Voltage-gated sodium (Naᵥ) channels are critical to the regulation of brain activity, cardiac rhythm, and muscle contraction. Expressed in the membranes of excitable cells, Naᵥ channels respond to membrane depolarization to open a pore that facilitates the selective flow of sodium current into the cell, initiating the action potential. In mammals, the Naᵥ channel family consists of nine subtypes (Naᵥ1.1–1.9), distributed throughout the central and peripheral nervous system, as well as in cardiac and skeletal muscle (Yu and Catterall, 2003). The Naᵥ1.4 subtype is predominantly expressed in skeletal myofibers, where it initiates muscle contraction. Genetic mutations in this subtype are associated with various motor dysfunctions, such as both hyperkalemic and hypokalemic periodic paralyses (Mantegazza et al., 2021; Venance et al., 2006; Jurkat-Rott et al., 2000). Naᵥ1.7 is found in peripheral sensory neurons and is responsible for nociception. Several pain disorders, such as inherited erythromelalgia and small fiber neuropathy arise from gain-of-function Naᵥ1.7 mutations (Dib-Hajj et al., 2013). Both Naᵥ subtypes have been investigated as promising pharmacological targets for the treatment of myopathy and pain conditions (Dib-Hajj et al., 2013; Ghovanloo et al., 2021).

Structurally, Naᵥ channels consist of four homologous domains (DI–DIV) arranged in a domain-swapped configuration (Figure 1A–B). Each domain comprises six transmembrane helices (S1–S6). The central pore domain is formed by S5, S6 helices and selectivity filter (SF), while the four peripheral VSDs are formed by S1–S4 (de Lera Ruiz and Kraus, 2015; Pan et al., 2018). The pore domain also features lateral fenestrations that provide a pathway for the access of small molecules to the pore via the membrane (Gamal El-Din et al., 2018) and have been shown in computational studies to be accessible to lipid tails (Raju et al., 2013; Boiteux et al., 2014; Martin and Corry, 2014; Tao and Corry, 2022). Additionally, the C-terminal domain (CTD) extends from the DIV S6 helix into the cytoplasm, where it is thought to associate with the DIII–IV and DIV S4–S5 linkers in the resting state (Clairfeuille et al., 2019).

Figure 1

Download asset Open asset

Naᵥ channels adopt distinct functional states during the cycle of membrane depolarization and repolarization (Figure 1C). In the resting state, the pore is closed and VSD S4 helices are in the down, deactivated state. Membrane depolarization triggers the asynchronous transition of VSD I–III S4 helices to the up, activated conformation, causing Naᵥ channels to open (Cha et al., 1999; Capes et al., 2013; Goldschen-Ohm et al., 2013). During prolonged depolarization, VSD-IV moves upward, causing the channels to adopt a fast-inactivated state in which a three-residue Ile-Phe-Met (IFM) hydrophobic motif located on the intracellular linker between DIII and DIV (DIII–IV linker) allosterically closes the pore (Yan et al., 2017). The CTD is proposed to bind at the S4–S5 linker of VSD-IV and sequester the DIII-IV linker during the resting state. During fast inactivation, the CTD dissociates from VSD-IV and releases the DIII–IV linker to allow IFM binding (Clairfeuille et al., 2019). Upon repolarization, the VSDs deactivate, the IFM motif disassociates, the CTD rebinds to VSD-IV, and the pore returns to the resting state.

Phosphoinositides (PIPs) are important cellular signaling molecules found on the cytoplasmic leaflet of the mammalian cell membrane. They can exist in seven forms, with phosphorylation possible at one (PIP1), two (PIP2), or all three (PIP3) positions on the inositol ring, at the 3’, 4’, and/or 5’ carbons. PIPs, particularly PI(4,5)P₂, featuring phosphates at the 4’ and 5’ carbon positions (Figure 1D), are known to bind and modulate the activity of numerous ion channel families (Hille et al., 2015). These include voltage-gated ion channels, some of which have been resolved with PI(4,5)P₂ bound (Sun and MacKinnon, 2020; Gao et al., 2021). PIP is known to interact with the VSDs of different potassium channels, to stabilize the positive gating charges and support the voltage-sensing mechanism (Schmidt et al., 2006). By also binding at the VSD-pore interface in channels such as Kᵥ7.1, PIP is proposed to facilitate coupling of VSD movement to pore opening (Sun and MacKinnon, 2020; Kasimova et al., 2015; Ma et al., 2022). PI(4,5)P₂ also forms specific interactions with VSD-II of Caᵥ2.2 in the down state, making channel activation more difficult (Gao et al., 2021). Although PI(4,5)P₂ is known to bind to numerous voltage-gated ion channels, its effects on channel gating and function are complex and yet to be fully elucidated.

Recent experiments show that Naᵥ1.4 channel kinetics and voltage dependence are modulated by PI(4,5)P₂ (Gada et al., 2023). PI(4,5)P₂ inhibits Naᵥ1.4 by causing a depolarizing shift in voltage dependence of activation, accelerating transition to the inactivated state and slowing recovery from inactivation, resulting in reduced peak current and suppression of late current (summarized in Figure 1C). While this is likely to occur via a direct interaction with Naᵥ1.4, the structural basis of PI(4,5)P₂ modulation remains to be understood.

Here, we used a combination of coarse-grained and atomistic MD simulations to identify a putative PIP binding site to inactivated Naᵥ1.4 in VSD-IV and the DIII–IV linker. We analyze the atomistic level interactions between the positively charged residues at this site with PI(4,5)P₂ and PI(4)P, comparing this with structurally resolved PIP binding sites in the related ion channels Caᵥ2.2 and Kᵥ7.1. Consistent with the sequence conservation at the identified site, we find that PIPs also bind to Naᵥ1.7 in coarse-grained simulations, with notable differences dependent on VSD conformation states. This work provides insight into how PIPs can negatively regulate Naᵥ channels, a first step for the potential development of PIP-analogue sodium channel inhibitors.

To investigate how diverse lipid species interact with Naᵥ1.4, we carried out coarse-grained simulations of Naᵥ1.4 (PDB ID: 6agf, inactivated state) embedded in a complex mammalian membrane for 16 µs in triplicate (Figure 2A). Glycosphingolipid, PIP, and diacylglycerol (DG) were highly enriched around Naᵥ1.4 (Figure 2B). Additionally, we observed modest enrichment of lysophosphatidylcholine (LPC), phosphatidylinositol (PI), phosphatidylserine (PS), and phosphatidylethanolamine, and slight depletion of ceramide, sphingomyelin, phosphatidylcholine, and cholesterol. To investigate specific interactions, we generated z-density maps and calculated the per-residue occupancy of the 12 different lipid types (Figure 2C, Figure 2—figure supplements 1–2). Binding residues of interest were identified by constructing occupancy distributions by residue for each lipid type and identifying outlying values with high occupancies (Figure 2—figure supplement 3). DG lipids form significant interactions within the lateral fenestrations of Naᵥ1.4 (Figure 2—figure supplement 2). LPC and PI also frequently interact with different VSD residues (Figure 2—figure supplement 1).

