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Electromechanical coupling in the hyperpolarization-activated K+ channel KAT1

Abstract

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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Fig. 1: Function and architecture of A. thaliana KAT1em.
Fig. 2: KAT1 pore and voltage-sensing domain structure and alanine scanning of pore inner gate region.
Fig. 3: The KAT1 VSD–pore interface and lipid-binding conformation.
Fig. 4: Hypothetical modelling of the KAT1 VSD in the down state, and implications for electromechanical coupling and gating polarity.

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Data availability

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.

References

  1. Bezanilla, F. How membrane proteins sense voltage. Nat. Rev. Mol. Cell Biol. 9, 323–332 (2008).

    CAS  PubMed  Google Scholar 

  2. Männikkö, R., Elinder, F. & Larsson, H. P. Voltage-sensing mechanism is conserved among ion channels gated by opposite voltages. Nature 419, 837–841 (2002).

    ADS  PubMed  Google Scholar 

  3. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Blunck, R. & Batulan, Z. Mechanism of electromechanical coupling in voltage-gated potassium channels. Front. Pharmacol. 3, 166 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Altomare, C. et al. Integrated allosteric model of voltage gating of HCN channels. J. Gen. Physiol. 117, 519–532 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Long, S. B., Campbell, E. B. & Mackinnon, R. Voltage sensor of Kv1.2: structural basis of electromechanical coupling. Science 309, 903–908 (2005).

    ADS  CAS  PubMed  Google Scholar 

  7. Vardanyan, V. & Pongs, O. Coupling of voltage-sensors to the channel pore: a comparative view. Front. Pharmacol. 3, 145 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Hedrich, R. Ion channels in plants. Physiol. Rev. 92, 1777–1811 (2012).

    ADS  CAS  PubMed  Google Scholar 

  10. Hoshi, T. Regulation of voltage dependence of the KAT1 channel by intracellular factors. J. Gen. Physiol. 105, 309–328 (1995).

    CAS  PubMed  Google Scholar 

  11. 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).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hertel, B. et al. KAT1 inactivates at sub-millimolar concentrations of external potassium. J. Exp. Bot. 56, 3103–3110 (2005).

    CAS  PubMed  Google Scholar 

  13. Lee, C.-H. & MacKinnon, R. Structures of the Human HCN1 Hyperpolarization-Activated Channel. Cell 168, 111–120 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Yifrach, O. & MacKinnon, R. Energetics of pore opening in a voltage-gated K+ channel. Cell 111, 231–239 (2002).

    CAS  PubMed  Google Scholar 

  15. 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).

    CAS  PubMed  Google Scholar 

  16. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 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).

    CAS  PubMed  Google Scholar 

  18. 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).

    CAS  PubMed  Google Scholar 

  19. 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).

    CAS  PubMed  Google Scholar 

  20. 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).

    CAS  PubMed  Google Scholar 

  21. 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).

    CAS  PubMed  Google Scholar 

  22. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Li, Q. et al. Structural mechanism of voltage-dependent gating in an isolated voltage-sensing domain. Nat. Struct. Mol. Biol. 21, 244–252 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Guo, J. et al. Structure of the voltage-gated two-pore channel TPC1 from Arabidopsis thaliana. Nature 531, 196–201 (2016).

    ADS  CAS  PubMed  Google Scholar 

  25. Yan, Z. et al. Structure of the Nav1.4-β1 complex from electric eel. Cell 170, 470–482.e11 (2017).

    CAS  PubMed  Google Scholar 

  26. Xu, H. et al. Structural basis of Nav1.7 inhibition by a gating-modifier spider toxin. Cell 176, 702–715.e14 (2019).

    CAS  PubMed  Google Scholar 

  27. 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).

    CAS  PubMed  Google Scholar 

  28. Whicher, J. R. & MacKinnon, R. Structure of the voltage-gated K+ channel Eag1 reveals an alternative voltage sensing mechanism. Science 353, 664–669 (2016).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Kusch, J. et al. Interdependence of receptor activation and ligand binding in HCN2 pacemaker channels. Neuron 67, 75–85 (2010).

    CAS  PubMed  Google Scholar 

  31. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Perissinotti, L. L. et al. Determinants of isoform-specific gating kinetics of hERG1 channel: combined experimental and simulation study. Front. Physiol. 9, 207 (2018).

    PubMed  PubMed Central  Google Scholar 

  34. Papanatsiou, M. et al. Optogenetic manipulation of stomatal kinetics improves carbon assimilation, water use, and growth. Science 363, 1456–1459 (2019).

    ADS  CAS  PubMed  Google Scholar 

  35. Kasimova, M. A. et al. Helix breaking transition in the S4 of HCN channel is critical for hyperpolarization-dependent gating. eLife 8, e53400 (2019).

    PubMed  PubMed Central  Google Scholar 

  36. Lee, C. H. & MacKinnon, R. Voltage sensor movements during hyperpolarization in the HCN channel. Cell 179, 1582–1589 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 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).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Rohou, A. & Grigorieff, N. CTFFIND4: Fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 192, 216–221 (2015).

