The cardiac IKs ion channel comprises KCNQ1, calmodulin, and KCNE1 in a dodecameric complex which provides a repolarizing current reserve at higher heart rates and protects from arrhythmia syndromes that cause fainting and sudden death. Pharmacological activators of IKs are therefore of interest both scientifically and therapeutically for treatment of IKs loss-of-function disorders. One group of chemical activators are only active in the presence of the accessory KCNE1 subunit and here we investigate this phenomenon using molecular modeling techniques and mutagenesis scanning in mammalian cells. A generalized activator binding pocket is formed extracellularly by KCNE1, the domain-swapped S1 helices of one KCNQ1 subunit and the pore/turret region made up of two other KCNQ1 subunits. A few residues, including K41, A44 and Y46 in KCNE1, W323 in the KCNQ1 pore, and Y148 in the KCNQ1 S1 domain, appear critical for the binding of structurally diverse molecules, but in addition, molecular modeling studies suggest that induced fit by structurally different molecules underlies the generalized nature of the binding pocket. Activation of IKs is enhanced by stabilization of the KCNQ1-S1/KCNE1/pore complex, which ultimately slows deactivation of the current, and promotes outward current summation at higher pulse rates. Our results provide a mechanistic explanation of enhanced IKs currents by these activator compounds and provide a map for future design of more potent therapeutically useful molecules.
By combining electrophysiological analysis of mutant channels and molecular dynamics simulations, this important study identifies a common binding site for two structurally distinct activators of KCNQ1-KCNE1 channels. The findings represent an important advance for the field, with convincing functional and computational data to support the claims. The work will be of interest to those studying the binding of small molecule drugs to membrane protein complexes.https://doi.org/10.7554/eLife.87038.3.sa0
Potassium ion (K+) channel activators are important compounds in human health, as partial or complete loss of function of many K+ channels may lead to inherited or acquired diseases that have significant morbidity and mortality. The delayed cardiac rectifier potassium current, IKs, plays an important role, especially at high heart rates, in the physiological shortening of the cardiac action potential (Sanguinetti et al., 1996). Unsurprisingly, mutations in both KCNQ1 (α-subunit) and KCNE1 (β-subunit), which when paired together give rise to the IKs current (Barhanin et al., 1996; Sanguinetti et al., 1996; Bendahhou et al., 2005), have been implicated in cardiac arrhythmia syndromes such as long QT syndrome (LQTS) and atrial fibrillation (Jervell and Lange-Nelsen, 1957; Wang et al., 1996; Chen et al., 2003; Eldstrom and Fedida, 2011; Olesen et al., 2014). With nearly all mutations seen in LQTS patients identified as loss-of-function and 50% of those loss-of-function mutations identified in the KCNQ1 subunit (Hedley et al., 2009; Ackerman et al., 2011), enhancing and activating IKs currents has long been suggested as a promising therapeutic approach for treating LQTS. It is a curiosity of IKs channels that the known activators fall into two general groupings: those that work best on the α-subunit, KCNQ1, alone, including zinc pyrithione, L-364,373, and one of the most studied activators, ML277 (Mattmann et al., 2012; Yu et al., 2013; Xu et al., 2015; Eldstrom et al., 2021); and those that work best in the presence of the auxiliary β-subunit, KCNE1, compounds like mefenamic acid and DIDS (4,4'-diisothiocyano-2,2'- stilbene disulfonic acid) (Abitbol et al., 1999; Wang et al., 2020).
Using cryo-EM, we recently visualized the binding of ML277 deep in the central core of KCNQ1 channels in a pocket lined inferiorly by the S4-S5 linker, laterally by the S5 and S6 helices of two separate subunits, and above by pore domain residues (Willegems et al., 2022). The location of the binding pocket and its structural inter-relationships help to explain the underlying mechanism of action of ML277, its specificity for KCNQ1, and the lack of efficacy due to steric hindrance in the presence of a β-subunit. However, it is known neither where activators of IKs bind that require the presence of the β-subunit, such as phenylboronic acid (Mruk and Kobertz, 2009), hexachlorophene, stilbenes such as DIDS and SITS (4-acetamido-4'-isothiocyanatostilbene-2,2'-disulfonic acid), and diclofenac acid derivatives such as mefenamic acid (Abitbol et al., 1999; Zheng et al., 2012; Wang et al., 2020), nor how they mediate their activator action. We have some clues for DIDS and mefenamic acid that their binding sites are not in the central channel core, as is the case for ML277, but depend on KCNE1 β-subunit residues at the extracellular surface of the channel, residues 39–43 in KCNE1 for DIDS (Abitbol et al., 1999) and K41 for mefenamic acid (Wang et al., 2020). Our recent cysteine scanning data revealed that although other extracellular KCNE1 residues in the same region to varying degrees impacted the effect of mefenamic acid, only the K41C mutation completely abolished mefenamic acid effect up to a concentration of 1 mM (Wang et al., 2020). Previous cross-linking studies have identified key interactions between this extracellular region of KCNE1 and the S1 and S6 transmembrane segments of KCNQ1 (Xu et al., 2008; Chung et al., 2009), suggesting that residues in either of these regions could also provide important clues to explain mefenamic acid’s mechanism of action.
To further explore the dependence of residues in KCNE1 and those in adjacent KCNQ1 sites on the binding of mefenamic acid to IKs, we first examined the role of K41C in preventing the drug effect. Docking in combination with molecular dynamics (MD) simulations of mefenamic acid binding to IKs followed by mutagenesis were used to map out critical KCNE1 and KCNQ1 residues. Further, we expanded on the idea that the stilbene, DIDS (Abitbol et al., 1999), which is structurally quite different from mefenamic acid, shares a common binding site. Our results showed that both compounds bind in the same general region formed by elements of the pore and S6 domains of KCNQ1 and the near extracellular region of KCNE1, but depend on different critical residues for their binding stability. Exposure of the channel complex to either compound induces subtle structural changes that subsequently stabilize the conformation of the S1/outer pore/S6 of KCNQ1 and slow the IKs deactivation gating kinetics. The results suggest the existence of a common drug-induced binding site and a mechanism of action for small molecule IKs activators that is distinct from that of specific compounds that activate KCNQ1 alone.
Exposure of wild type IKs complexes (4:4 ratio; WT EQ) to 100 µM mefenamic acid transforms the slowly activating IKs current into one with an almost linear waveform and completely inhibits tail current decay at –40 mV (Figure 1A; Abitbol et al., 1999; Unsöld et al., 2000; Wang et al., 2020). G-V relations obtained from peak initial tail currents show that 100 µM mefenamic acid hyperpolarizes the G-V (ΔV1/2 = -105.7 mV, Figure 1B and C) and decreases the slope (control k=19.4 mV, Mef k=41.3 mV, Table 1). We previously showed that introduction of a cysteine mutation at residue K41 in all four KCNE1 subunits (4:4 ratio of mutant K41C-KCNE1 to KCNQ1; K41C-EQ) itself had only a minor effect on the G-V, but prevented changes to currents and the G-V relationship on exposure to 100 µM or 1 mM mefenamic acid (Figure 1B and C and Table 1; Wang et al., 2020). The data suggest that residue K41 in KCNE1 is critical to the action of mefenamic acid, but give no information why K41 is so important, and prompted us to question how this residue and other adjacent residues in KCNE1, and those nearby on KCNQ1 acted together to form a binding pocket for mefenamic acid.
Initially, to visualize potential drug binding sites and understand how mutation of KCNE1 and KCNQ1 residues might prevent drug action, in-silico experiments of drug docking with subsequent MD simulations were performed on a model of IKs channels. The IKs model was constructed based on the recent cryo-EM structure of KCNQ1-KCNE3, which is thought to represent the activated-open state of the channel complex (Sun and MacKinnon, 2020). Taking into consideration the sequence similarity of KCNE1 and KCNE3 subunits in their transmembrane segments, it has been suggested that the main interface of these subunits with KCNQ1 is preserved in this region. Our initial data indicated that the extracellular residues of KCNE1 are involved in the action of IKs activators, so we constructed a model where external KCNE3 residues R53-Y58 were substituted with homologous KCNE1 residues, D39-A44 (Figure 2A). The resulting 4:4 IKs channel complex was termed pseudo-KCNE1-KCNQ1, ps-IKs, and Figure 2B shows the essential elements of the ps-IKs subunits which form the extracellular interface of KCNQ1 and KCNE and served as a basis for drug docking and MD simulations. Details of the docking procedure are described in the Materials and methods section and schematically summarized in Figure 2—figure supplement 1.
Briefly, conformational sampling was performed on ps-IKs residues D39-A44, and conformations showing the lowest free energy were selected for docking using a four-dimensional (4D) docking approach to find the best binding pose of the ligand. A conformation with the best docking score (Figure 2B) shows mefenamic acid binding to the pocket formed between extracellular KCNE1 residues, the external S6 transmembrane helix of one subunit, and the S1 transmembrane domain of the neighboring subunit and the pore turret of a third subunit. The estimated volume of the pocket in the mefenamic acid bound state is ~307 Å3 with a hydrophobicity value of ~0.65 kcal/mol (Figure 2D). The free energy of the mefenamic acid-ps-IKs interaction estimated by the MM/GBSA method from analysis of 300 ns MD simulations was –37.9±1.22 kcal/mol (Figure 4A, Table 4), while a similar value of –31.8±1.46 kcal/mol was calculated using the Poisson-Boltzmann surface area (MM/PBSA) model. The MM/GBSA and MM/PBSA data were used to identify ps-IKs residues contributing to the free energy of interaction with mefenamic acid for further analysis (Figure 2—figure supplement 2), although it should be noted that root mean square deviation (RMSD) analysis of trajectories indicated that the binding was dynamic as there were other binding conformations possible (Figure 2—figure supplement 3). Mefenamic acid in this complex was stabilized by two hydrogen bonds formed between the drug and ps-KCNE1 residues Y46 and E43 (Figure 2C). Analysis of MD simulations showed that these two H-bonds were the most stable, the Y46 H-bond to mefenamic acid had a mean frequency of 79.7%, and E43 22.5% of the drug bound time (Figure 2—figure supplement 4; Table 2).
The energy decomposition per amino acid using MM/GBSA and MM/PBSA methods revealed several residues in KCNQ1 and ps-KCNE1 with significant contributions to mefenamic acid coordination (Figure 2C, Figure 2—figure supplement 2). As expected, these are the KCNE1 residues located at the external region of the auxiliary subunit – amino acids K41 to Y46. In addition, residues W323 and V324 located on the S6 helix as well as residues L142, Q147, and Y148 located on the S1 helix exhibited the lowest interaction free energy. We focused on functional validation of the KCNE1 residues K41, L42, E43, A44, and Y46, and KCNQ1 residues W323, V324, L142, Q147, and Y148 by mutation to cysteine, alanine and/or tryptophan, and examining the sensitivity of fully saturated EQ channel complexes to 100 µM mefenamic acid. Although A300, located in the turret region was identified as potentially important (but less so than KCNE residues and W323), we could not get adequate expression from A300C for functional analysis.
The effect of mutations on the current waveform and tail current response to 100 µM mefenamic acid treatment was examined on IKs channels identified as: x-EQ-y where ‘x’ denotes a KCNE1 mutation and ‘y’ denotes a KCNQ1 mutation. In the absence of mefenamic acid (control), most mutations, with the exception of EQ-L142C (Figure 3—figure supplement 2), produced slowly activating currents with rapid tail current decay (Figure 3A).