Figure 2 with 3 supplements see all

Download asset Open asset

Given our interest in understanding the modulation of Naᵥ1.4 by PIPs, we focus on their interactions for the remainder of this manuscript. Despite the very low concentration of PIPs (0.5% each of PIP1, PIP2, and PIP3) in the mammalian membrane, they are highly enriched around Naᵥ1.4, particularly near the DIII–IV linker and the VSD-IV (Figure 2C). Contact analysis revealed a putative PIP binding site involving the K1330 residue of the DIII–IV linker and residues R1463, K1466, and R1469 in the DIV S4–S5 linker, which connects the pore and VSD-IV (Figure 2D). Across all three replicates, only residues within this site were occupied by PIPs for more than 80% (quartile 3+1.5×interquartile range) of simulation time on average (Figure 2D). We note that these residues have higher occupancies for PIP compared to other lipids, including other negatively charged phospholipids (PA, PS, and PI) (Figure 2D).

Simulations in a complex mammalian membrane showed that PIP species bind specifically and selectively to Naᵥ1.4 in the presence of other negatively charged lipids and at low, physiological concentrations. However, the large membrane required, and long PIP binding durations prevented sampling of large numbers of binding and unbinding events. To address this, we carried out additional simulations where Naᵥ1.4 was placed in a smaller POPC membrane with a 5% concentration of each PIP species in the cytoplasmic leaflet (Figure 3A).

Figure 3 with 3 supplements see all

Download asset Open asset

Across 10 replicates of these enriched PIP simulations, each carried out for 80 µs, we observed all three PIP species binding at the identified site on VSD-IV and DIII–IV linker (Figure 3B). PIPs can approach and bind to this site from either side of the VSD, however, PIP1 only forms stable interactions when it approaches and binds from the VSD-I side while PIP2 and PIP3 usually bind from the VSD-III side (Figure 3B). These enriched PIP simulations also revealed additional positively charged residues in the DIII–IV linker (K1329, K1333, and K1352) and DIV S4 (R1460) which support binding.

There were 156 PIP binding/unbinding events with duration greater than 2 µs occurring in the identified site (Figure 3C). Of these, 43 were with PIP1, 44 with PIP2, and 33 with PIP3. The number of short-term (2–10 µs) interactions decreased with headgroup charge. That is, PIP1 formed the greatest number of short-term interactions while PIP3 had the fewest. Of the 31 binding events with duration greater than 10 µs, 7 were with PIP1, 15 with PIP2, and 9 with PIP3 (Figure 3—figure supplements 1–2).

When we analyzed interactions occurring during 2–80+ µs binding events, we found that the number of interacting basic residues changes depending on the PIP headgroup charge (Figure 3D). PIP1 (headgroup charge: –3e) binding is most frequently coordinated by two positive charges in VSD-IV and zero or one residue in the DIII–IV linker. PIP2 (headgroup charge: –5e) binding most frequently involves one or two interactions from the DIII–IV linker and three from VSD-IV for a total of five interactions. Interestingly, despite its greater negative charge, PIP3 (headgroup charge: –7e) interacts similarly to PIP1, and has fewer interactions than PIP2.

The minimum distance between the interacting PIP headgroup and each binding residue across simulation time is shown for the five 40+ µs PIP binding events (Figure 3E). Of these, three are PIP2 binding events which almost span the entire 80 µs of simulation. One 40+ µs binding event each for PIP1 and PIP3 were also observed. The stable PIP1 binding event observed involved interactions with R1460 and R1469 in VSD-IV, as well as fluctuating interactions with the four residues of the DIII–IV linker (K1329, K1330, K1333, K1352). The three long-term PIP2 binding events observed were similar to each other, mainly stabilized by R1460, R1463, and R1469. As with PIP1, the number and identity of interacting DIII–IV linker residues varied across the span of each simulation and between replicates, owing to the flexibility of the linker. Like PIP2, PIP3 binding was characterized by stable interactions with R1460, R1463, and R1469 (VSD-IV) as well as K1329 and K1330 (DIII–IV linker). In silico mutation of the eight residues implicated in PIP binding to leucine (charge neutralization while preserving side chain size) or glutamate (charge reversal) significantly reduced PIP binding (Figure 3—figure supplement 3).

Coarse-grained simulations enhance sampling by reducing the number of particles, enabling larger time steps and construction of a smoother energy surface. To examine interactions and protein conformational changes in atomistic detail, we backmapped representative snapshots from our PIP-enriched simulations, where we observed stable, long-term binding events between Naᵥ1.4 and PIP1/PIP2. The coarse-grained PIP1 and PIP2 molecules were converted to the atomistic PI(4)P and PI(4,5)P₂, respectively. For each system, 7.5 µs of atomistic simulations were performed (5 replicates, 1.5 µs each).

In these simulations, the PI(4,5)P₂ headgroup was stable at the binding site identified in coarse-grained simulations (root mean square deviation [RMSD] <2.2 Å) (Figure 4A, Figure 4—figure supplement 1). PI(4,5)P₂ binding is predominantly coordinated by R1469 on the DIV S4–S5 linker, as well as R1466 and R1463. In one replicate, PI(4,5)P₂ associates with R1460 via the phosphate group (PO4) that connects the headgroup to the PIP tails. The PI(4,5)P₂ headgroup also forms electrostatic interactions with K1329 and K1330 in the DIII–IV linker, but forms few contacts with K1333 (Figure 4B, Figure 4—figure supplement 2). This loop portion of the DIII–IV linker is highly flexible (Figure 4—figure supplement 1), thus the lysine side chains can flip between binding the PI(4,5)P₂ headgroup and facing the intracellular space.

Figure 4 with 3 supplements see all

Download asset Open asset

The 4’-phosphate formed interactions with residues belonging to the DIII–IV linker (K1329 and K1333) and DIV S4–S5 linker (K1666 and R1469) with similar frequency, in 55–60% of simulation frames (Figure 4B). In contrast, the 5’-phosphate formed contacts with three VSD-IV S4–S5 residues (R1463, K1466, and R1469) in 70–98% of simulation frames and DIII–IV linker residues in 37–48% of frames. Taken together, this data suggests that although the headgroup is flexible when bound, the 5’-phosphate is more important for coordinating VSD-IV S4–S5 residues while the 4’-phosphate associates with both regions of the binding site.

Atomistic simulations of PI(4)P bound from the VSD-III side show that this is also a stable pose (RMSD <2 Å), where the headgroup interacts with the same positively charged residues as seen for PI(4,5)P₂ (Figure 4A, Figure 4—figure supplement 1). The residues on the S4–S5 linker, R1463, K1466, and R1469, predominantly bind to the 4’-phosphate (Figure 4B). Due to the more buried location of PI(4)P binding, the PO4 phosphate can associate more with DIII–IV linker lysines (Figure 4B), however, the absence of the 5’-phosphate leads to a reduced number of total electrostatic interactions (Figure 4—figure supplement 3).

