The selectivity filter of voltage-gated potassium (Kv) channels is a specialized molecular structure, responsible for the fast and selective conduction of potassium (K⁺) over other ionic species. It is well established that the selectivity filter of Kv channels can exist in both conductive and non-conductive states (Figure 1). Known as the process of (slow) C-type inactivation (Hoshi et al., 1990; Ostmeyer et al., 2013), the conductive to non-conductive conformational transition is of great physiological importance as it contributes to fine-tune long-term activity of Kv channels. The canonical conductive conformational state of the filter was first revealed with the crystallographic structure of the prototypical bacterial K⁺ channel KcsA at high resolution (e.g. PDB id 1BL8 [Doyle et al., 1998] or 1K4C [Zhou et al., 2001]). Molecular dynamics (MD) studies confirm that ion conduction along this filter conformation is possible and unopposed by large free-energy barriers (Bernèche and Roux, 2001; Kopec et al., 2019). Since, additional structures have been resolved for other Kv channels of the (eukaryotic) Shaker family, broadening our knowledge of the conductive state of the filter (Long et al., 2007). Recently, high-resolution structures of Kv channels revealed a novel conformation of the selectivity filter that is partially dilated at its outer end and constricted near its internal face (Tan et al., 2022; Reddi et al., 2022; Yangyu et al., 2024). Because a number of mutations known to strongly enhance the process of C-type inactivation were introduced in the construct used in structural determination (Perozo et al., 1993), this ‘dilated’ conformation has been interpreted as the non-conductive conformational state of the selectivity filter, accounting for the phenomenon of C-type inactivation. Reinforcing that notion, the most recent structure of the wild-type Shaker B channel displays the same dilated conformation of the filter at low concentration of external potassium (Stix et al., 2023).

Figure 1

Download asset Open asset

The ‘dilated’ and ‘conductive’ filter conformations differ markedly. The all-atom root-mean-square deviations (RMSD) of the selectivity filter between the dilated and the conductive conformation is about 3.0 Å, arising mostly from rearrangements of the side chain of the highly conserved tyrosine and aspartic acid along the signature of the selectivity filter TVGYGD (Figure 1). Yet, despite the considerable conformational differences, a simple inspection reveals no apparent physical barrier opposing ion conduction along the permeation pathway of the ‘dilated’ structure, challenging its postulated non-conductivity. The selectivity filter in the dilated conformation is accessible to the intracellular solution via a large open and hydrated vestibular cavity. Incoming ions can bind to sites S₄ and S₃ and translocate to the extracellular side via a hydrated crevice corresponding to the widened sites S₂, S₁, and S₀. The open and fully hydrated vestibular cavity is clearly conductive in the dilated conformation, which disagrees with previous cysteine modification and blocker-protection assays indicating that the cavity of the channel is changed in the slow-inactivated state compared with the open state (Panyi and Deutsch, 2007). Besides, the voltage drop across the selectivity filter through the conductive and dilated conformations is similar (Figure 1C). In contrast to classic chemical and peptide blockers or the constricted filter associated with the inactivation of the KcsA bacterial channel that typically inhibit conduction by providing a physical barrier along the permeation pathway (Ostmeyer et al., 2013; Yangyu et al., 2024; Eriksson and Roux, 2002; Banerjee et al., 2013; Cuello et al., 2010), the dilated conformation does not appear to impose any structural impediment to ionic conduction. These observations beg for a quantitative assessment of the ion conduction properties of the dilated conformation, and the relation of the latter to the functional C-type inactivated state identified in electrophysiological experiments.

To address these questions, detailed all-atom MD simulations were carried out on the basis of the high-resolution X-ray structure of the kv1.2-kv2.1-3m chimera channel containing the triple mutation W362F, S367T, and V377T (Reddi et al., 2022). These mutations enhance C-type inactivation in the kv1.2-kv2.1 chimera, making the pore similar to the pore of the Shaker channel. To circumvent the uncertainties inherent with MD and achieve robust conclusions, simulations based on two established force fields, AMBER (Maier et al., 2015; Joung and Cheatham, 2008) and CHARMM36m (Huang et al., 2017), were considered. Additional simulations were also carried out using a version of CHARMM36m force field with a few modified interactions to explain the difference in ion conduction with the AMBER force field (Materials and methods, Supplementary files 1 and 2) In all the simulations, the channel was embedded in a fully hydrated phospholipid bilayer at 150 mM KCl and simulated with an applied transmembrane (TM) of +200 mV.

The kv1.2-kv2.1-3m channel with the dilated selectivity filter appears remarkably stable during a 10 μs simulation using the AMBER force field (Figure 2). The structural RMSD of the selectivity filter relative to the initial X-ray structure (Reddi et al., 2022) is less than 2.8 Å, indicative of structural stability (Figure 2B, structural deviation R). Simulation of kv1.2-kv2.1-3m with the CHARMM36m force field also illustrates the stability of the dilated conformation over the microsecond timescale, with structural deviation of the selectivity filter less than 2.5 Å (Figure 2C, structural deviation R). The average density of the K⁺ ions along the permeation pathway from the AMBER trajectory is consistent with a predominant occupancy of the sites S₄, S₃, and S_ext. Not resolved in the X-ray structure of kv1.2-kv2.1-3m (Reddi et al., 2022), the density at the external site S_ext results from close interactions of K⁺ and the carboxylate group of D375. The acidic side chain adopts a relaxed conformation throughout the simulation, in which the carboxylate moiety is fully exposed to the external solution in an orientation similar to that previously reported in the cryogenic electron microscopy (cryo-EM) structure of the homologous Kv1.3 channel with a dilated selectivity filter (Selvakumar et al., 2022). According to a hard-knock mechanism at the level of binding sites S₄ and S₃ (Figure 2—figure supplement 1; Stix et al., 2023), there is an early voltage-driven K⁺ conduction event across the selectivity filter within the first ~2 μs of the simulation (Figure 2B, trajectory of K⁺ along the permeation pathway Z). Conduction increases the residence time of the ion in the dilated region of the selectivity filter and accounts for a minor density peak at the level of site S₁ (Figure 2B, linear density of K⁺), which is consistent with the resolved electron density of the cation in the experimental structures (Tan et al., 2022; Reddi et al., 2022; Yangyu et al., 2024; Stix et al., 2023).