In the presence of 100 µM mefenamic acid, the waveforms of WT EQ, S6 and pore mutations EQ-V324A, EQ-V324W, EQ-Q147C and EQ-Y148C were all transformed into ones with instantaneous current onset and slowed tail current decay (Figure 3A, Figure 3—figure supplement 1). Only the EQ-W323A waveform and tail current were largely unaffected by 100 µM mefenamic acid (like K41C-EQ, Figure 1). The EQ-W323C and A44C-EQ current waveforms were also unchanged by 100 µM mefenamic acid, but their tail current decay was slowed (Figure 3A, Figure 3—figure supplement 1). We interpreted this to mean that mefenamic acid binds to EQ-W323C and A44C-EQ mutant open channels and slows closing, but that the drug-channel complex is less stable and mefenamic acid unbinds during the interpulse interval, which relieves drug action between pulses. The summary of the normalized response of the different mutants to mefenamic acid is shown in Figure 3D. G-V plots were obtained from the tail current amplitudes in the absence and presence of 100 µM mefenamic acid, and unlike WT EQ (Figure 1), minimal change in the shape and position of the EQ-W323A G-V plot was seen after exposure to 100 µM mefenamic acid V1/2 shift of –14.1 mV compared with –105.7 mV seen in WT EQ (Figure 1, Figure 3B and E, and Table 1). For the W323C mutation, a less drastic decrease in size compared to alanine, the V1/2 shift seen with 100 µM mefenamic acid increased (EQ-W323C ΔV1/2 = -26.5 mV), suggesting that the size of the W323 residue is important (Figure 3E and Table 1). However, when the neighboring V324 KCNQ1 residue was mutated to a smaller (alanine) or bulkier (tryptophan) residue they both showed the same response to mefenamic acid. Both the V324W and V324A mutations reduced V1/2 shifts caused by 100 µM mefenamic acid to between –14 to –19 mV (Figure 3E, Table 1, Figure 3—figure supplement 1).
Extracellular S1 residues identified in the MD simulations also proved to be important for mefenamic acid binding, although less so than W323 and K41 residues found in the KCNQ1 extracellular regions of the S6 segment and KCNE1, respectively. Compared to WT, EQ-Y148C reduced the V1/2 shift after exposure to 100 µM mefenamic acid (EQ-Y148C ΔV1/2 = -19.3 mV) and lessened the slope of the G-V relationship (Figure 3C). The Q147C mutant on the other hand, only partially prevented the V1/2 shift observed after mefenamic acid treatment (EQ-Q147C ΔV1/2 = -37.6 mV, Figure 3E).
Other KCNE1 residues located at the N-terminal limit of the KCNE1 transmembrane segment (L42, E43, A44, Y46) were also investigated. Unlike with K41C, but similar to WT EQ, tail current decay was inhibited in the presence of 100 μM mefenamic acid in all of these mutants, reflected by the normalized response (Figure 3D, Figure 3—figure supplement 1). In addition, a reduced but still significant shift in V1/2 was observed with 100 μM mefenamic acid compared to control in most mutants tested, except K41C. (K41C ΔV1/2 = -3.8 mV; L42C ΔV1/2 = -37.1 mV; E43C ΔV1/2 = -3.9 mV; A44C ΔV1/2 = -9.7 mV; Figure 3E). Representative current traces and G-V curves for all mutants are shown in Figure 3—figure supplement 1. Consistent with previous literature (Gofman et al., 2012; Wang et al., 2012; Kuenze et al., 2020), Y46C-EQ in control conditions produced a current with faster activation and a complex GV curve which made analysis and an assignment of slope and V1/2 difficult, though a small left shift in the GV curve was visible (data not shown). In lieu of Y46C, G-V data from the Y46A-EQ mutant showed a potent effect of mefenamic acid, equivalent to that of WT with a ΔV1/2 = -106.2 mV (Figure 3—figure supplement 1D). These results suggest that mutation of residues further away from the N-terminus of KCNE1 than K41 has diminishing effects on the activator action of mefenamic acid.
Unlike other mutations, the control EQ-L142C current waveform displayed an almost instantaneous current onset, and tail currents showed no decay with our standard protocol (Figure 3—figure supplement 2A). As the control EQ-L142C current waveform qualitatively resembled WT EQ currents in the presence of 100 µM mefenamic acid, G-V plots at different interpulse intervals were compared. At the standard interval of 15 s, the EQ-L142C G-V plot (blue) closely overlapped that of WT EQ in the presence of Mef (solid grey), and at a 30 s interpulse interval, the position of the WT EQ +Mef (grey open circles) and EQ-L142C plots (green) both depolarized significantly (Figure 3—figure supplement 2B). It appeared that the L142C mutation augmented channel activation as much as 100 µM mefenamic acid on WT channels. In addition, mefenamic acid made the EQ-L142C G-V plot voltage independent, overlapping with the G-V obtained for EQ-L142C with a 7 s interpulse interval (pink and red respectively, Figure 3—figure supplement 2B). The data indicate that EQ-L142C is still responsive to mefenamic acid but does so from a heightened state of activation (Table 3).
To explore the role played by critical residues, in-silico homologous mutations K41C, Y46C and W323A were introduced into the mefenamic acid-bound ps-IKs channel (Mef-ps-IKs) and the stability of the Mef-mutant complexes was assessed in MD simulations. Remarkably, mefenamic acid detached from mutant K41C and W323A channels within 120 ns in all three independent MD simulations with an AMBER force field (Table 4). In contrast, mefenamic acid remained bound during the entire simulation time to WT and Y46C-ps-IKs complexes (Table 4). The last mutant was tested as we could not determine the functional effect of mefenamic acid on this residue in electrophysiological experiments, but in the simulation, Y46C-ps-IKs behaved like Y46A from electrophysiological data (Figure 3, Figure 3—figure supplement 1). Mefenamic acid unbound from A44C-ps-IKs channel complexes within 80, 100, and 110 ns during three different simulations, changing binding pose several times before doing so.
Similar results were obtained in MD simulations with the CHARMM force field where Mef-ps-IKs was embedded in the lipid membrane (Video 1), although in two of five 1250 ns long-duration simulations we observed unbinding of Mef from WT ps-IKs complexes, which suggested that the lipid environment allowed a more dynamic interaction between Mef and IKs. In contrast, Mef left the binding site in all five 500 ns runs for W323A and K41C mutants (Figure 4—figure supplement 1, Videos 2 and 3), and when the Y46C mutant was placed in a lipid environment, mefenamic acid left the binding site or changed its binding conformation in three of five 500 ns MD simulations (Video 4). A significantly reduced free interaction energy (ΔG) of ligand binding for K41C and W323A mutant Mef-ps-IKs complexes in 300 ns simulations was observed compared to WT Mef-ps-IKs (Figure 4A, Table 4), and the small but statistically significant change in free energy observed for the Y46C mutant complex confirms that the Y46 residue is not as important as K41 and W323 for mefenamic acid binding. It should be noted that the absolute values of ΔG or corresponding dissociation constants (Kd) calculated from MD simulations do not reflect apparent ΔG and Kd values determined from electrophysiological experiments or biochemical essays.
The flexibility of the external ps-KCNE1 protein residues of the mutants W323A, K41C, and Y46C ps-IKs channels was also analyzed from 300 ns duration trajectories, by monitoring their average root mean square fluctuation (RMSF) during the last 100 ns of simulations after the detachment of mefenamic acid from the molecular complex or in the case of Y46C without introduction of the ligand. The RMSF values obtained from the mutant channels were then compared to that of WT ps-IKs channels where MD simulations of the same duration were conducted after removing the mefenamic acid molecule from the complex (Figure 4B). The results indicate that K41C and W323A mutations modestly increased the RMSF of D39-A44 ps-KCNE1 residues when compared to the WT ps-IKs channel complex without mefenamic acid bound (Figure 4C and D). The Y46C mutation seems to impact the mobility of the N-terminal portion of KCNE1 much less (Figure 4E), perhaps owing to its location further into the membrane. These results suggest that the side chains of K41 and W323 residues normally stabilize the conformation of the external region of KCNE1, so that mutation of these residues increases random motion and reduces the ability of drugs like mefenamic acid to remain bound at this site.
To establish whether the binding pocket for mefenamic acid can be generalized to other IKs activators, we also examined the binding of the structurally unrelated IKs activator, DIDS. Stilbenes such as DIDS (Figure 5A) also activate IKs (Abitbol et al., 1999; Bollmann et al., 2020), and given their molecular differences from fenamates, it is of interest to explore common structural and dynamic features of their binding to the IKs channel complex. 100 µM DIDS had no effect on endogenous currents in GFP-transfected tsA201 cells but treatment of WT EQ channels with 100 µM DIDS transformed the slowly activating waveform into one with faster onset (although not instantaneous like mefenamic acid) and inhibited tail current decay (Figure 5B). G-V plots were obtained from the tail amplitudes in the absence and presence of 100 µM DIDS (Figure 5C and D). The overall shape, slope, and V1/2 of the WT EQ G-V relationship changed with 100 µM DIDS in the direction of increased activation (ΔV1/2 = -46.6 mV; Table 5; control k=20.3 mV, DIDS k=25.3 mV). However, the effects on WT EQ were less pronounced with DIDS than mefenamic acid at the 100 µM concentration.
The ps-IKs construct and docking procedures used previously to explore the binding site of mefenamic acid were used as a basis for DIDS docking and subsequent MD simulations. We found that DIDS also bound to a location formed between extracellular KCNE1 residues, the external S6 transmembrane helix of one subunit, the S1 transmembrane domain of the neighboring subunit, and the pore turret of the opposite subunit (Figure 6A). In docking experiments, DIDS is stabilized by its hydrophobic and van der Waals contacts with KCNQ1 and KCNE1 subunits as well as by two hydrogen bonds formed between the drug and ps-KCNE1 residue L42 and KCNQ1 residue Q147 (Figure 6B). These hydrogen bonds however were not stable during MD simulations and instead H-bonding occurs between E43, Y46, and I47 in KCNE1 as well as W323, S298 and Y299 in KCNQ1 (Figure 6—figure supplement 1, Table 2). The energy decomposition per amino acid using MM/GBSA and MM/PBSA methods based on 300 ns simulations revealed several residues in KCNQ1 and ps-KCNE1 with significant contributions to DIDS coordination (Figure 6C). Although residues identified as critical to mefenamic acid binding and action also generally appear to be important for DIDS, especially W323, K41 was noticeably not as critical to DIDS coordination, since the ΔG values for this residue were significantly smaller compared to those of the Mef (see Figure 2—figure supplement 2).
Similar to mefenamic acid, in AMBER force field simulations, DIDS stayed bound to WT ps-IKs, but detached from the W323A mutant during two of three runs of 300 ns duration, and from Y46C channels in one of three runs after 240, 239, and 122 ns of simulation, respectively (Table 6). Interestingly, DIDS did not unbind from K41C mutant ps-IKs channels, even after 300 ns of simulation, although the binding pose did change in one run. Calculations of free interaction energy shown in Table 6 using both MM/PBSA and MM/GBSA methods are of mean values before the detachment of the drug if that occurred. If DIDS detachment did not occur during 300 ns MD runs, the average free energy was calculated over the duration of the entire MD run for that construct. Only DIDS binding to W323A, and to Y46C in one simulation run, showed significantly lower free energy than DIDS binding to WT, using both methodologies. The small change in free energy and more stable binding of DIDS to K41C suggest that this residue is not as important as W323 or Y46 for DIDS binding. In longer 500 ns simulations using a CHARMM force field with the channel complexes embedded in lipid, trajectory analysis revealed clusters of DIDS poses in complex with the WT ps-IKs channel, which are much more closely related than those identified for mefenamic acid (Figure 6—figure supplement 2, note the ordinate scale). Still, DIDS remained bound to WT ps-IKs, during all runs throughout 500 ns simulations (Figure 6—figure supplement 3, Video 5), and to the K41C mutant in two runs (Video 6). Both W323A and Y46C mutants unbound during 500 ns simulations (all runs for Y46C, Figure 6—figure supplement 3, Videos 7 and 8), as was seen with the AMBER force field. These model predictions were tested experimentally using fully saturated mutant IKs channel complexes, K41C-EQ, L42C-EQ, A44C-EQ, Y46A-EQ, EQ-W323A, EQ-L142C, and EQ-Y148C (Figure 7A, Figure 7—figure supplement 1). Y46 was identified as an important residue for DIDS binding in MD simulations, but as before, the complexity of the GV curve and low functional expression of the Y46C construct led us to substitute Y46A for this mutation in electrophysiology experiments (Figure 7 and Figure 7—figure supplement 1).
