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.