Figure 2 with 2 supplements see all

Download asset Open asset

The knock-on mechanism depends on the concentration of incoming ions in the central cavity of the channel. Although the last conduction event takes place around ~2 μs of simulation, note that only after ~4 μs of simulation, the knock-on mechanism is persistently disrupted as ions are excluded from the central cavity of the channel by the isoleucine side chains at position 398. More specifically, ion access to the central cavity of the channel and their subsequent conduction across the selectivity filter completely cease after ~4 μs of simulation time when the side chain of isoleucine at position 398 from all four S6 segments twist toward the central axis of the channel, dehydrating and blocking the permeation pathway immediately beneath the selectivity filter (Figure 2B, density of water along the pore axis). The C_β-C_β distance between I398 in opposing subunits decreases over a period of 3–4 μs, reaching a value of about 9 Å—in stark contrast with the initial distance of 15 Å in the X-ray structure (Reddi et al., 2022; Figure 2, separation distance L of I398). As a consequence, the pore radius becomes locally constricted (≤2 Å), blocking ion conduction (Figure 2B, pore radius r) (Treptow and Tarek, 2006).

In contrast, ion conduction events are observed in the simulation with the CHARMM36m force field. This observation is in sharp contrast with the conclusion drawn from previous simulations of the dilated conformation of Shaker B also based on the CHARMM36m force field, where no ion conduction event was observed even with an applied membrane potential of +300 mV (Tan et al., 2022; Stix et al., 2023). Importantly, dihedral restraints were applied in these simulations to preclude deviations from the X-ray structure; it is possible that those restraints apparently prevented ion conduction through the open-dilated conformation. Because this is the same conformation of the selectivity filter and the same CHARMM36m force field, suggesting that the key difference in the simulation results is more likely due to different MD simulation conditions used than the very minor differences in the pore domain between Kv1.2-2.1-3m and Shaker. Because the CHARMM36m force field favors the open configuration of the isoleucine gate, the simulation shows that the dilated conformation is highly conductive under membrane depolarization. The distribution of ions along the permeation pathway in the CHARMM36m simulation is distinct from that inferred from the AMBER simulations, as the increased mobility of K⁺ in the selectivity filter accounts for a more pronounced reallocation of the ionic density from sites S₄/S₃ to sites S₂/S₁.

To further clarify the ion conduction properties of the dilated filter and understand the role of isoleucine 398, we simulate the channel with the AMBER force field in the presence of harmonic distance restraint to keep the isoleucine gate in the open configuration (Figure 2—figure supplement 2). Compared to the unrestrained simulation with the AMBER force field, a larger number of ions access the central cavity of the channel, triggering two spontaneous conduction events across the selectivity filter in the early ~4 μs of the simulation. The density profile of the K⁺ ion along the channel axis is distinct from that of the unconstrained simulation. Partial reallocation of the ionic density from sites S₄/S₃ to sites S₂/S₁ reflects the increased mobility of K⁺ in the selectivity filter, thereby corroborating the hypothesis that the dilated conformation of the selectivity filter is conductive when the isoleucine gate is open. This conclusion is further confirmed from additional simulations based on a hybrid CHARMM36m force field referred to as CHARMM36m-NBFIX in which only the ion-carbonyl, ion-water, and water-carbonyl interactions were adjusted to mimic the values from the AMBER force field (Figure 2—figure supplement 2, Supplementary file 2). While the changes are fairly small and reproduce known behavior for the conductive state of the selectivity filter (Materials and methods, Supplementary file 3), the simulations recapitulate the ion conduction properties of the dilated conformation from AMBER, demonstrating how these three key interactions are directly responsible for the observed differences with CHARMM36m. In the CHARMM36m-NBFIX simulation, the local concentration of ions in the central cavity of the channel is significantly smaller than that of the original trajectory. Pronounced density peaks at sites S₄/S₃ indicates that K⁺ binds more strongly to the selectivity filter. The combined effect yields a conductive AMBER-like channel, characterized by fewer conduction events per simulation time.

These simulations strongly suggest that there is nothing about the dilated conformation of the filter that inherently impedes ion conduction, and that the conformational motion of I398 is necessary to truly block conduction. Thus, it is the conformational change implicating the isoleucine gate that leads the channel toward a true non-conductive state. As a significant modification of the channel structure, the local rearrangement of I398 seems to be coupled to motions of other regions of the main pore, including V402 at the highly conserved PVP motif (Figure 2). Analysis indicates that CHARMM36m favors the open and fully hydrated state of the I398 gate. The average intersubunit C_β-C_β distance of the isoleucine side chains (~16 Å) is close to the reference value in the X-ray structure (~15 Å) most of the simulation time. Gating fluctuations of I398 are, however, clearly observed in the late stages of the simulation and correlate well with the reduction of ions in the central cavity of the channel and with the conduction across the selectivity filter. Particularly important, the CHARMM36m simulation adds support to the assumption that the dilated conformation of the selectivity filter is conductive, and that closure of the isoleucine gate is required to shut down ion transport across the channel.

Despite intrinsic force-field differences with respect to channel conductivity, all three atomistic models support the conclusion that the dilated conformation of the selectivity filter is, by itself, conductive and the isoleucine gate seems to be important to block K⁺ current across the channel. According to the voltage-driven MD trajectories in which the isoleucine gate is open, the total number of conduction events across the dilated conformation of the selectivity filter over the total simulation time is 44 (ions)/30 μs (Supplementary file 4). Based on the number of crossing events per simulation time, the ionic current of a macroscopic population of ion channels in the open-gate dilated state is ~0.2 pA at the TM voltage of +200 mV (single-channel conductance of ~1.17 pS in symmetric 150 mM KCl). In all likelihood underestimation of the channel conductance as a consequence of the well-documented force-field limitations in reproducing the ionic current in Kv channels at low voltages (<300 mV) (Jensen et al., 2013), the estimate of ~0.2 pA is still orders of magnitude larger than the measured current in the triple-mutant channel upon C-type inactivation (vide infra), and, therefore, the conductivity properties of the ‘dilated’ conformation of the selectivity filter cannot explain alone the inactivation of kv1.2-kv2.1-3m under membrane depolarization. Consistent with single-channel measurements (Yang et al., 1997), the estimate of ~1.17 pS is actually more comparable to simulation predictions of the single-channel conductance of the conductive selectivity filter i.e., ~3.5 pS in symmetric 300 mM KCl (Stix et al., 2023). Beside the dilated conformation of the selectivity filter, the isoleucine gate then appears to be a critical molecular element of the channel machinery, largely implicated in C-type inactivation.