K41C-EQ G-V plots were obtained from peak tail current amplitudes in the absence and presence (Figure 7B) of 100 µM DIDS. Unlike with mefenamic acid, but in agreement with the modeling, K41C only partially prevented the action of DIDS. Treatment of K41C-EQ with 100 µM DIDS hyperpolarized the V1/2 and changed the shape of the G-V relationship compared to control (Figure 7B and D, Table 5). The current waveform of K41C-EQ activated more quickly with less sigmoidicity after treatment with DIDS, and tail current decay was slowed as indicated by the normalized response (Figure 7B and C). As K41C-EQ remained responsive to DIDS, and L42C responded similarly to K41C-EQ (for K41C-EQ, ΔV1/2 was –24.7 mV, and for L42C-EQ ΔV1/2 was –34 mV), we hypothesized that DIDS binding to IKs channel complexes may involve KCNE1 residues closer to the transmembrane region. In agreement with this idea, A44C showed little response to 100 µM DIDS both in terms of current waveform and tail current decay. The shift in V1/2 was also reduced compared to WT-EQ (A44C-EQ ΔV1/2 = -18.5 mV; WT EQ ΔV1/2 = -46.6mV, Figure 7C and D). In Y46A, the effect of DIDS on the current waveform was greatly reduced as was slowing of tail current decay (Figure 7A and C). We could only estimate an activation V1/2 for Y46A as the G-V curve was right shifted to very positive potentials and non-saturating. Nevertheless, hyperpolarization of the V1/2 (Figure 7D, Figure 7—figure supplement 1) in the presence of DIDS was observed, suggesting that unstable and short-lived binding of the drug to the channel complex was sufficient to interfere with channel gating.
The modeling studies (Figure 6) suggested that W323 remained a key residue for IKs activator sensitivity, and in agreement with this, EQ-W323A was only partially responsive to DIDS. In EQ-W323A, the tail current decay was affected but the slowly-activating current waveform was preserved in the presence of DIDS, supporting the idea that the drug dissociates from the channel complex between pulses (Figure 7A and C, Table 6). However, a significant shift in the V1/2 remained (ΔV1/2 = -27.3 mV; Figure 7D, Table 5). Comparable effects observed in EQ-W323A, A44C-EQ, and Y46A-EQ are consistent with the idea that the DIDS binding pocket involves the N-terminal end of S6 and the deeper extracellular surface of the KCNE1 transmembrane segment. In EQ-Y148C, the V1/2 and the shape of the G-V plot were not altered by 100 µM DIDS (Figure 7D, Table 5), and tail current decay was not markedly slowed (Figure 7A and C). This result suggests that unlike mefenamic acid, DIDS binding determinants extend to the S1 region of KCNQ1 and that Y148 is an important contributor.
Notably, as with mefenamic acid, the L142C-EQ G-V plot became voltage-independent after treatment with 100 µM DIDS (data not shown) and so it proved hyper-responsive to both of the IKs activators, DIDS and mefenamic acid.
Mefenamic acid, a nonsteroidal anti-inflammatory drug (NSAID), and the structurally distinct stilbenes DIDS and SITS, among other compounds, have previously been identified by numerous groups to enhance IKs currents in various expression systems including canine and guinea-pig ventricular myocytes (Magyar et al., 2006; Toyoda et al., 2006) as well as heterologous expression systems such as Xenopus laevis oocytes, CHO, COS-7, tsA201 and LM cells (Busch et al., 1994; Abitbol et al., 1999; Unsöld et al., 2000; Toyoda et al., 2006; Wang et al., 2020). The extracellular region of KCNE1 between residues 39 and 44 was found important in mediating the effect of DIDS on IKs (Abitbol et al., 1999) and residue K41, located on the extracellular end of KCNE1, was found to be critical in mediating mefenamic acid’s activating effect on the fully saturated IKs channel complex (Wang et al., 2020). Consistent with this idea, when all four WT KCNE1 subunits are replaced with mutant K41C-KCNE1, mefenamic acid up to a concentration of 1 mM is largely ineffective (Figure 1). Unlike in the WT complex, there is little change to the current waveform, slope or V1/2 of the G-V plot during activator exposure, suggesting that all the drug binding site(s) on the channel complex are impaired, or that the mechanism of action is disabled, or that the mutation causes a combination of these actions. In the present study, we analysed these activator compound actions using molecular modeling approaches, and complementary mutational analysis. Our data describe the formation of a drug binding pocket between the immediate extracellular residues of KCNE1, S1 and pore residues of two KCNQ1 subunits, stabilized by the presence of structurally different activator compounds. The effect of mutations that negate the activator compound actions is to destabilize the binding pocket itself, reduce drug binding and limit activator residency time on the channel complex.
Using data that suggested an extracellular binding site for mefenamic acid was at the KCNE1-channel interface, MD simulations were used to explore drug-channel interactions further. The drug binding pocket was defined as the extracellular space formed by KCNE1, the domain-swapped S1 helices of one KCNQ1 subunit and the pore/turret region made up of two other KCNQ1 subunits (Figure 2; Video 1). Docking and molecular simulations identified a binding site made up of several residues across all of these elements (Figure 2 and Figure 2—figure supplement 2, Table 7). Mutagenesis revealed that most mutations impacted at least one out of the three mefenamic acid effects: V1/2 shift, GV-plot slope change, and current waveform change related to slowed deactivation. However, only three of the mutants impacted all of the mefenamic acid actions (Figure 3). These were W323A in KCNQ1, as well as K41C, and A44C (to a lesser extent) in KCNE1, which define key components of the binding pocket for mefenamic acid. That W323 forms an important medial wall of the hydrophobic pocket to which mefenamic acid binds is suggested by data from mutants in which the size of the side chain at this location is systematically made smaller to cysteine and then alanine, which results in diminished effectiveness of the drug, particularly with alanine. This mutation resulted in the detachment of mefenamic acid as well as lower interaction energy of drug-channel complexes during MD simulations (Figure 4A; Video 3, Figure 4—figure supplement 1C) particularly loss of interaction strength with KCNE1 (Table 7). Likewise, MD simulations revealed detachment of mefenamic acid and reduced interaction energy values for K41C-Mef-IKs complexes (Video 2, Figure 4) again accompanied by loss of interaction strength with KCNE1 (Table 7). The drug also detached from the A44C complex, another mutant with a reduced response in terms of changes in waveform, V1/2 and slope. In contrast, while the free interaction energy of the mutant Y46C channel was significantly decreased in comparison to the WT channel complex (Figure 4A), detachment of mefenamic acid from the mutant channel during 300 ns MD simulations was not observed (Table 4). However, when placed in a lipid environment mefenamic only remained bound in three out of five 500 ns simulations with Y46C, suggesting a more dynamic interaction in this model (Figure 4—figure supplement 1D, Video 4). Comparison of energies at specific residues shows a loss of interaction with Q147 and Y148 in S1 with this mutant and a small shift towards the turret at S298 and A300 (Figure 8A; Table 7, Figure 8—source data 1). While we were not able to obtain good electrophysiological data from Y46C, data from Y46A suggested that mefenamic acid still had potent actions on this mutant. Overall, the combination of binding studies and electrophysiological data indicate that Y46 was not as important for mefenamic acid binding and action as other KCNE1 N-terminal residues.
Although it was suggested that DIDS and mefenamic acid have the same binding site (Abitbol et al., 1999), the extent of overlap was unknown. Initially, we examined the effect of DIDS on WT EQ. Consistent with previous studies (Abitbol et al., 1999; Bollmann et al., 2020) we confirmed that 100 µM DIDS enhanced WT EQ activity (Figure 5B) with a V1/2 shift of –46.6 mV and a decrease in the slope of the G-V curve (Table 5). As most IKs activators are dependent on the KCNQ1-KCNE1 stoichiometry, this more potent effect of DIDS seen in our study may be explained by the higher dose we used and our KCNE1-KCNQ1 linked channel constructs, which ensured fully KCNE1-saturated complexes. Furthermore, due to the large size and complex folds found on the surface of oocytes, higher drug concentrations than those used for cultured cells are often required in order to facilitate a similar effect in both expression systems (Kvist et al., 2011). All previous studies utilized Xenopus laevis oocytes, whereas in this study transiently transfected tsA201 cells were used.
The results of in-silico experiments, including some binding properties and stability of some mutations, were only partially validated by electrophysiology data, which might be explained by the limitations of the applied methods. Nevertheless, docking analyses revealed that both mefenamic acid and DIDS bind to the same general extracellular inter-subunit interface with some differences in key residues revealed by the electrophysiology data. Mutation of Y148 in KCNQ1 to a cysteine (Y148C) or Y46 in KCNE1 to an alanine (Y46A) was found to reduce the effects of 100 µM DIDS, particularly in the case of Y148C (Figure 7). These data suggest that DIDS resides deeper in the binding pocket formed by KCNE1 residues and the KCNQ1 pore/S6, and so was less dependent on the side chain of K41 to retain the activator on the channel complex (compare Video 1 and Video 5). DIDS associated more strongly with the pore of the Y148C mutant, particularly with V324 and T327, and less across the ps-KCNE1, S1 and turret regions (Table 7). The Y46C mutation resulted in shifts away from ps-KCNE1 and towards the S1 domain (T144, E146 and Q147). In the case of A44C and DIDS, the changes were more subtle, with stronger interactions moving from central residues in KCNE1 (K41, L42, and E43) and S1 (V141 and L142; colored grey in Figure 8B, cf. Figure 8—source data 2) to more peripheral residues (I145, E146, and Q147) and lower in the KCNE TMD (A44 and M45; colored magenta in Figure 8B).
Molecular modeling and docking revealed that mefenamic acid and DIDS induce conformational changes in the channel upon binding, to shape a binding pocket formed by residues from the external S1 domain, KCNE1 and the pore domain of IKs (Figure 2, Videos 1 and 5). This cryptic binding pocket is not detectable in the absence of the drug (Figure 4B), which suggests that it has been induced in a similar manner to previous reports of toxin interactions with KcsA-Kv1.3 that induce conformational changes in both the toxin and the channel structure to generate a high-affinity binding site (Lange et al., 2006; Zachariae et al., 2008). Indeed, analysis of the binding site before and after mefenamic acid unbinds shows the involved residues in the channel complex moving into the space vacated by mefenamic acid (Figure 4—figure supplement 2, Video 9), a collapse of the pocket created by the drug-channel interactions.
Given the high modeled binding energy of the channel-drug activated state complexes, ~–39 kcal/mol for mefenamic acid and ~–35 kcal/mol for DIDS, but the relatively low affinities, given the micromolar concentrations required experimentally, we can imagine a dynamic complex where mefenamic acid and DIDS bind/unbind from IKs at high frequency. Slowed deactivation may therefore be the result of these rapid binding/unbinding interactions slowing the dissociation of the S1/KCNE1/pore domain/drug complex by either providing steric hindrance to dissociation or by stabilizing the activated complex. MD simulations suggest the latter is most likely the case. Cross-linking studies have previously shown that placing cysteines at key locations in the KCNE1 N-terminus, the top of S1 and in S6 can lead to disulfide bond formation and slowing or elimination of deactivation (Chung et al., 2009) similar to what we observe when IKs is exposed to mefenamic acid and DIDS. Our data indicate that mutation of residue W323 to an alanine would not only destabilize the external S1/KCNE1/pore domain interface (Figure 4D, Table 1) but also eliminate direct hydrophobic contacts which normally occur between the W323 side chain and mefenamic acid, thus facilitating drug dissociation (Figure 2C, Video 3).