To investigate structural changes that might occur in the presence of PI(4)P/PI(4,5)P₂, we also carried out simulations of the inactivated Naᵥ1.4 structure without any PIPs bound for comparison. Although the DIII–IV linker remains bound throughout simulations both with and without PIPs, residues P1336 and R1337 in the DIII–IV linker downstream of the IFM motif are significantly less mobile with PI(4,5)P₂ present (Figure 4C). Additionally, seven residues belonging to the DIV S4–S5 linker, including binding residues K1466 and R1469, also have significantly lower mobility when PI(4,5)P₂ is bound (Figure 4C). In the PI(4)P bound simulations, there were no significant differences in DIII–IV linker or S4–S5 linker mobility.

To further probe whether PIP can stabilize the DIII–IV linker and the inactivation gate, we applied coarse-grained simulations with the DIII–IV linker unrestrained. In simulations of WT Naᵥ1.4, the IFM has a reduced stability within its binding pocket when PIP is excluded from the membrane (Figure 4E). To accentuate this effect, we simulated an inactivation-deficient variant of Naᵥ1.4, where the IFM motif is mutated to IQM (Liu et al., 2023). We find that the IQM motif has a greater probability of being tightly associated with the receptor pocket in the presence of PIP compared to without it (Figure 4E), supporting our observation that PIP can stabilize the channel in the inactivated state. This suggests that the presence of PIP may partially rescue some of the structural defects associated with inactivation dysfunction in Naᵥ mutants.

The PIP binding poses seen in our simulations are similar to resolved binding poses for PI(4,5)P₂ in cryo-EM structures of Caᵥ2.2 and Kᵥ7.1 (Figure 5A). A sequence alignment of Naᵥ1.4 VSDs shows that there are more positively charged residues present in the S4/S4–S5 linker regions of DIV compared to the other domains (Figure 5B). PIP is bound at similar VSD residues on both these ion channels, with PIP forming interactions with a gating charge further up the S4 helix in Caᵥ2.2 due to the VSD being in a different state. Additionally, the high sequence similarity in the S4/S5–S5 linker and DIII–IV linker regions between the nine human Naᵥ channel subtypes suggests conservation of the PIP binding site (Figure 5C).

Figure 5

Download asset Open asset

To assess possible state- and subtype-dependent differences in PIP binding, we simulated three structures of Naᵥ1.7 with different VSD conformations in a PIP-enriched membrane (Figure 6A). The inactivated Naᵥ1.7 structure (blue, PDB ID: 6j8g) contains all VSDs in the activated/up state and a bound DIII–IV linker. We also simulated a NaᵥPas (American cockroach) chimera structure with a human Naᵥ1.7 VSD-IV in the deactivated/down state (VSDs I–III are activated, and from NaᵥPas) (pink, PDB ID: 6nt4). This structure (Clairfeuille et al., 2019) features a dissociated DIII–IV linker and a resolved CTD bound to the DIV S4–S5 linker (forming the ‘inactivation switch’) at residues identified to form part of our PIP binding site. To assess PIP binding at VSD I–III in the deactivated/down state, we modeled the Naᵥ1.7 resting state based on templates structures in which different VSDs have been captured in deactivated states with the aid of toxins. These structures feature three or more of the gating charge residues below the hydrophobic constriction site (HCS) and displacement of the S4–S5 linker (Figure 6—figure supplement 1). Given that no resting state mammalian Naᵥ channel structure has been resolved, it is possible that the modeled VSDs may not reflect the fully deactivated state.

Figure 6 with 3 supplements see all

Download asset Open asset

In triplicate 50 µs coarse-grained simulations, PIPs bind to the analogous site to that seen in inactivated Naᵥ1.4 in the inactivated Naᵥ1.7 structure, interacting with residues belonging to both the DIII–IV linker and VSD-IV for durations comparable to Naᵥ1.4, (Figure 6B–C, Figure 6—figure supplement 2). Binding of PIP to the DIV S4–S5 linker to the deactivated VSD-IV in the Naᵥ1.7-NaᵥPas chimera and resting state model was also observed (Figure 6B–C). However, in the Naᵥ1.7-NaᵥPas chimera, the PIP bound at the S4–S5 linker cannot simultaneously associate with the DIII–IV linker (Figure 6—figure supplement 2), due to its sequestration by the CTD, which moves the lysines away from the binding VSD-IV residues (Figure 6D). Instead, K1491, K1492, and K1495 (on the DIII–IV linker) are occupied by different PIPs on the other side of the VSD. In the resting state model, which features an unbound DIII–IV linker PIPs binding at the DIV S4–S5 residues also do not associate with the DIII–IV linker (Figure 6—figure supplement 2). Comparison of binding durations at this site across the three systems reveals a greater number of long PIP interactions (>20 µs) with inactivated Naᵥ1.7 (Dib-Hajj et al., 2013) compared to Naᵥ1.7-NaᵥPas (Venance et al., 2006) or the resting state model (Mantegazza et al., 2021) where VSD-IV is down, and the DIII–IV linker is dissociated.

There are state-dependent differences in PIP occupancy at the gating charges in each VSD (Figure 6E–F, Figure 6—figure supplement 3A and B). PIP can associate with the lowest three gating charges VSD-I of the resting state model, but not in the inactivated state (Figure 6E). This is likely due to the large displacement in the DI-S4 helix, which moves down three helical turns in the resting state model so that all gating charges are below the HCS (Figure 6—figure supplement 1). In VSD-II and -III, the PIP interaction differences between inactivated and resting state are present but less pronounced, owing to a smaller difference in the relative displacement between the gating charges between states (Figure 6—figure supplement 1). PIP occupancy is also higher at VSD-IV when it is in the deactivated conformation, however, the presence of a bound CTD as seen in the Naᵥ1.7-NaᵥPas model reduces this occupancy of PIP (Figure 6E). Additional simulations of a resting state model of Naᵥ1.4 built using our Naᵥ1.7 resting state model as a template suggest that similar gating charge interactions occur for Naᵥ1.4 when the VSDs are deactivated (Figure 6—figure supplement 3C).

Recently, PI(4,5)P₂ was shown to be a negative regulator of Naᵥ1.4, modulating channel kinetics and voltage dependence. Presence of PI(4,5)P₂ causes a depolarizing shift in the voltage dependence of activation, that is a stronger stimulus is required to produce Naᵥ1.4 opening. Additionally, it stabilizes the inactivated state of Naᵥ1.4, marked by both shortened times to inactivation and slowed recovery from the inactivation.

