Voltage-gated potassium (Kv) channels coordinate electrical signalling and control cell volume by gating in response to membrane depolarization or hyperpolarization. However, although voltage-sensing domains transduce transmembrane electric field changes by a common mechanism involving the outward or inward translocation of gating charges1,2,3, the general determinants of channel gating polarity remain poorly understood4. Here we suggest a molecular mechanism for electromechanical coupling and gating polarity in non-domain-swapped Kv channels on the basis of the cryo-electron microscopy structure of KAT1, the hyperpolarization-activated Kv channel from Arabidopsis thaliana. KAT1 displays a depolarized voltage sensor, which interacts with a closed pore domain directly via two interfaces and indirectly via an intercalated phospholipid. Functional evaluation of KAT1 structure-guided mutants at the sensor–pore interfaces suggests a mechanism in which direct interaction between the sensor and the C-linker hairpin in the adjacent pore subunit is the primary determinant of gating polarity. We suggest that an inward motion of the S4 sensor helix of approximately 5–7 Å can underlie a direct-coupling mechanism, driving a conformational reorientation of the C-linker and ultimately opening the activation gate formed by the S6 intracellular bundle. This direct-coupling mechanism contrasts with allosteric mechanisms proposed for hyperpolarization-activated cyclic nucleotide-gated channels5, and may represent an unexpected link between depolarization- and hyperpolarization-activated channels.
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Cryo-EM density maps of KAT1 have been deposited in the Electron Microscopy Data Bank under accession codes EMD-21019 (full molecule) and EMD-21018 (transmembrane-focused refinement). The atomic models of the KAT1 tetramer and octamer have been deposited in the Protein Data Bank under accession code 6V1X and 6V1Y, respectively. All other data are available upon reasonable request to the corresponding author.
Bezanilla, F. How membrane proteins sense voltage. Nat. Rev. Mol. Cell Biol. 9, 323–332 (2008).
Männikkö, R., Elinder, F. & Larsson, H. P. Voltage-sensing mechanism is conserved among ion channels gated by opposite voltages. Nature 419, 837–841 (2002).
Latorre, R. et al. Molecular coupling between voltage sensor and pore opening in the Arabidopsis inward rectifier K+ channel KAT1. J. Gen. Physiol. 122, 459–469 (2003).
Blunck, R. & Batulan, Z. Mechanism of electromechanical coupling in voltage-gated potassium channels. Front. Pharmacol. 3, 166 (2012).
Altomare, C. et al. Integrated allosteric model of voltage gating of HCN channels. J. Gen. Physiol. 117, 519–532 (2001).
Long, S. B., Campbell, E. B. & Mackinnon, R. Voltage sensor of Kv1.2: structural basis of electromechanical coupling. Science 309, 903–908 (2005).
Vardanyan, V. & Pongs, O. Coupling of voltage-sensors to the channel pore: a comparative view. Front. Pharmacol. 3, 145 (2012).
Chowdhury, S. & Chanda, B. Perspectives on: conformational coupling in ion channels: thermodynamics of electromechanical coupling in voltage-gated ion channels. J. Gen. Physiol. 140, 613–623 (2012).
Hedrich, R. Ion channels in plants. Physiol. Rev. 92, 1777–1811 (2012).
Hoshi, T. Regulation of voltage dependence of the KAT1 channel by intracellular factors. J. Gen. Physiol. 105, 309–328 (1995).
Moroni, A. et al. Mutation in pore domain uncovers cation- and voltage-sensitive recovery from inactivation in KAT1 channel. Biophys. J. 78, 1862–1871 (2000).
Hertel, B. et al. KAT1 inactivates at sub-millimolar concentrations of external potassium. J. Exp. Bot. 56, 3103–3110 (2005).
Lee, C.-H. & MacKinnon, R. Structures of the Human HCN1 Hyperpolarization-Activated Channel. Cell 168, 111–120 (2017).
Yifrach, O. & MacKinnon, R. Energetics of pore opening in a voltage-gated K+ channel. Cell 111, 231–239 (2002).