A reduced interaction with S1 could also conceivably curtail the ability of IKs activators to slow dissociation of the activated complex and restore faster deactivation rates such that there is no longer an enhanced step current in the presence of the drug, as seen in mutants K41C and A44C for mefenamic acid (Figures 1A and 3A) and A44C, Y46A, W323A, and Y148C for DIDS (Figure 7A). Similarly, electrostatic interactions of residue K41, which acts as a lid for the binding pocket for mefenamic acid (Video 2), could help stabilize the S1/KCNE1/pore complex by its contacts with drug, which in its turn links the static pore domain to the dynamic voltage-sensor. Mutation of residue K41 to a cysteine may prevent stable IKs activator binding to the channel by increasing the fluctuations of the external KCNE1 region (Figure 4C) and by reducing the contacts with the drug (Figure 2C). In simulations, mefenamic acid was seen dissociating from mutant K41C, A44C, and W323A channel complexes much more often compared to wild type IKs, and this explains why the electrophysiological effects of IKs activators were prevented by amino acid substitutions at these locations (Videos 2 and 3). Shifts in, and particularly reductions in ps-KCNE1 association for both drugs (Table 7) that lead to loss of efficacy suggest that it is the interactions with KCNE1 and S1 that are key to maintaining the activated state. This is similar to the proposal for tight interactions between S1 and KCNE3 in maintaining constitutive activity in KCNE3-associated channels (Kasuya and Nakajo, 2022).
In close proximity to this drug-binding pocket are known gain-of function mutations in S1, S140G and V141M, which have previously been reported to slow deactivation in the presence of KCNE1 (El Harchi et al., 2010; Peng et al., 2017). As their current waveforms are qualitatively similar to those seen after exposure of WT EQ to IKs activators, we propose that the same mechanism may underlie the effects of mefenamic acid, DIDS and these IKs S1 mutations. In addition, we find that mutation of the neighboring residue, L142, also produces current waveforms and G-V plots which mirror those seen when WT EQ is treated with 100 µM mefenamic acid (Figure 3—figure supplement 2) or DIDS. In the cryo-EM structure of KCNQ1-KCNE3 (Sun and MacKinnon, 2020), residue L142 interacts with KCNE3, whereas V141 interacts with both KCNE3 and the pore domain. This suggests that both the V141M and L142C mutations could directly alter the interaction of the S1 domain with the pore and/or the position or movement of KCNE1. The co-evolved interface between the extracellular end of S1 and the pore domain is thought to be important for bracing the VSD, to allow efficient force transmission to the pore (Lee et al., 2009), but can also impact permeation, as the S140G and V141M mutations also enhance rubidium permeation through IKs complexes (Peng et al., 2017). The importance of this S1-pore coupling to channel function is supported by mutational analyses of S1 residues (Chen et al., 2003; Hong et al., 2005; Wang et al., 2011; Campbell et al., 2013) as well as the L142C mutation examined in this study (Figure 3—figure supplement 2), which all display current waveforms with instantaneous onset. Incidentally, A300, a residue in the pore region of KCNQ1, which also interacts with both mefenamic acid and DIDS in MD simulations (Figure 2—figure supplement 2, Figure 6), has also been implicated in the same gain-of-function cleft as S140G and V141M (Smith et al., 2007). The A300T mutant has a ~–20 mV shift in V1/2 of activation and faster rates of activation (Bianchi et al., 2000), once again showing how a mutation can mirror the effects of the two activators studied here.
Binding of mefenamic acid and DIDS to the extracellular end of KCNE1 and the KCNQ1 S6 and S1 helices is facilitated by a number of key residues. Residue K41 acts as a ‘lid’ holding mefenamic acid in place, while residue W323 impacts the size of the binding pocket. Size reduction mutations of either residue destabilize mefenamic acid binding and ultimately lead to drug detachment from the channel complex. This explains why, when all four IKs subunits are mutated, as in the case of K41C-EQ, EQ-W323A, and A44C-EQ, little to no effect of the drug is seen. The larger drug, DIDS, interacts with many of the same residues but those deeper in the pocket appear more important than for mefenamic acid. Furthermore, the qualitative similarities between the S1 mutant channel, EQ-L142C and WT EQ in the presence of 100 µM mefenamic acid suggest that IKs activators most likely cause their effects by modulating interactions between the S1 helix, pore turret, KCNE1 and the S6 helix. Upon binding, both DIDS and mefenamic acid induce conformational changes in an occult binding pocket and stabilize the S1/KCNE1/pore complex, which ultimately slows deactivation. The results indicate that this extracellular inter-subunit interface forms a generalized binding site which different drugs can access and through which induce common effects on channel activation and deactivation. The presence of such a binding site and the variable nature of IKs complex composition may serve as starting points for future drug development projects targeted at discovering therapeutically-useful IKs agonists.
A model of the IKs channel complex – termed pseudo-KCNE1-KCNQ1 (ps-IKs) – was created based on the cryogenic electron microscopy (cryo-EM) structure of KCNQ1-KCNE3 (PDB ID: 6v01; Sun and MacKinnon, 2020). In this structure, the extracellular residues of KCNE3, R53-Y58, were substituted with homologous KCNE1 residues D39-A44. Conformational sampling was then performed for substituted residues and the lowest free energy conformations were selected for subsequent docking experiments applying a four-dimensional (4D) docking approach which accounts for the flexibility of the receptor site configuration (Bottegoni et al., 2009). After docking, a receptor ensemble with multiple conformations of the putative binding site region was created via another round of conformational sampling for the external part of ps-KCNE1 and its KCNQ1 neighborhood (8 Å cut-off distance) and generated conformations were used for a new docking iteration. A ligand-channel conformation with the lowest free energy was chosen from the final docking iterations. The docking and conformational sampling as well as substitution of amino acids were performed with ICM-pro 3.8 software (Neves et al., 2012). A schematic representation of the general workflow for ps- IKs model construction and drug docking can be found in Figure 2—figure supplement 1.
The coordinates of mefenamic acid-bound ps-IKs channel complexes with the lowest free energy were then used for two sets of MD simulations with AMBER and CHARMM force fields (see below). Considering the complexity of the binding site located on the periphery of the pore, ps-KCNE1 and VSD, docking and subsequent MD simulations were performed for only one binding site (Figure 2D). To keep the voltage-sensing domain (VSD) in its activated state conformation, we restrained the PIP2 molecules in their cryo-EM positions with a force constant of 1000 kJ/mol/nm2 during MD simulations.
Three 300 ns duration simulations were performed in a water environment with AMBER20 using a ff14SB force field for protein and GAFF/AM1-BCC scheme for the ligand parameterization and calculation of the atomic point charges (Jakalian et al., 2002; Case et al., 2005; Maier et al., 2015). The complex was solvated with TIP3P water models and Na+/Cl- at 100 mM concentration. The system was minimized and equilibrated in the NVT and NPT ensembles for 10 ns, gradually releasing spatial restraints from the backbone and sidechains. During the last 5 ns only backbone atoms were restrained with a force constant of 50 kJ/mol/nm2. The same procedure was used for equilibration of mutant complexes after introduction of mutations using ICM-pro 3.8 software. We used the Langevin thermostat with a collision frequency of 2 ps–1, a reference temperature of 310 K, and Monte Carlo barostat with reference pressure at 1 bar (Oliver et al., 1997; Wu et al., 2016). The long-range electrostatic interactions with a cut-off at 10 Å were treated with the Particle Mesh Ewald (PME) algorithm. Bonds involving only hydrogens were constrained with the SHAKE algorithm and a 2 fs integration time step was used.
Five 500 ns duration simulations were performed using a CHARMM36m force field and ps-IKs complex inserted into a POPC membrane. The same MD parameters described in our previous study were used for this set of simulations (Willegems et al., 2022). Trajectories from MD simulations were clustered with TTclust based on ligand and its binding site RMSD. The elbow method was used to find the optimum number of clusters. From each cluster, a centroid with the lowest RMSD to all other ligand conformations in the cluster was selected as a representative structure. GROMACS-2021.4 was used for RMSD, RMSF, and H-bond calculation. RMSD was calculated from the initial position of the ligand after the least squares fit alignment of the protein backbone. For RMSF calculations only backbone and C-beta atoms were used.
Trajectories from MD simulations with a CHARMM36m force field are available at https://doi.org/10.5281/zenodo.8226585. The 2D diagrams and other molecular visualizations were generated by ICM-pro and VMD software. The MM/PBSA and MM/GBSA methods were used to calculate the free energy of binding (Miller et al., 2012). We collected 1000 snapshots with equal intervals (every 0.3 ns) if ligands did not leave the binding site during 300 ns.
DIDS (Sigma-Aldrich, Mississauga, ON, Canada) was prepared as a 50 mM stock solution dissolved in 100% dimethyl sulfoxide. Stock DIDS solution was diluted in control whole-cell bath solution to obtain a final DIDS concentration of 100 µM which was perfused onto mammalian cells for whole-cell experiments. All other reagents and solutions were prepared as described (Wang et al., 2020). Mefenamic acid (Tocris Bioscience, Oakville, ON, Canada) was used to activate IKs at concentrations of 100 μM and 1 mM.
tsA201 transformed human embryonic kidney 293 cells were purchased directly from Sigma-Aldrich. After culturing, they were plated for whole-cell experiments and subsequently transfected with Lipofectamine2000 (Murray et al., 2016; Westhoff et al., 2019; Wang et al., 2020). All mutations were first generated using site-directed mutagenesis and Pfu turbo, then sequence confirmed. Whole-cell experiments were conducted 24–48 hr post transfection. For wild type (WT) EQ and mutant x-EQ-Y (where ‘x’ denotes a KCNE1 mutation and ‘y’ denotes a KCNQ1 mutation; for example, K41C-EQ, EQ-W323A), cells were transfected with a linked KCNE1 and KCNQ1 cDNA (2–3 µg was used) which assembles as a fully saturated 4:4 ratio of KCNE1 to KCNQ1. All constructs were also co-transfected with 0.8 µg of GFP to allow transfected cells to be identified. Data were obtained using an Axopatch 200B amplifier, Digidata 1440 A digitizer and pCLAMP 11 software (Molecular Devices, LLC, San Jose, CA).
Conductance-voltage (G-V) relationships were obtained from the normalized peak of the initial tail current (G/Gmax) and plotted against the corresponding voltage. G-V plots were fitted with a Boltzmann sigmoid equation to obtain the voltage at half-maximal activation (V1/2) and slope (k) values (Tables 1, 3 and 5). For each EQ mutant, the change in activation V1/2 (∆V1/2=V1/2 in the presence of drug-V1/2 control) was also determined (Figures 3E and 7D). In some cases, the foot of the G-V curve did not reach 0 (essentially in the presence of drug) and as a result, the V1/2 was read directly from the normalized plots at the voltage point where the curve crossed the 0.5 value on the y coordinate. Slowing of tail current decay was used as a measure of mefenamic acid and DIDS impact on WT and mutated EQ channel complexes. Specifically, the peak to end difference currents were calculated by subtracting the minimum amplitude of the deactivating current from the peak amplitude of the deactivating current. The difference current in mefenamic acid or DIDS was normalized to the maximum control (in the absence of drug) difference current and subtracted from 1.0 to obtain the normalized response (Figures 3D and 7C). Data files collected during this study and used in the preparation of results and figures are available at https://doi.org/10.5281/zenodo.8226585.
GraphPad Prism 9 (GraphPad Software, San Diego, CA) was used to analyze all the data. Where applicable, unpaired t-test or one-way ANOVA followed by the Fisher’s least significant difference (LSD) test was used to determine statistical significance. In all figures ****, ***, **, * denotes a significance where p<0.0001, p<0.001, p<0.01 and p<0.05, respectively. All data in the figures and tables are shown as mean ± SD or SEM. Bar graphs showing mean ∆V1/2 (Figures 3E and 7D) were generated by calculating changes in V1/2 induced by drug treatment vs. control in separate cells. ∆V1/2 values reported in the Results were calculated from the mean V1/2 values shown in Tables 1 and 3.
All original electrophysiological data files summarized in figures and figure supplements and CHARMM trajectory data are available at https://doi.org/10.5281/zenodo.8226585.
