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

Area of Science:

• Computational Chemistry and Molecular Modeling
• Cardiovascular Pharmacology
• Drug Discovery and Development

Background:

• Off-target drug interactions with the human ether-à-go-go related gene 1 (hERG1) channel cause severe cardiotoxicity, leading to drug withdrawals.
• Predicting hERG liability is essential in drug discovery to ensure cardiovascular safety.
• Understanding atomic-level drug-hERG interactions is key to developing safer medications.

Purpose of the Study:

• To develop and validate an integrated computational workflow for characterizing drug-hERG channel interactions.
• To identify potential drug binding sites within the hERG channel.
• To provide qualitative and quantitative insights into drug binding affinity and mechanisms.

Main Methods:

• Utilized the cryo-electron microscopy (cryo-EM) structure of the hERG channel.
• Employed a structure-based computational workflow incorporating various analytical approaches.
• Validated computational findings using fluorescence polarization binding assays and electrophysiology (patch clamp) on wild-type and mutant hERG channels.

Main Results:

• The computational workflow accurately differentiated strong hERG blockers from weak ones.
• Identified three potential anchoring sites within the hERG channel crucial for high-affinity drug binding.
• Demonstrated that drugs binding to hERG disrupt native structural networks, enhancing their affinity.

Conclusions:

• The developed computational workflow is effective for predicting drug-hERG interactions and guiding safe drug development.
• The identified anchoring sites provide valuable targets for designing drugs with reduced cardiotoxic potential.
• Drug binding to hERG involves hijacking intrinsic channel structural networks, a mechanism critical for understanding cardiotoxicity.