Department of Physiology and Membrane Biology, University of California School of Medicine, Davis, CA, USA.
Biophysics Graduate Group, University of California School of Medicine, Davis, CA, USA.
Channels (Austin). 2024 Dec;18(1):2325032. doi: 10.1080/19336950.2024.2325032. Epub 2024 Mar 6.
Ion channels play key roles in human physiology and are important targets in drug discovery. The atomic-scale structures of ion channels provide invaluable insights into a fundamental understanding of the molecular mechanisms of channel gating and modulation. Recent breakthroughs in deep learning-based computational methods, such as AlphaFold, RoseTTAFold, and ESMFold have transformed research in protein structure prediction and design. We review the application of AlphaFold, RoseTTAFold, and ESMFold to structural modeling of ion channels using representative voltage-gated ion channels, including human voltage-gated sodium (Na) channel - Na1.8, human voltage-gated calcium (Ca) channel - Ca1.1, and human voltage-gated potassium (K) channel - K1.3. We compared AlphaFold, RoseTTAFold, and ESMFold structural models of Na1.8, Ca1.1, and K1.3 with corresponding cryo-EM structures to assess details of their similarities and differences. Our findings shed light on the strengths and limitations of the current state-of-the-art deep learning-based computational methods for modeling ion channel structures, offering valuable insights to guide their future applications for ion channel research.
离子通道在人体生理学中起着关键作用，是药物发现的重要靶点。离子通道的原子尺度结构为深入了解通道门控和调节的分子机制提供了宝贵的见解。基于深度学习的计算方法（如 AlphaFold、RoseTTAFold 和 ESMFold）的最新突破改变了蛋白质结构预测和设计的研究。我们综述了 AlphaFold、RoseTTAFold 和 ESMFold 在使用代表性电压门控离子通道（包括人电压门控钠（Na）通道-Na1.8、人电压门控钙（Ca）通道-Ca1.1 和人电压门控钾（K）通道-K1.3）进行离子通道结构建模方面的应用。我们将 Na1.8、Ca1.1 和 K1.3 的 AlphaFold、RoseTTAFold 和 ESMFold 结构模型与相应的冷冻电镜结构进行了比较，以评估它们相似和不同之处的细节。我们的研究结果揭示了当前最先进的基于深度学习的计算方法在建模离子通道结构方面的优缺点，为指导其未来在离子通道研究中的应用提供了有价值的见解。
