Structural insights into insect-selective sodium channel toxins drive AI-enhanced biopesticide design

Domain: chemistry
Published: 2026-03-06
Editorial outcome: accept

Abstract
Many voltage-gated sodium channel-targeting animal peptide toxins are renowned for their potency and selectivity against insects. Understanding why these toxins selectively target insect sodium channels over their mammalian counterparts is crucial for developing safer and more effective pest control agents. Here, we present the cryoelectron microscopy (cryo-EM) structures of the insect sodium channel Na PaS bound to two naturally occurring insect-selective toxins, Av3 from the sea anemone and LqhαIT from the scorpion. Both toxins bind to the voltage-sensing domain 4 (VSD4) of Na PaS and disrupt fast inactivation by stabilizing the S4 segment in a deactivated conformation. While Av3 engages a membrane-embedded site between VSD4 and pore domain 1 (PD1), LqhαIT binds to the classical neurotoxin site 3, illustrating distinct binding modes that converge on a shared mechanism of action. These structures reveal the molecular determinants of insect selectivity and highlight the molecular coevolution of toxin-channel interactions, as corroborated by electrophysiology and toxicity assays. Leveraging these insights, we apply AI-driven protein design tools to increase the insecticidal potency of LqhαIT, resulting in a variant with a remarkable doubling in efficacy, as we confirm by insecticidal bioassays. This study illuminates the diverse mechanisms of sodium channel modulation and provides a framework for the structure-guided, AI-driven design of toxin-based biopesticides.

Introduction
Voltage-gated sodium (Na ) channels, integral membrane proteins found in the plasma membranes of excitable cells, are critical for initiating and propagating action potentials1, 2. These channels respond to membrane depolarization by rapidly opening, allowing an influx of Na⁺ ions, and then swiftly transitioning to an inactivated state within milliseconds3. Eukaryotic Na channels comprise a pore-forming α subunit and auxiliary β subunits 4. The α subunit contains four homologous domains (DI-DIV), each containing six transmembrane helices (S1-S6). The S1-S4 helices form the voltage-sensing domain (VSD), whereas the S5 and S6 helices, together with two intervening pore helices (P1 and P2), constitute the pore domain (PD)5. Sodium ion selectivity is conferred by the selectivity filter (SF), which is formed by specific residues S(D, E, K, and A)6. Within the VSD, positively charged residues in the S4 helix are critical for voltage sensing7. Upon depolarization, conformational shifts in VSD1-3 open the central pore, whereas the movement of VSD4 triggers fast inactivation8. This occurs through binding of the IFM (Ile-Phe-Met) motif, located in the DIII-IV linker, to a cavity outside the S6 bundle, allosterically blocking ion flow9, 10. Inactivation is also regulated by the C-terminal region, specifically through interactions between an EF-hand domain with the DIII-IV linker and fibroblast-homologous factors, and by calmodulin binding to an IQ domain11-13. This intricate mechanism highlights the structural and functional complexity of Na channels, which are finely tuned for precise neural and muscular signaling14.

Insect Na channels are well-established targets for insecticides, including widely used pesticides such as dichlorodiphenyltrichloroethane (DDT), pyrethroids, and indoxacarb15-17. However, the extensive use of these chemical pesticides has led to major challenges, including the emergence of resistance in pest populations and environmental concerns. To address these issues, animal toxins found in the venoms of numerous metazoans offer promising alternatives for pesticide development. A notable example is SPEAR® biopesticides developed by Vestaron Corp., which utilize the peptide toxin GS-Omega/Kappa-Hxtx-Hv1a derived from the funnel-web spider Hadronyche versuta. When combined with Bacillus thuringiensis kurstaki (Btk), a biopesticide that disrupts the insect midgut, orally administered GS-Omega/Kappa-Hxtx-Hv1a can reach its target, nicotinic acetylcholine receptors (nAChRs), in the insect nervous system, where it induces hyperexcitability by enhancing channel opening18. This innovative strategy highlights the potential of animal toxins in the development of next-generation green biopesticides that are both effective and environmentally sustainable.

In nature, animal venoms are complex mixtures containing multiple components that target ion channels from both insect and mammalian systems, serving functions related to prey capture and predator defense, respectively. Many animal toxins have evolved to selectively target insect Na channels, either by modifying channel gating or through direct channel blocking19. Toxins from scorpions, sea anemones, spiders, and cone snails exhibit remarkable selectivity for insect Na channels, making them ideal candidates for ecofriendly bioinsecticides. Despite their potential, the molecular mechanisms underlying their insect-specific selectivity remain poorly understood. Unraveling how these toxins interact with and modulate Na channels not only advances our understanding of channel function but also opens the door to the design of highly specific and sustainable bioinsecticides20, 21.

Av3 and LqhαIT are two cysteine-rich peptide toxins with remarkable insect selectivity. Av3, a 27-amino-acid toxin derived from the venom of the sea anemone Anemonia viridis22, and LqhαIT, a 66-amino-acid α-scorpion toxin (ScTx) found in the venom of the deathstalker scorpion Leiurus hebraeus, specifically target Na channels in arthropods such as insects and crustaceans23. Importantly, both toxins exhibit minimal effects on mammalian Na channels, positioning them as promising candidates for developing eco-friendly bioinsecticides24-26. The structures of these toxins are stabilized by multiple disulfide bonds, three in Av3 and four in LqhαIT. Structurally, Av3 adopts a compact, turn-based conformation, whereas LqhαIT features the characteristic β1-α1-β2-β3 scaffold shared by all ScTxs22, 27, 28. Previous mutagenesis studies suggest that these two toxins share a common but not identical binding site near the extracellular region of VSD4, referred to as neurotoxin binding site 320, 25, 28. By targeting this site, they inhibit fast inactivation of the Na channel, leading to prolonged neuronal firing, paralysis of insect muscles, and ultimately death. Despite extensive research, the precise receptor sites and molecular mechanisms underlying their modulation and insect-specific selectivity remain incompletely understood.

Advances in cryo-EM have provided significant insights into the structural basis of Na channel modulation by animal toxins29-35. For example, structural studies of human Na 1.7 with the mammal-selective spider toxins HWTX-IV and ProTx-II have revealed their distinct binding sites and provided insights into their mechanisms of Na channel gating modulation. However, structural data on Na channels bound to insect-selective toxins are largely unavailable. This knowledge gap limits our understanding of species-specific selectivity and toxin-channel coevolution, as well as the development of insect-selective biopesticides. Further structural studies are critical to unravel the molecular basis of insect selectivity and to advance the design and optimization of toxin-based biopesticides.