To validate and corroborate the key role of the isoleucine gate in C-type inactivation inferred from the MD simulations, electrophysiology experiments were carried out (Materials and methods, Figure 3). In stark contrast with recordings of the kv1.2-kv2.1 chimera channel (Figure 3A), C-type inactivation is greatly enhanced in the triple-mutant channel kv1.2-kv2.1-3m, as is evidenced by the fast decay of the ionic current in the ms timescale and the macroscopic current appearing on similar scales as gating current (Figure 3B, Figure 3—figure supplement 1). However, substitution of the isoleucine by the polar amino acid asparagine, with similar side-chain volume, restores the conductivity of the triple-mutant channel (Figure 3C). While not disturbing the current-voltage relationship of the triple-mutant channel, mutation I398N drastically increases ionic conduction without any apparent time-dependent inactivation (Figure 3D and E). Importantly, restoration of ion conduction with I398N is not caused by inadvertently stabilizing the selectivity filter in the conductive state, as demonstrated by pore-blocking toxin assays. Agitoxin-II (AgTxII), dendrotoxin, and charybdotoxin (CTX) are potent toxin blockers of Kv channels (Takacs et al., 2009), binding to the outer mouth of the channel (Yangyu et al., 2024; Eriksson and Roux, 2002; Banerjee et al., 2013). Such pore-blocking toxins preferentially bind and occlude the conductive conformation of the selectivity filter of Kv channels and are not effective in blocking channels that have W434F-like selectivity filter, as indicated by previous functional studies (Kitaguchi et al., 2004) as well as implicit solvent binding free-energy calculations (Materials and methods, Figure 3F, Figure 3—figure supplement 2, Supplementary file 5). Whereas AgTxII blocks the ionic current across the conductive kv1.2-kv2.1 channel by binding to the outer mouth of the filter in the conductive conformation (Figure 3G), it fails to bind and occlude the kv1.2-kv2.1-3m channel with its filter mostly in the dilated conformation (Figure 3H). Similar effect is observed in Kv1.2-kv2.1-3m_I398N, suggesting the resumed ion permeation is not caused by any stabilization effect of I398N on the selectivity filter and most likely the ions are conducting through the dilated filter (Figure 3I). Because kv1.2-kv2.1-3m and kv1.2-kv2.1-3m_I398N are not affected by the toxin (Figure 3J), the ion conduction in kv1.2-kv2.1-3m_I398N mutant indicates that the permeation pathway must be altered at some location along the permeation pathway other than the selectivity filter. Such structural modifications of the permeation pathway are specific of C-type inactivation since a double mutant (S367T/V377T) of the chimera channel kv1.2-kv2.1-2m that does not inactivate and is blocked by AgTxII, irrespective of the I398N mutation (Figure 3—figure supplement 3), suggesting that I398N mutation neither affect the selectivity filter of kv1.2-kv2.1-2m nor kv1.2-kv2.1-3m. The critical C-type inactivation mutation W362F (Perozo et al., 1993) is missing in the kv1.2-kv2.1-2m channel, hence its inability to slow-inactivate.

Figure 3 with 5 supplements see all

Download asset Open asset

Consistent with experiment, a polar amino acid allows ion conduction by keeping the permeation pathway constitutively hydrated and open. Additional simulations of the triple-mutant channel support this view by revealing that I398N prevents closure of the gate at position 398 (Supplementary file 1). Figure 3—figure supplement 4 shows the AMBER simulation of the triple-mutant channel with the I398N mutation. The conformation of the dilated selectivity filter is stable in presence of the mutation. The average intersubunit C_β-C_β distance of I398N fluctuates between ~11 Å and ~15 Å in the beginning of the AMBER trajectory, before reaching the value of ~9 Å in the final stage of the simulation. Compared to the wild-type simulation, the local pore radius and water density are significantly enhanced under I398N mutation (Supplementary file 6). The open and hydrated configuration of the gate in the I398N mutant allows ion conduction across the dilated selectivity filter as long as structural fluctuations of the PVP motif (V402) do not obstruct the permeation pathway, as expected because that motif is the main gate of the channel. Similar conclusions can be drawn from CHARM36m simulation in which long-lived fluctuations of the mutant gate I398N allows intermittent conduction of ions across the dilated conformation of the selectivity filter over the microsecond timescale.

Taken together, the present computational and experimental results demonstrate that the dilated conformation of the selectivity filter of kv1.2-kv2.1-3m is conductive and that an isoleucine gate is critical to block K⁺ currents during C-type inactivation of the channel. Judged by the primary sequence conservation of I398 (Figure 3—figure supplement 5), the isoleucine gate seems to be relevant for potassium channels that undergo C-type inactivation in general, and, potentially, for other voltage-gated Na⁺ channels possessing distinct selectivity filters (Liu et al., 2023). The action of the isoleucine gate in C-type inactivation is averted by the I398N mutation because the pathway remains hydrated with the polar asparagine side. In the homologous Shaker B channel, the single mutation I470C affects the rate of inactivation (Holmgren et al., 1997; Peters et al., 2013) while the double mutation T449V/I470C (Olcese et al., 2001) converts the slow inactivated state into a conductive state. Both mutations alter the slow-inactivation phenotype under long depolarizations, strongly corroborating our findings. Closing of the I398 gate in the dilated conformation of kv1.2-kv2.1-3m involves occlusion of the binding site of internally applied quaternary ammonium (QA) blockers (Lenaeus et al., 2005), explaining the previously reported 20-fold decreased affinity of TEA for the inactivated state of Shaker-IR compared with that of the open state (Panyi and Deutsch, 2007). On the other hand, the mechanism whereby I398N renders the gate constitutively open, not occluding the binding site of QA blockers, is also consistent with the demonstration that I470C in Shaker-IR morphs the channel that does not trap QA blockers into one that does (Holmgren et al., 1997) and with the fact that MTSEA modifications on the inactivated state are sixfold slower than in the open state of the T449K/I470C Shaker-IR (Panyi and Deutsch, 2007). Across all these measured effects, the modus operandi of the isoleucine gate is expected to be coupled to the PVP motif, and, therefore, to reflect to some extent the conformational allostery between the selectivity filter and the bundle-crossing region previously reported for C-type inactivation (Panyi and Deutsch, 2007; Cuello et al., 2017; Labro et al., 2018). While these experimental results were indicative of the role of the isoleucine gate its mechanistic significance with regards to the non-conductive C-type inactivation seems to have been overlooked (Tan et al., 2022; Reddi et al., 2022; Yangyu et al., 2024; Stix et al., 2023).