4,4’-Diisothiocyanato-2,2’-Stilbenedisulfonic Acid (DIDS) Modulates the Activity of KCNQ1/KCNE1 Channels by an Interaction with the Central Pore RegionCellular Physiology and Biochemistry 54:321–332.https://doi.org/10.33594/000000222
Four-dimensional docking: a fast and accurate account of discrete receptor flexibility in ligand dockingJournal of Medicinal Chemistry 52:397–406.https://doi.org/10.1021/jm8009958
Positive regulation by chloride channel blockers of I sK channels expressed in Xenopus oocytesMolecular Pharmacology 46:750–753.
Selective targeting of gain-of-function KCNQ1 mutations predisposing to atrial fibrillationCirculation. Arrhythmia and Electrophysiology 6:960–966.https://doi.org/10.1161/CIRCEP.113.000439
The voltage-gated channel accessory protein KCNE2: multiple ion channel partners, multiple ways to long QT syndromeExpert Reviews in Molecular Medicine 13:e38.https://doi.org/10.1017/S1462399411002092
ML277 regulates KCNQ1 single-channel amplitudes and kinetics, modified by voltage sensor stateThe Journal of General Physiology 153:e202112969.https://doi.org/10.1085/jgp.202112969
The genetic basis of long QT and short QT syndromes: a mutation updateHuman Mutation 30:1486–1511.https://doi.org/10.1002/humu.21106
De novo KCNQ1 mutation responsible for atrial fibrillation and short QT syndrome in uteroCardiovascular Research 68:433–440.https://doi.org/10.1016/j.cardiores.2005.06.023
Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validationJournal of Computational Chemistry 23:1623–1641.https://doi.org/10.1002/jcc.10128
L-364,373 fails to activate the slow delayed rectifier K+ current in canine ventricular cardiomyocytesNaunyn-Schmiedeberg’s Archives of Pharmacology 373:85–89.https://doi.org/10.1007/s00210-006-0047-4
ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SBJournal of Chemical Theory and Computation 11:3696–3713.https://doi.org/10.1021/acs.jctc.5b00255
MMPBSA.py: An Efficient Program for End-State Free Energy CalculationsJournal of Chemical Theory and Computation 8:3314–3321.https://doi.org/10.1021/ct300418h
Docking and scoring with ICM: the benchmarking results and strategies for improvementJournal of Computer-Aided Molecular Design 26:675–686.https://doi.org/10.1007/s10822-012-9547-0
Atrial fibrillation: the role of common and rare genetic variantsEuropean Journal of Human Genetics 22:297–306.https://doi.org/10.1038/ejhg.2013.139
Structural models for the KCNQ1 voltage-gated potassium channelBiochemistry 46:14141–14152.https://doi.org/10.1021/bi701597s
Modulation of functional properties of KCNQ1 channel by association of KCNE1 and KCNE2Biochemical and Biophysical Research Communications 344:814–820.https://doi.org/10.1016/j.bbrc.2006.03.213
Gating-related molecular motions in the extracellular domain of the IKs channel: implications for IKs channelopathyThe Journal of Membrane Biology 239:137–156.https://doi.org/10.1007/s00232-010-9333-7
Probing the structural basis for differential KCNQ1 modulation by KCNE1 and KCNE2The Journal of General Physiology 140:653–669.https://doi.org/10.1085/jgp.201210847
Self-guided Langevin dynamics via generalized Langevin equationJournal of Computational Chemistry 37:595–601.https://doi.org/10.1002/jcc.24015
KCNQ1 and KCNE1 in the IKs Channel Complex Make State-dependent Contacts in their Extracellular DomainsThe Journal of General Physiology 131:589–603.https://doi.org/10.1085/jgp.200809976
Chan et al. attempted to identify the binding sites or pockets for the KCNQ1-KCNE1 activator mefenamic acid. Because the KCNQ1-KCNE1 channel is responsible for cardiac repolarization, genetic impairment of either the KCNQ1 or KCNE1 gene can cause cardiac arrhythmias. Therefore, the development of activators without side effects is highly desired. Since mefenamic acid binding requires both KCNQ1 and KCNE1 subunits, the authors performed drug docking simulations using the KCNQ1-psKCNE1 structural model with substitution of the extracellular five amino acids (R53-Y58) of KCNE3 to D39-A44 of KCNE1. They successfully identified some critical amino acid residues, including W323 of KCNQ1 and K41 and A44 of KCNE1. They then tested these identified amino acid residues by analyzing the point mutants and confirmed that they were critical for the binding of the activator. They also examined another activator, but structurally different DIDS, and reported that DIDS and mefenamic acid share the binding pocket, and concluded that the extracellular region composed of S1, S6, and KCNE1 is a generic binding pocket for the IKS activators.
The limitation of this study is that they had to use the KCNQ1-KCNE3-based structural model for the docking simulation. Although they only focused on the extracellular region substituted by the six amino acid residues of KCNE1, the binding mode or location of KCNE1 might be different from KCNE3. Another weakness is that unbinding may be facilitated in the closed state, whereas they had to use the open channel for the MD simulation. Therefore, their MD simulations do not necessarily reflect the unbinding process in the closed state, which should occur in the comparable electrophysiological experiments. Nevertheless, the data are solid and well support their conclusions. This work should be valuable to the field, not only for future drug design but also for the biophysical understanding of the binding/unbinding of drugs to ion channel complexes.https://doi.org/10.7554/eLife.87038.3.sa1
The voltage-gated potassium channel KCNQ1/KCNE1 (IKs) plays important physiological functions, for instance in the repolarization phase of the cardiac action potential. Loss-of-function of KCNQ1/KCNE1 is linked to disease. Hence, KCNQ1/KCNE1 is a highlighted pharmacological target and mechanistic insights into how channel modulators enhance the function of the channel is of great interest. The authors have through several previous studies provided mechanistic insights into how small-molecule activators like ML277 act on KCNQ1. However, less is known about the binding site and mechanism of action of other type of channel activators, which require KCNE1 for their effect. In this study, Chan and co-workers use molecular dynamics approaches, mutagenesis and electrophysiology to propose an overall similar binding site for the KCNQ1/KCNE1 activators mefenamic acid and DIDS, located at the extracellular interface of KCNQ1 and KCNE1. The authors propose an induced-fit model for the binding site, which critically engages residues in the N-terminus of KCNE1. Moreover, the authors discuss possible mechanisms of action of how drug binding to this site may enhance channel function.
The authors address an important question, of broad relevance to researchers in the field. The manuscript is well written and the text easy to follow. A strength of the work is the parallel use of experimental and simulation approaches, which enables both functional testing and mechanistic predictions and interpretations. For instance, the authors have experimentally assessed the putative relevance of a large set of residues based on simulation predictions. A minor limitation is that not all residues of putative importance for drug binding/effects can be reliable evaluated in experiments, which is, however, clearly discussed by the authors and a challenge shared by electrophysiologists in the field.https://doi.org/10.7554/eLife.87038.3.sa2
The authors identified the mefenamic (Mef) binding site and DIDS binding site on the KCNQ1 KCNE1 complex. The authors also identified the mechanism of interactions using electrophysiological recording, calculating V1/2 of different mutants, and looking at the instantaneous and tail currents. The contribution of each residue within the binding pocket was analysed using GBSA and PBSA and traditional molecular dynamics simulation.
The manuscript has been substantially revised from the previous version with a greater depth of computational analysis.https://doi.org/10.7554/eLife.87038.3.sa3
The following is the authors’ response to the original reviews.
Reviewer #1 (Public Review):
Chan et al. tried identifying the binding sites or pockets for the KCNQ1-KCNE1 activator mefenamic acid. Because the KCNQ1-KCNE1 channel is responsible for cardiac repolarization, genetic impairment of either the KCNQ1 or KCNE1 gene can cause cardiac arrhythmia. Therefore, the development of activators without side effects is highly demanded. Because the binding of mefenamic acid requires both KCNQ1 and KCNE1 subunits, the authors performed drug docking simulation by using KCNQ1-KCNE3 structural model (because this is the only available KCNQ1-KCNE structure) with substitution of the extracellular five amino acids (R53-Y58) into D39-A44 of KCNE1. That could be a limitation of the work because the binding mode of KCNE1 might differ from that of KCNE3. Still, they successfully identified some critical amino acid residues, including W323 of KCNQ1 and K41 and A44 of KCNE1. They subsequently tested these identified amino acid residues by analyzing the point mutants and confirmed that they attenuated the effects of the activator. They also examined another activator, yet structurally different DIDS, and reported that DIDS and mefenamic acid share the binding pocket, and they concluded that the extracellular region composed of S1, S6, and KCNE1 is a generic binding pocket for the IKS activators.
The data are solid and well support their conclusions, although there are a few concerns regarding the choice of mutants for analysis and data presentation.
1. One of the limitations of this work is that they used psKCNE1 (mostly KCNE3), not real KCNE1, as written above. It is also noted that KCNQ1-KCNE3 is in the open state. Unbinding may be facilitated in the closed state, although evaluating that in the current work is difficult.
We agree that it is difficult to evaluate the role of unbinding from our model. Our data showing that longer interpulse intervals have a normalizing effect on the GV curve (Figure 3-figure supplement 2) could be interpreted to suggest that unbinding occurs in the closed state. Alternatively, the slowing of deactivation caused by S1-S6 interactions and facilitated by the activators may effectively be exceeded at the longer interpulse intervals.
1. According to Figure 2-figure supplement 2, some amino acid residues (S298 and A300) of the turret might be involved in the binding of mefenamic acid. On the other hand, Q147 showing a comparable delta G value to S298 and A300 was picked for mutant analysis. What are the criteria for the following electrophysiological study?
EP experiments interrogated selected residues with significant contributions to mefenamic acid and DIDs coordination as revealed by the MM/GBSA and MM/PBSA methods. A300 was identified as potentially important. We did attempt A300C but were never able to get adequate expression for analysis.
1. It is an interesting speculation that K41C and W323A stabilize the extracellular region of KCNE1 and might increase the binding efficacy of mefenamic acid. Is it also the case for DIDS? K41 may not be critical for DIDS, however.
Yes, we found K41 was not critical to the binding/action of DIDS compared to MEF. In electrophysiological experiments with the K41C mutation, DIDS induced a leftward GV shift (~ -25 mV) whereas the normalized response was statistically non-significant. In MD simulation studies, we observed detachment of DIDS from K41C-Iks only in 3 runs out of 8 simulations. This is in contrast to Mef, where the drug left the binding site of K41C-Iks complex in all simulations.
1. Same to #2, why was the pore turret (S298-A300) not examined in Figure 7?
Again, we attempted A300C but could not get high enough expression.
Reviewer #3 (Public Review):
1. The computational aspect of the work is rather under-sampled - Figure 2 and Figure 4. The lack of quantitative analysis on the molecular dynamic simulation studies is striking, as only a video of a single representative replica is being shown per mutant/drug. Given that the simulations shown in the video are extremely short; some video only lasts up to 80 ns. Could the author provide longer simulations in each simulation condition (at least to 500 ns or until a stable binding pose is obtained in case the ligand does not leave the binding site), at least with three replicates per each condition? If not able to extend the length of the simulations due to resources issue, then further quantitative analysis should be conducted to prove that all simulations are converged and are sufficient. Please see the rest of the quantitative analysis in other comments.
We provide more quantitative analysis for the existing MD simulations and ran five additional simulations with 500 ns duration by embedding the channel in a POPC lipid membrane. For the new MD simulations, we used a different force field in order to minimize ambiguity related to force fields as well. Analysis of these data has led to new data and supplemental figures regarding RMSD of ligands during the simulations (Figure 4-figure supplement 1 and Figure 6-figure supplement 3), clustering of MD trajectories based on Mef conformation (Figure 2-figure supplement 3 and Figure 6 -figure supplement 2), H-bond formation over the simulations (Figure 2-figure supplement 4 and Figure 6-figure supplement 1). We have edited the manuscript to include this new information where appropriate.