In this study, we resolve the cryo-EM structures of the American cockroach Na channel Na PaS in complex with the insect-selective toxins Av3 and LqhαIT at resolutions of 2.8 Å and 2.9 Å, respectively. Using mutagenesis, electrophysiology, and surface plasmon resonance (SPR), we identify key residues responsible for species-specific recognition and reveal toxin-induced conformational changes that disrupt fast inactivation. Additionally, by leveraging complex structure analysis and artificial intelligence (AI)-driven design, we engineer a mutant LqhαIT variant with doubled potency against insect Na channels. This work elucidates the molecular mechanisms underlying insect selectivity and offers structural templates for the development of biopesticides.

Results
Av3 and LqhαIT Selectively Modulate Insect Na Channels
Av3 and LqhαIT are natural toxins produced by sea anemones and scorpions to prey on arthropods. To assess their species-specific modulatory properties, we compared their activities on insect and mammalian Na channels. Na PaS, a Na channel from the American cockroach Periplaneta americana, is highly stable and retains essential Na channel features, making it ideal for structural studies. However, it does not conduct sodium currents when expressed in Xenopus oocytes. Therefore, for electrophysiological analyses, we employed the orthologous insect Na channel, BgNa 1-1a, from the German cockroach Blattella germanica, which was also used in a previous study to functionally validate the Na PaS-Dc1a complex structure29. BgNa 1-1aS shares approximately 59% sequence identity with Na PaS, with higher conservation in the extracellular toxin-interacting regions, making it a suitable surrogate for electrophysiological studies36, 37. As a control, we used the cardiac isoform of the human Na channel (hNa 1.5), which provides preliminary assurance of mammalian safety. hNa 1.5 is representative of mammalian Na channels and shares 42% overall sequence identity with Na PaS. Both Av3 and LqhαIT selectively modulate BgNa 1-1a by significantly increasing the peak Na⁺ current and inhibiting fast inactivation at nanomolar concentrations, with EC₅₀ values of 191.6 nM and 321.9 nM, respectively (Fig. 1A, B). In contrast, both toxins had minimal effects on hNa 1.5, even at the highest tested concentration of 40 µM (Fig. 1A, B), highlighting their strong insect selectivity.

Av3 and LqhαIT Target Distinct Sites on VSD4
To investigate the molecular mechanisms by which Av3 and LqhαIT modulate insect Na channels, we determined the cryo-EM structures of Na PaS bound to these toxins at resolutions of 2.8 Å and 2.9 Å, respectively. Previous studies have shown that Av3 competes with other site 3 toxins, suggesting that it targets the same or overlapping regions within VSD438. This was further supported by docking studies showing that Av3 sits on top of VSD439. Mutagenesis experiments have also indicated that certain mutations within site 3 differentially affect Av3 and scorpion toxins, implying distinct binding modes, although the precise binding pose of Av3 remains unclear25. Our cryo-EM structure of Na PaS-Av3 reveals that Av3 occupies a membrane-embedded site on Na channels. Owing to its small size, high flexibility, and hydrophobic nature, most part of Av3 partitions into the membrane and is sequestered within a deep transmembrane cleft formed between VSD4 and the S5/S6 helices of PD1. The C-terminal tail of Av3 extends beyond the membrane to interact with the extracellular glycan moiety of PD1, increasing the stability of its binding (Fig. 1C).

In contrast, LqhαIT binds to the extracellular side of VSD4, interacting with the glycan moiety from PD1 and positioning itself above the membrane (Fig. 1D). This binding site closely resembles the region targeted by other known α-ScTxs, including AaH2, Lqh3, and MTα-530, 33, 40 (Supplementary Fig. 1). Notably, the binding sites of Av3 and LqhαIT are adjacent with minimal overlap, underscoring their divergent modes of interaction with the channel (Supplementary Fig. 2A, B).

Av3 and LqhαIT Bindings Induce Conformational Changes in Na PaS
Although Av3 and LqhαIT interact with Na PaS through distinct mechanisms, both toxins induce a similar conformational change in the VSD4. In the unbound state, VSD4 adopts a partially activated conformation, as observed in apo-Na PaS cryo-EM structure (PDB ID: 5X0M). In this state, two gating charges, R2 and R3, are positioned above the hydrophobic constriction site (HCS) (Phe1213) and interact with the extracellular negative cluster (ENC), which includes Asn1183, Asp1190 and Asn1206. Upon Av3 or LqhαIT binding, the S4 gating charges shift one position down along the gating charge-transfer pathway, converting the VSD from a partially activated state to a deactivated state. In this toxin-bound state, R2 remains coordinated to Asn1183 and Asn1206, while R3 aligns with the HCS and R4 undergoes a significant ~10 Å downward shift across the HCS to interact with the intracellular negative cluster (INC) (Fig. 2A, Supplementary Movie 1).

This downward movement of the S4 helix subsequently generates two electrostatic interfaces: the one between Arg1277 (R6) from the S4 and Arg1293 from the S4-S5 linker with the acidic residues on the C-terminal EF-hand domain (Interface I), and the other between Asp1420 from the S6 and the basic residues on the DIII-IV linker (Interface II). In the apo-Na PaS structure, only one electrostatic bridge is observed (Asp1420 with Arg1138 from Interface II). Av3 induces the formation of three additional electrostatic bridges, including Arg1277 (R6)-Glu1435, Arg1293-Asp1427, and Asp1420-Arg1142. In contrast, LqhαIT binding induces only two of these bridges, including Arg1277 (R6)-Glu1435 and Asp1420-Arg1142, alongside the pre-existing Asp1420-Arg1138 interaction (Fig. 2B). These interactions contribute to restraining the DIII-IV linker, thereby impairing the fast inactivation of the sodium channel. This mechanism aligns with the structural changes induced by the toxin AaH2, further supporting a common role of electrostatic interactions in toxin-induced channel modulation30. However, several key interacting residues in Na PaS, such as Arg1138 and Arg1293, are substituted by neutral residues (e.g., alanine, methionine, or asparagine) in other Na isoforms (Fig. 2C). These differences suggest that the precise electrostatic network and conformational changes induced by toxin binding may not be fully conserved across other Na channels.