Soler-Llavina, G. J., Chang, T. H. & Swartz, K. J. Functional interactions at the interface between voltage-sensing and pore domains in the Shaker Kv channel. Neuron 52, 623–634 (2006).
Ledwell, J. L. & Aldrich, R. W. Mutations in the S4 region isolate the final voltage-dependent cooperative step in potassium channel activation. J. Gen. Physiol. 113, 389–414 (1999).
Liu, K., Li, L. & Luan, S. An essential function of phosphatidylinositol phosphates in activation of plant shaker-type K+ channels. Plant J. 42, 433–443 (2005).
Carter, P. J., Winter, G., Wilkinson, A. J. & Fersht, A. R. The use of double mutants to detect structural changes in the active site of the tyrosyl-tRNA synthetase (Bacillus stearothermophilus). Cell 38, 835–840 (1984).
Yarov-Yarovoy, V. et al. Structural basis for gating charge movement in the voltage sensor of a sodium channel. Proc. Natl Acad. Sci. USA 109, E93–E102 (2012).
Lai, H. C., Grabe, M., Jan, Y. N. & Jan, L. Y. The S4 voltage sensor packs against the pore domain in the KAT1 voltage-gated potassium channel. Neuron 47, 395–406 (2005).
Grabe, M., Lai, H. C., Jain, M., Jan, Y. N. & Jan, L. Y. Structure prediction for the down state of a potassium channel voltage sensor. Nature 445, 550–553 (2007).
Vargas, E. et al. An emerging consensus on voltage-dependent gating from computational modeling and molecular dynamics simulations. J. Gen. Physiol. 140, 587–594 (2012).
Li, Q. et al. Structural mechanism of voltage-dependent gating in an isolated voltage-sensing domain. Nat. Struct. Mol. Biol. 21, 244–252 (2014).
Guo, J. et al. Structure of the voltage-gated two-pore channel TPC1 from Arabidopsis thaliana. Nature 531, 196–201 (2016).
Yan, Z. et al. Structure of the Nav1.4-β1 complex from electric eel. Cell 170, 470–482.e11 (2017).
Xu, H. et al. Structural basis of Nav1.7 inhibition by a gating-modifier spider toxin. Cell 176, 702–715.e14 (2019).
Sesti, F., Rajan, S., Gonzalez-Colaso, R., Nikolaeva, N. & Goldstein, S. A. N. Hyperpolarization moves S4 sensors inward to open MVP, a methanococcal voltage-gated potassium channel. Nat. Neurosci. 6, 353–361 (2003).
Whicher, J. R. & MacKinnon, R. Structure of the voltage-gated K+ channel Eag1 reveals an alternative voltage sensing mechanism. Science 353, 664–669 (2016).
Chen, S., Wang, J., Zhou, L., George, M. S. & Siegelbaum, S. A. Voltage sensor movement and cAMP binding allosterically regulate an inherently voltage-independent closed-open transition in HCN channels. J. Gen. Physiol. 129, 175–188 (2007).
Kusch, J. et al. Interdependence of receptor activation and ligand binding in HCN2 pacemaker channels. Neuron 67, 75–85 (2010).
Alvarez-Baron, C. P., Klenchin, V. A. & Chanda, B. Minimal molecular determinants of isoform-specific differences in efficacy in the HCN channel family. J. Gen. Physiol. 150, 1203–1213 (2018).
Wang, W. & MacKinnon, R. Cryo-EM structure of the open human ether-à-go-go-related K+ channel hERG. Cell 169, 422–430.e10 (2017).
Perissinotti, L. L. et al. Determinants of isoform-specific gating kinetics of hERG1 channel: combined experimental and simulation study. Front. Physiol. 9, 207 (2018).
Papanatsiou, M. et al. Optogenetic manipulation of stomatal kinetics improves carbon assimilation, water use, and growth. Science 363, 1456–1459 (2019).
Kasimova, M. A. et al. Helix breaking transition in the S4 of HCN channel is critical for hyperpolarization-dependent gating. eLife 8, e53400 (2019).
Lee, C. H. & MacKinnon, R. Voltage sensor movements during hyperpolarization in the HCN channel. Cell 179, 1582–1589 (2019).
