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Structural basis for gating charge movement in the voltage sensor of a sodium channel
Open accessAbstract
Voltage-dependent gating of ion channels is essential for electrical signaling in excitable cells, but the structural basis for voltage sensor function is unknown. We constructed high-resolution structural models of resting, intermediate, and activated states of the voltage-sensing domain of the bacterial sodium channel NaChBac using the Rosetta modeling method, crystal structures of related channels, and experimental data showing state-dependent interactions between the gating charge-carrying arginines in the S4 segment and negatively charged residues in neighboring transmembrane segments. The resulting structural models illustrate a network of ionic and hydrogen-bonding interactions that are made sequentially by the gating charges as they move out under the influence of the electric field. The S4 segment slides 6–8 Å outward through a narrow groove formed by the S1, S2, and S3 segments, rotates ∼30°, and tilts sideways at a pivot point formed by a highly conserved hydrophobic region near the middle of the voltage sensor. The S4 segment has a 3 10 -helical conformation in the narrow inner gating pore, which allows linear movement of the gating charges across the inner one-half of the membrane. Conformational changes of the intracellular one-half of S4 during activation are rigidly coupled to lateral movement of the S4–S5 linker, which could induce movement of the S5 and S6 segments and open the intracellular gate of the pore. We confirmed the validity of these structural models by comparing with a high-resolution structure of a NaChBac homolog and showing predicted molecular interactions of hydrophobic residues in the S4 segment in disulfide-locking studies.
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Cited by
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Kuang, Q., Purhonen, P., & Hebert, H. (2015) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-015-1948-5 · Cited by 291
Structure and Pharmacology of Voltage-Gated Sodium and Calcium Channels
Catterall, W. A., Lenaeus, M. J., & Gamal El-Din, T. M. (2020) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010818-021757 · Cited by 281
Self-citationMolecular Insights from Conformational Ensembles via Machine Learning
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Metadata
- Authors
- Yarov-Yarovoy, V., DeCaen, P. G., Westenbroek, R. E., Pan, C.-Y., Scheuer, T., Baker, D., & Catterall, W. A.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2011-12-12
- Volume
- 109
- Issue
- 2
- Pages
- E93-102
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2164437435
- Cited by
- 242
- References
- 0
- FWCI
- 6.70
- Type
- journal article
- Language
- en
- Open access
- green
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