Exploring voltage-gated sodium channel conformations and protein-protein interactions using AlphaFold2
Open accessPreprint — published as 10.1085/jgp.202413705 · Paper page
Abstract
Voltage-gated sodium (Na V ) channels are vital regulators of electrical activity in excitable cells. Given their importance in physiology, Na V channels are key therapeutic targets for treating numerous conditions, yet developing subtype-selective drugs remains challenging due to the high sequence and structural conservation among Na V subtypes. Recent advances in cryo-electron microscopy have resolved most human Na V channels, providing valuable insights into their structure and function. However, limitations persist in fully capturing the complex conformational states that underlie Na V channel gating and modulation. This study explores the capability of AlphaFold2 to sample multiple Na V channel conformations and assess AlphaFold Multimer’s accuracy in modeling interactions between the Na V α-subunit and its protein partners, including auxiliary β-subunits and calmodulin. We enhance conformational sampling to explore Na V channel conformations using a subsampled multiple sequence alignment approach and varying the number of recycles. Our results demonstrate that AlphaFold2 models multiple Na V channel conformations, including those observed in experimental structures, states that have not been described experimentally, and potential intermediate states. Correlation and clustering analyses uncover coordinated domain behavior and recurrent state ensembles. Furthermore, AlphaFold Multimer models Na V complexes with auxiliary β-subunits and calmodulin with high accuracy, and the presence of protein partners significantly alters both the modeled conformational landscape of the Na V α-subunit and the coupling between its functional states. These findings highlight the potential of deep learning-based methods to expand our understanding of Na V channel structure, gating, and modulation, while also underscoring the limitations of predicted models that remain hypotheses until validated by experimental data. Summary: Lopez-Mateos et al.’s study demonstrates AlphaFold2’s potential to sample multiple states of human Na V channels. Additionally, Na V α-subunit interactions with β-subunits and calmodulin reshape Na V α-subunit conformational landscape. This study reveals potential of deep learning methods to model structural diversity of ion channels.
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AlphaFold2 captures conformational transitions in the voltage-gated channel superfamily
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- Authors
- Lopez-Mateos, D., Narang, K., & Yarov-Yarovoy, V.
- Affiliations
- University of California, Davis (US)
- Venue
- The Journal of General Physiology
- Publisher
- openRxiv
- Published
- 2024-10-18
- Volume
- 158
- Issue
- 2
- ISSN
- 0022-1295 (ISSN-L)
- PMC
- PMC11507785
- OpenAlex
- W4417523715
- Cited by
- 5
- References
- 83
- Citations by year
- 2026: 3 · 2025: 2
- FWCI
- 1.49
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- The Journal of General Physiology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- Funders
- National Institute of Neurological Disorders and Stroke (R61NS127285)
- National Heart, Lung, and Blood Institute (R01HL128537, R01HL174001)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Ion channel regulation and function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiac electrophysiology and arrhythmias
- Keywords
- voltage-gated sodium channels, protein conformational sampling, protein-protein interaction, AlphaFold2, AlphaFold-Multimer, calmodulin, channel gating, conformational ensembles, multiple sequence alignment, deep learning protein structure prediction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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