Furthermore, Av3 binding expands the binding pocket by wedging itself between VSD4 and PD1, simultaneously pushing the glycan and PD1 toward the pore. Despite this structural rearrangement, the pore size remains unchanged, indicating minimal impact on single-channel conductance or ion selectivity (Supplementary Fig. 3). Additionally, Av3 binding induces the formation of a rigid α-helix in the loop connecting helices S3 and S4, suggesting that toxins can alter the secondary structure of ion channels and supporting an induced-fit binding model (Fig. 2D). In contrast, LqhαIT primarily interacts with VSD4 and lightly leans on PD1, affecting the conformation of the S3-S4 loop with minimal impact on PD1 (Fig. 2D).

Insect-Specific Binding Determinants of Av3
The Av3 binding site spans three regions: VSD4, the S5-S6 helices of PD1, and the glycan moiety of PD1 (Fig. 3A). The most extensive interaction occurs at VSD4, where approximately 350 Å² of surface area is buried by Av3. This binding is driven primarily by hydrophobic interactions, complemented by specific hydrogen bonds. Key residues at the VSD4 interface, including Pro5, Tyr7, Trp13, and Tyr18, contribute to Av3 activity (Fig. 3C). Electrophysiological assays show that alanine substitutions at these positions reduce Av3 efficacy by 2.1-, 2.6-, 25.3-, and 7.2-fold, respectively, highlighting their functional relevance (Fig. 3F). Surface plasmon resonance (SPR) analysis further confirmed a marked loss of binding for the Y7A, W13A, and Y18A mutants (Supplementary Table 1). At the second interface, residues Trp8, Pro12, and Gln15 interact extensively with the S5 and S6 helices of PD1, with Trp8 forming a Pi-Pi interaction with Trp391 (Fig. 3D). Alanine substitutions at these residues notably reduce the binding affinity and efficacy of Av3, with Trp8 and Pro12 having more pronounced effects (Fig. 3F, Supplementary Table 1). The glycan interface, involving Pro19, Glu20, and Val27, also contributes to Av3 binding. However, alanine substitutions at these positions have minimal impact, suggesting a supporting role in the overall interaction25 (Fig. 3B).

Additionally, some hydrophobic residues in Av3, such as Trp8 and Trp13, face the toxin-lipid interface in our complex structure, suggesting that they may help stabilize the toxin conformation through interactions with the lipid membrane. A well-defined lipid-like density was observed near Trp8 of Av3 and Trp391 of PD1 (Fig. 3E). Based on its shape and position, analogous to lipids observed at equivalent sites in other human Na structures, it was modeled as a putative phospholipid, suggesting a conserved lipid-channel interaction41-43. Notably, Av3 does not displace the lipid but rather leans on it, forming a sandwich-like arrangement with Na PaS. Electrophysiological assays showed that substitution of Trp8 and Trp13 with alanine markedly reduced Av3 activity (Fig. 3F), supporting a contributing role of membrane interactions in Av3 function.

Binding to Na PaS induces significant structural changes in Av3 itself. The apo structure of Av3, determined by NMR, has no defined secondary structure, reflecting its high flexibility22. This flexibility likely allows Av3 to fit into the narrow niche between PD1 and VSD4. The root mean square deviation (RMSD) between the Cα atoms of the bound and apo forms of Av3 is approximately 3 Å, with the glycan-interacting C-terminal region exhibiting a more substantial displacement of ~7 Å upon binding (Fig. 3A).

Av3 is highly active against insects and crustaceans but is inactive in mammals. While most interacting residues at the VSD4 interface are conserved across species, our complex structure identified five invertebrate-specific residues in PD1, including Ile279, Val283, Trp391, His392, and Leu394 (Fig. 3D, H). Electrophysiological assays demonstrated that substituting Val283, Trp391, and His392 with their mammalian counterparts significantly reduced the efficacy of Av3 by 3.0-, 16.3- and, 49.4-fold (Fig. 3G), underscoring their importance in species selectivity. Sequence- and structure-based alignment of toxin-binding regions revealed a high degree of conservation across hNa 1.1-1.9, suggesting that Av3’s lack of activity against hNa 1.5 likely extends to other human Na isoforms (Fig. 3H, Supplementary Fig. 2C, D).

Additionally, the glycan moiety linked to Asn330, which contributes to Av3 binding, is conserved across species. However, variations in glycosylation patterns may influence toxin selectivity (Supplementary Fig. 4), warranting further investigation into the role of these modifications in mediating toxin specificity.

Insect-Specific Binding Determinants of LqhαIT
The binding of LqhαIT is mediated primarily by interactions involving the β1-α1 loop, β2-β3 loop, and C-terminal tail (CT) of LqhαIT with VSD4, covering a buried surface area of approximately 450 Å². This is further complemented by interactions between the CT and the glycan of PD1, which contribute an additional buried surface area of ~246 Å² (Fig. 4A-C). Several key contact residues are conserved across all the α-ScTxs. For example, Asn44 from the β2-β3 loop in the VSD4 interface and His64 from the CT in the glycan interface are crucial for binding. Substituting these residues with alanine reduces LqhαIT activity by 47.1-fold and 37.1-fold, respectively (Fig. 4F). On the channel side, Asp1252 from the S3-S4 loop, which interacts with Gly43 from LqhαIT, is a highly conserved residue essential for α-ScTx binding44. In line with its critical role, the D1252A mutation reduces LqhαIT activity 98.2-fold, whereas the D1252R mutation completely abolishes it (Fig. 4C, G).

The overall structure of LqhαIT remains largely unchanged upon binding, with a Cα RMSD of ~0.77 Å compared with the apo structure, indicating that LqhαIT is more rigid than Av3. However, the side chains of several residues near the LqhαIT-VSD4 interface, including Glu15, Phe17, and Arg18, undergo coordinated conformational shifts toward the S3-S4 loop upon binding (Fig. 4D). These movements likely reflect an induced-fit mechanism driven by hydrophobic packing, electrostatic complementarity, and local structural accommodation. Among them, the conserved Arg18 rotates ~120°, but does not form direct contacts with Na PaS. However, the R18D mutation abolishes toxin activity, suggesting an indirect role in toxin-channel recognition, as confirmed by electrophysiology and SPR (Fig. 4F, Supplementary Table 1). We propose that Arg18 may assist in toxin orientation via electrostatic steering or transient membrane interactions prior to stable binding.

