Diverse binding poses of agonistic neurotoxins on human Na_v1.6

Abstract Voltage-gated sodium (Nav) channels are key targets of various venomous toxins. Deciphering the binding poses and mechanisms of action of representative toxins will help to dissect the functional mechanism of the channels and facilitate therapeutic development targeting Nav channels1,2. Here we present cryo-electron microscopy (cryo-EM) structures of distinct binding poses of three agonistic peptide toxins on the human Nav1.6–β1 channel complex. The globular β-scorpion toxin Cn2 nestles between the extracellular segment of voltage-sensing domain (VSD) in the second repeat of the Nav1.6 core α-unit (VSDII) and the pore extracellular loops in the third repeat of the Nav1.6 core α-unit (ECLIII), where it is stabilized by interactions with both protein regions and the branched N1372-glycan. Cone snail ι-conotoxin RXIA adopts an elongated conformation, spanning VSDI and VSDIV to wrap around the shoulder of the pore domain (PD). The bullet ant-derived toxin δ-paraponeritoxin-Pc1a exists as a transmembrane helix that stands between VSDII and PDIII. Our findings, corroborated by functional characterizations, illustrate the diversity in peptide toxin binding poses and mechanisms of action, link stabilization of the up state of VSDI or VSDII to channel activation, and provide clues to the rational design of selective Nav channel modulators. This is a preview of subscription content, access via your institution Access options Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription £17.99 / 30 days cancel any time Subscribe to this journal Receive 52 print issues and online access £199.00 per year only £3.83 per issue Buy this article - Purchase on SpringerLink - Instant access to the full article PDF. £ 29.95 Prices may be subject to local taxes which are calculated during checkout Data availability Data supporting the findings of this study are available within the Article and its Supplementary Information. The cryo-EM maps have been deposited in the EMDB under accession codes EMD-80133 (Nav1.6–Cn2), EMD-80134 (Nav1.6–ι-RXIA) and EMD-80135 (Nav1.6–Pc1a). The coordinates have been deposited in the PDB under accession codes 25IH (Nav1.6–Cn2), 25II (Nav1.6–ι-RXIA) and 25IJ (Nav1.6–Pc1a). Source data are provided with this paper. References Arbuckle, K. Evolutionary context of venom in animals. Evol. Venom. Anim. Toxins 24, 3–31 (2017). Huang, J., Pan, X. & Yan, N. Structural biology and molecular pharmacology of voltage-gated ion channels. Nat. Rev. Mol. Cell Biol. 25, 904–925 (2024). de Lera Ruiz, M. & Kraus, R. L. Voltage-gated sodium channels: structure, function, pharmacology, and clinical indications. J. Med. Chem. 58, 7093–7118 (2015). Armstrong, C. M. Na channel inactivation from open and closed states. Proc. Natl Acad. Sci. USA 103, 17991–17996 (2006). Chanda, B. & Bezanilla, F. Tracking voltage-dependent conformational changes in skeletal muscle sodium channel during activation. J. Gen. Physiol. 120, 629–645 (2002). Lazarovici, P. Snake-and spider-venom-derived toxins as lead compounds for drug development. Methods Mol. Biol. https://doi.org/10.1007/978-1-4939-9845-6_1 (2020). Kalia, J. et al. From foe to friend: using animal toxins to investigate ion channel function. J. Mol. Biol. 427, 158–175 (2015). Luz Morales-Lazaro, S., Hernández-García, E., Serrano-Flores, B. & Rosenbaum, T. Organic toxins as tools to understand ion channel mechanisms and structure. Curr. Top. Med. Chem. 15, 581–603 (2015). Escoubas, P. & Bosmans, F. Spider peptide toxins as leads for drug development. Expert Opin. Drug Discov. 2, 823–835 (2007). Gilchrist, J. & Bosmans, F. Using voltage-sensor toxins and their molecular targets to investigate NaV1.8 gating. J. Physiol. 596, 1863–1872 (2018). Fan, X. et al. Phrixotoxin-3 binds to three distinct antagonistic sites on human Nav1.6. Cell Res. 35, 610–613 (2025). Schaller, K. L., Krzemien, D. M., Yarowsky, P. J., Krueger, B. K. & Caldwell, J. H. A novel, abundant sodium channel expressed in neurons and glia. J. Neurosci. 