The auditory cortex sends massive projections to the inferior colliculus, but the organization of this pathway is not yet well understood. Previous work has shown that the corticocollicular projection emanates from both layers 5 and 6 of the auditory cortex and that neurons in these layers have different morphological and physiological properties. It is not yet known in the mouse if both layer 5 and layer 6 project bilaterally, nor is it known if the projection patterns differ based on projection location. Using targeted injections of Fluorogold into either the lateral cortex or dorsal cortex of the inferior colliculus, we quantified retrogradely labeled neurons in both the left and right lemniscal regions of the auditory cortex, as delineated using parvalbumin immunostaining. After dorsal cortex injections, we observed that approximately 18–20% of labeled cells were in layer 6 and that this proportion was similar bilaterally. After lateral cortex injections, only ipsilateral cells were observed in the auditory cortex, and they were found in both layer 5 and layer 6. The ratio of layer 5:layer 6 cells after lateral cortex injection was similar to that seen after dorsal cortex injection. Finally, injections of different tracers were made into the two inferior colliculi, and an average of 15–17% of cells in the auditory cortex were double-labeled, and these proportions were similar in layers 5 and 6. These data suggest that (1) only the dorsal cortex of the inferior colliculus receives bilateral projections from the auditory cortex, (2) both the dorsal and lateral cortex of the inferior colliculus receive similar layer 5 and layer 6 auditory cortical input, and (3) a subpopulation of individual neurons in both layers 5 and 6 branch to innervate both dorsal cortices of the inferior colliculus. The auditory corticocollicular system consists of a large set of descending projections from the auditory cortex (AC) to the inferior colliculus (IC), which is the midbrain integration center . The proThe corticocollicular system is one of multiple auditory corticofugal pathways that have cascading connectivity to ultimately influence auditory processing at the level of the auditory periphery . AlthougDespite the anatomical differences between layer 5 and layer 6 projections to the IC, many questions remain about the functional organization of this projection. For example, it is unknown in the mouse if both layer 5 and layer 6 project to both the DC and LC of the IC, and if so, if they project in equal proportions. In addition, it is unknown if both layer 5 and layer 6 project to both ICs in the mouse, and if so, if individual neurons branch to project to both sides. Therefore, in the current study, we injected a sensitive retrograde tracer, Fluorogold, unilaterally into either the DC or the LC of the mouse and determined the extent to which the projection was in the ipsi- vs. contralateral lemniscal AC fields. To determine if the bilaterality of the projection was due to individual neurons that branch to both ICs, or comprise separate ipsi- and contralateral projections, different tracers were placed in each IC, and examination was done for double-labeled cells in the AC. We found that while both LC and DC received layer 5 and layer 6 AC inputs in similar proportions, only the DC received bilateral inputs from the AC. In addition, we observed that after injections of different tracers to the two different ICs, double-labeled cells were observed in both layers 5 and 6, suggesting that a subset of cells from each layer branch to innervate each DC.Experiments were performed in adult CBA/CaJ  or Swiss Webster  mice of both sexes ranging from ages 4 to 6 months. Swiss Webster mice were only used for the three injections involving red retrobeads. Thirteen mice were used in the quantitative analysis of this study, and their data are summarized in Sterile instruments and aseptic techniques were used for all surgical procedures. Mice were anesthetized intraperitoneally with a mixture of ketamine hydrochloride (100 mg/kg) and xylazine (3 mg/kg) and acepromazine (3 mg/kg). The mouse was placed into a Kopf Model 940 Small Animal Stereotaxic Instrument with digital readout. The head was shaved and disinfected with Povidone–iodide and 70% ethanol. An incision was made in the scalp and the surrounding skin was injected with lidocaine  intradermally as local anesthetic and carprofen  was given subcutaneously for post-operative pain management. Moisture Eyes ophthalmic ointment was applied to each eye to protect the cornea from drying. A small craniotomy was made over the IC using a surgical drill, and a small glass micropipette (tip size approximately 10 microns) was filled with Fluorogold , 1% in phosphate-buffered saline (PBS) for unilateral injections, and 0.1 acetate buffer (pH 