Using a multiscale simulation approach, we identified a putative PIP binding site comprised of positively charged residues belonging to the S4 helix/S4–S5 linker of VSD-IV (R1460, R1463, K1466, R1469) and DIII–IV linker (K1329, K1330, K1333, K1352). Coarse-grained simulations of Naᵥ1.4 embedded in a complex membrane showed that PIP interacts with residues belonging to VSD-IV and the DIII–IV linker. In coarse-grained enriched PIP simulations, PIP2 formed longer duration interactions with Naᵥ1.4 than PIP1 and PIP3, supported by a greater number of charged interactions. Atomistic simulations verified the stability of PI(4,5)P₂ (the most common PIP2 species in the plasma membrane) at this site and showed that the binding of PI(4,5)P₂ reduces the mobility of some DIV S4–S5 and DIII–IV linker residues. Simulations of Naᵥ1.7 with VSDs in different conformational states showed that the PIP binding site is conserved in Naᵥ1.7 and that PIP interactions at VSD gating charges are functional state dependent, with more interactions being formed when the VSDs are deactivated.

The DIII–IV linker, CTD, and S4–S5 linkers all play key roles throughout the Naᵥ conformational cycle. Mutation of the IFM inactivation motif as well as other residues in the DIII–IV linker alter fast inactivation and recovery from fast inactivation (McPhee et al., 1998; West et al., 1992). While the precise role of the Naᵥ CTD and its conformation during the Naᵥ activation cycle remain elusive, it is likely to be important for coordinating fast inactivation (Clairfeuille et al., 2019; Mantegazza et al., 2001). CTD binding to DIV S4–S5 and sequestration of the DIII–IV linker is proposed to occur in the resting state. After the pore opens, activation of VSD-IV is thought to cause CTD dissociation, releasing the DIII–IV linker to allow fast inactivation. Residues in ‘switch 1’ of the CTD binding site on the DIV S4–S5 stably bind PIP in our simulations of inactivated Naᵥ1.4 and Naᵥ1.7. We hypothesize that PIP binding at this location makes it more difficult for the CTD to reassociate with VSD-IV, a conformational change which is required during recovery from inactivation.

More generally, the S4–S5 linkers in all four domains couple the VSD to the pore helices and adopt different orientations depending on VSD activation states. When the VSD is activated (in the open and inactivated states), the S4–S5 linkers lie parallel to the membrane. In the resting state, when the voltage sensor is deactivated, the S4–S5 linkers move downward below the plane of the membrane. We propose that PIP binding at the identified site could additionally stabilize both the DIV S4–S5 linker and DIII–IV linker to favor the inactivated state. Although the recovery from fast inactivation occurs on the order of several milliseconds (Bezanilla and Armstrong, 1977), and is beyond atomistic simulation timescales, we observed statistically significant reductions in the RMSF of several DIII–IV linker and DIV S4–S5 linker residues when PI(4,5)P₂ was bound in 1.5 µs atomistic simulations. Reduction in the mobility of the DIII–IV linker may slow the dissociation of the upstream IFM motif and stabilization of the S4–S5 linker prevents the downward movement required for the channel to transition back to the resting state.

The PIP binding residues identified here are conserved in Naᵥ1.1–1.9 (Figure 5C), suggestive of a shared binding site and mechanism for PIP-mediated modulation across subtypes. Mutations at these conserved residues in other subtypes lead to various gain-of-function diseases (Table 1). For example, analogous to the Naᵥ1.4 R1469 residue, the R1642C mutation (in Naᵥ1.3) leads to developmental epileptic encephalopathy (Zaman et al., 2018), and R1644C/H mutations in Naᵥ1.5 (analogous to R1469 in Naᵥ1.4) cause cardiac arrythmias, characterized by accelerated rates of channel recovery from inactivation (Frustaci et al., 2005). Consistent with our observations, these diseases with mutations on the DIII–IV linker are likely to reduce PI(4,5)P₂ binding, which could be a contributing factor to instability of inactivated state in these pathogenic variants. These inactivation-deficient variants, as well as the IQM variant that we simulated, further emphasize that interactions between PI(4,5)P₂ with Naᵥ channels could be important for prolonging the fast-inactivated state.

The PIP binding site identified here harbors sequence and structural similarity to PI(4,5)P₂ binding sites found in other cation channels (Figure 5B). For example, PI(4,5)P₂ is resolved at a similar site near the VSD and S4–S5 linker in a recent cryo-EM structure of Kᵥ7.1, where the phosphate headgroup forms analogous contacts to R249 and R243 (PDB ID: 6v01) (Sun and MacKinnon, 2020). Despite differences in the role of PI(4,5)P₂, which negatively regulates Naᵥ1.4 but is required for Kᵥ7.1 pore opening, the binding site appears to be conserved. Based on the PI(4,5)P₂ binding site, a structurally similar compound was developed as an activator of Kᵥ7 channels and proposed to be a future antiarrhythmic therapy (Liu et al., 2020).

PI(4,5)P₂ also binds to the down, deactivated state of VSD-II in Caᵥ2.2 (PDB ID: 7mix) (Gao et al., 2021). In this structure, the PI(4,5)P₂ headgroup interacts with two VSD-II gating charges, R584 and K587. Compared to the positioning of PI(4,5)P₂ in our simulations of Naᵥ1.4 with an activated VSD-IV, the headgroup associates further up the S4 helix in Caᵥ2.2 due to the VSD being in a deactivated state. This is also seen in Kᵥ7.1 which contains an extended GGT loop in the S4–S5 linker which prevents PI(4,5)P₂ binding in the VSD-down state (Mandala and MacKinnon, 2023). In our coarse-grained simulations of the resting Naᵥ1.7 model, we observe a similar state-dependent difference in PI(4,5)P₂ interactions with the deactivated states of each VSD. Since activation of VSDs I–III are known to be coupled to channel opening (Goldschen-Ohm et al., 2013), we propose that PIP binding at these VSDs impedes their ability to activate and thus increases the voltage threshold required of opening. PI(4,5)P₂ binding at VSD-IV is prevented by the presence of the CTD (and absence of the DIII–IV linker) in the resting state, thus not affecting the kinetics of inactivation onset.

The leftward shift in voltage dependence of inactivation is less pronounced when PI(4,5)P₂ is converted to PI(4)P rather than completely dephosphorylated to PI (Gada et al., 2023). This suggests that PI(4)P may play a compensatory role when PI(4,5)P₂ is not present. This is supported by our simulations which show that PI(4)P can also stably occupy the identified binding site, albeit with shorter duration and form less electrostatic interactions compared to PI(4,5)P₂. Our atomistic simulations also showed that the 5’-phosphate is more important than the 4’-phosphate for forming interactions with the DIV S4–S5 linker residues of the inactivation switch. These factors suggest that PI(4,5)P₂ binding is preferred over PI(4)P at this site and can better compete to bind over the CTD, implying that PI(4,5)P₂ is more effective at stabilizing the inactivated state and inhibiting recovery to the resting state.