While conserved residues are critical for general toxin-channel recognition, the species specificity of α-ScTxs is primarily dictated by non-conserved surface residues. α-ScTxs can be broadly classified into insect-selective and mammal-selective subtypes, exemplified by LqhαIT and AaH2, respectively. Although they share a conserved βαββ structural scaffold, they diverge at key surface-exposed positions that define their Na channel subtype selectivity. Compared with rNa 1.2 and hNa 1.5, LqhαIT displays over 1,000-fold selectivity for Drosophila melanogaster Na (DmNa ), whereas AaH2 demonstrates more than 100-fold selectivity for rNa 1.2 over DmNa 24, 45. To explore the molecular basis of α-ScTx species selectivity, we compared the binding modes observed in our Na PaS-LqhαIT structure with the previously published structure of AaH2 bound to the VSD of hNa 1.7 in a Na 1.7/Na PaS chimeric channel. Our analysis identified two key interfaces contributing to insect specificity. At the VSD4 interface, Lys41 from the β2-β3 loop of insect-selective α-ScTxs is in close proximity to a negatively charged site on the S2 helix, formed by two negatively charged residues, Glu1200 and Asp1203 (Fig. 4E). In contrast, mammal-selective α-toxins feature a proline at this position. Sequence alignment indicates that most mammalian Na s possess a neutral interface at this site, with the exception of Na 1.4 and Na 1.7, which contain a single negatively charged residue (Fig. 4I). As expected, mutating Lys41 to proline significantly reduces LqhαIT activity against insect Na channels. Additionally, LqhαIT residues that face the S3-S4 linker, including Lys8, Val13, and Phe17, play key roles in conferring insect specificity. Substituting these residues with their counterparts from mammal-selective α-ScTxs reduce LqhαIT activity by 45.2-fold, 10.5-fold, and 26.0-fold, respectively (Fig. 4C, F). On the channel side, substituting the insect-specific Ile1253 in the S3-S4 linker with leucine, the corresponding residue in mammalian Na s, leads to a significant reduction in LqhαIT activity (Fig. 4G, I), highlighting the importance of species-specific interactions in determining toxin sensitivity.

At the PD1 interface, nonconserved residues in the β1-α1 loop and C-terminal region of α-ScTxs, which interact with distal glycan regions, significantly influence toxin selectivity. While the glycans near the conserved glycosylation site are relatively similar across Na channels, their distal regions vary (Supplementary Fig. 4). Substituting insect-selective LqhαIT sequences in this region with those from mammal-selective AaH2 greatly reduces activity, including the N9D, Y10V, I57T, and V59G substitutions which significantly decrease LqhαIT activity (Fig. 4B, F, H). Although glycosylation in Xenopus oocytes may not reflect the native insect or mammalian patterns, the observed differences suggest that the distal glycan structure may plausibly contribute to differential toxin binding and selectivity.

For selected mutants, binding affinities were also assessed by SPR (Supplementary Table 1), which showed consistent trends with the electrophysiological results. Given the conserved nature of these binding interfaces, the observed insect selectivity of LqhαIT is also likely to apply across mammalian Na isoforms (Fig. 4I, Supplementary Fig. 2C, D).

Together, these findings offer structural insights into the molecular determinants of α-ScTx selectivity and may guide the design of more selective and effective neurotoxins for various applications.

AI-assisted Structure-based Design of LqhαIT-derived Biopesticides
LqhαIT has demonstrated high potency against a broad spectrum of pest insects while exhibiting low activity on mammalian Na s, making it an attractive candidate for development as a biopesticide. Leveraging the complex structure of Na PaS-LqhαIT, we implemented an AI-driven design pipeline that integrates three core models to identify mutations that enhance the toxin's properties (Fig. 5A-E).

First, we employed our self-developed ComplexDDG algorithm to predict beneficial mutations that could enhance toxin-channel binding affinity, using the complex structure as input. ComplexDDG integrates sequence- and structure-level representations within a geometric deep learning framework (Fig. 5B). Residue embeddings derived from the pretrained protein language model ESM-1v are used as the initial node features of a geometric attention network, capturing evolutionary, structural, and contextual information46, 47. These are combined with 22 physicochemical energy terms calculated by FoldX, which quantify energetic differences between the wild-type and mutant complexes48. The fused sequence-structure-energy features are then used to predict the binding free-energy change (ΔΔG). Based on these predictions, 101 LqhαIT mutations with ΔΔG values below zero were shortlisted as candidates for further analysis.

Next, Evolutionary Scale Modeling 2 (ESM2), a large language model designed to predict the likelihood of amino acid substitutions based on natural mutation probabilities, was applied to identify stability-enhancing mutations49. Among the initial candidates, 30 mutations commonly selected by both ComplexDDG and ESM2 were further refined. To ensure the feasibility of protein expression and purification, we used the solubility prediction model Aggrescan3D (A3D) 2.0 to exclude mutations likely to cause aggregation50, discarding candidates with solubility scores above 1.5. Additionally, we applied manual filtering based on empirical knowledge and included substitutions targeting key sites identified by ComplexDDG. Ultimately, 28 mutations were selected for experimental validation.

The candidate mutants were initially screened via SPR binding assays. Five mutants presented a greater than twofold increase in binding affinity (Fig. 5C). Electrophysiological experiments confirmed functional enhancements, with mutants E15F and A39L showing significant improvements in modifying BgNa 1-1a (Fig. 5D). In vivo activity was assessed by injecting the mutants into Galleria mellonella larvae, a representative lepidopteran pest. Both mutants demonstrated enhanced toxicity compared to wild-type LqhαIT, with A39L achieving a remarkable doubling in insecticidal efficacy (Fig. 5E).

Glu15 is located within the β1-α1 loop of LqhαIT and interacts with Val1253 and Ile1254 of the S3-S4 loop. Substituting glutamate with the bulkier, hydrophobic phenylalanine residue likely enhances hydrophobic interactions within this region (Fig. 5F). Interestingly, Ala39, located in the β2-β3 loop, does not directly interact with Na but instead faces the aqueous and membrane interface (Fig. 5F). The A39L mutation likely stabilizes interactions with membrane lipids, facilitating toxin docking and insertion into the membrane. This additional pivot point may enhance the toxin binding process. These findings highlight the power of structure-based and AI-driven strategies in advancing the development of highly effective biopesticides.

Methods
Ethical Statement
All procedures involving Xenopus oocytes were approved by Hainan University Institutional Animal Use and Care Committee and conducted in accordance with institutional guidelines and applicable national regulations of China.