Shaya, D. et al. Voltage-gated sodium channel (NaV) protein dissection creates a set of functional pore-only proteins. Proc. Natl Acad. Sci. USA 108, 12313–12318 (2011).
Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).
Rohou, A. & Grigorieff, N. CTFFIND4: Fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 192, 216–221 (2015).
Kimanius, D., Forsberg, B. O., Scheres, S. H. W. & Lindahl, E. Accelerated cryo-EM structure determination with parallelisation using GPUs in RELION-2. eLife 5, 1–21 (2016).
Rosenthal, P. B. & Henderson, R. Optimal determination of particle orientation, absolute hand, and contrast loss in single-particle electron cryomicroscopy. J. Mol. Biol. 333, 721–745 (2003).
Chen, S. et al. High-resolution noise substitution to measure overfitting and validate resolution in 3D structure determination by single particle electron cryomicroscopy. Ultramicroscopy 135, 24–35 (2013).
Scheres, S. H. W. & Chen, S. Prevention of overfitting in cryo-EM structure determination. Nat. Methods 9, 853–854 (2012).
Kucukelbir, A., Sigworth, F. J. & Tagare, H. D. Quantifying the local resolution of cryo-EM density maps. Nat. Methods 11, 63–65 (2014).
Biasini, M. et al. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 42, W252-8 (2014).
Arnold, K., Bordoli, L., Kopp, J. & Schwede, T. The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics 22, 195–201 (2006).
Stein, N. CHAINSAW: a program for mutating pdb files used as templates in molecular replacement. J. Appl. Cryst. 41, 641–643 (2008).
Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D 66, 213–221 (2010).
Afonine, P. V. et al. Real-space refinement in PHENIX for cryo-EM and crystallography. Acta Crystallogr. D 74, 531–544 (2018).
Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D 60, 2126–2132 (2004).
Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).
Brown, A. et al. Tools for macromolecular model building and refinement into electron cryo-microscopy reconstructions. Acta Crystallogr. D 71, 136–153 (2015).
Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).
Shin, T. M., Smith, R. D., Toro, L. & Goldin, A. L. High-level expression and detection of ion channels in Xenopus oocytes. Methods Enzymol. 293, 529–556 (1998).
Carvalho-de-Souza, J. L. & Bezanilla, F. Nonsensing residues in S3-S4 linker’s C terminus affect the voltage sensor set point in K+ channels. J. Gen. Physiol. 150, 307–321 (2018).
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).
Shaw, D. E. et al. Anton, a special-purpose machine for molecular dynamics simulation. Commun. ACM 51, 91–97 (2008).
Šali, A. & Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).
Khalili-Araghi, F. et al. Calculation of the gating charge for the Kv1.2 voltage-activated potassium channel. Biophys. J. 98, 2189–2198 (2010).
Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. 14, 33–38, 27–28 (1996).
Phillips, J. C. et al. Scalable molecular dynamics with NAMD. J. Comput. Chem. 26, 1781–1802 (2005).
Best, R. B. et al. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ(1) and χ(2) dihedral angles. J. Chem. Theory Comput. 8, 3257–3273 (2012).
Klauda, J. B. et al. Update of the CHARMM all-atom additive force field for lipids: validation on six lipid types. J. Phys. Chem. B 114, 7830–7843 (2010).
Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983).
Martyna, G. J., Tobias, D. J. & Klein, M. L. Constant pressure molecular dynamics algorithms. J. Chem. Phys. 101, 4177–4189 (1994).
Feller, S. E., Zhang, Y., Pastor, R. W. & Brooks, B. R. Constant pressure molecular dynamics simulation: The Langevin piston method. J. Chem. Phys. 103, 4613–4621 (1995).
Essmann, U. et al. A smooth particle mesh Ewald method. J. Chem. Phys. 103, 8577–8593 (1995).
Roux, B. The membrane potential and its representation by a constant electric field in computer simulations. Biophys. J. 95, 4205–4216 (2008).
Martyna, G. J., Klein, M. L. & Tuckerman, M. Nosé–Hoover chains: The canonical ensemble via continuous dynamics. J. Chem. Phys. 97, 2635–2643 (1992).