1. Given that the protein is a tetramer, at least 12 datasets could have been curated to improve the statistic. It was also unclear how frequently the frames from the simulations were taken in order to calculate the PBSA/GBSA.
By using one ligand for each ps-IKs channel complex we tried to keep the molecular system and corresponding analysis as simple as was possible. Our initial results have shown that 4D docking and subsequent MD simulations with only one ligand bound to ps-IKs was complicated enough. Our attempts to dock 4 ligands simultaneously and analyze the properties of such a system were ineffective due to difficulties in: (i) obtaining stable complexes during conformational sampling and 4D docking procedures, since the ligand interaction covers a region including three protein chains with dynamic properties, (ii) possible changes of receptor conformation properties at three other subunits when one ligand is already occupying its site, (iii) marked diversity of the binding poses of the ligand as cluster analysis of ligand-channels complex shows (Figure 2-figure supplement 3).We have added a line in the methods to clarify the use of only one ligand per channel complex in simulations.
In order to calculate MMPBSA/MMGBSA we used a frame every 0.3 ns throughout the 300 ns simulation (1000 frames/simulation) or during the time the ligand remained bound. We have clarified this in the Methods.
1. The lack of labels on several structures is rather unhelpful (Figure 2B, 2C, 4B). The lack of clarity of the interaction map in Figures 2D and 6A.
We updated figures considering the reviewer's comments and added labels. For 2D interaction maps, we provided additional information in figure legends to improve clarity.
1. The RMSF analysis is rather unclear and unlabelled thoroughly. In fact, I still don't quite understand why n = 3, given that the protein is a tetramer. If only one out of four were docked and studied, this rationale needs to be explained and accounted for in the manuscript.
The rationale of conducting MD simulations with one ligand bound to IKs is explained in response to point 2 of the reviewer’s comments.
RMSF analysis in Figure 4C-E was calculated using the chain to which Mef was docked but after Mef had left the binding site. Details were added to the methods.
1. For the condition that the ligands suppose to leave the site (K42C for Mef and Y46A for DIDS), can you please provide simulations at a sufficient length of time to show that ligand left the site over three replicates? Given that the protein is a tetramer, I would be expecting three replicates of data to have four data points from each subunit. I would be expecting distance calculation or RMSD of the ligand position in the binding site to be calculated either as a time series or as a distribution plot to show the difference between each mutant in the ligand stability within the binding pocket. I would expect all the videos to be translatable to certain quantitative measures.
We have shown in the manuscript that the MEF molecule detaches from the K41C/IKs channel complex in all three simulations (at 25 ns, 70 ns and 20 ns, Table. 4). Similarly, the ligand left the site in all five new 500 ns duration simulations. We did not provide simualtions for Y46A, but Y46C left the binding site in 4 of 5 500 ns simulations and changed binding pose in the other.
Difficulties encountered upon extending the docking and MD simulations for 4 receptor sites of the channel complex is discussed in our response to point # 2 of the reviewer.
1. Given that K41 (Mef) and Y46 are very important in the coordination, could you calculate the frequency at which such residues form hydrogen bonds with the drug in the binding site? Can you also calculate the occupancy or the frequency of contact that the residues are making to the ligand (close 4-angstrom proximity etc.) and show whether those agree with the ligand interaction map obtained from ICM pro in Figure 2D?
We thank the reviewer for the suggestion to analyze the H-bond contribution to ligand dynamics in the binding site. In the plots shown in Figure 2-figure supplement 4 and Figure 6-figure supplement 1, we now provide detailed information about the dynamics of the H-bond formation between the ligand and the channel-complex throughout simulations. In addition, we have quantified this and have added these numbers to a table (Table 2) and in the text of the results.
1. Given that the author claims that both molecules share the same binding site and the mode of ligand binding seems to be very dynamic, I would expect the authors to show the distribution of the position of ligand, or space, or volume occupied by the ligand throughout multiple repeats of simulations, over sufficient sampling time that both ligand samples the same conformational space in the binding pocket. This will prove the point in the discussion - Line 463-464. "We can imagine a dynamic complex... bind/unbind from Its at a high frequency".
To support our statement regarding a dynamic complex we analyzed longer MD simulations and clustered trajectories, from this an average conformation from each cluster was extracted and provided as supplementary information which shows the different binding modes for Mef (Figure 2-figure supplement 3). DIDS was more stable in MD simulations and though there were also several clusters, they were similar enough that when using the same cut-off distance as for mefenamic acid, they could be grouped into one cluster. (Note the scale differences on dendrogram between Figure 2-figure supplement 3 and Figure 6-figure supplement 2).
1. I would expect the authors to explain the significance and the importance of the PBSA/GBSA analysis as they are not reporting the same energy in several cases, especially K41 in Figure 2 - figure supplement 2. It was also questionable that Y46, which seems to have high binding energy, show no difference in the EPhys works in figure 3. These need to be commented on.
Several studies indicate that ΔG values calculated using MM/PBSA and MM/GBSA methods may vary. Some studies report marked differences and the reasons for such a discrepancy is thoroughly discussed in a review by Genheden and Ryde (PMID: 25835573). Therefore, we used both methods to be sure that key residues contributing to ligand binding identified with one method appear in the list of residues for which the calculations are done with the other method.
Y46C which showed only a slightly less favorable binding energy and did not unbind during 300 ns simulations, unbound, or changed pose in 4 out of 5 of the longer simulations in the presence of a lipid membrane (Figure 4-figure supplement 1). The discrepancy between electrophysiological and MD data is commented in the manuscript (pages 12-13).
1. Can the author prove that the PBSA/GBSA analysis yielded the same average free energy throughout the MD simulation? This should be the case when the simulations are converged. The author may takes the snapshots from the first ten ns, conduct the analysis and take the average, then 50, then 100, then 250 and 500 ns. The author then hopefully expects that as the simulations get longer, the system has reached equilibrium, and the free energy obtained per residue corresponds to the ensemble average.
As we mention in the manuscript, MEF- channel interactions are quite dynamic and vary even from simulation to simulation. The frequent change of the binding pose of the ligands observed during simulations (represented in Figure 2 - figure supplement 3 as clusters) is a clear reflection of such a dynamic process. Therefore, we do not expect the same average energy throughout the simulation but we do expect that ΔG values stands above the background for key residues, which was generally the case (Figure 2 - figure supplement 2 and Figure 6.)
1. The phrase "Lowest interaction free energy for residues in ps-KCNE1 and selected KCNQ1 domains are shown as enlarged panels (n=3 for each point)" needs further explanation. Is this from different frames? I would rather see this PBSA and GBSA calculated on every frame of the simulations, maybe at the one ns increment across 500 ns simulations, in 4 binding sites, in 3 replicas, and these are being plotted as the distribution instead of plotting the smallest number. Can you show each data point corresponding to n = 3?
The MMPBSA/MMGBSA was calculated for 1000 frames across 3x300 ns simulations with 0.3 ns sampling interval, together 3000 frames, shown in Figure 2-figure supplement 2 and includes error bars to show the differences across runs. We have updated the legend for greater clarity.
1. I cannot wrap my head around what you are trying to show in Figure 2B. This could be genuinely improved with better labelling. Can you explain whether this predicted binding pose for Mef in the figure is taken from the docking or from the last frame of the simulation? Given that the binding mode seems to be quite dynamic, a single snapshot might not be very helpful. I suggest a figure describing different modes of binding. Figure 2B should be combined with figure 2C as both are not very informative.
We have updated Figure 2B with better labelling and added a new figure showing the different modes of binding (Figure 2-figure supplement 3).
1. Similar to the comment above, but for Figure 4B. I do not understand the argument. If the author is trying to say that the pocket is closed after Mef is removed - then can you show, using MD simulation, that the pocket is openable in an apo to the state where Mef can bind? I am aware that the open pocket is generated through batches of structures through conformational sampling - but as the region is supposed to be disordered, can you show that there is a possibility of the allosteric or cryptic pocket being opened in the simulations? If not, can you show that the structure with the open pocket, when the ligand is removed, is capable of collapsing down to the structure similar to the cryo-EM structure? If none of the above work, the author might consider using PocketMiner tools to find an allosteric pocket (https://doi.org/10.1038/s41467-023-36699-3) and see a possibility that the pocket exists.
Please see Figure 4 – figure supplement 2 which depicts the binding pocket from the longest run we performed (1250 ns) before drug detachment (grey superimposed structures) and after (red superimposed structures). Mefenamic acid is represented as licorice and colored green. Snapshots for superimposition were collected every 10 ns. As can be seen in the figure, when the drug leaves the binding site (after 500 ns, structures colored red), the N-terminal residue of psKCNE1, W323, and other residues that form the pocket shift toward the binding site, overlapping with where Mefenamic acid once resided. The surface structure in Figure 4B shows this collapse.
In the manuscript, we propose that drug binding occurs by the mechanism that could be best described by induced fit models, which state that the formation of the firm complexes (channel-Mef complex) is a result of multiple-states conformational adjustments of the bimolecular interaction. These interactions do not necessarily need to have large interfaces at the initial phase. This seems to be the case in Mef with IKS interactions, since we could not identify a pocket of appropriate size either using PocketMiner software suggested by the reviewer or with PocketFinder tool of ICM-pro software.
1. Figure 4C - again, can you show the RMSF analysis of all four subunits leading to 12 data points? If it is too messy to plot, can you plot a mean with a standard deviation? I would say that a 1-1.5 angstroms increase in the RMSF is not a "markedly increased", as stated on line 280. I would also encourage the authors to label whether the RMSF is calculated from the backbone, side-chain or C-alpha atoms and, ideally, compare them to see where the dynamical properties are coming from.
Please see the answer to comment #4. We agree that the changes are not so dramatic and modified the text accordingly. RMSD was calculated for backbone atom to compare residues with different side chains, a note of this is now in the methods and statistical significance of ps-IKs vs K41C, W323A and Y46C is indicated in Figures 4C-4E.
1. In the discussion - Lines 464-467. "Slowed deactivation of the S1/KCNE1/Pore domain/drug complex... By stabilising the activated complex. MD simulation suggests the latter is most likely the case." Can you point out explicitly where this has been proven? If the drug really stabilised the activated complex, can you show which intermolecular interaction within E1/S1/Pore has the drug broken and re-form to strengthen the complex formation? The authors have not disproven the point on steric hindrance either. Can this be disproved by further quantitative analysis of existing unbiased equilibrium simulations?
The stabilization of S1/KCNE1/Pore by drugs does not necessarily have to involve a creation of new contacts between protein parts or breakage of interfaces between them. The stabilization of activated complexes by drugs may occur when the drug simultaneously binds to both moveable parts of the channel, such as voltage sensor(s) or upper KCNE1 region, and static region(s) of the channel, such as the pore domain. We have changed the corresponding text for better clarity.
1. Figure 4D - Can you show this RMSF analysis for all mutants you conducted in this study, such as Y46C? Can you explain the difference in F dynamics in the KCNE3 for both Figure 4C and 4D?
We now show the RMSF for K41C, W323A and Y46C in Figure 4C-E. We speculate that K41 (magenta) and W323 (yellow), given their location at the lipid interface (see Author response image 1), may be important stabilizing residues for the KCNE N-terminus, whereas Y46 (green) which is further down the TMD has less of an impact.
1. Line 477: the author suggested that K41 and Mef may stabilise the protein-protein interface at the external region of the channel complex. Can you prove that through the change in protein-protein interaction, contact is made over time on the existing MD trajectories, whether they are broken or formed? The interface from which residues help to form and stabilise the contact? If this is just a hypothesis for future study, then this has to be stated clearly.
It is known that crosslinking of several residues of external E1 with the external pore residues dramatically stabilizes voltage-sensors of KCNQ1/KCNE1 complex in the up-state conformation. This prevents movable protein regions in the voltage-sensors returning to their initial positions upon depolarization, locking the channel in an open state. We suggest that MEF may restrain the backward movement of voltage-sensors in a similar way that stabilizes open conformation of the channel. The stabilization of the voltage sensor domain through MEF occurs due to contacts of the drug with both static (pore domain) and dynamic protein parts (voltage-sensors and external KCNE1 regions). We have changed the corresponding part of the text.