Simulations using the Martini2.2 forcefield have previously been used to investigate lipid-protein interactions (Corradi et al., 2018) and successfully predict specific lipid binding sites, including the PIP binding site on K_ir channels (Stansfeld et al., 2009). While the Martini2.2 PIP species can be parameterized for a specific sub-species (e.g. PI(3,4)P₂ vs PI(4,5)P₂), we instead employed atomistic simulations to complement and strengthen findings from coarse-grained simulations, allowing us to identify the specific contribution of the 4’- and 5’-phosphate groups to binding as well as to investigate the conformational changes associated with PI(4,5)P₂ binding. Since the precise protonation of the PI(4,5)P₂ headgroup in a physiological setting is unclear, we explore one case with the 4’-phosphate protonated. Given that the PI(4,5)P₂ headgroup can adopt slightly different binding orientations and can fluctuate over the course of atomistic simulations (Figure 4—figure supplement 2), we expect the alternate protonation state to have similar affinity for the binding site.

In this work we made use of the Martini2.2 model for our coarse-grained simulations, however recently the refined Martini3 (Souza et al., 2021) has become available and will be a useful tool for further interrogating protein-lipid interactions, as a greater number of lipid parameters become available. Our coarse-grained simulations also allow us to investigate the association of other lipid types with Naᵥ1.4. While we focus on PIP here, there are other lipid species that have modulatory effects on Naᵥ channels, such as cholesterol (Amsalem et al., 2018), glycolipids, DG, LPC, and PI, and their interactions warrant further investigation.

Conclusion

Using multiscale simulations, we show that PI(4,5)P₂ binds stably to inactivated structures of Naᵥ1.4 and Naᵥ1.7 at a conserved site within the DIV S4–S5 linker. As the CTD is proposed to also bind here during recovery from inactivation, we hypothesize that PI(4,5)P₂ competes with the CTD to bind to this site, prolonging inactivation. At this site, PI(4,5)P₂ simultaneously binds to the DIII–IV linker which is responsible for allosterically blocking the pore during fast inactivation (Figure 7). Its binding reduces the mobility of both the DIV S4–S5 and DIII–IV linkers, potentially slowing the conformational changes required for the channel to recover to the resting state. We also propose that in the resting state, PIPs form additional interactions with S4 gating charges, particularly in VSD-I, anchoring them to the membrane in a way which may make the upward movement required for their activation more difficult. Our results provide insight into how sodium channels are modulated by PIPs, an important step for the development of novel therapies to treat Naᵥ-related diseases.

Figure 7

Download asset Open asset

All analysis code is available on GitHub (nav_pip_project; copy archived at Tao, 2023).

1. 1. Amsalem M
   2. Poilbout C
   3. Ferracci G
   4. Delmas P
   5. Padilla F

   (2018) Membrane cholesterol depletion as a trigger of Nav1.9 channel-mediated inflammatory pain

   The EMBO Journal 37:e97349.

   https://doi.org/10.15252/embj.201797349

   • PubMed
   • Google Scholar
2. 1. Åqvist J
   2. Wennerström P
   3. Nervall M
   4. Bjelic S
   5. Brandsdal BO

   (2004) Molecular dynamics simulations of water and biomolecules with a Monte Carlo constant pressure algorithm

   Chemical Physics Letters 384:288–294.

   https://doi.org/10.1016/j.cplett.2003.12.039

   • Google Scholar
3. Software

   1. Bauer P
   2. Hess B
   3. Lindahl E

   (2023) GROMACS 2022.5 manual (2022.5), version 2022.5

   Gromacs.

   https://manual.gromacs.org/2022.5/index.html
4. 1. Bezanilla F
   2. Armstrong CM

   (1977) Inactivation of the sodium channel. I. Sodium current experiments

   The Journal of General Physiology 70:549–566.

   https://doi.org/10.1085/jgp.70.5.549

   • PubMed
   • Google Scholar
5. 1. Boiteux C
   2. Vorobyov I
   3. Allen TW

   (2014) Ion conduction and conformational flexibility of a bacterial voltage-gated sodium channel

   PNAS 111:3454–3459.

   https://doi.org/10.1073/pnas.1320907111

   • Google Scholar
6. 1. Bouysset C
   2. Fiorucci S

   (2021) ProLIF: a library to encode molecular interactions as fingerprints

   Journal of Cheminformatics 13:72.

   https://doi.org/10.1186/s13321-021-00548-6

   • PubMed
   • Google Scholar
7. 1. Capes DL
   2. Goldschen-Ohm MP
   3. Arcisio-Miranda M
   4. Bezanilla F
   5. Chanda B

   (2013) Domain IV voltage-sensor movement is both sufficient and rate limiting for fast inactivation in sodium channels

   The Journal of General Physiology 142:101–112.

   https://doi.org/10.1085/jgp.201310998

   • PubMed
   • Google Scholar
8. Book

   1. Case DA

   (2020)

   AMBER 2020

   San Francisco: University of California.

   • Google Scholar
9. 1. Cha A
   2. Ruben PC
   3. George AL
   4. Fujimoto E
   5. Bezanilla F

   (1999) Voltage sensors in domains III and IV, but not I and II, are immobilized by Na+ channel fast inactivation

   Neuron 22:73–87.

   https://doi.org/10.1016/s0896-6273(00)80680-7

   • PubMed
   • Google Scholar
10. 1. Chang Lin Y
    2. Buyan A
    3. Corry B

    (2022) Characterizing the lipid fingerprint of the mechanosensitive channel Piezo2

    Biophysical Journal 121:369a.

    https://doi.org/10.1016/j.bpj.2021.11.892

    • Google Scholar
11. 1. Clairfeuille T
    2. Cloake A
    3. Infield DT

    (2019) Structural basis of α-scorpion toxin action on Nav channels

    Science 363:eaav8573.

    https://doi.org/10.1126/science.aav8573

    • Google Scholar
12. 1. Corradi V
    2. Mendez-Villuendas E
    3. Ingólfsson HI
    4. Gu R-X
    5. Siuda I
    6. Melo MN
    7. Moussatova A
    8. DeGagné LJ
    9. Sejdiu BI
    10. Singh G
    11. Wassenaar TA
    12. Delgado Magnero K
    13. Marrink SJ
    14. Tieleman DP

    (2018) Lipid-protein interactions are unique fingerprints for membrane proteins

    ACS Central Science 4:709–717.

    https://doi.org/10.1021/acscentsci.8b00143

    • PubMed
    • Google Scholar
13. 1. de Jong DH
    2. Singh G
    3. Bennett WFD
    4. Arnarez C
    5. Wassenaar TA
    6. Schäfer LV
    7. Periole X
    8. Tieleman DP
    9. Marrink SJ

    (2013) Improved parameters for the martini coarse-grained protein force field

    Journal of Chemical Theory and Computation 9:687–697.

    https://doi.org/10.1021/ct300646g

    • Google Scholar
14. 1. de Lera Ruiz M
    2. Kraus RL

    (2015) Voltage-gated sodium channels: structure, function, pharmacology, and clinical indications

    Journal of Medicinal Chemistry 58:7093–7118.

    https://doi.org/10.1021/jm501981g

    • PubMed
    • Google Scholar
15. 1. Dib-Hajj SD
    2. Yang Y
    3. Black JA
    4. Waxman SG

    (2013) The Na(V)1.7 sodium channel: from molecule to man

    Nature Reviews. Neuroscience 14:49–62.

