Key result
Machine learning and molecular dynamics identify key hERG interacting residues showing stable ligand complex behavior.
Why the study?
To understand cardiotoxicity relating to the hERG gene using molecular docking and dynamics studies.
Machine learning and molecular dynamics simulations identified key interacting residues for hERG-drug interactions, providing computational insights into drug-induced cardiotoxicity.
May aid computational cardiotoxicity screening; leaves open clinical translation pending validation.
value was between 10 μM and less, it was classified as a blocker (hERG-positive), and if it was more, it was classified as a non-blocker (hERG-negative). Matthew's correlation coefficient formula and F1score were applied to compare and track the developed models' performance. Molecular docking and dynamics studies were performed to understand the cardiotoxicity relating to the hERG-gene. The hERG residues interacting after 100 ns are LEU:697, THR:708, PHE:656, HIS:674, HIS:703, TRP:705 and ASN:709 and the hERG-ligand-16 complex trajectory showed stable behaviour with lesser fluctuations in the entire simulation of 200 ns.Communicated by Ramaswamy H. Sarma.
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Das et al. (2023) studied hERG-drug interactions and cardiotoxicity. Machine-learning, QSAR, and molecular dynamics was evaluated on hERG blocking classification and molecular interactions. Machine learning and molecular dynamics simulations identified key hERG interacting residues with the hERG-ligand-16 complex showing stable behavior over a 200 ns trajectory.
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