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Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold

Yarov-Yarovoy, Vladimir1; Nguyen, Phuong Tran1; Harris, Brandon John1; Mateos, Diego Lopez1; González, Adriana Hernández1; Murray, Adam Michael2

Published Mar 14, 2024 on Dryad. https://doi.org/10.5061/dryad.z08kprrn0

Data files

Mar 14, 2024 version files 6.03 MB

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.