    https://doi.org/10.1038/nrn3404

    • PubMed
    • Google Scholar
16. 1. Evans DJ
    2. Holian BL

    (1985) The Nose–Hoover thermostat

    The Journal of Chemical Physics 83:4069–4074.

    https://doi.org/10.1063/1.449071

    • Google Scholar
17. 1. Frustaci A
    2. Priori SG
    3. Pieroni M
    4. Chimenti C
    5. Napolitano C
    6. Rivolta I
    7. Sanna T
    8. Bellocci F
    9. Russo MA

    (2005) Cardiac histological substrate in patients with clinical phenotype of Brugada syndrome

    Circulation 112:3680–3687.

    https://doi.org/10.1161/CIRCULATIONAHA.105.520999

    • PubMed
    • Google Scholar
18. 1. Gada KD
    2. Kamuene JM
    3. Chandrashekar A
    4. Kissell RC
    5. Yauch AK
    6. Plant LD

    (2023) PI(4,5)P2 regulates the gating of NaV1.4 channels

    The Journal of General Physiology 155:e202213255.

    https://doi.org/10.1085/jgp.202213255

    • PubMed
    • Google Scholar
19. 1. Gamal El-Din TM
    2. Lenaeus MJ
    3. Zheng N
    4. Catterall WA

    (2018) Fenestrations control resting-state block of a voltage-gated sodium channel

    PNAS 115:13111–13116.

    https://doi.org/10.1073/pnas.1814928115

    • Google Scholar
20. 1. Gao S
    2. Yao X
    3. Yan N

    (2021) Structure of human Ca_v2.2 channel blocked by the painkiller ziconotide

    Nature 596:143–147.

    https://doi.org/10.1038/s41586-021-03699-6

    • PubMed
    • Google Scholar
21. 1. Ghovanloo M-R
    2. Choudhury K
    3. Bandaru TS
    4. Fouda MA
    5. Rayani K
    6. Rusinova R
    7. Phaterpekar T
    8. Nelkenbrecher K
    9. Watkins AR
    10. Poburko D
    11. Thewalt J
    12. Andersen OS
    13. Delemotte L
    14. Goodchild SJ
    15. Ruben PC

    (2021) Cannabidiol inhibits the skeletal muscle Nav1.4 by blocking its pore and by altering membrane elasticity

    The Journal of General Physiology 153:e202012701.

    https://doi.org/10.1085/jgp.202012701

    • PubMed
    • Google Scholar
22. 1. Goldschen-Ohm MP
    2. Capes DL
    3. Oelstrom KM
    4. Chanda B

    (2013) Multiple pore conformations driven by asynchronous movements of voltage sensors in a eukaryotic sodium channel

    Nature Communications 4:1350.

    https://doi.org/10.1038/ncomms2356

    • PubMed
    • Google Scholar
23. 1. Goujon M
    2. McWilliam H
    3. Li W
    4. Valentin F
    5. Squizzato S
    6. Paern J
    7. Lopez R

    (2010) A new bioinformatics analysis tools framework at EMBL-EBI

    Nucleic Acids Research 38:W695–W699.

    https://doi.org/10.1093/nar/gkq313

    • PubMed
    • Google Scholar
24. 1. Hille B
    2. Dickson EJ
    3. Kruse M
    4. Vivas O
    5. Suh BC

    (2015) Phosphoinositides regulate ion channels

    Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids 1851:844–856.

    https://doi.org/10.1016/j.bbalip.2014.09.010

    • Google Scholar
25. 1. Hopkins CW
    2. Le Grand S
    3. Walker RC
    4. Roitberg AE

    (2015) Long-time-step molecular dynamics through hydrogen mass repartitioning

    Journal of Chemical Theory and Computation 11:1864–1874.

    https://doi.org/10.1021/ct5010406

    • PubMed
    • Google Scholar
26. 1. Huang J
    2. Rauscher S
    3. Nawrocki G
    4. Ran T
    5. Feig M
    6. de Groot BL
    7. Grubmüller H
    8. MacKerell AD Jr

    (2017) CHARMM36m: an improved force field for folded and intrinsically disordered proteins

    Nature Methods 14:71–73.

    https://doi.org/10.1038/nmeth.4067

    • PubMed
    • Google Scholar
27. 1. Huang G
    2. Wu Q
    3. Li Z
    4. Jin X
    5. Huang X
    6. Wu T
    7. Pan X
    8. Yan N

    (2022) Unwinding and spiral sliding of S4 and domain rotation of VSD during the electromechanical coupling in Na _v 1.7

    PNAS 119:e2209164119.

    https://doi.org/10.1073/pnas.2209164119

    • Google Scholar
28. 1. Humphrey W
    2. Dalke A
    3. Schulten K

    (1996) VMD: visual molecular dynamics

    Journal of Molecular Graphics 14:33–38.

    https://doi.org/10.1016/0263-7855(96)00018-5

    • PubMed
    • Google Scholar
29. 1. Ingólfsson HI
    2. Melo MN
    3. van Eerden FJ
    4. Arnarez C
    5. Lopez CA
    6. Wassenaar TA
    7. Periole X
    8. de Vries AH
    9. Tieleman DP
    10. Marrink SJ

    (2014) Lipid organization of the plasma membrane

    Journal of the American Chemical Society 136:14554–14559.

    https://doi.org/10.1021/ja507832e

    • PubMed
    • Google Scholar
30. 1. Jo S
    2. Kim T
    3. Iyer VG
    4. Im W

    (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM

    Journal of Computational Chemistry 29:1859–1865.

    https://doi.org/10.1002/jcc.20945

    • PubMed
    • Google Scholar
31. 1. Jorgensen WL
    2. Chandrasekhar J
    3. Madura JD
    4. Impey RW
    5. Klein ML

    (1983) Comparison of simple potential functions for simulating liquid water

    The Journal of Chemical Physics 79:926–935.

    https://doi.org/10.1063/1.445869

    • Google Scholar
32. 1. Jurkat-Rott K
    2. Mitrovic N
    3. Hang C
    4. Kouzmenkine A
    5. Iaizzo P
    6. Herzog J
    7. Lerche H
    8. Nicole S
    9. Vale-Santos J
    10. Chauveau D
    11. Fontaine B
    12. Lehmann-Horn F

    (2000) Voltage-sensor sodium channel mutations cause hypokalemic periodic paralysis type 2 by enhanced inactivation and reduced current

    PNAS 97:9549–9554.

    https://doi.org/10.1073/pnas.97.17.9549

    • Google Scholar
33. 1. Kasimova MA
    2. Zaydman MA
    3. Cui J
    4. Tarek M

    (2015) PIP₂-dependent coupling is prominent in Kv7.1 due to weakened interactions between S4-S5 and S6

    Scientific Reports 5:7474.