Toxin expression and purification.
The optimized coding sequences for LqhαIT and Av3 were cloned and inserted into the pLIC-MBP expression vector with the MalE signal sequence, a 6xHis tag, and an MBP fusion tag followed by a tobacco etch virus (TEV) protease recognition site directly preceding the toxin sequence. Toxin mutants were generated via mutagenesis using a QuikChange mutagenesis kit (Tiangen) following the manufacturer’s instructions, with primer sequences provided in Supplementary Table 2. The plasmid was transformed into the E. coli BL21(DE3) strain (New England Biolabs) for toxin production. The cells were grown in 2xYT medium at 37°C and induced with 400 μM IPTG when the OD600 reached 0.8. The cells were cultured at 18°C for an additional 16 h before being collected by centrifugation for 15 min at 5,000 x g. The cells were resuspended in 10 mM HEPES (pH 7.4) and 250 mM NaCl, disrupted via sonication and centrifuged for 30 min at 12,000 x g. The supernatant was loaded onto Ni-NTA resin (GenScript), and the unbound protein was removed by washing with 20 mM imidazole. The target protein was eluted with 500 mM imidazole, followed by concentration and buffer exchange to remove imidazole. One milligram of TEV protease was added to every 40 mg of toxin, and the mixture was incubated at 4°C for 12 hr. The cleaved His6-MBP and His6-TEV were precipitated by the addition of 1% trifluoroacetic acid (TFA), and then the sample was centrifuged at 14,000 x g for 30 minutes. The supernatant was purified via RP-HPLC on a C18 column (Waters, 10 x 250 mm, particle size 5 μm) with a gradient of 20-50% Solvent B (0.05% TFA in acetonitrile) in Solvent A (0.05% TFA in water) over 40 min. The eluted toxin was lyophilized and redissolved in the required buffers. The integrity of the toxins was confirmed via MALDI-TOF and functional bioassays.

Na PaS expression and purification.
The optimized coding DNA for Na PaS was cloned and inserted into the BacMam vector with a Twin-Strep tag and a FLAG tag in tandem at the N-terminus. The BacMam viruses were amplified in Sf9 cells according to the standard protocol. HEK293 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with fetal bovine serum under 5% CO2 at 37°C and infected for 48 hrs before collection. The collected cells were resuspended in 50 mM Tris (pH 7.4) and 150 mM NaCl supplemented with 1% digitonin, 25 µg/ml DNase, protease inhibitor cocktail and 1 mM PMSF and incubated at 4°C for 2 hr. After ultracentrifugation at 150,000 x g for 40 minutes, the supernatant was incubated with anti-FLAG resin (BIMAKE) for 1 hour. The resin was washed with wash buffer (50 mM Tris pH 7.4, 150 mM NaCl, 0.06% GDN). The target protein was eluted with wash buffer supplemented with 300 μg/ml FLAG peptide and then applied to Strep-Tactin XT resin. The resin was washed with wash buffer. The protein was eluted with wash buffer supplemented with 50 mM biotin. The eluent was then concentrated and injected into a Superose6 10/300 column (GE Healthcare). For Cryo-EM data collection, the peak fractions were concentrated to approximately 3 mg/ml and incubated with LqhαIT or Av3 at a 1:4 molar ratio for 30 minutes prior to cryogrid preparation.

Surface plasmon resonance
SPR experiments were carried out via a Biacore 8K instrument (GE Healthcare). Na PaS was immobilized via standard N-hydroxy succinimide (NHS)/1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDCS) amine coupling on a carboxyl methyl dextran (CM5) sensor chip (GE Healthcare). Before the covalent immobilization of Na PaS, the sensor surface was activated by a mixed solution of 0.4 M EDC and 0.1 M NHS (1:1) for 7 min at a flow rate of 10 μl/min. The purified Na PaS protein was diluted to 20 μg/ml in immobilization buffer (10 mM sodium acetate, pH 4.5, and 0.04% GDN) and immobilized on the sensor chip to a level of 15,000 response units (RUs). Interactions between Na PaS and substrate toxins were monitored by injecting various concentrations of peptides (twofold serial dilutions starting from 400 nM) in running buffer containing 10 mM Na HPO , 1.8 mM KH PO , pH 7.4, 137 mM NaCl, and 2.7 mM KCl at a flow rate of 30 μl/min for 120 s. Dissociation was performed by running the buffer without toxins at a rate of 30 μl/min for at least 120 s. The RU was obtained by subtracting a control for unspecific binding (the signal from a blank flow cell). As the inclusion of detergent impaired toxin-channel binding (Supplementary Fig. 5A), all screenings were conducted in detergent-free buffer. The reliability of the assay was confirmed by injecting wild-type LqhαIT as control at the beginning and end of each run, which produced highly consistent responses (Supplementary Fig. 5B, C).

Expression of BgNa 1-1a and hNa 1.5 in Xenopus Oocytes.
The methodologies for oocyte preparation, cRNA synthesis, and injection were performed as previously described, with slight modifications58. Briefly, plasmids (wild-type or mutant) were linearized and purified using phenol-chloroform extraction. cRNA was synthesized in vitro using T7 polymerase and the mMESSAGE mMACHINE High Yield Capped RNA Kit to ensure high-quality and efficient transcription. For optimal functional expression, BgNa 1-1a cRNA was coinjected with TipE cRNA into Xenopus oocytes at a 1:1 molar ratio, following an established protocol shown to increase the expression of insect sodium channels in heterologous systems59. Similarly, hNav1.5 cRNA was coinjected with the auxiliary β1 subunit into Xenopus oocytes at a 1:1 molar ratio to support proper channel expression and functionality.