15, 3231–3242 (1995). Freeman, S. A., Desmazières, A., Fricker, D., Lubetzki, C. & Sol-Foulon, N. Mechanisms of sodium channel clustering and its influence on axonal impulse conduction. Cell. Mol. Life Sci. 73, 723–735 (2016). Caldwell, J. H., Schaller, K. L., Lasher, R. S., Peles, E. & Levinson, S. R. Sodium channel Nav1.6 is localized at nodes of Ranvier, dendrites, and synapses. Proc. Natl Acad. Sci. USA 97, 5616–5620 (2000). Hu, W. et al. Distinct contributions of Nav1.6 and Nav1.2 in action potential initiation and backpropagation. Nat. Neurosci. 12, 996–1002 (2009). O’Brien, J. E. & Meisler, M. H. Sodium channel SCN8A (Nav1.6): properties and de novo mutations in epileptic encephalopathy and intellectual disability. Front. Genet. 4, 213 (2013). Liu, Y. et al. Neuronal mechanisms of mutations in SCN8A causing epilepsy or intellectual disability. Brain 142, 376–390 (2019). Blanchard, M. G. et al. De novo gain-of-function and loss-of-function mutations of SCN8A in patients with intellectual disabilities and epilepsy. J. Med. Genet. 52, 330–337 (2015). Sole, L., Wagnon, J. L. & Tamkun, M. M. Functional analysis of three Nav1.6 mutations causing early infantile epileptic encephalopathy. Biochim. Biophys. Acta 1866, 165959 (2020). Tanaka, B. S. et al. A gain-of-function mutation in Nav1.6 in a case of trigeminal neuralgia. Mol. Med. 22, 338–348 (2016). Wagnon, J. L. et al. Loss-of-function variants of SCN8A in intellectual disability without seizures. Neurol. Genet. 3, e170 (2017). Wagnon, J. L. et al. Partial loss-of-function of sodium channel SCN8A in familial isolated myoclonus. Hum. Mutat. 39, 965–969 (2018). Samanta, D. et al. Current and emerging precision therapies for developmental and epileptic encephalopathies. Pediatr. Neurol. 168, 67–81 (2025). Tchaicha, S. et al. Areas of research priorities in epilepsy: a position paper of the European Reference Network for Rare and Complex Epilepsies, EpiCARE. Epilepsia Open 11, 291–321 (2026). Pan, X. et al. Molecular basis for pore blockade of human Na+ channel Nav1.2 by the μ-conotoxin KIIIA. Science 363, 1309–1313 (2019). Shen, H., Liu, D., Wu, K., Lei, J. & Yan, N. Structures of human Nav1.7 channel in complex with auxiliary subunits and animal toxins. Science 363, 1303–1308 (2019). Huang, G. et al. High-resolution structures of human Nav1.7 reveal gating modulation through α-π helical transition of S6IV. Cell Rep. 39, 110735 (2022). Li, Y. et al. Structure of human NaV1. 6 channel reveals Na+ selectivity and pore blockade by 4,9-anhydro-tetrodotoxin. Nat. Commun. 14, 1030 (2023). Wu, T., Yang, X., Jin, X., Yan, N. & Li, Z. Critical role of extracellular loops in differential modulations of TTX-sensitive and TTX-resistant Nav channels. Proc. Natl Acad. Sci. USA 122, e2510355122 (2025). Shen, H. et al. Structural basis for the modulation of voltage-gated sodium channels by animal toxins. Science 362, eaau2596 (2018). Clairfeuille, T. et al. Structural basis of α-scorpion toxin action on Nav channels. Science 363, eaav8573 (2019). Jiang, D. et al. Structural basis for voltage-sensor trapping of the cardiac sodium channel by a deathstalker scorpion toxin. Nat. Commun. 12, 128 (2021). Fiedler, B. et al. Specificity, affinity and efficacy of iota-conotoxin RXIA, an agonist of voltage-gated sodium channels Nav1.2, 1.6 and 1.7. Biochem. Pharmacol. 75, 2334–2344 (2008). Jimenez, E. C. et al. Novel excitatory Conus peptides define a new conotoxin superfamily. J. Neurochem. 