1. Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold
- Author
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Nguyen, Phuong Tran, Harris, Brandon John, Mateos, Diego Lopez, González, Adriana Hernández, Murray, Adam Michael, and Yarov-Yarovoy, Vladimir
- Subjects
Biochemistry and Cell Biology ,Biological Sciences ,Machine Learning and Artificial Intelligence ,Networking and Information Technology R&D (NITRD) ,1.1 Normal biological development and functioning ,Humans ,Calcium ,Ion Channels ,Potassium ,Structural modeling ,voltage-gated sodium channels ,voltage-gated calcium channels ,voltage-gated potassium chnanels ,AlphaFold ,RoseTTAFold ,ESMFold ,Other Physical Sciences ,Genetics ,Biochemistry & Molecular Biology ,Biochemistry and cell biology - Abstract
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 (NaV) channel - NaV1.8, human voltage-gated calcium (CaV) channel - CaV1.1, and human voltage-gated potassium (KV) channel - KV1.3. We compared AlphaFold, RoseTTAFold, and ESMFold structural models of NaV1.8, CaV1.1, and KV1.3 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.
- Published
- 2024