Electrophysiological recording and analysis
Sodium currents were measured and analyzed using the two-electrode voltage clamp technique60. Recordings were conducted in ND96 bath buffer (2.0 mM KCl, 1.8 mM CaCl , 96.0 mM NaCl, 10.0 mM 4-(2-hydroxyethyl) piperazine-1-ethanesulfonic acid, and 1.0 mM MgCl , adjusted to pH 7.5 with NaOH) at 18-24°C. Glass electrodes were fabricated using a P-1000 puller, filled with a mixture of 3 M KCl and 0.5% agarose, and had resistance of less than 1 MΩ when constructed from borosilicate glass. Sodium currents were recorded using an OC725D oocyte clamp instrument and a Digidata1550A interface. Data acquisition and analysis were performed with pClamp 10.6 software, with the data filtered at 2 kHz and digitized at a 20 kHz sampling frequency. To ensure optimal recording conditions, the amount of cRNA injected into Xenopus oocytes and the incubation period were meticulously adjusted, limiting the maximum peak sodium current to ≤ 2.0 μA. Leak currents were corrected via P/4 subtraction and kept below 0.3 μA. The percentage of BgNa 1-1a modification by LqhαIT and Av3 was calculated using the following Equation (1):

M = I20 / Ipeak (1)

where Ipeak represents the peak current elicited by a 50 ms step depolarization to -10 mV, I20 represents the non-inactivated current at the end of depolarization at 20 ms, and the I20/Ipeak ratio reflects the efficacy of the toxin in modulating fast inactivation61. Similarly, the percentages of hNa 1.5 modified by LqhαIT and Av3 were measured using the following Equation (2):

M = I6 / Ipeak (2)

where I6 was used to represents the non-inactivated current at the end of depolarization at 6 ms33. EC₅₀ values were obtained by fitting the Hill equation to each replicate. Data are presented as the mean ± SEM.

Insect bioassay
To evaluate the insecticidal effects of the toxins, 5 µL of toxin dissolved in PBS buffer was injected into the second abdominal proleg of last-instar Galleria mellonella larvae. Each wild-type and mutant toxin was tested in three independent experiments, with eight larvae per group. As a control, 5 µL of PBS solution was injected into larvae following the same procedure. After injection, the larvae were maintained at room temperature. Death or uncoordinated body movement was recorded as an effective response. Effective rates were calculated 24 hours postinjection.

Cryo-EM sample preparation and data acquisition
For cryo-EM sample preparation, 4 μL of Na PaS-LqhaIT or Na PaS-Av3 was loaded on glow-discharged grids (R1.2/1.3, Au 300, Quantifoil). The grids were blotted for 4 s and flash-frozen in liquid ethane cooled with liquid nitrogen using a Vitrobot Mark IV (Thermo Fisher Scientific Inc.) operated at 4°C and 100% humidity. The grids were subsequently transferred to a Titan Krios electron microscope (Thermo Fisher Scientific, Inc.) operated at 300 kV and equipped with a Gatan K3 Summit direct electron detector and a GIF Quantum energy filter. Movie stacks were automatically collected using an EPU with a preset defocus ranging from -1.2 μm to -1.8 μm in superresolution mode. Data collection was performed at a normal magnification of 81,000x for Na PaS-LqhaIT and 105,000x for Na PaS-Av3, with pixel sizes of 1.072 and 0.855 Å/pixel, respectively. The slit width on the energy filter was 20 eV, and the total dose was approximately 50 e-/Å2 for each micrograph stack.

Image processing
Image processing was carried out in cryoSPARC62, and the strategies are shown in Supplementary Fig. 6A and Supplementary Fig. 7A. The movie stacks were motion-corrected via MotionCor263, and the defocus values were estimated with patch CTF estimation. Micrographs with contamination or a maximum resolution lower than 5 Å were excluded from the calculation, resulting in a total of 15,651 micrographs (Na PaS-LqhαIT) and 4,515 micrographs (Na PaS-Av3) for structure determination, respectively.

For the Na PaS-LqhαIT dataset, a total of 40,338 particles were autopicked via Blob Picker, extracted with a box size of 300 pixels from 500 micrographs, and classified into 50 classes via 2D classification. Representative 2D class averages were selected as the training dataset for Topaz Train64. Then, 2,070,023 particles were autopicked from 15,651 micrographs using Topaz. After one round of 2D classification, 1,976,679 particles were selected and then subjected to Ab initio reconstruction in four classes, which were used as templates for the heterogeneous refinement of all the selected particles. After the particles were selected from the most abundant subset, 46.0% of the total particles were subjected to homogeneous refinement, which yielded a reconstruction with a resolution of 3.19 Å. To better resolve the density, another round of heterogeneous refinement was performed, and five volume maps were generated. One density map with 483,207 particles was selected and subjected to nonuniform refinement followed by CTF refinement and nonuniform refinement, improving the map resolution up to 2.87 Å65, 66.

For the Na PaS-Av3 dataset, a total of 95,454 particles were autopicked via Blob Picker, extracted with a box size of 400 pixels from 200 micrographs, and classified into 50 classes via 2D classification. Representative 2D class averages were selected as the training dataset for Topaz Train. Then, 1,366,255 particles were autopicked from 4,515 micrographs with Topaz. After two rounds of 2D classification, 1,064,365 particles were selected and subjected to ab initio reconstruction in four classes, which were used as templates for the heterogeneous refinement of all the selected particles. After that, the particles selected from the most abundant subset, accounting for 44.0% of the total particles, were subjected to homogeneous refinement, which yielded a reconstruction with a resolution of 3.14 Å. To better resolve the density, another round of heterogeneous refinement was performed, and four volume maps were generated. One density map with 353,627 particles was selected and subjected to nonuniform refinement to improve the resolution of the density to 2.79 Å65, 66.

Model building and refinement
To generate initial models, the cryo-EM structure of Na PaS (PDB 5X0M) was docked into the Cryo-EM density maps using Chimera67. The models were iteratively rebuilt in COOT and refined in Phenix68, 69. The density maps provided sufficient detail for the reliable modeling of side chains and structural motifs (Supplementary Fig. 8A, B). The statistics for the Cryo-EM data collection and model refinement are reported in Supplementary Table 3. The refined coordinates and Cryo-SEM data were deposited into the PDB and EM Data Banks, respectively.

AI-driven mutation selection
A mutation-selection procedure was conducted using the complex structure of Na PaS-LqhαIT as input. The binding free energy (∆G) was calculated to evaluate the thermodynamic properties of the interface. The change in protein-protein binding affinity (∆∆G), representing the difference in binding free energy between the wild-type and mutant complexes, was predicted using ComplexDDG to identify beneficial mutations. Comprehensive details of the ComplexDDG model architecture, benchmarking performance, and ablation studies are provided in the Supplementary Note, Supplementary Figs. 9-10, and Supplementary Tables 4-5. Next, the Evolutionary Scale Modeling 2 (ESM2) Large Language Model (pretrained model: esm2_t36_3B_UR50D) was employed to predict the impact of genetic variations on protein structure and function, facilitating the selection of beneficial mutations49. Residue-type probability distributions were computed for each position by masking the residue using ESM2. Additionally, Aggrescan3D (A3D) 2.0 was used to evaluate the potential effects of mutations on protein aggregation50.