85, 610–621 (2003). Schiavon, E. et al. Resurgent current and voltage sensor trapping enhanced activation by a β-scorpion toxin solely in Nav1.6 channel significance in mice Purkinje neurons. J. Biol. Chem. 281, 20326–20337 (2006). Aili, S. R. et al. An integrated proteomic and transcriptomic analysis reveals the venom complexity of the bullet ant Paraponera clavata. Toxins 12, 324 (2020). Buczek, O. et al. Structure and sodium channel activity of an excitatory I1-superfamily conotoxin. Biochemistry 46, 9929–9940 (2007). Buczek, O., Yoshikami, D., Bulaj, G., Jimenez, E. C. & Olivera, B. M. Post-translational amino acid isomerization: a functionally important d-amino acid in an excitatory peptide. J. Biol. Chem. 280, 4247–4253 (2005). Piek, T. et al. Poneratoxin, a novel peptide neurotoxin from the venom of the ant, Paraponera clavata. Compar. Biochem. Physiol. C 99, 487–495 (1991). Robinson, S. D. et al. Ant venoms contain vertebrate-selective pain-causing sodium channel toxins. Nat. Commun. 14, 2977 (2023). Fan, X., Huang, J., Jin, X. & Yan, N. Cryo-EM structure of human voltage-gated sodium channel Nav1.6. Proc. Natl Acad. Sci. USA 120, e2220578120 (2023). Wu, Q. et al. Structural mapping of Nav1.7 antagonists. Nat. Commun. 14, 3224 (2023). Shen, H. et al. Structural basis for the modulation of voltage-gated sodium channels by animal toxins. Science https://doi.org/10.1126/science.aau2596 (2018). Huang, G. et al. Unwinding and spiral sliding of S4 and domain rotation of VSD during the electromechanical coupling in Nav1.7. Proc. Natl Acad. Sci. USA 119, e2209164119 (2022). Szolajska, E. et al. Poneratoxin, a neurotoxin from ant venom: structure and expression in insect cells and construction of a bio-insecticide. Eur. J. Biochem. 271, 2127–2136 (2004). Fan, X. et al. Open-state structure of veratridine-activated human Nav1.7 reveals the molecular choreography of fast inactivation. Vita https://doi.org/10.15302/vita.2026.01.0003 (2026). Jin, X., Huang, J., Wang, H., Wang, K. & Yan, N. A versatile residue numbering scheme for Nav and Cav channels. Cell Chem. Biol. 31, 1394–1404 (2024). Israel, M. R. et al. The E15R point mutation in scorpion toxin Cn2 uncouples its depressant and excitatory activities on human Nav1.6. J. Med. Chem. 61, 1730–1736 (2018). Jiang, D. et al. Open-state structure and pore gating mechanism of the cardiac sodium channel. Cell 184, 5151–5162 (2021). Wisedchaisri, G. & Gamal El-Din, T. M. Druggability of voltage-gated sodium channels-exploring old and new drug receptor sites. Front. Pharmacol. 13, 858348 (2022). Fattorusso, A. et al. The pharmacoresistant epilepsy: an overview on existant and new emerging therapies. Front. Neurol. 12, 674483 (2021). Stevens, M., Peigneur, S. & Tytgat, J. Neurotoxins and their binding areas on voltage-gated sodium channels. Front. Pharmacol. 2, 71 (2011). Bagal, S. K., Marron, B. E., Owen, R. M., Storer, R. I. & Swain, N. A. Voltage gated sodium channels as drug discovery targets. Channels 9, 360–366 (2015). Blumenfeld, H. et al. Role of hippocampal sodium channel Nav1.6 in kindling epileptogenesis. Epilepsia 50, 44–55 (2009). Phulera, S. et al. Scorpion α-toxin LqhαIT specifically interacts with a glycan at the pore domain of voltage-gated sodium channels. Structure https://doi.org/10.1016/j.str.2024.07.021 (2024). Cui, C. et al. Mechanisms of KCNQ1 gating modulation by KCNE1/3 for cell-specific function. Cell Res. 35, 876–886 (2025). Goldschen-Ohm, M. P., Capes, D. L., Oelstrom, K. M. & Chanda, B. Multiple pore conformations driven by asynchronous movements of voltage sensors in a eukaryotic sodium channel. Nat. Commun. 4, 1350 (2013). Gao, S. et al. Structural basis for human Cav1.2 inhibition by multiple drugs and the neurotoxin calciseptine. Cell 186, 5363–5374 (2023). Gao, S., Yao, X. & Yan, N. Structure of human Cav2.2 channel blocked by the painkiller ziconotide. Nature 596, 143–147 (2021). Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021). Smart, O. S., Neduvelil, J. G., Wang, X., Wallace, B. A. & Sansom, M. S. HOLE: a program for the analysis of the pore dimensions of ion channel structural models. J. Mol. Graph. 14, 354–360 (1996). Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017). Lei, J. & Frank, J. Automated acquisition of cryo-electron micrographs for single particle reconstruction on an FEI Tecnai electron microscope. J. Struct. Biol. 150, 69–80 (2005). Bepler, T. et al. Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. Nat. Methods 16, 1153–1160 (2019). Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010). Pintar, A., Possani, L. D. & Delepierre, M. Solution structure of toxin 2 from Centruroides noxius Hoffmann, a β-scorpion neurotoxin acting on sodium channels. J. Mol. Biol. 287, 359–367 (1999). Afonine, P. V. et al. Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr. D 68, 352–367 (2012). Croll, T. I. ISOLDE: a physically realistic environment for model building into low-resolution electron-density maps. Acta Crystallogr. D 74, 519–530 (2018). Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024). Li, Z. et al. Structure of human Nav1.5 reveals the fast inactivation-related segments as a mutational hotspot for the long QT syndrome. Proc. Natl Acad. Sci. USA 118, e2100069118 (2021). Huang, X. et al. Structural basis for high-voltage activation and subtype-specific inhibition of human Nav1.8. Proc. Natl Acad. Sci. USA 119, e2208211119 (2022). Sastry, G. M., Adzhigirey, M., Day, T., Annabhimoju, R. & Sherman, W. Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J. Comput. Aided Mol. Des. 27, 221–234 (2013). Lu, C. et al. OPLS4: improving force field accuracy on challenging regimes of chemical space. J. Chem. Theory Comput. 17, 4291–4300 (2021). Lomize, A. L., Todd, S. C. & Pogozheva, I. D. Spatial arrangement of proteins in planar and curved membranes by PPM 3.0. Protein Sci. 31, 209–220 (2022). Mark, P. & Nilsson, L. Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. J. Phys. Chem. A 105, 9954–9960 (2001). McGibbon, R. T. et al. MDTraj: a modern open library for the analysis of molecular dynamics trajectories. Biophys. J. 109, 1528–1532 (2015). Pérez-Hernández, G. & Hildebrand, P. W. mdciao: accessible analysis and visualization of molecular dynamics simulation data. PLoS Comput. Biol. 21, e1012837 (2025). Acknowledgements We thank the members of the cryo-EM facility at Princeton Imaging and Analysis Center; and the staff at the Structural Biology Core Facility at the Bio-Tech Center and the Computing Labware for Electron-microscopy Visualization and Experimental Research (CLEVER) at the Biomedical Data Center of Shenzhen Medical Academy of Research and Translation (SMART) for their technical support. Funding This work is supported by the National Natural Science Foundation of China (32330052, 92478205 and 32501082). X.F. was supported by the HFSP long-term fellowship (LT000754/2020-L) from the International Human Frontier Science Program Organization (HFSPO) from 2020 to 2023. J.H. and X.F. are supported by the start-up funding from Shenzhen Medical Academy of Research and Translation (SMART). J.H. is also supported by the Guangdong Pearl River Talent Program (ZJQNRC20241219163128017). X.J. is supported by the Beijing Natural Science Foundation (7264373). N.Y. thanks the Parkland Foundation for the Mindray Endowed Professorship. Author information Authors and Affiliations Contributions N.Y., X.F. and J.H. conceived the project. X.F., J.H., L.Y., J.C., H.W., X.H., J.G., Q.W., Y.X., F.L., Q.G., Z.S., X.J. and N.Y. designed experiments. X.F., J.H. and L.Y. performed all experiments related to cryo-EM studies, including protein expression, purification and three-dimensional reconstructions. J.C., Q.W., Q.G., X.H., Z.S. and X.J. carried out experiments related to electrophysiology. X.H., Y.X. and F.L. prepared the plasmids for electrophysiological analyses. H.W. performed MDS. X.F., J.H., L.Y., J.C., H.W., X.H., J.G., Q.W., Y.X., F.L., Q.G., Z.S., X.J. and N.Y. analysed data. X.F., J.H. and N.Y. wrote the manuscript with input from all of the authors. J.C. and H.W. made equal contributions to this work. All of the authors approved the final manuscript. Corresponding authors Ethics declarations Competing interests