    https://doi.org/10.1038/srep07474

    • PubMed
    • Google Scholar
34. 1. Lee J
    2. Cheng X
    3. Swails JM
    4. Yeom MS
    5. Eastman PK
    6. Lemkul JA
    7. Wei S
    8. Buckner J
    9. Jeong JC
    10. Qi Y
    11. Jo S
    12. Pande VS
    13. Case DA
    14. Brooks CL
    15. MacKerell AD
    16. Klauda JB
    17. Im W

    (2016) Charmm-gui input generator for namd, gromacs, amber, openmm, and charmm/openmm simulations using the charmm36 additive force field

    Journal of Chemical Theory and Computation 12:405–413.

    https://doi.org/10.1021/acs.jctc.5b00935

    • PubMed
    • Google Scholar
35. 1. Liu Y
    2. Xu X
    3. Gao J
    4. Naffaa MM
    5. Liang H
    6. Shi J
    7. Wang HZ
    8. Yang N-D
    9. Hou P
    10. Zhao W
    11. White KM
    12. Kong W
    13. Dou A
    14. Cui A
    15. Zhang G
    16. Cohen IS
    17. Zou X
    18. Cui J

    (2020) A PIP₂ substitute mediates voltage sensor-pore coupling in KCNQ activation

    Communications Biology 3:385.

    https://doi.org/10.1038/s42003-020-1104-0

    • PubMed
    • Google Scholar
36. 1. Liu Y
    2. Bassetto CAZ Jr
    3. Pinto BI
    4. Bezanilla F

    (2023) A mechanistic reinterpretation of fast inactivation in voltage-gated Na+ channels

    Nature Communications 14:5072.

    https://doi.org/10.1038/s41467-023-40514-4

    • Google Scholar
37. 1. Loncharich RJ
    2. Brooks BR
    3. Pastor RW

    (1992) Langevin dynamics of peptides: the frictional dependence of isomerization rates of N-acetylalanyl-N’-methylamide

    Biopolymers 32:523–535.

    https://doi.org/10.1002/bip.360320508

    • PubMed
    • Google Scholar
38. 1. López CA
    2. Sovova Z
    3. van Eerden FJ
    4. de Vries AH
    5. Marrink SJ

    (2013) Martini force field parameters for glycolipids

    Journal of Chemical Theory and Computation 9:1694–1708.

    https://doi.org/10.1021/ct3009655

    • PubMed
    • Google Scholar
39. 1. Ma D
    2. Zhong L
    3. Yan Z
    4. Yao J
    5. Zhang Y
    6. Ye F
    7. Huang Y
    8. Lai D
    9. Yang W
    10. Hou P
    11. Guo J

    (2022) Structural mechanisms for the activation of human cardiac KCNQ1 channel by electro-mechanical coupling enhancers

    PNAS 119:e2207067119.

    https://doi.org/10.1073/pnas.2207067119

    • Google Scholar
40. 1. Mandala VS
    2. MacKinnon R

    (2023) The membrane electric field regulates the PIP ₂ -binding site to gate the KCNQ1 channel

    PNAS 120:e2301985120.

    https://doi.org/10.1073/pnas.2301985120

    • Google Scholar
41. 1. Mantegazza M
    2. Yu FH
    3. Catterall WA
    4. Scheuer T

    (2001) Role of the C-terminal domain in inactivation of brain and cardiac sodium channels

    PNAS 98:15348–15353.

    https://doi.org/10.1073/pnas.211563298

    • Google Scholar
42. 1. Mantegazza M
    2. Cestèle S
    3. Catterall WA

    (2021) Sodium channelopathies of skeletal muscle and brain

    Physiological Reviews 101:1633–1689.

    https://doi.org/10.1152/physrev.00025.2020

    • PubMed
    • Google Scholar
43. 1. Martin LJ
    2. Corry B

    (2014) Locating the route of entry and binding sites of benzocaine and phenytoin in a bacterial voltage gated sodium channel

    PLOS Computational Biology 10:e1003688.

    https://doi.org/10.1371/journal.pcbi.1003688

    • PubMed
    • Google Scholar
44. 1. McPhee JC
    2. Ragsdale DS
    3. Scheuer T
    4. Catterall WA

    (1998) A critical role for the S4-S5 intracellular loop in domain IV of the sodium channel alpha-subunit in fast inactivation

    The Journal of Biological Chemistry 273:1121–1129.

    https://doi.org/10.1074/jbc.273.2.1121

    • PubMed
    • Google Scholar
45. 1. Michaud-Agrawal N
    2. Denning EJ
    3. Woolf TB
    4. Beckstein O

    (2011) MDAnalysis: a toolkit for the analysis of molecular dynamics simulations

    Journal of Computational Chemistry 32:2319–2327.

    https://doi.org/10.1002/jcc.21787

    • PubMed
    • Google Scholar
46. 1. Pan X
    2. Li Z
    3. Zhou Q
    4. Shen H
    5. Wu K
    6. Huang X
    7. Chen J
    8. Zhang J
    9. Zhu X
    10. Lei J
    11. Xiong W
    12. Gong H
    13. Xiao B
    14. Yan N

    (2018) Structure of the human voltage-gated sodium channel Na_v1.4 in complex with β1

    Science 362:eaau2486.

    https://doi.org/10.1126/science.aau2486

    • PubMed
    • Google Scholar
47. 1. Parrinello M
    2. Rahman A

    (1981) Polymorphic transitions in single crystals: A new molecular dynamics method

    Journal of Applied Physics 52:7182–7190.

    https://doi.org/10.1063/1.328693

    • Google Scholar
48. 1. Qi Y
    2. Ingólfsson HI
    3. Cheng X
    4. Lee J
    5. Marrink SJ
    6. Im W

    (2015) CHARMM-GUI martini maker for coarse-grained simulations with the martini force field

    Journal of Chemical Theory and Computation 11:4486–4494.

    https://doi.org/10.1021/acs.jctc.5b00513

    • PubMed
    • Google Scholar
49. 1. Raju SG
    2. Barber AF
    3. LeBard DN
    4. Klein ML
    5. Carnevale V

    (2013) Exploring volatile general anesthetic binding to a closed membrane-bound bacterial voltage-gated sodium channel via computation

    PLOS Computational Biology 9:e1003090.

    https://doi.org/10.1371/journal.pcbi.1003090

    • PubMed
    • Google Scholar
50. 1. Sali A
    2. Blundell TL

    (1993) Comparative protein modelling by satisfaction of spatial restraints

    Journal of Molecular Biology 234:779–815.

    https://doi.org/10.1006/jmbi.1993.1626

    • PubMed
    • Google Scholar
51. 1. Schmidt D
    2. Jiang QX
    3. MacKinnon R

    (2006) Phospholipids and the origin of cationic gating charges in voltage sensors

    Nature 444:775–779.

    https://doi.org/10.1038/nature05416

    • PubMed
    • Google Scholar
52. 1. Shen H
    2. Liu D
    3. Wu K
    4. Lei J
    5. Yan N

    (2019) Structures of human Na_v1.7 channel in complex with auxiliary subunits and animal toxins

    Science 363:1303–1308.