1. The author stated on lines 305-307 that "DIDS is stabilised by its hydrophobic and vdW contacts with KCNQ1 and KCNE1 subunits as well as by two hydrogen bonds formed between the drug and ps-KCNE1 residue L42 and KCNQ1 residue Q147" Can you show, using H-bond analysis that these two hydrogen bonds really exist stably in the simulations? Can you show, using minimum distance analysis, that L42 are in the vdW radii stably and are making close contact throughout the simulations?
We performed a detailed H-bond analysis (Figure 6-supplement figure 1) which shows that DIDS forms multiple H-bond over the simulations, though only some of them (GLU43, TYR46, ILE47, SER298, TYR299, TRP323 ) are stable. Thus, the H-bonds that we observed in DIDS-docking experiments were unstable in MD simulations. As in the case of the IKs-MEF complex, the prevailing H-bonds exhibit marked quantitative variability from simulation to simulation. We have added a table detailing the most frequent H-bonds during MD simulations (Table 2).
1. Discussion - In line 417, the author stated that the "S1 appears to pull away from the pore" and supplemented the claim with the movie. This is insufficient. The author should demonstrate distance calculation between the S1 helix and the pore, in WT and mutants, with and without the drug. This could be shown as a time series or distribution of centre-of-mass distance over time.
We tried to analyze the distance changes between the upper S1 and the pore domain but failed to see a strong correlation We have removed this statement from the discussion.
1. Given that all the work were done in the open state channel with PIP2 bound (PDB entry: 6v01), could the author demonstrate, either using docking, or simulations, or alignment, or space-filling models - that the ligand, both DIDS and Mef, would not be able to fit in the binding site of a closed state channel (PDB entry: 6v00). This would help illustrate the point denoted Lines 464-467. "Slowed deactivation of the S1/KCNE1/Pore domain/drug complex... By stabilising the activated complex. MD simulation suggests the latter is most likely the case."
As of now, a structure representing the closed state of the channel does not exist. 6V00 is the closed inactivated state of the channel pore with voltage-sensors in the activated conformation. In order to create simulation conditions that reliably describe the electrophysiological experiments, at least a good model for closed channels with resting state voltage sensors is necessary.
1. The author stated that the binding pose changed in one run (lines 317 to 318). Can you comment on those changes? If the pose has changed - what has it changed to? Can you run longer simulations to see if it can reverse back to the initial confirmation? Or will it leave the site completely?
Longer simulations and trajectory clustering revealed several binding modes, where one pose dominated in approximately 50% of all simulations in Figure 2-figure supplement 3 encircled with a blue frame.
1. Binding free energy of -32 kcal/mol = -134 kJ/mol. If you try to do dG = -RTlnKd, your lnKd is -52. Your Kd is e^-52, which means it will never unbind if it exists. I am aware that this is the caveat with the methodologies. But maybe these should be highlighted throughout the manuscript.
We thank the reviewer for this comment. ΔΔG values, and corresponding Kd values, calculated from simulation of Mef-ps-IKs complex do not reflect the apparent Kd values determined in electrophysiological experiments, nor do they reflect Kd values of drug binding that could be determined in biochemical essays. Important measures are the changes observed in simulations of mutant channel complexes relative to wild type. We now briefly mention this issue in the manuscript.
Reviewer #1 (Recommendations For The Authors):
1. It would be nice to have labels of amino acid residues in Figure 2B.
We updated Figure 2B and added some residue labels.
1. Fig. 3A and 7A. In what order the current traces are presented? I don't see the rule.
We have now arranged the current traces in a more orderly manner, listing them first by ascending KCNE1 residue numbers and then by ascending KCNQ1 residue numbers. Now consistent with Fig 3 and 7 (normalized response and delta V1/2).
1. Line 312 "A44 and Y46 were more so." A44 may be more critical, but I can't see Y46 is more, according to Figure 2-figure supplement2 and Figure 6.
Indeed, comparison of the energy decomposition data indicates approximately the same ΔG values for Y46. We have revised this in the text correspondingly.
1. Line 267 "Mefenamic acid..." I would like to see the movie.
We no longer have access to this original movie
1. In supplemental movies 5-7, the side chains of some critical amino acid residues (W323, K41) would be better presented as in movies 1-4.
We have retained the original presentations of these movies as the original files are no longer available.
Reviewer #2 (Recommendations For The Authors):
1. To determine the effect of mefenamic acid and DIDS on channel closing kinetics, a protocol in which they step from an activating test pulse to a repolarizing tail pulse to -40 mV for 1 s is used. If I understand it right, the drug response is assessed as the difference in instantaneous tail current amplitude and the amplitude after 1 s (row 599-603). The drug response of each mutant is then normalized to the response of the WT channel. However, for several mutants there is barely any sign of current decay during this relatively brief pulse (1 s) at this specific voltage. To determine drug effects more reliably on channel closing kinetics/the extent of channel closing, I wonder if these protocols could be refined? For instance, to cover a larger set of voltages and consider longer timescales?
To clarify, the drug response of each mutant is not normalized to the response of the WT channel. In fact, our analysis is not meant to compare mutant and WT tail current decay but rather how isochronal tail current decay is changed in response to drug treatment in each channel construct. As acknowledged by the reviewer, the peak to end difference currents were calculated by subtracting the minimum amplitude of the deactivating current from the peak amplitude of the deactivating current. But the difference current in mefenamic acid or DIDS was normalized to the maximum control (in the absence of drug) difference current and subtracted from 1.0 to obtain the normalized response. Thus, the difference in tail current decay in the absence and in the presence of drug is measured within the same time scale and allow a direct comparison between before and after drug treatment. As shown in Fig 3D and 7C, a large drug response such as the one measured in WT channels is reflected by a value close to 1. A smaller drug response is indicated by low values. We recognize that some mutations resulted in an intrinsic inhibition of tail current decay in the absence of drug, which potentially lead to underestimating the normalized response value.Our goal was not to study in detail the effects of the drug on channel closing kinetics, but only to determine the impact of the mutation on drug binding by using tail current decay as a readout. Consequently, we believe that the duration of the deactivating tail current used in this experiment was sufficient to detect drug-induced tail current decay inhibition.
1. The effect of mefenamic acid seems to be highly dependent on the pulse-to-pulse interval in the experiments. For instance, for WT in Figure 3 - Figure supplement 1, a 15 s pulse-to-pulse interval provides a -100 mV shift in V1/2 induced by mefenamic acid, whereas there is no shift induced when using a 30 s pulse-to-pulse interval. Can the authors explain why they generally consider a 15 s pulse-to-pulse interval more suitable (physiologically relevant?) in their experiments to assess drug effects?
In our previous experiments, we have determined that a 15 s inter-pulse interval is generally adequate for the WT IKs channels to fully deactivate before the onset of the next pulse. Consistent with our previous work (Wang et al. 2019), we observed that in wild-type EQ channels, there is no current summation from one pulse to the next one (see Fig 1A, bottom panel). This is important as the IKs channel complex is known to be frequency dependent i.e. current amplitude increases as the inter-pulse interval gets shorter. Such current summation results in a leftward shift of the conductance-voltage (GV) relationship. This is also important with regards to drug effects. As indicated by the reviewer, mefenamic acid effects are prominent with a 15 sec inter-pulse interval but less so with a 30 sec inter-pulse interval when enough time is given for channels to more completely deactivate. Full effects of mefenamic acid would have therefore been concealed with a 30sec inter-pulse interval.
Moreover, our patch-clamp recordings aim to explore the distinct responses of mutant channels to mefenamic acid and DIDS in comparison to the wild-type channel. It is important to note that the inter-pulse interval's physiological relevance is not necessarily crucial in this context.
1. Related to comment 1 and 2, there is a large diversity in the intrinsic properties of tested mutants. For instance, V1/2 ranges from 4 to 70 mV. Also, there is large variability in the slope of the G-V curves. Whether channel closing kinetics, or the impact of pulse-to-pulse interval, vary among mutants is not clear. Could the authors please discuss whether the intrinsic properties of mutants may affect their ability to respond to mefenamic acid and DIDS? Also, please provide representative current families and G-V curves for all assessed mutants in supplementary figures.
The intrinsic properties of some mutants vary from the WT channels and influence their responsiveness to mefenamic acid and DIDS. The impact of the mutations on the IKs channel complex are reflected by changes in V1/2 (Table 1, 4) and tail current decay (Figs. 3, 7). But, it is the examination of the drug effects on these intrinsic properties (i.e. GV curve and tail current decay) that constitutes the primary endpoint of our study. We consider that the degree by which mef and DIDS modify these intrinsic properties reflects their ability to bind or not to the mutated channel. In our analysis, we compared each mutant's response to mefenamic acid and DIDS with its respective control. Consequently, the intrinsic properties of the mutant channels have already been considered in our evaluation. As requested, we have provided representative current families and G-V curves for all assessed mutants in Figure 3-figure supplement 1 and Figure 7-figure supplement 1.
1. The A44C and Y148C mutants give strikingly different currents in the examples shown in Figure 3 and Figure 7. What is the reason for this? In the examples in figure 7, it almost looks like KCNE1 is absent. Although linked constructs are used, is there any indication that KCNE1 is not co-assembled properly with KCNQ1 in those examples?
The size of the current is critical to determining its shape, as during the test pulse there is some endogenous current mixed in which impacts shape. A44C and Y148C currents shown in Figure 7 are smaller with a larger contribution of the endogenous current, mostly at the foot of the current trace. In our experience there is little endogenous current in the tail current at -40 mV and for this reason we focus our measurements there.
Although constructs with tethered KCNQ1 and KCNE1 were used, we cannot rule out the possibility that Q1 and E1 interaction was altered by some of the mutations. Several KCNE1 and KCNQ1 residues have been identified as points of contact between the two subunits. For instance, the KCNE1 loop (position 36-47) has been shown to interact with the KCNQ1 S1-S2 linker (position 140-148) (Wang et al, 2011). Thus, it is conceivable that mutation of one or several of those residues may alter KCNQ1/KCNE1 interaction and modify the activation/deactivation kinetics of the IKs channel complex.
1. I had a hard time following the details of the simulation approaches used. If not already stated (I could not find it), please provide: (i) details on whether the whole channel protein was considered for 4D docking or a docking box was specified, (ii) information on how simulations with mutant ps-IKs were prepared (for instance with the K41C mutant), especially whether the in silico mutated channel was allowed to relax before evaluation (and for how long). Also, please make sure that information on simulation time and number of repeats are provided in the Methods section.
For 4D docking, only residues within 0.8 nm of psKCNE1 residues D39-A44 were selected. Complexes with mutated residues were relaxed using the same protocol as the WT channel, (equilibration with gradually releasing restraints with a final equilibration for 10 ns where only the backbone was constrained with 50 kcal/mol/nm2). We have updated the methods accordingly.
In figure legends, please provide information on whether data represents mean +/- SD or SEM. Also, please provide information on which statistical test was used in each figure.
We revised the figure legend to add the nature of the statistical test used.
G-V curves are normalized between 0 and 1. However, for many mutants the G-V relationship does not reach saturation at depolarized voltages. Does this affect the estimated V1/2? I could not really tell as I was not sure how V1/2 was determined for different mutants (could the explanation on row 595-598 be clarified)?
The primary focus here is in the shift between the control response and drug response for each mutant, rather than the absolute V1/2 values. The isochronal G-V curves that are generated for each construct (WT and mutant) utilize an identical voltage protocol. This approach ensures a uniform comparison among all mutants. By observing the shifts in these curves, we can gain insight into the response of mutant channels to the drug. This information ultimately helps elucidate the inherent properties of the mutant channels and contributes to our understanding of the drug's binding mechanism to the channel.
