highlights the importance of using hERG structural models accounting for ligand-induced fit effects and allowed us to select the best-performing protein conformation (made available in the Supporting Information, SI) to be employed for a reliable structure-based prediction of hERG-related cardiotoxicity.

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Section: Introductionmentioning

confidence: 99%

Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study

Creanza

Delre

Ancona

et al. 2021

J. Chem. Inf. Model.

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“…Specifically, a small rotation of the S6 segment from its conformation in the hERG cryo-EM structure was suggested to position F656 sidechains toward a drug-accessible receptor site ( Helliwell et al, 2018 ; Kalyaanamoorthy et al, 2020 ). Indeed, molecular dynamics simulations revealed that conformational dynamics of F656 sidechains may contribute to state-dependent drug binding ( Perissinotti et al, 2019 ; Dickson et al, 2020 ; Kalyaanamoorthy et al, 2020 ; Kudaibergenova et al, 2020 ; Yang et al, 2020 ; DeMarco et al, 2021 ; Koulgi et al, 2022 ; Das et al, 2023 ). Similarly, F656A and Y652A mutations can dramatically change hERG channel pore conformational preferences, which was not directly tested in our RosettaLigand docking calculations but can be probed by molecular dynamics simulations in our follow-up study.…”

Section: Resultsmentioning

confidence: 99%

“…We consider our RosettaLigand method-based computational docking simulations of hERG–drug interactions to be complementary to previous multiple computational molecular docking and MD simulation studies ( Durdagi et al, 2012 ; Helliwell et al, 2018 ; Vaz et al, 2018 ; Munawar et al, 2019 ; Negami et al, 2019 ; Perissinotti et al, 2019 ; Dickson et al, 2020 ; Kalyaanamoorthy et al, 2020 ; Kudaibergenova et al, 2020 ; Yang et al, 2020 ; DeMarco et al, 2021 ; Koulgi et al, 2022 ; Das et al, 2023 ). Experimental measurements and molecular dynamics simulations are needed to test computational docking-based structural hypotheses.…”

Section: Discussionmentioning

confidence: 99%

Structural modeling of hERG channel–drug interactions using Rosetta

Emigh Cortez,

DeMarco,

Furutani

et al. 2023

Front. Pharmacol.

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The human ether-a-go-go-related gene (hERG) not only 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, which predisposes individuals to potentially deadly arrhythmias. However, not all hERG-blocking drugs are proarrhythmic, 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 and mutant variants (Y652A, F656A, and Y652A/F656 A) and a closed-state wild-type 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 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.

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“…A computation model that is based on docking scores is regarded as a highly efficient way to assess the hERG–drug interaction ( Koulgi et al., 2021) . In the reference study, the docking score is further integrated with the protein–ligand interaction fingerprints to characterize the behavior of small molecules in the binding site of target proteins and to elucidate fundamental biochemical processes.…”

Section: Discussionmentioning

confidence: 99%

On QSAR-based cardiotoxicity modeling with the expressiveness-enhanced graph learning model and dual-threshold scheme

et al. 2023

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Introduction: Given the direct association with malignant ventricular arrhythmias, cardiotoxicity is a major concern in drug design. In the past decades, computational models based on the quantitative structure–activity relationship have been proposed to screen out cardiotoxic compounds and have shown promising results. The combination of molecular fingerprint and the machine learning model shows stable performance for a wide spectrum of problems; however, not long after the advent of the graph neural network (GNN) deep learning model and its variant (e.g., graph transformer), it has become the principal way of quantitative structure–activity relationship-based modeling for its high flexibility in feature extraction and decision rule generation. Despite all these progresses, the expressiveness (the ability of a program to identify non-isomorphic graph structures) of the GNN model is bounded by the WL isomorphism test, and a suitable thresholding scheme that relates directly to the sensitivity and credibility of a model is still an open question.Methods: In this research, we further improved the expressiveness of the GNN model by introducing the substructure-aware bias by the graph subgraph transformer network model. Moreover, to propose the most appropriate thresholding scheme, a comprehensive comparison of the thresholding schemes was conducted.Results: Based on these improvements, the best model attains performance with 90.4% precision, 90.4% recall, and 90.5% F1-score with a dual-threshold scheme (active: <1μM; non-active: >30μM). The improved pipeline (graph subgraph transformer network model and thresholding scheme) also shows its advantages in terms of the activity cliff problem and model interpretability.

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Molecular dynamics of hERG channel: insights into understanding the binding of small molecules for detuning cardiotoxicity

Cited by 6 publications

References 61 publications

Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study

Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study

Structural modeling of hERG channel–drug interactions using Rosetta

On QSAR-based cardiotoxicity modeling with the expressiveness-enhanced graph learning model and dual-threshold scheme

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