Mutations with predicted ∆∆G scores less than 0 and ESM2 scores greater than those of the wild-type residues were considered candidate beneficial mutations. These were further screened using the A3D 2.0 RESTful service, with the aggregation analysis distance set to 10 Å. A threshold value of <1.5 was applied to minimize the risk of aggregation. To further enrich for functionally favorable variants, a multi-criteria filtering strategy was applied based on structural and physicochemical considerations. Specifically, mutations were prioritized if they: (i) occurred at toxin-channel interface residues identified from the cryo-EM structure; (ii) were predicted to enhance hydrophobic packing or introduce favorable electrostatic interactions; and (iii) did not involve structurally critical residues, such as those involved in disulfide bonds or conserved functional motifs. The resulting candidate mutations were selected for experimental validation.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript \"Structural Insights into Insect-Selective Sodium Channel Toxins Drive AI-Enhanced Biopesticide Design\" by Heng Jiang is devoted to studies one of the most critical ion channels: voltage gated sodium channel from an insect (Periplaneta Americana) sodium channel NavPaS). The significance of this study is obvious to me, since for the first time the details of CryoEM 3D structure of that popular model channel complexed with two aniaml toxins is determined (Av3 from sea anemone and LqhαIT from the scorpion). Great novelty is somehow compromised by the fact, the structure of one of two systems reported has been recently published by an independent research group (Phulera, S., et al., Scorpion alpha-toxin LqhalphaIT specifically interacts 809 with a glycan at the pore domain of voltage-gated sodium channels. Structure,810 2024. 32(10): p. 1611-1620 e4).This fact is clearly pointed out in the manuscript. The newer structure of LqhalphaIT - NavPaS has better resolution which facilitates detailed molecular analysis of interactions and possibly design of new pesticides.\nI expect that relatively large community of biophysicist, biologist and some chemists will be interested in presented data. Topic of effective pest control is a serious global issue.\nTill now detailed orientations and docking positions of those rater complex peptide toxins systems were a matter of debate, now the issue is resolved. The molecular details plus AI based analysis of promising mutation positions in scorpion toxin helped to design new more potent and hopefully also selective anti-insect peptides. The synthesis of those new compounds revealed their postulated efficacy. This is a direct proof that AI-assisted improvement of peptide-protein binding strength is possible. Such approach not only suggest new anti-insect effective compounds but offers opportunity to use similar pipelines in rational AI-assisted drug design. This refers to situation when a potent inhibitors of known protein targets are sought.\nI rank this manuscript high, it contains a lot of new facts regarding two important molecular complexes, with significant impact on economy and possibly human health. The methods used are divers and link technical expertise with goal focused experiments. I am not in position to judge how appropriate were experimental details regarding proteins expression, purification and mutagenesis, but structural studies and analysis seems to be fine.\nThe quality and clarity of presentation are very good. Conclusions are supported by data. I have only a few critical comments:\n1. Using different sodium channels for electrophysiology and other one for all structural studies is somehow risky and not common. Perhaps adding event more comments on the utility of such an approach (?previous studies) would better justify that part of the manuscript (we understand argument on lack of conductance of NavPaS in used cell system, but may be other arguments might facilitate transferring measurements results on orthologous Nav channel BgNav1-1a, from the German cockroach Blattella germanica to NavPaS.\n2. Line 148: “BgNav1-1a shares approximately 59% sequence identity with NavPaS and is well suited for electrophysiological studies [36, 37]. As a control, we used the cardiac isoform of the human Nav channel (hNav1.5), which represents mammalian Nav channels.”\nHow much sequence identity is shared between NavPaS and hNav1.5?\n3. Line 71 : calmodulin?\n4. Line 165-166, not quite precise. The authors may add some review comments on results of molecular docking/MD of both toxins to NavPaS made in the past, for example: (1) Scorpion Toxins: Positive Selection at a Distal Site Modulates Functional Evolution at a Bioactive Site, Limei Zhu, Bin Gao , Shouli Yuan, Shunyi Zhu; Mol Biol Evol. 2019 Feb 1;36(2):365-375. doi: 10.1093/molbev/msy223; (2) Niklas, B. et al. Interactions of Sea Anemone Toxins with Insect Sodium Channel—Insights from Electrophysiology and Molecular Docking Studies. Molecules 26, 1302 (2021), etc.\n5. Lines 239-241. “Thus, mutations at these residues could disrupt interactions with both the Nav channel and lipid membrane, emphasizing the critical role of membrane interactions in Av3 activity.” – in my opinion this statement is quite speculative and too strong. Is interaction with a membrane CRITICAL in Av3 activity?\n6. Line 348 “we applied manual filtering based on empirical knowledge and included substitutions targeting key sites identified by ComplexDDG “ – I am concerned about substantial subjective unspecified judgement revealed in this point. Could authors’ results be reproduced?\n7. Line 423. It should be indicated here in Discussion section in what molecule this A39L mutation is.\n8. Numerous references deserve better editing, pages range is missing, for example [2], [13], [18], [29-33]. Authors are not properly shown in [15].\n9. A general remark: What is the translation of the toxin's effect on fast inactivation from the NavPas channel to other insect Nav channels, given that NavPas is non-functional, has no inactivation particle IFM (MFM) motif, and it is unknown whether it preserves the fast inactivation process?\n10. May be authors should be more critical about prospects of animal derived toxins in pest control. Those toxins have been known for decades and have failed to market as their excellent in vitro effects do not translate into in vivo effects.\n11. Is computational pipeline to design new variants of toxins available for interested scientists?", "reviewer_2": "This is a well-executed and timely study that provides valuable structural and functional insights into insect-selective sodium channel toxins. The authors effectively integrate cryo-EM, surface plasmon resonance, and electrophysiology to elucidate how Av3 and LqhαIT differentially engage NavPaS, modulate channel gating, and confer insect-specific potency at the structural level. The discovery of a previously uncharacterized membrane-embedded binding site for Av3 is particularly compelling, as it may open new opportunities for rational design targeting this unique pocket. Furthermore, the application of AI-guided protein engineering led to a redesigned LqhαIT variant with significantly enhanced potency. These findings not only deepen our understanding of toxin–channel coevolution but also establish a strong framework for the development of next-generation biopesticides with improved specificity and efficacy. There are still a few issues that should be addressed, but if properly resolved, I would support publication.