Ngo Khoa, Yang Pei-Chi, Yarov-Yarovoy Vladimir, Clancy Colleen E, Vorobyov Igor
Center for Precision Medicine and Data Science, University of California, Davis, Davis, United States.
Department of Physiology and Membrane Biology, University of California, Davis, Davis, United States.
Elife. 2025 Jul 14;13:RP104901. doi: 10.7554/eLife.104901.
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 the resolution of discrete conformational states of transmembrane ion channel proteins. An example is K11.1 (hERG), comprising the primary cardiac repolarizing current, . 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. While prior studies have applied AlphaFold to predict alternative protein conformations, we show that the inclusion of carefully chosen structural templates can guide these predictions toward distinct functional states. This targeted modeling approach is validated through comparisons with experimental data, including proposed state-dependent structural features, drug interactions from molecular docking, and ion conduction properties from molecular dynamics simulations. Remarkably, AlphaFold not only predicts inactivation mechanisms of the hERG channel that prevent ion conduction but also uncovers novel molecular features explaining enhanced drug binding observed during inactivation, offering a deeper understanding of hERG channel function and pharmacology. Furthermore, leveraging AlphaFold-derived states enhances computational screening by significantly improving agreement with experimental drug affinities, an important advance for hERG as a key drug safety target where traditional single-state models miss critical state-dependent effects. By mapping protein residue interaction networks across closed, open, and inactivated states, we identified critical residues driving state transitions validated by prior mutagenesis studies. This innovative methodology sets a new benchmark for integrating deep learning-based protein structure prediction with experimental validation. It also offers a broadly applicable approach using AlphaFold to predict discrete protein conformations, reconcile disparate data, and uncover novel structure-function relationships, ultimately advancing drug safety screening and enabling the design of safer therapeutics.
为了设计安全、有选择性且有效的新疗法，必须深入了解药物靶点的结构和功能。要解决的最困难问题之一一直是跨膜离子通道蛋白离散构象状态的解析。一个例子是K11.1（hERG），它构成了主要的心脏复极电流。hERG是一个臭名昭著的药物抗靶点，所有有前景的药物都要针对它进行筛选，以确定其致心律失常的可能性。药物与hERG失活状态的相互作用与心律失常风险升高有关，并且药物可能在通道关闭时被困住。虽然先前的研究已应用AlphaFold来预测蛋白质的替代构象，但我们表明，纳入精心选择的结构模板可以将这些预测导向不同的功能状态。这种有针对性的建模方法通过与实验数据进行比较得到验证，这些实验数据包括提议的状态依赖性结构特征、分子对接的药物相互作用以及分子动力学模拟的离子传导特性。值得注意的是，AlphaFold不仅预测了阻止离子传导的hERG通道的失活机制，还揭示了解释失活过程中观察到的增强药物结合的新分子特征，从而更深入地理解hERG通道的功能和药理学。此外，利用AlphaFold推导的状态通过显著提高与实验药物亲和力的一致性来增强计算筛选，这对于作为关键药物安全靶点的hERG来说是一个重要进展，因为传统的单状态模型会忽略关键的状态依赖性效应。通过绘制跨关闭、开放和失活状态的蛋白质残基相互作用网络，我们确定了驱动状态转变的关键残基，这些残基已通过先前的诱变研究得到验证。这种创新方法为将基于深度学习的蛋白质结构预测与实验验证相结合树立了新的标杆。它还提供了一种广泛适用的方法，即使用AlphaFold来预测离散的蛋白质构象、协调不同的数据并揭示新的结构-功能关系，最终推动药物安全筛选并实现更安全疗法的设计。