It is important to address the approximate and imperfect nature of the atomic models used in the present MD simulations. Even with well-established force fields like AMBER and CHARMM36m, ion conduction through the selectivity filter in the canonical ‘conductive’ conformation tends to be too small compared to experimental values, especially at physiological voltages (less 50 mV) (Jensen et al., 2013). With AMBER, there is observable ion conduction via the hard-knock mechanism (no water molecules between the ions) but a fairly high voltage (200–300 mV) is required to generate a significant ionic current (Köpfer et al., 2014). With CHARMM36m, there is also observable conduction via the hard-knock mechanism at high voltages, albeit less so than with AMBER. Thus, the simulated conductance through K⁺ channels is generally too small, and it is relative to this baseline that conduction through the open dilated conformation of the filter based on the of kv1.2-kv2.1-3m structure must be critically assessed. Here, along the dilated conformation of the selectivity filter, we find that there is no substantial ion conduction with the AMBER force field because the ion binds strongly to the selectivity filter sites S₄ and S_3. This behavior is reproduced by the CHARMM36m-NBFIX force field, with its AMBER-like ion-carbonyl interactions. However, there is noticeable ion conduction with the CHARMM36m force field because the ion does not bind strongly to the sites S₄ and S₃. In fact, multiple conduction events across the open-dilated conformation of the triple-mutant chimera channel were observed in the simulation with the CHARMM36m force field, with no applied restraints.

Our physiological experiments demonstrate that by mutating I398 to a polar residue, ion permeation can be resumed in the triple mutation. More importantly, this effect is not a result of reverting C-type inactivation and stabilizing the filter in the conductive state, as shown by the toxin blocking experiments (Figure 3, Figure 3—figure supplement 3). This implies that the potassium ions are permeating through the dilated filter in kv1.2-kv2.1-3m_I398N, providing strong evidence that I398 is in fact the residue that is responsible for blocking the ion conduction during C-type inactivation instead of the dilated filter. We note that the activation and deactivation become extremely fast in the kv1.2-kv2.1-3m_I398N construct which maybe the consequence of rendering the slow-inactivated state conductive, a fact that should be explored further but is beyond the scope of this study. However, the main conclusion still stands: I398 most likely form the barrier for conduction in C-type inactivated channels and the dilated filter itself is not sufficient.

Based on our findings, we propose that the structures of kv1.2-kv2.1-3m (Reddi et al., 2022), Shaker-W434F (Tan et al., 2022), Kv1.2-W366F (Yangyu et al., 2024), and Shaker-lowK (Stix et al., 2023) corresponds to a conductive metastable intermediate on the path toward the true non-conductive C-type inactivated state wherein the permeation pathway is blocked by the isoleucine gate. The free-energy landscape associated with the with C-type inactivation process comprises two sequential transitions between three metastable states: conductive → dilated-intermediate → deeply inactivated. Each transition was characterized by a separate potential of mean force (PMF) calculation (Materials and methods, Figure 4, Figure 4—figure supplements 1 and 2). The first PMF monitoring the conformation of the selectivity filter while the isoleucine gate is in the open conformation shows two metastable states: conductive and dilated. The transition, which favors the latter state with a downhill free-energy change of –12 kcal/mol, involves a significant conformational change of Y373 from its buried configuration next to T367 in the conductive state toward its externally exposed orientation in the intermediate state resulting from unfavorable close interactions between the polar side chain of Y373 and F362. This is actually a metastable intermediate state that remains conductive. As shown by the second PMF monitoring the closure of the isoleucine gate toward a non-conductive deep inactivated state while the filter remains in dilated conformation shows a downhill change of –6.0 kcal/mol. Notwithstanding the limited accuracies of these computational estimates, the forward and backward free-energy barriers for these two transitions are in qualitative agreement with C-type inactivation of the triple-mutant channel in the sense that kv1.2-kv2.1-3m inactivates substantially faster than recovers from it—at +200 mV, the time constant of C-type inactivation is predicted to be in the same microsecond range of the simulated deep inactivation of the channel (Figure 3, Figure 3—figure supplement 1). The overall free-energy landscapes pointing toward the greater stability of the ‘deep inactivated’ state is qualitatively correct.

