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A structure-based computational workflow to predict liability and binding modes of small molecules to hERG.

ABSTRACT: Off-target interactions of drugs with the human ether-à-go-go related gene 1 (hERG1) channel have been associated with severe cardiotoxic conditions leading to the withdrawal of many drugs from the market over the last decades. Consequently, predicting drug-induced hERG-liability is now a prerequisite in any drug discovery campaign. Understanding the atomic level interactions of drug with the channel is essential to guide the efficient development of safe drugs. Here we utilize the recent cryo-EM structure of the hERG channel and describe an integrated computational workflow to characterize different drug-hERG interactions. The workflow employs various structure-based approaches and provides qualitative and quantitative insights into drug binding to hERG. Our protocol accurately differentiated the strong blockers from weak and revealed three potential anchoring sites in hERG. Drugs engaging in all these sites tend to have high affinity towards hERG. Our results were cross-validated using a fluorescence polarization kit binding assay and with electrophysiology measurements on the wild-type (WT-hERG) and on the two hERG mutants (Y652A-hERG and F656A-hERG), using the patch clamp technique on HEK293 cells. Finally, our analyses show that drugs binding to hERG disrupt and hijack certain native-structural networks in the channel, thereby, gaining more affinity towards hERG.

SUBMITTER: Kalyaanamoorthy S

PROVIDER: S-EPMC7530726 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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A structure-based computational workflow to predict liability and binding modes of small molecules to hERG.

Kalyaanamoorthy Subha S Lamothe Shawn M SM Hou Xiaoqing X Moon Tae Chul TC Kurata Harley T HT Houghton Michael M Barakat Khaled H KH

Scientific reports 20201001 1

Off-target interactions of drugs with the human ether-à-go-go related gene 1 (hERG1) channel have been associated with severe cardiotoxic conditions leading to the withdrawal of many drugs from the market over the last decades. Consequently, predicting drug-induced hERG-liability is now a prerequisite in any drug discovery campaign. Understanding the atomic level interactions of drug with the channel is essential to guide the efficient development of safe drugs. Here we utilize the recent cryo-E ...[more]

PMID: 33004839

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A structure-based computational workflow to predict liability and binding modes of small molecules to hERG.

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A structure-based computational workflow to predict liability and binding modes of small molecules to hERG.

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