Harnessing AlphaFold to reveal state secrets: Prediction of hERG closed and inactivated states
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Abstract
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 K V 11.1 (hERG), comprising the primary cardiac repolarizing current, I Kr . 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 myriad available experimental data. Drug docking simulations demonstrated hERG state-specific drug interactions aligning well 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 a novel method to predict discrete protein conformations and novel linkages from structure to function.
Significance Statement
It has been a longstanding goal to reveal the distinct conformational states of proteins to better understand their function. In pursuit of this goal, an extensive array of approaches including mutagenesis, electrophysiological, structural and computational methods have been undertaken. While published studies have yielded important insights, none of the existing approaches have proven adequate to unambiguously identify natural protein conformational states. Here, we demonstrate the successful application of deep-learning based AlphaFold2 to predict conformational states of the key cardiac hERG K + ion channel. The implications are broad as understanding hERG is critical for drug cardiac safety. Moreover, the approach can be readily generalized to other ion channels, offering a versatile framework for identifying protein structure and its link to function.