Shan, Y., Klepeis, J. L., Eastwood, M. P., Dror, R. O. & Shaw, D. E. Gaussian split Ewald: a fast Ewald mesh method for molecular simulation. J. Chem. Phys. 122, 54101 (2005).
Goddard, T. D. et al. UCSF ChimeraX: meeting modern challenges in visualization and analysis. Protein Sci. 27, 14–25 (2018).
Pintilie, G. D., Zhang, J., Goddard, T. D., Chiu, W. & Gossard, D. C. Quantitative analysis of cryo-EM density map segmentation by watershed and scale-space filtering, and fitting of structures by alignment to regions. J. Struct. Biol. 170, 427–438 (2010).
Pintilie, G., Chen, D.-H., Haase-Pettingell, C. A., King, J. A. & Chiu, W. Resolution and probabilistic models of components in CryoEM maps of mature P22 bacteriophage. Biophys. J. 110, 827–839 (2016).
Pravda, L. et al. MOLEonline: a web-based tool for analyzing channels, tunnels and pores (2018 update). Nucleic Acids Res. 46 (W1), W368–W373 (2018).
James, Z. M. & Zagotta, W. N. Structural insights into the mechanisms of CNBD channel function. J. Gen. Physiol. 150, 225–244 (2018).
Afonine, P. V. et al. New tools for the analysis and validation of cryo-EM maps and atomic models. Acta Crystallogr. D 74, 814–840 (2018).
Smart, O. S., Neduvelil, J. G., Wang, X., Wallace, B. A. & Sansom, M. S. P. HOLE: a program for the analysis of the pore dimensions of ion channel structural models. J. Mol. Graph. 14, 354–360 (1996).
We thank U. Baxa and T. J. Edwards at NCEF for cryo-EM data collection; P. Rodriguez, J. Austin II and T. Lavoie at the University of Chicago Advanced Electron Microscopy Facility for microscope maintenance and training; and P. Bezanilla, M. Zhao, T. Li and B. Reddy for advice and discussions at all stages of the project. Anton 2 computer time was provided by the Pittsburgh Supercomputing Center (PSC) through Grant R01GM116961 from the National Institutes of Health. The Anton 2 machine at PSC was generously made available by D. E. Shaw Research. This research was partly supported by the National Cancer Institute’s National Cryo-EM Facility at the Frederick National Laboratory for Cancer Research. This work was supported by the Consortium for Membrane Protein Dynamics and by grant R01GM088406 to EP. MDC was supported by F30MH116647 and T32GM007281.
The authors declare no competing interests.
Peer review information Nature thanks Sergei Noskov, Ingeborg Schmidt-Krey and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data figures and tables
a, Two major classes of channels, domain-swapped and non-domain-swapped are distinguished by the relative positions of voltage-sensing and pore domains. b, Solved structures of non-domain-swapped ion channels, two subunits shown for clarity c, G(V) relations of each channel subclass d, Gradient depiction of cyclic-nucleotide and voltage sensitivity for subclass members. Figure inspired by ref. 75.
a, SEC of KAT1em purified in digitonin, run on a Superose 6 column. b, Stain-free SDS–PAGE of purified KAT1em. SEC and SDS–PAGE results correspond to the preparation used for imaging (d) and are representative of three independent purifications. c, SEC of KAT1em in 2N2 nanodiscs (yellow trace), showing putative octamer, tetramer and empty nanodisc. Fluorescence detection SEC of full-length KAT1–GFP (blue trace) showing putative octamer and tetramer. These two samples were not subjected to any cryo-EM experiments, and are included only for the purpose of comparison. d, KAT1em cryo-EM workflow. From 1,500 movies, 120,000 particles were picked and subjected to 2D classification, which then yielded 110,000 particles, which were classified in 3D without imposing symmetry (4 coloured classes). Particles from the best two classes (blue and green classes, 91,000 total) were subsequently refined, imposing C4 symmetry, and using successive masks to focus on one of the tetramers and finally on the transmembrane region of one of the tetramers. Additional details are given in Methods.
