Data from: Harnessing AlphaFold to reveal hERG channel conformational state secrets

To design safe, selective, and effective new therapies, there must be a
deep understanding of the structure and function of the drug target. One
of the most difficult problems to solve has been resolution of discrete
conformational states of transmembrane ion channel proteins. An example is
KV11.1 (hERG), comprising the primary cardiac repolarizing current, IKr.
hERG is a notorious drug anti-target against which all promising drugs are
screened to determine potential for arrhythmia. Drug interactions with the
hERG inactivated state are linked to elevated arrhythmia risk, and drugs
may become trapped during channel closure. However, the structural details
of multiple conformational states have remained elusive. Here, we guided
AlphaFold2 to predict plausible hERG inactivated and closed conformations,
obtaining results consistent with multiple available experimental data.
Drug docking simulations demonstrated hERG state-specific drug
interactions in good agreement with experimental results, revealing that
most drugs bind more effectively in the inactivated state and are trapped
in the closed state. Molecular dynamics simulations demonstrated ion
conduction that aligned with earlier studies. Finally, we identified key
molecular determinants of state transitions by analyzing interaction
networks across closed, open, and inactivated states in agreement with
earlier mutagenesis studies. Here, we demonstrate a readily generalizable
application of AlphaFold2 as an effective and robust method to predict
discrete protein conformations, reconcile seemingly disparate data and
identify novel linkages from structure to function.