As requested by the reviewer, we also clarified the way V1/2 was generated: When the G-V curve did not reach zero, the V1/2 value was directly read from the plot at the voltage point where the curve crossed the 0.5 value on the y coordinate.
A general comment is that the Discussion is fairly long and some sections are quite redundant to the Results section. The authors could consider focusing the text in the Discussion.
We changed the discussion correspondingly wherever it was appropriate.
I found it a bit hard to follow the authors interpretation on whether their drug molecules remain bound throughout the experiments, or whether there is fast binding/unbinding. Please clarify if possible.
In the 300 ns MD simulations mefenamic acid and DIDS remained stably bound to WT-ps-IKS, binding of drugs to mutant complexes are described in the Table 3 and Table 5. In longer simulations with the channel embedded in a lipid environment, mefenamic acid unbinds in two out of five runs for WT-ps-IKs (Figure 4 – figure supplement 1), and DIDS shows a few events where it briefly unbinds (Figure 6 -figure supplement 3). Based on electrophysiological data we speculate that drugs might bind and unbind to WT-ps-IKs during the gating process. We do not see bind-unbinding in MD simulations, since the model we used in simulations reflects only open conformation of the channel-complex with an activated-state voltage-sensor, whereas a resting-state voltage sensor condition was not considered.
The authors have previously shown that channels with no, one or two KCNE1 subunits are not, or only to a small extent, affected by mefenamic acid (Wang et al., 2020). Could the details of the binding site and proposed mechanisms of action provide clues as to why all binding sites need to be occupied to give prominent drug effects?
In the manuscript, we propose that the binding of drugs induces conformational changes in the pocket region that stabilize S1/KCNE1/Pore complex. In the tetrameric channel with 4:4 alpha to beta stoichiometry the drugs are likely to occupy all four sites with complete stabilization of S1/KCNE1/Pore. When one or more KCNE1 subunits is absent, as in case of EQQ, or EQQQQ constructs, drugs will bind to the site(s) where KCNE1 is available. This will lead to stabilization of the only certain part of the S1/KCNE1/Pore complex. We believe that the corresponding effect of the drug, in this case will be partially effective.
There is a bit of jumping in the order of when some figures are introduced (e.g. row 178 and 239). The authors could consider changing the order to make the figures easier to follow.
We have changed the corresponding section appropriately to improve the reading flow.
Row 237: "Data not shown", please show data.
The G-V curve of the KCNE1 Y46C mutant displays a complex, double Boltzmann relationship which does not allow for the calculation of a meaningful V1/2 nor would it allow for an accurate determination of drug effects. Consequently, we have excluded it from the manuscript.
In the Discussion, the author use the term "KCNE1/3". Does this correspond to the previous mention of "ps-KCNE1"?
Yes, this refers to ps-KCNE1. We have changed it correspondingly.
Row 576: When was HMR 1556 used?
While HMR 1556 was used in preliminary experiments to confirm that the recorded current was indeed IKs, it does not provide substantial value to the data presented in our study or our experiments. As a result, we have excluded HMR 1556 experiments from the final results and have revised the Methods section accordingly.
Reviewer #3 (Recommendations For The Authors):
1. Figures 2D and 6A are very unclear. Can the authors provide labels as text rather than coloured circles, whether the residue is on Q1 or E1? There is also a distance label in the figure in the small font with the faintest shade of grey, which I believe is supposed to be hydrogen bonds. Can this be improved for clarity?
We feel that additional labels on the ligand diagrams to be more confusing, instead, we updated the description in the legend and added labels to Figure 2B and Figure 6B to improve the clarity of residue positions. In addition, we have added 2 new figures with more detailed information about H-bonds (Figure 2-figure supplement 4, Figure 6- figure supplement 1).
1. Figure 2B - all side chains need labelling in different binding modes. The green ligand on blue protein is very difficult to see. Suddenly, the ligand turns light blue in panel 2C. Can this be consistent throughout the manuscript?
Figure 2B is updated according to this comment.
1. Figure 2 - figure supplement 2, and figure 6B. Can the author show the residue number on the x-axis instead of just the one-letter abbreviation? This requires the reader to count and is not helpful when we try to figure out where the residue is at a glance. I would suggest a structure label adjacent to the plot to show whether they are located with respect to the drug molecule.
Since the numbers for residues on either end of the cluster are indicated at the bottom of each boxed section, we feel that adding residue numbers would just further clutter the figure.
1. Figure 2 - figure supplement 2, and Figure 6B. Can you explain what is being shown in the error bar? I assume standard deviation?
Error bars on Figure 2-figure supplement 2 represent SEM. We added corresponding text in the figure legend.
1. Figure 2 - figure supplement 2, and figure 6B. Can you explain how many frames are being accounted for in this PBSA calculation?
For Figure 2- figure supplement 2 and Figure 6B a frame was made every 0.3 ns over 3x300 ns simulation, 1000 frames for each simulation, 3000 frames overall.
1. Figure 3D/E and 7C/D, it would be helpful to show which mutant show agreeable results with the simulations, PBSA/GBSA and contact analyses as suggested above.
The inconsistencies and discrepancies between the results of MD simulations and electrophysiological experiments are discussed throughout the manuscript.
1. Figure legend, figure 3E - I assume that there is a type that is different mutants with respect to those without the drug. Otherwise, how could WT, with respect to WT, has -105 mV dV1/2?
The reviewer is correct in that the bars indicate the difference in V1/2 between control and drug treatment. Thus, the difference in V1/2 (∆V1/2) between the V1/2 calculated for WT control and the V1/2 for mefenamic acid is indeed -105 mV. We have now revised Figure 3E's legend to accurately reflect this and ensure a clear understanding of the data presented.
1. Figure 3 - figure supplement 1B is very messy, and I could not extract the key point from it. Can this be plotted on a separate trace? At least 1 WT trace and one mutant trace, 1 with WT+drug and one mut+drug as four separate plots for clarity?
The key message of this figure is to illustrate the similarities of EQ WT + Mef and EQ L142C data. Thus, after thorough consideration, we have concluded that maintaining the current figure, which displays the progressive G-V curve shift in EQ WT and L142C in a superimposed manner, best illustrates the gradual shift in the G-V curves. This presentation allows for a clearer and more immediate comparison of the curve shifts, which may be more challenging to discern if the G-V curves were separated into individual figures. We believe that the existing format effectively communicates the relevant information in a comprehensive and accessible manner.
1. Figure 4B - the label Voltage is blended into the orange helix. Can the label be placed more neatly?
We altered the labels for this figure and added that information in the figure description.
1. Can you show the numerical label of the residue, at least only to the KCNE1 portion in Figures 4C and 4D?
We updated these figures and added residue numbering for clarity.
1. Can you hide all non-polar hydrogen atoms in figure 8 and colour each subunit so that it agrees with the rest of the manuscripts? Can you adjust the position of the side chain so that it is interpretable? Can you summarise this as a cartoon? For example, Q147 and Y148 are in grey and are very far hidden away. So as S298. Can you colour-code your label? The methionine (I assume M45) next to T327 is shown as the stick and is unlabelled. Maybe set the orthoscopic view, increase the lighting and rotate the figures in a more interpretable fashion?
We agree that Fig.8 is rather small as originally presented. We have tried to emphasize those residues we feel most critical to the study and inevitably that leads to de-emphasis of other, less important residues. As long as the figure is reproduced at sufficient size we feel that it has sufficient clarity for the purposes of the Discussion.
1. Line 538-539. Can you provide more detail on how the extracellular residues of KCNE3 are substituted? Did you use Modeller, SwissModel, or AlphaFold to substitute this region of the KCNEs?
We used ICM-pro to substitute extracellular residues of KCNE3 and create mutant variants of the Iks channel. This information is provided in the methods section now.
1. Line 551: The PIP2 density was solved using cryo-EM, not X-ray crystallography.
We corrected this.
1. Line 555: The system was equilibrated for ten ns. In which ensemble? Was there any restraint applied during the equilibration run? If yes, at what force constant?
The system was equilibrated in NVT and NPT ensembles with restraints. These details are added to methods. In the new simulations, we did equilibrations gradually releasing spatial from the backbone, sidechains, lipids, and ligands. A final 30 ns equilibration in the NPT ensemble was performed with restraint only for backbone atoms with a force constant of 50 kJ/mol/nm2. Methods were edited accordingly.
1. Line 557: Kelvin is a unit without a degree.
1. Line 559: PME is an electrostatic algorithm, not a method.
1. Line 566: Collecting 1000 snapshots at which intervals. Given your run are not equal in length, how can you ensure that these are representative snapshots?
Please see comment #5.
1. Table 3 - Why SD for computational data and SEM for experimental data?
There was no particular reason for using SD in some graphs. We used appropriate statistical tests to compare the groups where the difference was not obvious.https://doi.org/10.7554/eLife.87038.3.sa4
- David Fedida
- David Fedida
- David Fedida
- Vitya Vardanyan
- Vitya Vardanyan
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
We thank Fariba Ataei for her assistance in cell culture and for making mutants. This research was funded by Natural Sciences and Engineering Research Council of Canada (grant #RGPIN-2022–03021), Canadian Institutes of Health Research (#PJT-175024) and Heart and Stroke Foundation of Canada (#G-21–0031566) grants to DF, and grants from the Volkswagen Foundation (#AZ86659 and AZ 92111) to VV. MC holds an NSERC CGS-M scholarship. YW holds a CIHR– Vanier CGS scholarship.
- Merritt Maduke, Stanford University, United States
- Stephan A Pless, University of Copenhagen, Denmark
You can cite all versions using the DOI https://doi.org/10.7554/eLife.87038. This DOI represents all versions, and will always resolve to the latest one.
© 2023, Chan, Sahakyan, Eldstrom et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Pancreatic a-cells secrete glucagon, an insulin counter-regulatory peptide hormone critical for the maintenance of glucose homeostasis. Investigation of the function of human a-cells remains a challenge due to the lack of cost-effective purification methods to isolate high-quality a-cells from islets. Here, we use the reaction-based probe diacetylated Zinpyr1 (DA-ZP1) to introduce a novel and simple method for enriching live a-cells from dissociated human islet cells with ~ 95% purity. The a-cells, confirmed by sorting and immunostaining for glucagon, were cultured up to 10 days to form a-pseudoislets. The a-pseudoislets could be maintained in culture without significant loss of viability, and responded to glucose challenge by secreting appropriate levels of glucagon. RNA-sequencing analyses (RNA-seq) revealed that expression levels of key a-cell identity genes were sustained in culture while some of the genes such as DLK1, GSN, SMIM24 were altered in a-pseudoislets in a time-dependent manner. In conclusion, we report a method to sort human primary a-cells with high purity that can be used for downstream analyses such as functional and transcriptional studies.
Eukaryotic cells control inorganic phosphate to balance its role as essential macronutrient with its negative bioenergetic impact on reactions liberating phosphate. Phosphate homeostasis depends on the conserved INPHORS signaling pathway that utilizes inositol pyrophosphates and SPX receptor domains. Since cells synthesize various inositol pyrophosphates and SPX domains bind them promiscuously, it is unclear whether a specific inositol pyrophosphate regulates SPX domains in vivo, or whether multiple inositol pyrophosphates act as a pool. In contrast to previous models, which postulated that phosphate starvation is signaled by increased production of the inositol pyrophosphate 1-IP7, we now show that the levels of all detectable inositol pyrophosphates of yeast, 1-IP7, 5-IP7, and 1,5-IP8, strongly decline upon phosphate starvation. Among these, specifically the decline of 1,5-IP8 triggers the transcriptional phosphate starvation response, the PHO pathway. 1,5-IP8 inactivates the cyclin-dependent kinase inhibitor Pho81 through its SPX domain. This stimulates the cyclin-dependent kinase Pho85-Pho80 to phosphorylate the transcription factor Pho4 and repress the PHO pathway. Combining our results with observations from other systems, we propose a unified model where 1,5-IP8 signals cytosolic phosphate abundance to SPX proteins in fungi, plants, and mammals. Its absence triggers starvation responses.