Figure 4 with 3 supplements see all

Download asset Open asset

The new structural insights into the intricate mechanism of C-type inactivation suggests new research directions in the field. Worth of investigation is the experimental observation that Shaker B leaks Na⁺ in the absence or low concentration of K⁺ in the C-type inactivation state (Starkus et al., 1997). AMBER and CHARM36m simulations of kv1.2-kv2.1-3m in presence of 150 mM NaCl show that one or two Na⁺ ions can stably bind the dilated conformation of the selectivity filter at sites S₃, S₄, and S_ext, while favoring the open configuration of the isoleucine gate (Materials and methods, Figure 4—figure supplement 3 and Supplementary file 1). The internal [Na⁺]/[K⁺] concentration ratio seems to affect the closing of the isoleucine gate with functional implications for sodium leak in C-type inactivation. Also important, it is the more extensive investigation of the structure-function relationship of the inactivation gate according to the amino acid sequence within the pore domain. In particular, note that except for hERG, most studied potassium channels including, Kv1.2, Shaker-B, KcsA, and MthK, display either isoleucine, leucine, valine, or phenylalanine at position 398 (Figure 3—figure supplement 5). The hERG channel has a tyrosine at position 398 which in the cryo-EM structure (Wang and MacKinnon, 2017) is twisted toward the central cavity in the open state. Because tyrosine is bulkier than asparagine, there is a chance that C-type inactivation in hERG also involves constriction of the permeation pathway at position 398 when the selectivity filter is dilated—another fascinating assumption worth of investigation in structural studies. As a piece of the molecular machinery implicated in multiple states, the isoleucine gate might interfere in a state-dependent manner with the mechanism of action of a broad class of QA blockers (Panyi and Deutsch, 2007) and negatively charged activators that bind beneath the selectivity filter and operate as master keys to open a variety of K⁺ channels (Schewe et al., 2019). Of particular importance, the understanding of this last aspect could offer a distinct advantage in the development of drugs that modulate gating states of potassium channels more specifically.

All data considered in the study, including molecular configurations and scripts for MD simulations, MD trajectories, docking configurations and electrophysiology data, can be downloaded from the Zenodo repository https://doi.org/10.5281/zenodo.10938041.

1. 1. Werner T

   (2024) Zenodo

   Isoleucine gate blocks K+ conduction in C-type inactivation.

   https://doi.org/10.5281/zenodo.10938041

1. 1. Armstrong CM
   2. Bezanilla F

   (1973) Currents related to movement of the gating particles of the sodium channels

   Nature 242:459–461.

   https://doi.org/10.1038/242459a0

   • PubMed
   • Google Scholar
2. 1. Baker NA
   2. Sept D
   3. Joseph S
   4. Holst MJ
   5. McCammon JA

   (2001) Electrostatics of nanosystems: application to microtubules and the ribosome

   PNAS 98:10037–10041.

   https://doi.org/10.1073/pnas.181342398

   • PubMed
   • Google Scholar
3. 1. Banerjee A
   2. Lee A
   3. Campbell E
   4. Mackinnon R

   (2013) Structure of a pore-blocking toxin in complex with a eukaryotic voltage-dependent K(+) channel

   eLife 2:e00594.

   https://doi.org/10.7554/eLife.00594

   • PubMed
   • Google Scholar
4. 1. Bernèche S
   2. Roux B

   (2001) Energetics of ion conduction through the K+ channel

   Nature 414:73–77.

   https://doi.org/10.1038/35102067

   • PubMed
   • Google Scholar
5. 1. Bontems F
   2. Gilquin B
   3. Roumestand C
   4. Ménez A
   5. Toma F

   (1992) Analysis of side-chain organization on a refined model of charybdotoxin: structural and functional implications

   Biochemistry 31:7756–7764.

   https://doi.org/10.1021/bi00149a003

   • PubMed
   • Google Scholar
6. 1. Branduardi D
   2. Gervasio FL
   3. Parrinello M

   (2007) From A to B in free energy space

   The Journal of Chemical Physics 126:054103.

   https://doi.org/10.1063/1.2432340

   • PubMed
   • Google Scholar
7. 1. Callenberg KM
   2. Choudhary OP
   3. de Forest GL
   4. Gohara DW
   5. Baker NA
   6. Grabe M

   (2010) APBSmem: A graphical interface for electrostatic calculations at the membrane

   PLOS ONE 5:e12722.

   https://doi.org/10.1371/journal.pone.0012722

   • PubMed
   • Google Scholar
8. 1. Crooks GE
   2. Hon G
   3. Chandonia JM
   4. Brenner SE

   (2004) WebLogo: a sequence logo generator

   Genome Research 14:1188–1190.

   https://doi.org/10.1101/gr.849004

   • PubMed
   • Google Scholar
9. 1. Cuello LG
   2. Jogini V
   3. Cortes DM
   4. Pan AC
   5. Gagnon DG
   6. Dalmas O
   7. Cordero-Morales JF
   8. Chakrapani S
   9. Roux B
   10. Perozo E

   (2010) Structural basis for the coupling between activation and inactivation gates in K(+) channels

   Nature 466:272–275.

   https://doi.org/10.1038/nature09136

   • PubMed
   • Google Scholar
10. 1. Cuello LG
    2. Cortes DM
    3. Perozo E

    (2017) The gating cycle of a K+ channel at atomic resolution

    eLife 6:e28032.

    https://doi.org/10.7554/eLife.28032

    • Google Scholar
11. 1. Doyle DA
    2. Morais Cabral J
    3. Pfuetzner RA
    4. Kuo A
    5. Gulbis JM
    6. Cohen SL
    7. Chait BT
    8. MacKinnon R

    (1998) The structure of the potassium channel: molecular basis of K+ conduction and selectivity

    Science 280:69–77.

    https://doi.org/10.1126/science.280.5360.69

    • PubMed
    • Google Scholar
12. 1. Eastman P
    2. Swails J
    3. Chodera JD
    4. McGibbon RT
    5. Zhao Y
    6. Beauchamp KA
    7. Wang LP
    8. Simmonett AC
    9. Harrigan MP
    10. Stern CD
    11. Wiewiora RP
    12. Brooks BR
    13. Pande VS

    (2017) OpenMM 7: Rapid development of high performance algorithms for molecular dynamics

    PLOS Computational Biology 13:e1005659.

    https://doi.org/10.1371/journal.pcbi.1005659

    • PubMed
    • Google Scholar
13. 1. Eriksson MAL
    2. Roux B

    (2002) Modeling the structure of agitoxin in complex with the Shaker K+ channel: a computational approach based on experimental distance restraints extracted from thermodynamic mutant cycles

    Biophysical Journal 83:2595–2609.

    https://doi.org/10.1016/S0006-3495(02)75270-3

    • PubMed
    • Google Scholar
14. 1. Finn RD
    2. Clements J
    3. Eddy SR

    (2011) HMMER web server: interactive sequence similarity searching

    Nucleic Acids Research 39:W29–W37.