The authors declare no competing interests. Peer review Peer review information Nature thanks Frank Bosmans and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Additional information Extended data figures and tables Extended Data Fig. 1 Electrophysiological characterizations of Nav1.6 modulation by three peptide toxins. a, Examination of the dual modulation of Nav1.6 by Cn2. Representative traces for blocking Nav1.6-WT by Cn2 at indicated concentrations (top). Statistical results of the time constant (tau) for Nav1.6-WT currents. Sodium currents were elicited by a voltage step from a holding potential of -120 mV to 0 mV (external solution, ES, n = 30) or -40 mV (1 μM Cn2, n = 18). sample sizes (n) are shown in the data set (bottom). Beside are the representative traces. b, Potentiation of Nav1.6 by ι-RXIA. Representative traces for the peak currents of Nav1.6-WT in the presence of ι-RXIA at indicated concentrations. c, Voltage-dependent inactivation curves of Nav1.6-WT treated with 1 μM (n = 5) or 10 μM (n = 5) ι-RXIA. d, Representative traces of Nav1.6-WT peak current in the presence of Pc1a at indicated concentrations. The channels were expressed without (panel a) or with (panel b-d) β1 for electrophysiological recordings. Data are presented as mean ± SEM. Sample sizes (n) shown in the data set represent biologically independent cells in panels a and c, and ****P < 0.0001 in a. Statistical analysis was conducted using a two-tailed unpaired t-test in a. Detailed statistics are provided in Source data. Extended Data Fig. 2 Functional characterization of Nav1.6 mutants treated with Cn2. a, Functional characterization of Nav1.6 variants. Voltage-dependent activation (top, n = 19, 7, 18, 14 for variants) and inactivation (bottom, n = 18, 6, 14, 9 for variants) curves of Nav1.6 variants related to Cn2 binding or selectivity. b, Representative traces for blocking Nav1.6-K1427E, Nav1.6-D1436K and Nav1.6-E838Q by Cn2 at the indicated concentrations. c, Voltage-dependent activation curves with a brief pre-pulse to + 40 mV of Nav1.6-K1427E (n = 19, 10, 10, 11, 11, 10, 9, 8), Nav1.6-D1436K (n = 19, 14, 11, 11, 12, 8) and Nav1.6-E838Q (n = 16, 10, 6, 6, 7, 7, 6, 6) when applied different concentrations of Cn2. d, The less conserved residues may be responsible for the subtype selectivity of Cn2. Sequences corresponding to the Cn2 binding residues across nine human Nav channel subtypes are shown. The subscript annotation for each residue conforms to a structure-based residue numbering system53. Data are presented as mean ± SEM. Sample sizes (n) shown in the data set represent biologically independent cells in panels a and c. Detailed statistics are provided in Source data. Extended Data Fig. 3 Functional validation of the structural observation for the Nav1.6 and ι-RXIA complex. a, ι-RXIA and its binding segments are well resolved. Shown here are the densities, contoured at 6 σ, for the overall ι-RXIA and the S3I, S4I, S4-5III, and S6III segments that are involved in ι-RXIA binding. b, Reported NMR analysis of free ι-RXIA (PDB code: 2JRY). Left: Conformer ensemble of the free ι-RXIA structure in solution determined by NMR (PDB: 2JRY). Right: The R.M.S.D. plot for the NMR conformers highlights structural instability at both the C- and N-termini. c, Lipids are involved in the toxin binding. The lipid densities, contoured at 4 σ, are found surrounding ι-RXIA and Nav1.6. d, Functional characterization of ι-RXIA’s impact on human Nav1.6 variants that were co-expressed with β1. Left: Characterization of the agonistic effect of ι-RXIA on human Nav1.6 variants. The ratio of peak currents with (I’Na) and without (IoNa) toxin depicts the modulation of Nav1.6 variants by 1 μM ι-RXIA. The sample sizes (n) for Nav1.6 variants are n = 22, 7, 10, 10, 7, 8, 8, 13, 12, 12. Right, Representative traces of Nav1.6 variants in the absence and presence of 1 μM ι-RXIA. Peak currents were elicited by a voltage step from