    https://doi.org/10.1126/science.aaw2493

    • PubMed
    • Google Scholar
53. 1. Sievers F
    2. Wilm A
    3. Dineen D
    4. Gibson TJ
    5. Karplus K
    6. Li W
    7. Lopez R
    8. McWilliam H
    9. Remmert M
    10. Söding J
    11. Thompson JD
    12. Higgins DG

    (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega

    Molecular Systems Biology 7:539.

    https://doi.org/10.1038/msb.2011.75

    • PubMed
    • Google Scholar
54. 1. Souza PCT
    2. Alessandri R
    3. Barnoud J
    4. Thallmair S
    5. Faustino I
    6. Grünewald F
    7. Patmanidis I
    8. Abdizadeh H
    9. Bruininks BMH
    10. Wassenaar TA
    11. Kroon PC
    12. Melcr J
    13. Nieto V
    14. Corradi V
    15. Khan HM
    16. Domański J
    17. Javanainen M
    18. Martinez-Seara H
    19. Reuter N
    20. Best RB
    21. Vattulainen I
    22. Monticelli L
    23. Periole X
    24. Tieleman DP
    25. de Vries AH
    26. Marrink SJ

    (2021) Martini 3: a general purpose force field for coarse-grained molecular dynamics

    Nature Methods 18:382–388.

    https://doi.org/10.1038/s41592-021-01098-3

    • PubMed
    • Google Scholar
55. 1. Stansfeld PJ
    2. Hopkinson R
    3. Ashcroft FM
    4. Sansom MSP

    (2009) PIP(2)-binding site in Kir channels: definition by multiscale biomolecular simulations

    Biochemistry 48:10926–10933.

    https://doi.org/10.1021/bi9013193

    • PubMed
    • Google Scholar
56. 1. Sun J
    2. MacKinnon R

    (2020) Structural basis of human kcnq1 modulation and gating

    Cell 180:340–347.

    https://doi.org/10.1016/j.cell.2019.12.003

    • PubMed
    • Google Scholar
57. 1. Tao E
    2. Corry B

    (2022) Characterizing fenestration size in sodium channel subtypes and their accessibility to inhibitors

    Biophysical Journal 121:193–206.

    https://doi.org/10.1016/j.bpj.2021.12.025

    • PubMed
    • Google Scholar
58. Software

    1. Tao E

    (2023) etaoster.github.io, version swh:1:rev:817cf0e788ae97d248d82c1f5d9bbe76b1ab5eb2

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:07d16e3c2fb81d6d58f37ba0d262e72aa8f61a39;origin=https://github.com/etaoster/etaoster.github.io;visit=swh:1:snp:0c2d1b2ad3c5911a93e24df77c39c0b117604fce;anchor=swh:1:rev:817cf0e788ae97d248d82c1f5d9bbe76b1ab5eb2
59. 1. Venance SL
    2. Cannon SC
    3. Fialho D
    4. Fontaine B
    5. Hanna MG
    6. Ptacek LJ
    7. Tristani-Firouzi M
    8. Tawil R
    9. Griggs RC
    10. CINCH investigators

    (2006) The primary periodic paralyses: diagnosis, pathogenesis and treatment

    Brain 129:8–17.

    https://doi.org/10.1093/brain/awh639

    • PubMed
    • Google Scholar
60. 1. Wassenaar TA
    2. Ingólfsson HI
    3. Böckmann RA
    4. Tieleman DP
    5. Marrink SJ

    (2015) Computational lipidomics with insane: a versatile tool for generating custom membranes for molecular simulations

    Journal of Chemical Theory and Computation 11:2144–2155.

    https://doi.org/10.1021/acs.jctc.5b00209

    • PubMed
    • Google Scholar
61. 1. Waterhouse AM
    2. Procter JB
    3. Martin DMA
    4. Clamp M
    5. Barton GJ

    (2009) Jalview Version 2--a multiple sequence alignment editor and analysis workbench

    Bioinformatics 25:1189–1191.

    https://doi.org/10.1093/bioinformatics/btp033

    • PubMed
    • Google Scholar
62. 1. West JW
    2. Patton DE
    3. Scheuer T
    4. Wang Y
    5. Goldin AL
    6. Catterall WA

    (1992) A cluster of hydrophobic amino acid residues required for fast Na(+)-channel inactivation

    PNAS 89:10910–10914.

    https://doi.org/10.1073/pnas.89.22.10910

    • Google Scholar
63. 1. Wisedchaisri G
    2. Tonggu L
    3. Gamal El-Din TM
    4. McCord E
    5. Zheng N
    6. Catterall WA

    (2021) Structural basis for high-affinity trapping of the na1.7 channel in its resting state by tarantula toxin

    Molecular Cell 81:38–48.

    https://doi.org/10.1016/j.molcel.2020.10.039

    • PubMed
    • Google Scholar
64. Book

    1. Wu EL

    (2014)

    CHARMM‐GUI Membrane Builder toward Realistic Biological Membrane Simulations

    Wiley Online Library.

    • Google Scholar
65. 1. Yan Z
    2. Zhou Q
    3. Wang L
    4. Wu J
    5. Zhao Y
    6. Huang G
    7. Peng W
    8. Shen H
    9. Lei J
    10. Yan N

    (2017) Structure of the nav1.4-β1 complex from electric eel

    Cell 170:470–482.

    https://doi.org/10.1016/j.cell.2017.06.039

    • Google Scholar
66. 1. Yu FH
    2. Catterall WA

    (2003) Overview of the voltage-gated sodium channel family

    Genome Biology 4:1–7.

    https://doi.org/10.1186/gb-2003-4-3-207

    • PubMed
    • Google Scholar
67. 1. Zaman T
    2. Helbig I
    3. Božović IB
    4. DeBrosse SD
    5. Bergqvist AC
    6. Wallis K
    7. Medne L
    8. Maver A
    9. Peterlin B
    10. Helbig KL
    11. Zhang X
    12. Goldberg EM

    (2018) Mutations in SCN3A cause early infantile epileptic encephalopathy

    Annals of Neurology 83:703–717.

    https://doi.org/10.1002/ana.25188

    • PubMed
    • Google Scholar

Author details

1. Yiechang Lin

   Research School of Biology, Australian National University, Canberra, Australia

   Contribution

   Conceptualization, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Elaine Tao

   For correspondence

   yiechang.lin@anu.edu.au

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9836-132X

2. Elaine Tao

   Research School of Biology, Australian National University, Canberra, Australia

   Contribution

   Conceptualization, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Yiechang Lin

   For correspondence

   elaine.tao@anu.edu.au

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0332-1646

3. James P Champion

   Research School of Biology, Australian National University, Canberra, Australia

   Contribution

   Investigation

   Competing interests

   No competing interests declared

4. Ben Corry

   Research School of Biology, Australian National University, Canberra, Australia

   Contribution

   Supervision, Funding acquisition, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   ben.corry@anu.edu.au

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6324-442X

• 936

  views

• 75

  downloads

• 1

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.