Université de Lorraine, CNRS, LPCT, F-54000 Nancy, France.
Faculty of Biology, Lomonosov Moscow State University, 1-12 Leninskie Gory, 119234 Moscow, Russia.
J Phys Chem B. 2024 Sep 12;128(36):8724-8736. doi: 10.1021/acs.jpcb.4c03182. Epub 2024 Aug 30.
Owing to recent advancements in cryo-electron microscopy, voltage-gated ion channels have gained a greater comprehension of their structural characteristics. However, a significant enigma remains unsolved for a large majority of these channels: their gating mechanism. This mechanism, which encompasses the conformational changes between open and closed states, is pivotal to their proper functioning. Beyond the binary states of open and closed, an ensemble of intermediate states defines the transition path in-between. Due to the lack of experimental data, one might resort to molecular dynamics simulations as an alternative to decipher these states and the transitions between them. However, the high-energy barriers and the colossal time scales involved hinder access to the latter. We present here an application of deep learning as a reliable pipeline for a comprehensive exploration of voltage-gated ion channel conformational rearrangements during gating. We showcase the pipeline performance specifically on the Kv1.2 voltage sensor domain and confront the results with existing data. We demonstrate how our physics-based deep learning approach contributes to the theoretical understanding of these channels and how it might provide further insights into the exploration of channelopathies.
由于最近在冷冻电子显微镜方面的进展，电压门控离子通道对其结构特征有了更深入的了解。然而，对于大多数这些通道来说，一个重大的谜团仍然没有解开：它们的门控机制。这种机制包括开放和关闭状态之间的构象变化，对于它们的正常功能至关重要。除了开放和关闭的二元状态外，一系列中间状态定义了它们之间的过渡路径。由于缺乏实验数据，人们可能会求助于分子动力学模拟来解析这些状态及其之间的转变。然而，高能量障碍和巨大的时间尺度阻碍了对后者的探索。在这里，我们提出了一种将深度学习应用于全面探索门控过程中电压门控离子通道构象重排的可靠方法。我们特别在 Kv1.2 电压传感器结构域上展示了该管道的性能，并将结果与现有数据进行了对比。我们展示了我们基于物理的深度学习方法如何有助于对这些通道的理论理解，以及它如何为通道病的探索提供进一步的见解。