a holding potential of -120 mV to −30 mV for 50 ms. e, Representative traces of Nav1.6-R220E in the presence of ι-RXIA at indicated concentrations (top). Characterization of the modulation of Nav1.6-R220E by ι-RXIA. ι-RXIA inhibits Nav1.6-R220E at steady state, with the IC50 of 0.25 ± 0.06 μM. The sample sizes (n) for low to high concentrations are n = 5, 6, 6, 11, 6, 8 (bottom). f, Changes in inactivation Tau (τinac) values of Nav1.6 variants with 1 μM ι-RXIA. The sample sizes (n) for Nav1.6 variants are n = 10, 8, 7, 5, 6, 5, 5, 5, 5, 6, 8. g, Identification of the channel loci that are responsible for ι-RXIA’s subtype selectivity. Shown here is the sequence alignment of the ι-RXIA coordinating residues across nine human Nav channel subtypes. Data are presented as mean ± SEM. Sample sizes (n) shown in the data set represent biologically independent cells in panels d, e and f. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Statistical analysis was conducted using one-way ANOVA with Dunnett’s multiple-comparison test (d and f). Exact P values and detailed statistics are provided in Source data. Extended Data Fig. 4 The C-terminus of ι-RXIA stabilizes the up conformation of Nav1.6-S4I. a, ι-RXIA acts as an agonist on Nav1.6-WT but exhibits reversed inhibitory effects on the R220E mutant. Shown here are normalized I-V curves for Nav1.6-WT (upper panel, n = 14, 10, 8 for ES, 1 and 10 μM ι-RXIA) and Nav1.6-R220E (lower panel, n = 15, 12, 8 for ES, 1 and 10 μM ι-RXIA), both co-expressed with β1, in the absence and presence of 1 or 10 μM ι-RXIA during electrophysiological recording. Data are presented as mean ± SEM. n represent biologically independent cells. b, The carboxyl group at the C-terminus of ι-RXIA stabilizes the S4I segment in the up state through essential interactions with R220. Shown here are close-up views of the interactions between the carboxyl terminus of ι-RXIA and Nav1.6 S4I, derived from the final clustering results of 100 ns MD simulations under various conditions. c, Interaction frequency between the carboxyl terminus of ι-RXIA and Nav1.6 S4I within a 3.5-Å distance during MD simulations with three replicates. Stable R220-ι-RXIA interactions are observed only with WT Nav1.6 VSDI (up) and full-length ι-RXIA. d, Relative binding energies of ι-RXIA to Nav1.6 under different conditions. The relative binding energies, calculated using the MM/GBSA method, are presented as mean ± SEM (n = 1 for WT control group Nav1.6 VSDI,up and full-length ι-RXIA, n = 3 for the rest conditions). e, The F*44 F mutant exhibits higher flexibility around the C-terminal region. Shown here are the root-mean-square-fluctuation (RMSF) plots of WT ι-RXIA and F*44 F mutants in MD simulation. F* indicates D-Phe. Extended Data Fig. 5 Characterization of functional residues in Nav1.6 for the subtype selection and MOA of Pc1a. a, Sequence alignment reveals three residues that may underlie the subtype-specific response of Nav1.6 to Pc1a. Shown here is the sequence alignment of Pc1a binding residues across nine human Nav channel subtypes. b-e, Electrophysiological characterizations of Nav1.6 variants treated with Pc1a at indicated concentrations. Voltage-dependent activation (n = 26, 15, 22, 21 for variants) and inactivation (n = 11, 9, 8, 8 for variants) curves (b), representative traces of the peak currents (c), G-V curves (n = 22, 15, 16, 15, 16, 14, 12, 10 for D1436A/N1437A; n = 21, 12, 13, 14, 14, 13, 10, 8 for K1427N/D1436Y/N1437S) (d), and normalized dose-response curves for the peak current and activation (e) of Nav1.6 variants, all co-expressed with β1, are presented. In panel (e), the sample sizes (n) in left panel for low to high concentrations are n = 25, 5, 7, 11, 14, 14, 11, 16, 13 for WT; n = 22, 15, 16, 15, 16, 14, 13, 10 for D1436A/N1437A; n = 21, 12, 13, 13, 16, 12, 12, 9 for K1427N/D1436Y/N1437S. The sample sizes (n) in middle and right panel are n = 12, 16, 13 for 10 nM and n = 14, 15, 