    PubMed  PubMed Central  Google Scholar 

  40. 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).

    Google Scholar 

  41. 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).

    CAS  PubMed  Google Scholar 

  42. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Scheres, S. H. W. & Chen, S. Prevention of overfitting in cryo-EM structure determination. Nat. Methods 9, 853–854 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Kucukelbir, A., Sigworth, F. J. & Tagare, H. D. Quantifying the local resolution of cryo-EM density maps. Nat. Methods 11, 63–65 (2014).

    CAS  PubMed  Google Scholar 

  45. Biasini, M. et al. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 42, W252-8 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 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).

    CAS  PubMed  Google Scholar 

  47. Stein, N. CHAINSAW: a program for mutating pdb files used as templates in molecular replacement. J. Appl. Cryst. 41, 641–643 (2008).

    CAS  Google Scholar 

  48. Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D 66, 213–221 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Afonine, P. V. et al. Real-space refinement in PHENIX for cryo-EM and crystallography. Acta Crystallogr. D 74, 531–544 (2018).

    CAS  Google Scholar 

  50. Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D 60, 2126–2132 (2004).

    PubMed  Google Scholar 

  51. Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Brown, A. et al. Tools for macromolecular model building and refinement into electron cryo-microscopy reconstructions. Acta Crystallogr. D 71, 136–153 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    CAS  PubMed  Google Scholar 

  54. 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).

    Google Scholar 

  55. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Shaw, D. E. et al. Anton, a special-purpose machine for molecular dynamics simulation. Commun. ACM 51, 91–97 (2008).

    Google Scholar 

  58. Šali, A. & Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).

    PubMed  Google Scholar 

  59. Khalili-Araghi, F. et al. Calculation of the gating charge for the Kv1.2 voltage-activated potassium channel. Biophys. J. 98, 2189–2198 (2010).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  60. Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. 14, 33–38, 27–28 (1996).

    Google Scholar 

  61. Phillips, J. C. et al. Scalable molecular dynamics with NAMD. J. Comput. Chem. 26, 1781–1802 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 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).

    ADS  CAS  Google Scholar 

  65. Martyna, G. J., Tobias, D. J. & Klein, M. L. Constant pressure molecular dynamics algorithms. J. Chem. Phys. 101, 4177–4189 (1994).

    ADS  CAS  Google Scholar 

  66. 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).

    ADS  CAS  Google Scholar 

  67. Essmann, U. et al. A smooth particle mesh Ewald method. J. Chem. Phys. 103, 8577–8593 (1995).

    ADS  CAS  Google Scholar 

  68. Roux, B. The membrane potential and its representation by a constant electric field in computer simulations. Biophys. J. 95, 4205–4216 (2008).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  69. Martyna, G. J., Klein, M. L. & Tuckerman, M. Nosé–Hoover chains: The canonical ensemble via continuous dynamics. J. Chem. Phys. 97, 2635–2643 (1992).

    ADS  Google Scholar 

  70. 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).

    ADS  PubMed  Google Scholar 

  71. Goddard, T. D. et al. UCSF ChimeraX: meeting modern challenges in visualization and analysis. Protein Sci. 27, 14–25 (2018).

    CAS  PubMed  Google Scholar 

  72. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 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).

    ADS  CAS  PubMed  Google Scholar 

  74. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. James, Z. M. & Zagotta, W. N. Structural insights into the mechanisms of CNBD channel function. J. Gen. Physiol. 150, 225–244 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 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).

    CAS  Google Scholar 

  77. 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).

    CAS  PubMed  Google Scholar 

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Acknowledgements

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.

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Authors

Contributions

M.D.C. and E.P. conceived the project. M.D.C. performed structural, biochemical and most electrophysiological experiments. G.F.C. performed limiting-slope analyses and processed all electrophysiology data. R.S. constructed down-state models and ran simulations. All authors contributed to manuscript preparation.

Corresponding author

Correspondence to Eduardo Perozo.

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The authors declare no competing interests.

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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

Extended Data Fig. 1 Structural and functional diversity of tetrameric ion channels.

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.

Extended Data Fig. 2 KAT1em biochemistry and cryo-EM workflow.

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.

Extended Data Fig. 3 Cryo-EM map and model validation.

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.

Extended Data Fig. 4 KAT1em pore domain and pseudo cyclic nucleotide-binding domain.

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.

Extended Data Fig. 5 The voltage-sensing domain of KAT1em in the up conformation.

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.

Extended Data Fig. 6 Structural and functional characterization of KAT1 VSD–pore interfaces.

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.

Extended Data Fig. 7 Detailed functional characterization of selected VSD–pore interface mutants.

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.

Extended Data Fig. 8 VSD movement during gating.

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.

Extended Data Fig. 9 A cysteine–Cd2+–cysteine bridge in the KAT1 VSD promotes channel opening.

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.

Extended Data Table 1 Cryo-EM data collection, refinement and validation statistics

Supplementary information

Supplementary Information

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).

Reporting Summary

Supplementary Figures

High resolution versions of Supplementary Figures 1-4 (see Supplementary Information document for details).

Supplementary Data

This file contains the uncropped gels.

Video 1

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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