a, ResMap colouring of unfiltered half map of full molecule b, Same ResMap colouring as a on sharpened full molecule map. c, d, Ninety-degrees-rotated angular-distribution plots for refined full molecule. e, FSC plot for map focused on the transmembrane region. FSC 0.143 criterion is used for resolution determination41. f, FSC (map and model) plot from phenix.mtriage76, indicating correspondence of tetramer atomic model to transmembrane domain-focused-refined density map. g, Details of sharpened cryo-EM density map are shown with fitted atomic model.
a, Side view of pore, with only two subunits shown for clarity. Permeation pathway is shown in blue, with inner gate radius calculated by MOLE74 (1.4 Å) or HOLE77 (1 Å), inner gate-forming I292 side chains shown as sticks. b, G(V) relations of pore alanine scan. Shaded error regions represent s.d., surrounding the symbols which represent the mean. Wild type (n = 11), L287A (n = 19), T288A (n = 4), L291A (n = 10), I292A (n = 10), T296A (n = 10), V299A (n = 8) and H301A (n = 10) are shown; n is the number of biologically independent cells. c, Overlay of KAT1em pseudo-CNBD (tan) and holoHCN1 CNBD (green, PDB ID: 5U6P). The ligand, HCN1-cAMP is shown as sticks in the cAMP-binding pocket. d, Overlay of KAT1em (tan) and EAG1 (blue, PDB ID: 5K7L). KAT1 lacks the ‘intrinsic ligand’ loop of EAG1. e, Top-down view of KAT1em (tan), holo HCN1 (green) and EAG1 (blue) overlay. Structures were aligned and superimposed on the basis of the transmembrane helices. Only C-linker hairpins are shown for clarity to compare relative rotation of the C-linker to the transmembrane domain for each structure. The relative rotation of the KAT1 C-linker matches that of EAG1 and not HCN1. f, g, Surface electrostatic potential of HCN1 (f) and KAT1 (g), respectively. Ligand-binding pockets are circled in black. KAT1 lacks a deep electropositive (blue) pocket as seen in HCN1.
a, Diagram of key VSD features, showing hydrophobic gasket (F102 and V70, yellow) as well as all S4 charges (blue) and distributed countercharges (or counter dipoles) (red). Dashed lines indicate likely interactions in the up conformation. b, c, Overlays of KAT1em (tan) with HCN (green, PDB ID: 5U6O) and Kv1.2/2.1 (pink, PDB ID: 2R9R), respectively, highlighting structural differences between S4 helices. Cα atoms of the positively charged residues of S4 are shown as spheres.
a, G(V) relations of S4–S5–C-linker interfacial mutants. Wild type (n = 11) and K187A (n = 8), D188A (n = 9), R190A (n = 6), F191A (n = 12), N192A (9), T303A (n = 13), R307A (n = 14), R314A (n = 31) and R314E (n = 9) mutants are shown; n is the number of biologically independent cells. Shaded regions represent s.d. and symbols represent the mean. b, G(V) relations of upper-interface mutants. Wild type (n = 11) and F81A (n = 12), F81L (n = 19), I166A (n = 18), M169A (n = 5), V178A (n = 19) and F215A (n = 19) mutants are shown; n is the number of biologically independent cells. Shaded regions represent s.d. and symbols represent the mean. c, Deactivation energies of upper-interface mutants calculated from G(V) relations in b (same sample sizes). d, Mapping of upper-interface functional data (shown in c). Displayed as sticks are key residues on S1: F80, F81, F83, key S4 residues: I166, M169, L172, V178, and key S5 residues: Y193, R197, K200, F207, C211, F215. e, f, Comparison of similar lipid-binding conformations observed in the structure (e) and after about 3.5 μs molecular dynamics simulation (f). g, Cryo-EM density map, with one bound lipid coloured green, contoured at the same contour level as the full map. h, SDS–PAGE and GFP in-gel imaging of Xenopus oocyte membrane fractions, extracted in gentle detergent (Methods). The experiment was performed once and each lane is derived from ten cells. i, Confocal imaging of Xenopus oocyte animal poles expressing various GFP-tagged constructs. Imaging was performed in a single session with normalized exposure times, and each image is representative of five independent oocytes.