    https://doi.org/10.1093/nar/gkr367

    • PubMed
    • Google Scholar
15. 1. Fu H
    2. Zhang H
    3. Chen H
    4. Shao X
    5. Chipot C
    6. Cai W

    (2018) Zooming across the free-energy landscape: Shaving barriers, and flooding valleys

    The Journal of Physical Chemistry Letters 9:4738–4745.

    https://doi.org/10.1021/acs.jpclett.8b01994

    • PubMed
    • Google Scholar
16. 1. Fu H
    2. Shao X
    3. Cai W
    4. Chipot C

    (2019) Taming rugged free energy landscapes using an average force

    Accounts of Chemical Research 52:3254–3264.

    https://doi.org/10.1021/acs.accounts.9b00473

    • PubMed
    • Google Scholar
17. 1. Gilson MK
    2. Given JA
    3. Bush BL
    4. McCammon JA

    (1997) The statistical-thermodynamic basis for computation of binding affinities: a critical review

    Biophysical Journal 72:1047–1069.

    https://doi.org/10.1016/S0006-3495(97)78756-3

    • PubMed
    • Google Scholar
18. 1. Holmgren M
    2. Smith PL
    3. Yellen G

    (1997) Trapping of organic blockers by closing of voltage-dependent K+ channels: evidence for a trap door mechanism of activation gating

    The Journal of General Physiology 109:527–535.

    https://doi.org/10.1085/jgp.109.5.527

    • PubMed
    • Google Scholar
19. 1. Hoshi T
    2. Zagotta WN
    3. Aldrich RW

    (1990) Biophysical and molecular mechanisms of Shaker potassium channel inactivation

    Science 250:533–538.

    https://doi.org/10.1126/science.2122519

    • PubMed
    • Google Scholar
20. 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

    (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
21. 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

    • Google Scholar
22. 1. Jensen MØ
    2. Jogini V
    3. Eastwood MP
    4. Shaw DE

    (2013) Atomic-level simulation of current-voltage relationships in single-file ion channels

    The Journal of General Physiology 141:619–632.

    https://doi.org/10.1085/jgp.201210820

    • PubMed
    • Google Scholar
23. 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
24. 1. Joung IS
    2. Cheatham TEI

    (2008) Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations

    The Journal of Physical Chemistry. B 112:9020–9041.

    https://doi.org/10.1021/jp8001614

    • PubMed
    • Google Scholar
25. 1. Kitaguchi T
    2. Sukhareva M
    3. Swartz KJ

    (2004) Stabilizing the closed S6 gate in the Shaker Kv channel through modification of a hydrophobic seal

    The Journal of General Physiology 124:319–332.

    https://doi.org/10.1085/jgp.200409098

    • PubMed
    • Google Scholar
26. 1. Kopec W
    2. Rothberg BS
    3. de Groot BL

    (2019) Molecular mechanism of a potassium channel gating through activation gate-selectivity filter coupling

    Nature Communications 10:5366.

    https://doi.org/10.1038/s41467-019-13227-w

    • PubMed
    • Google Scholar
27. 1. Köpfer DA
    2. Song C
    3. Gruene T
    4. Sheldrick GM
    5. Zachariae U
    6. de Groot BL

    (2014) Ion permeation in K

    Science 346:352–355.

    https://doi.org/10.1126/science.1254840

    • PubMed
    • Google Scholar
28. 1. Labro AJ
    2. Cortes DM
    3. Tilegenova C
    4. Cuello LG

    (2018) Inverted allosteric coupling between activation and inactivation gates in K⁺ channels

    PNAS 115:5426–5431.

    https://doi.org/10.1073/pnas.1800559115

    • PubMed
    • Google Scholar
29. 1. Lenaeus MJ
    2. Vamvouka M
    3. Focia PJ
    4. Gross A

    (2005) Structural basis of TEA blockade in a model potassium channel

    Nature Structural & Molecular Biology 12:454–459.

    https://doi.org/10.1038/nsmb929

    • PubMed
    • Google Scholar
30. 1. Lesage A
    2. Lelièvre T
    3. Stoltz G
    4. Hénin J

    (2017) Smoothed biasing forces yield unbiased free energies with the extended-system adaptive biasing force method

    The Journal of Physical Chemistry. B 121:3676–3685.

    https://doi.org/10.1021/acs.jpcb.6b10055

    • PubMed
    • Google Scholar
31. 1. Li Y
    2. Berke I
    3. Chen L
    4. Jiang Y

    (2007) Gating and inward rectifying properties of the MthK K+ channel with and without the gating ring

    The Journal of General Physiology 129:109–120.

    https://doi.org/10.1085/jgp.200609655

    • PubMed
    • Google Scholar
32. 1. Liu Y
    2. Bassetto CAZ
    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
33. 1. Long SB
    2. Tao X
    3. Campbell EB
    4. MacKinnon R

    (2007) Atomic structure of a voltage-dependent K+ channel in a lipid membrane-like environment

    Nature 450:376–382.

    https://doi.org/10.1038/nature06265

    • PubMed
    • Google Scholar
34. 1. Maier JA
    2. Martinez C
    3. Kasavajhala K
    4. Wickstrom L
    5. Hauser KE
    6. Simmerling C

    (2015) ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB

    Journal of Chemical Theory and Computation 11:3696–3713.

    https://doi.org/10.1021/acs.jctc.5b00255

    • Google Scholar
35. 1. Nandigrami P
    2. Szczepaniak F
    3. Boughter CT
    4. Dehez F
    5. Chipot C
    6. Roux B

    (2022) Computational assessment of protein-protein binding specificity within a family of synaptic surface receptors

    The Journal of Physical Chemistry. B 126:7510–7527.

    https://doi.org/10.1021/acs.jpcb.2c02173

    • PubMed
    • Google Scholar
36. 1. Olcese R
    2. Sigg D
    3. Latorre R
    4. Bezanilla F
    5. Stefani E

    (2001) A conducting state with properties of A slow inactivated state in A shaker K(+) channel mutant

    The Journal of General Physiology 117:149–163.