\nMajor points:\nLine 90: The authors state that ‘in nature, many animal toxins have evolved to selectively target insect Nav channels.’ Do the authors have a hypothesis for why this evolutionary pressure favored insect-selective targeting? Why wouldn’t similar toxins evolve to target mammalian Nav channels instead?”\nLine 147: The authors used NavPaS for structural studies, whereas BgNav1-1a was employed for electrophysiological analyses. Why not use BgNav1-1a for the cryo-EM studies as well? Doing so could provide a more direct correlation between structure and function.\"\nLine 150: Is there a specific reason the authors chose human Nav1.5 as a surrogate for other human Nav channels? It would be helpful if the authors could elaborate further—based on the resolved structures—on whether the observed selectivity toward Nav1.5 might also extend to other human Nav isoforms.\nLine 222: The authors state that ‘key residues at the VSD4 interface, including Pro5, Tyr7, Trp13, and Tyr18, are crucial for Av3 binding.’ However, based on Fig. 3c and the provided PDB file, Pro5 does not appear to participate in any direct interaction with the channel. Furthermore, the authors state, ‘Alanine substitutions at these residues nearly abolish binding, as confirmed by surface plasmon resonance (SPR) (Supplementary Table 2)’. This appears inconsistent with the data (Supplementary Table 2), as the P5A mutation shows a binding affinity similar to or slightly better than the wild-type (KD: 20.4 nM vs. 21.5 nM). The authors should revise this statement to accurately reflect the experimental results.”\nLine 274-277, 284-286: Could the authors provide a more detailed discussion of the mechanism underlying the significant conformational shifts of Glu15, Phe17, and Arg18?\nFigure 3H: Since the authors state in the legend that invertebrate-specific residues are marked with an asterisk, it is confusing that M281 is marked this way, suggesting it is invertebrate-specific. However, based on Figure 3H, M281 appears to be conserved across both invertebrate and mammalian Nav channels, except for hNav1.8, where it is a lysine (K). This should be clarified.\nAdditionally, in the main text (lines 253–257), the authors state: ‘The selectivity is attributed primarily to invertebrate-specific residues in PD1, including Ile279, Val283, Trp391, His392, and Leu394 (Fig. 3H). Substituting these invertebrate-specific residues with their mammalian counterparts significantly reduces the efficacy of Av3 by 2.6- to 44-fold, underscoring their importance in species selectivity (Fig. 3G).’ However, Figure 3G does not include data for the Ile279 and Leu394 mutation. The authors should either provide the corresponding data or revise the text to accurately reflect the figure contents.\nMinor points:\nLine 204-206: Add reference for this statement ‘This mechanism aligns with the structural changes induced by the toxin AaH2, further supporting a common role of electrostatic interactions in toxin-induced channel modulation.’\nLine 226, line 256, line 887, line 899: The authors should clarify which assay was used to measure Av3 activity here, in order to distinguish it from the previously mentioned SPR assay.”\nLine 239: The authors state that ‘mutations at these residues could disrupt interactions with both the Nav channel and lipid membrane,’ which implies that this is a speculative statement. However, Figure 3F clearly demonstrates that the W8A and W13A mutations substantially reduced activity. The authors should revise this sentence to reflect the experimental evidence rather than presenting it as a hypothesis.\nLine 244: The reference and PDB ID for the apo structure of Av3 should be provided.\nLine 271: The authors should add the data source (figure or table) supporting the statement ‘… LqhαIT activity was reduced by 40-fold and 37-fold, respectively.’\nLine 304: The author should also discuss the amino acid differences in S3-S4 linker between insect and mammal sodium channels.\nLine 309: The authors should provide reference and/or figure source to support the statement ‘While the glycans near the glycosylation site are relatively conserved across Nav channels, their distal regions vary’\nLine 585, Line 620: 50 e-/Å2 should be 50 e⁻/Å². 3,53,627 particles should be 353,627 particles.\nLine 711: The journal name Annual Review of Entomology is not in the abbreviated format. Please review all references to ensure consistent formatting throughout the reference list.\nLine 920-924: The authors showed EeNav1.4 in supplementary Fig.4 but did not mention it in legend.\nLine 950-951: The authors should clarify the meaning of ‘N.D.’ to avoid ambiguity.\nFigure 1A: To provide a more complete picture of the electrophysiological effects, the authors should show representative current traces of Av3 and LqhαIT on hNav1.5.\nFigure 1B: The hNav1.5 curve for Av3 appears relatively smooth, whereas the curve for LqhαIT shows large error bars. The authors should provide an explanation for this discrepancy. In addition, the figure legend should clearly indicate whether the data are presented as mean ± SD or mean ± SEM, and the number of replicates (n) should be specified.”\nFigure 2B: The authors should indicate what the dashed and solid lines represent in the right panel for clarity.\nFigure 3E: The authors should clarify how the lipid was identified as a phospholipid based on the density map. Additionally, the sigma value used for contouring the map should be specified in the figure legend.\nFigure 3F, Figure 3G, Figure 4F, Figure 4G, Figure 5B, Figure 5C, Figure 5D: To enhance transparency and allow better interpretation of data variability, the authors are encouraged to display individual replicate values and specify the number of replicates (n) in the bar plots.\nFigure 3H: D1 or DI? Please be consistent.\nFigure S3A and S3D: The text ‘86 residues’ appears to be unintended and should be removed.\nTable-S2: The authors also performed SPR analysis for LqhαIT variants, but this was not mentioned in the main text. Is there a specific reason for this omission?", "reviewer_3": "The structural basis for the action of peptide toxins acting selectively on insect sodium channels is poorly understood but can provide valuable information for the future design of effective and selective insecticides. In this study, Jiang and colleagues determined cryo-EM structures of the cockroach sodium channel NavPs in complex with either the LqhaIT or Av3 insecticidal toxin. The binding interfaces between toxin and channel were probed using mutagenesis, which included introducing mammalian substitutions into the insect channel. Mutants were tested using surface plasmon resonance studies with NavPs or two-electrode voltage-clamp studies with the BgNav1.1 channel from a different cockroach species (as NavPs is non-functional). This study also describes an AI modelling approach used to identify mutations of LqhaIT that were predicted to increase its binding affinity. One such mutant (A39L) was shown to be twice as effective as the wildtype toxin in a bioassay."}, "author_response": ""}]

Decision letter