a, G(V) relations for cRNA mixing-coinjection experiments. cRNA encoding loss-of-function mutants (I189A, R197K, K200Q, T306A and R310K), for which no currents were observed were selected. These loss-of-function mutant cRNAs were each individually mixed with cRNA encoding a gain-of-function double mutant (Q80A–177K). Data are mean ± s.e.m. Q80A–R177K (n = 12), I189A + Q80A–R177K (n = 7), K200Q + Q80A–R177K (n = 8), R197K + Q80A–R177K (n = 9), R310K + Q80A–R177K (n = 8) and T306A + Q80A–R177K (n = 9) are shown. b, Plot of activation midpoints (Vh) of G(V) relations shown in a. c, d, Limiting-slope analyses for wild-type KAT1 (c) and D188A (d). Top, raw currents evoked by voltage ramp protocol. Middle, conductance–voltage relations, with conductance plotted on a log scale. Data points are black, fits are red. Blue vertical lines mark the first and second inflection points of the curve, the region between which was used to calculate limiting-slope (z) values (Methods). Wild type, z = 2.83 ± 0.5; D188A, z = 3.28 ± 0.2. data are mean ± s.d. Bottom, data (black) and fits (red) on a linear scale. n is the number of biologically independent cells.
a, Schematic of double-mutant cycle analysis. The difference between ΔΔGx,y and the quantity (ΔΔGx + ΔΔGy) determines the extent of differential interaction between residues x and y in the up and down states. b, G(V) relations for single and double mutants, illustrating residue–residue pairs displaying additivity (gray) and non-additivity in different directions (green, up-state interaction; red, down-state interaction). Shaded regions represent s.d. and symbols represent the mean. Wild type (n = 11) and M64A (n = 11), V67A (n = 33), C77A (n = 15), Q80A (n = 11), D95A (n = 12), Q149A (n = 21), R165A (n = 21), S168A (n = 17), V178A (n = 19), M64A/V178A (n = 15), V67A/Q80A (n = 13), V67A/S168A (n = 16), V67A/V178A (n = 10), C77A/S168A (n = 14), Q80A/R165A (n = 6), D95A/R165A (n = 5) and Q149A/R165A (n = 14) mutants are shown; n is the number of biologically independent cells. c, Displacement of charge for the isolated VSD in the up, one-click down and two-click down conformations at different transmembrane potentials. Data are mean ± s.d. calculated using the last 40-ns snapshots (n = 4,000) of 50-ns trajectories. Each system was simulated once at each chosen potential. The gating charge was then calculated as the offset constant between the linear fits, resulting in a gating charge of 1.02 e and 0.55 e between the up and one-click down, and one-click down and two-click down states, respectively. d, Mapping of double-mutant cycle constraints onto up VSD structure. Thick red and green lines connect Cα carbons of interacting pairs. Thin grey lines connect negative-control pairs. e, Mapping of literature KAT1 down-state interacting pairs21 onto up structure. Thick red lines connect Cα carbons of interacting pairs.
a, Raw current traces for all four combinations of C77(S) and R165(C). On washing with 100 μM CdCl2, current increases only in the C77/R165C condition (red box, middle), and then decreases again upon EDTA wash. Representative data are shown from the same oocyte, and each experiment was repeated five independent times (five biologically independent oocytes) with similar results. b, Pulse protocol used during experiment. c, Mapping of C77 (on S1) and R165 (on S4) onto the up VSD structure of KAT1. Cα atoms are indicated by a red line.
Supplementary Methods, including extensive details on data analysis protocols for left-shifted G(V) curves and limiting slope data. Also included are four Supplementary Figures (higher resolution versions of these figures are provided separately).
High resolution versions of Supplementary Figures 1-4 (see Supplementary Information document for details).
This file contains the uncropped gels.
Features of the KAT1em structure including bound phospholipid and intracellular VSD-pore interface.
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Clark, M.D., Contreras, G.F., Shen, R. et al. Electromechanical coupling in the hyperpolarization-activated K+ channel KAT1. Nature 583, 145–149 (2020). https://doi.org/10.1038/s41586-020-2335-4
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