    https://doi.org/10.1085/jgp.117.2.149

    • PubMed
    • Google Scholar
37. 1. Ostmeyer J
    2. Chakrapani S
    3. Pan AC
    4. Perozo E
    5. Roux B

    (2013) Recovery from slow inactivation in K+ channels is controlled by water molecules

    Nature 501:121–124.

    https://doi.org/10.1038/nature12395

    • PubMed
    • Google Scholar
38. 1. Panyi G
    2. Deutsch C

    (2007) Probing the cavity of the slow inactivated conformation of shaker potassium channels

    The Journal of General Physiology 129:403–418.

    https://doi.org/10.1085/jgp.200709758

    • PubMed
    • Google Scholar
39. 1. Perozo E
    2. MacKinnon R
    3. Bezanilla F
    4. Stefani E

    (1993) Gating currents from a nonconducting mutant reveal open-closed conformations in Shaker K+ channels

    Neuron 11:353–358.

    https://doi.org/10.1016/0896-6273(93)90190-3

    • PubMed
    • Google Scholar
40. 1. Peters CJ
    2. Fedida D
    3. Accili EA

    (2013) Allosteric coupling of the inner activation gate to the outer pore of a potassium channel

    Scientific Reports 3:3025.

    https://doi.org/10.1038/srep03025

    • PubMed
    • Google Scholar
41. 1. Phillips JC
    2. Hardy DJ
    3. Maia JDC
    4. Stone JE
    5. Ribeiro JV
    6. Bernardi RC
    7. Buch R
    8. Fiorin G
    9. Hénin J
    10. Jiang W
    11. McGreevy R
    12. Melo MCR
    13. Radak BK
    14. Skeel RD
    15. Singharoy A
    16. Wang Y
    17. Roux B
    18. Aksimentiev A
    19. Luthey-Schulten Z
    20. Kalé LV
    21. Schulten K
    22. Chipot C
    23. Tajkhorshid E

    (2020) Scalable molecular dynamics on CPU and GPU architectures with NAMD

    The Journal of Chemical Physics 153:044130.

    https://doi.org/10.1063/5.0014475

    • PubMed
    • Google Scholar
42. 1. Reddi R
    2. Matulef K
    3. Riederer EA
    4. Whorton MR
    5. Valiyaveetil FI

    (2022) Structural basis for C-type inactivation in a Shaker family voltage-gated K⁺ channel

    Science Advances 8:eabm8804.

    https://doi.org/10.1126/sciadv.abm8804

    • PubMed
    • Google Scholar
43. 1. Roux B

    (2008) The membrane potential and its representation by a constant electric field in computer simulations

    Biophysical Journal 95:4205–4216.

    https://doi.org/10.1529/biophysj.108.136499

    • PubMed
    • Google Scholar
44. 1. Schewe M
    2. Sun H
    3. Mert Ü
    4. Mackenzie A
    5. Pike ACW
    6. Schulz F
    7. Constantin C
    8. Vowinkel KS
    9. Conrad LJ
    10. Kiper AK
    11. Gonzalez W
    12. Musinszki M
    13. Tegtmeier M
    14. Pryde DC
    15. Belabed H
    16. Nazare M
    17. de Groot BL
    18. Decher N
    19. Fakler B
    20. Carpenter EP
    21. Tucker SJ
    22. Baukrowitz T

    (2019) A pharmacological master key mechanism that unlocks the selectivity filter gate in K⁺ channels

    Science 363:875–880.

    https://doi.org/10.1126/science.aav0569

    • PubMed
    • Google Scholar
45. 1. Selvakumar P
    2. Fernández-Mariño AI
    3. Khanra N
    4. He C
    5. Paquette AJ
    6. Wang B
    7. Huang R
    8. Smider VV
    9. Rice WJ
    10. Swartz KJ
    11. Meyerson JR

    (2022) Structures of the T cell potassium channel Kv1.3 with immunoglobulin modulators

    Nature Communications 13:3854.

    https://doi.org/10.1038/s41467-022-31285-5

    • PubMed
    • Google Scholar
46. Conference

    1. Shaw DE
    2. Grossman JP
    3. Bank JA
    4. Batson B
    5. Butts JA
    6. Chao JC
    7. Deneroff MM
    8. Dror RO
    9. Even A
    10. Fenton CH
    11. Forte A
    12. Gagliardo J
    13. Gill G
    14. Greskamp B
    15. Ho CR
    16. Ierardi DJ
    17. Iserovich L
    18. Kuskin JS
    19. Larson RH
    20. Layman T
    21. Lee LS
    22. Lerer AK
    23. Li C
    24. Killebrew D
    25. Mackenzie KM
    26. Mok SYH
    27. Moraes MA
    28. Mueller R
    29. Nociolo LJ
    30. Peticolas JL
    31. Quan T
    32. Ramot D
    33. Salmon JK
    34. Scarpazza DP
    35. Schafer UB
    36. Siddique N
    37. Snyder CW

    (2014) Anton 2: Raising the Bar for Performance and Programmability in a Special-Purpose Molecular Dynamics Supercomputer” in SC ’14

    Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. pp. 41–53.

    https://doi.org/10.1109/SC.2014.9

    • Google Scholar
47. 1. Souza CS
    2. Amaral C
    3. Treptow W

    (2014) Electric fingerprint of voltage sensor domains

    PNAS 111:17510–17515.

    https://doi.org/10.1073/pnas.1413971111

    • PubMed
    • Google Scholar
48. 1. Starkus JG
    2. Kuschel L
    3. Rayner MD
    4. Heinemann SH

    (1997) Ion conduction through C-type inactivated Shaker channels

    The Journal of General Physiology 110:539–550.

    https://doi.org/10.1085/jgp.110.5.539

    • PubMed
    • Google Scholar
49. 1. Stefani E
    2. Bezanilla F

    (1998) Cut-open oocyte voltage-clamp technique

    Methods in Enzymology 293:300–318.

    https://doi.org/10.1016/s0076-6879(98)93020-8

    • PubMed
    • Google Scholar
50. 1. Stix R
    2. Tan XF
    3. Bae C
    4. Fernández-Mariño AI
    5. Swartz KJ
    6. Faraldo-Gómez JD

