Machine-learning technique, QSAR and molecular dynamics for hERG–drug interactions

Das, Nilima R.; Sharma, Tripti; Toropov, Andrey A.; Toropova, Alla P.; Tripathi, Manish Kumar; Achary, P. Ganga Raju

doi:10.1080/07391102.2023.2193641

Cited by 6 publications

(3 citation statements)

References 56 publications

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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%

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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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Section: Resultsmentioning

confidence: 99%

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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“…A detailed understanding of ligand-receptor interactions is crucial for the effective design of new drugs and for a general appreciation of cellular functioning mechanisms [1]. In this context, methods such as molecular docking and molecular dynamics, together with innovative machine learning techniques, are revolutionizing the approaches to the issue [2,3]. Molecular docking is a widely used tool for predicting and studying protein-ligand interactions [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

MD–Ligand–Receptor: A High-Performance Computing Tool for Characterizing Ligand–Receptor Binding Interactions in Molecular Dynamics Trajectories

Pieroni,

Madeddu,

Di Martino

et al. 2023

IJMS

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Molecular dynamics simulation is a widely employed computational technique for studying the dynamic behavior of molecular systems over time. By simulating macromolecular biological systems consisting of a drug, a receptor and a solvated environment with thousands of water molecules, MD allows for realistic ligand–receptor binding interactions (lrbi) to be studied. In this study, we present MD–ligand–receptor (MDLR), a state-of-the-art software designed to explore the intricate interactions between ligands and receptors over time using molecular dynamics trajectories. Unlike traditional static analysis tools, MDLR goes beyond simply taking a snapshot of ligand–receptor binding interactions (lrbi), uncovering long-lasting molecular interactions and predicting the time-dependent inhibitory activity of specific drugs. With MDLR, researchers can gain insights into the dynamic behavior of complex ligand–receptor systems. Our pipeline is optimized for high-performance computing, capable of efficiently processing vast molecular dynamics trajectories on multicore Linux servers or even multinode HPC clusters. In the latter case, MDLR allows the user to analyze large trajectories in a very short time. To facilitate the exploration and visualization of lrbi, we provide an intuitive Python notebook (Jupyter), which allows users to examine and interpret the results through various graphical representations.

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Aggregation-prone antimicrobial peptides target gram-negative bacterial nucleic acids and protein synthesis

Chen,

Zhang,

et al. 2024

Acta Biomaterialia

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Machine-learning technique, QSAR and molecular dynamics for hERG–drug interactions

Cited by 6 publications

References 56 publications

Structural modeling of hERG channel–drug interactions using Rosetta

Structural modeling of hERG channel–drug interactions using Rosetta

MD–Ligand–Receptor: A High-Performance Computing Tool for Characterizing Ligand–Receptor Binding Interactions in Molecular Dynamics Trajectories

Aggregation-prone antimicrobial peptides target gram-negative bacterial nucleic acids and protein synthesis

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