14 for 30 nM. f, Maximum change of V1/2 of Nav1.6 WT (n = 14, P < 0.0001) and the two mutants (n = 12, P < 0.0001 for D1436A/N1437A and n = 10, P < 0.0001 for K1427N/D1436Y/N1437S) treated with 1 μM Pc1a. g, Estimated activation time constants (Tau) for Nav1.6 variants with and without 1 μM Pc1a. Currents were elicited by a voltage step from a holding potential of −90 mV to −20 mV for 50 ms. The Tau values and sample sizes (n) for Nav1.6 variants without and with Pc1a are WT (n = 14): 0.53 ± 0.05 ms, 3.34 ± 0.46 ms, P < 0.0001; D1436A/N1437A (n = 12): 0.46 ± 0.03 ms, 1.47 ± 0.31 ms, P = 0.0284; K1427N/D1436Y/N1437S (n = 11): 0.60 ± 0.04 ms, 1.91 ± 0.25 ms, P = 0.0043. h, Electrophysiological characterization of Nav1.6-L1456A. Left: G-V curves of L1456A in the presence of 1 μM (n = 13) or 10 μM (n = 14) Pc1a. Right: Maximum change of V1/2 of Nav1.6 WT and L1456A treated with 1 μM (n = 19 and 13 for WT and L1456A, P < 0.0001) or 10 μM (n = 6 and 14 for WT and L1456A, P < 0.0001) Pc1a. Data are presented as mean ± SEM. n represent biologically independent cells. *P < 0.05, **P < 0.01, ****P < 0.0001. Statistical analysis was conducted using one-way ANOVA with Dunnett’s multiple-comparison test (f) and two-way ANOVA with Šídák’s multiple comparisons test (g and h). Detailed statistics are provided in Supplementary Tables 4-6 and Source data. Extended Data Fig. 6 Discrepancies between experimental and AlphaFold3-predicted binding poses for the three toxins. a, None of the predicted binding poses for the toxins matches the experimental structures. Shown here are pairwise comparisons of the AlphaFold3-predicted binding poses (toxins labelled with -AF) of the three toxins to experimental results (toxins labelled with -EM). The top five predictions are shown for each toxin. b, Comparison scores. The predicted template modelling (pTM) scores and the interface predicted template modelling (ipTM) scores are shown along with predicted aligned error (PAE) matrices for the prediction results. Extended Data Fig. 7 Comparative analysis of the binding poses of relevant toxins and auxiliary subunit. a, Comparison of the binding modes for Dc1a and Cn2. b, Comparison of the binding poses of KCNE1 with KCNQ1 (PDB: 9VEI) and Pc1a with Nav1.6. Extended Data Fig. 8 Binding of Pc1a between VSDII and PDIII stabilizes the up conformation of VSDII, accounting for its effect on channel activation. a-b, The down-to-up shifts of VSDI between Nav1.7-M11 and Nav1.7_open (a), and of VSDII between Cav1.1 and Cav1.2 (b), exhibit similar structural changes, including winding and unwinding of the S4 terminal helical turns, spiral sliding of S4, and rotation of the entire VSD. c, The binding pose of Pc1a favours the up conformation of the VSD. At the same time, it may hinder VSD movement, thereby slowing down the activation duration. The PDB codes for Nav1.7-M11, Nav1.7_open, Cav1.1 and Cav1.2 are 7XVE, 21TP, 5GJV, and 8WE6, respectively. Supplementary information Supplementary Information (download PDF ) Supplementary Figs. 1–8 and Supplementary Tables 1–9. Supplementary Video 1 (download MP4 ) Pc1a impedes fast inactivation of Nav channels by restricting the movements of S4-5II and S6III that are required to close the intracellular gate. The morph was generated based on the open-state Nav1.7 (PDB: 21TP) and Pc1a-bound Nav1.6 (this study), which represents an inactivated state. The channel and Pc1a are coloured with the same scheme as in Fig. 4. Supplementary Data (download ZIP ) Source data including all initial coordinates and MD simulation input files. Source Data (download XLSX ) Supplementary Fig. 5 Source data Rights and permissions About this article Cite this article Fan, X., Huang, J., Yang, L. et al. Diverse binding poses of agonistic neurotoxins on human Nav1.6. Nature (2026). https://doi.org/10.1038/s41586-026-10661-x Received: Accepted: Published: Version of record: DOI: https://doi.org/10.1038/s41586-026-10661-x