    (2023) Eukaryotic Kv channel Shaker inactivates through selectivity filter dilation rather than collapse

    Science Advances 9:eadj5539.

    https://doi.org/10.1126/sciadv.adj5539

    • PubMed
    • Google Scholar
51. 1. Takacs Z
    2. Toups M
    3. Kollewe A
    4. Johnson E
    5. Cuello LG
    6. Driessens G
    7. Biancalana M
    8. Koide A
    9. Ponte CG
    10. Perozo E
    11. Gajewski TF
    12. Suarez-Kurtz G
    13. Koide S
    14. Goldstein SAN

    (2009) A designer ligand specific for Kv1.3 channels from A scorpion neurotoxin-based library

    PNAS 106:22211–22216.

    https://doi.org/10.1073/pnas.0910123106

    • PubMed
    • Google Scholar
52. 1. Tan XF
    2. Bae C
    3. Stix R
    4. Fernández-Mariño AI
    5. Huffer K
    6. Chang TH
    7. Jiang J
    8. Faraldo-Gómez JD
    9. Swartz KJ

    (2022) Structure of the Shaker Kv channel and mechanism of slow C-type inactivation

    Science Advances 8:eabm7814.

    https://doi.org/10.1126/sciadv.abm7814

    • PubMed
    • Google Scholar
53. 1. Treptow W
    2. Tarek M

    (2006) Molecular restraints in the permeation pathway of ion channels

    Biophysical Journal 91:L26–L28.

    https://doi.org/10.1529/biophysj.106.087437

    • PubMed
    • Google Scholar
54. 1. Wang W
    2. MacKinnon R

    (2017) Cryo-EM structure of the open human Ether-à-go-go-related K+ channel hERG

    Cell 169:422–430.

    https://doi.org/10.1016/j.cell.2017.03.048

    • Google Scholar
55. 1. Webb B
    2. Sali A

    (2014) Comparative protein structure modeling using MODELLER

    Current Protocols in Bioinformatics 47:5.

    https://doi.org/10.1002/0471250953.bi0506s47

    • PubMed
    • Google Scholar
56. 1. Yan Y
    2. Tao H
    3. He J
    4. Huang SY

    (2020) The HDOCK server for integrated protein-protein docking

    Nature Protocols 15:1829–1852.

    https://doi.org/10.1038/s41596-020-0312-x

    • PubMed
    • Google Scholar
57. 1. Yang Y
    2. Yan Y
    3. Sigworth FJ

    (1997) How does the W434F mutation block current in Shaker potassium channels?

    The Journal of General Physiology 109:779–789.

    https://doi.org/10.1085/jgp.109.6.779

    • PubMed
    • Google Scholar
58. 1. Yangyu W
    2. Yangyang Y
    3. Youshan Y
    4. Shumin B
    5. Alberto R
    6. Ken A
    7. Fred JS

    (2024) Cryo-EM structures of Kv1.2 potassium channels, conducting and non-conducting

    eLife 12:e89459.

    https://doi.org/10.7554/eLife.89459

    • Google Scholar
59. 1. Ye S
    2. Li Y
    3. Jiang Y

    (2010) Novel insights into K+ selectivity from high-resolution structures of an open K+ channel pore

    Nature Structural & Molecular Biology 17:1019–1023.

    https://doi.org/10.1038/nsmb.1865

    • PubMed
    • Google Scholar
60. 1. Zadek B
    2. Nimigean CM

    (2006) Calcium-dependent gating of MthK, a prokaryotic potassium channel

    The Journal of General Physiology 127:673–685.

    https://doi.org/10.1085/jgp.200609534

    • PubMed
    • Google Scholar
61. 1. Zhou Y
    2. Morais-Cabral JH
    3. Kaufman A
    4. MacKinnon R

    (2001) Chemistry of ion coordination and hydration revealed by a K+ channel-FAB complex at 2.0 A resolution

    Nature 414:43–48.

    https://doi.org/10.1038/35102009

    • PubMed
    • Google Scholar

Author details

1. Werner Treptow

   1. Laboratório de Biologia Teórica e Computacional (LBTC), Universidade de Brasília, Brasilia, Brazil
   2. Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft

   Contributed equally with

   Yichen Liu and Carlos AZ Bassetto

   For correspondence

   treptow@unb.br

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4564-3205

2. Yichen Liu

   Department of Neurobiology, The University of Chicago, Chicago, United States

   Contribution

   Formal analysis, Methodology, Writing – original draft

   Contributed equally with

   Werner Treptow and Carlos AZ Bassetto

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0774-6932

3. Carlos AZ Bassetto

   Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, United States

   Present address

   Department of Physics and Astronomy, The University of Texas at San Antonio, San Antonio, United States

   Contribution

   Investigation, Methodology, Writing – original draft

   Contributed equally with

   Werner Treptow and Yichen Liu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7012-5699

4. Bernardo I Pinto

   Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, United States

   Contribution

   Investigation, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0200-1069

5. Joao Antonio Alves Nunes

   Laboratório de Biologia Teórica e Computacional (LBTC), Universidade de Brasília, Brasilia, Brazil

   Contribution

   Investigation, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

6. Ramon Mendoza Uriarte

   Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, United States

   Contribution

   performed new multi-microseconds simulations

   Competing interests

   No competing interests declared

7. Christophe J Chipot

   1. Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, United States
   2. Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana−Champaign, Unité Mixte de Recherche No. 7019, Université de Lorraine, Université de Lorraine, Vandœuvre-lès-Nancy, France
   3. NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, and Department of Physics, University of Illinois at Urbana−Champaign, Urbana, United States

   Contribution

   Conceptualization, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9122-1698

8. Francisco Bezanilla

   1. Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, United States
   2. Centro Interdisciplinario de Neurociencia de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile

   Contribution

   Conceptualization, Formal analysis, Supervision, Methodology, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6663-7931

9. Benoit Roux

   Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, United States

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing – original draft, Project administration

   For correspondence

   roux@uchicago.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5254-2712

• 286

  views

• 75

  downloads

• 0

  citations

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