3.3) for the bilateral injections. The left side was chosen for consistency, given left–right differences that have been described in the mouse auditory system . For bilFollowing a 7-day survival period, animals were anesthetized with overdose of ketamine and xylazine  and perfused transcardially with 4% paraformaldehyde (PFA) in PBS at pH 7.4. Brains were removed and post fixed overnight in the PFA solution. After being cryoprotected in an ascending series of sucrose solutions, each brain was embedded and cut into 40–50-μm-thick coronal sections on a cryostat that were collected serially in two sets.Parvalbumin (PV) immunostaining was done to delineate the borders of the lemniscal regions of the AC [primary AC and anterior auditory field ]. Sectiox–y plane of the IC. The stacks were collapsed into 2D maximum intensity projections and tiled into a single image using LAS X software. ImageJ software was used to adjust the color balance and to draw masks around the edge of the tissue to remove the embedding medium.Sections were imaged with a Leica SP8 laser scanning confocal microscope and LAS X control software or with an Olympus IX71 inverted epifluorescence microscope. For confocal images, each IC tissue section containing retrograde label, 40 × mosaic Z-stacks were taken throughout the entire depth and n = 4 in each injection location for unilateral injections, n = 5 for bilateral tracer injections), normality was not assumed and non-parametric statistics were used throughout with a threshold for significance of p < 0.05. Data are presented as median ± standard deviation.PV immunostaining was used to mark the borders of the lemniscal portions of the AC. For counting Fluorogold-labeled cells in the AC, neurons from six non-consecutive sections were selected. These sections were distributed across the anterior–posterior dimension of the AC. The labeled cells in AC within the PV-enriched zone were counted using ImageJ software. For counting double-labeled cells, to ensure that areas of maximum overlap were examined, only sections where at least 20 cells per tracer type were present were analyzed. Given the small numbers of animals (Thirteen adult (4–6 months old) male and female mice were used in this study. Four were injected with Fluorogold into the left LC, four were injected into the left DC, and five were injected into both ICs. For both DC and LC injection sites, there tended to be some spillover into the central nucleus of the IC (CNIC). See n = 4), there were significantly greater numbers of retrogradely labeled cells in the ipsilateral cortex , with a median ipsilateral:contralateral ratio = 6.63 ± 2.18, indicating that approximately 85% of all labeled cells were ipsilateral. After injection into the LC (n = 4), 100% of all labeled cells in all animals were found in the ipsilateral AC.The total number of cells in the lemniscal regions of the ipsilateral and contralateral AC were counted and compared. In all cases of DC injections .After DC injection, the proportions of layer 5 vs. layer 6 corticocollicular cells in the lemniscal AC were compared using a within-animal comparison. Although the total number of cells was significantly greater on the ipsilateral side, there was no difference in the proportion of layer 6 cells on the two sides  only the DC receives bilateral projections from the AC, (2) both the DC and LC receive similar proportions of layer 5 and layer 6 auditory cortical input, and (3) a subset of individual neurons in both layers 5 and 6 branch to innervate the DC bilaterally. These findings are summarized in the model shown in in vivo mapping of auditory fields prior to the injection of tracers may be helpful to determine if differences exist in the layer 5 vs. 6 projections from primary AC or anterior auditory field to the IC, or in the non-lemniscal regions of the auditory cortex.Cortical areas in this study were defined using PV immunostaining, which has been established to distinguish lemniscal auditory areas (primary AC and anterior auditory field) from non-lemniscal areas . Thus, wThe proportion of cells found to be double-labeled after injection of different tracers into different sides was found to be relatively low (range = 2.6–34.2%). Previous work has shown that this approach is susceptible to significant undercounting . For exaThe results of this study suggest that both the DC and LC receive similar proportions of layer 5 and layer 6 input, but that only DC receives input from the contralateral AC. These results differ somewhat from those of We also observed that a small proportion of cells branched to innervate both ICs (15.7% in layer 5 and 16.6% in layer 6). These values are higher than those seen in the guinea pig layer 5 [range = 2.5–11.9% ]. HoweveThe implications of having bilateral projections from AC to portions of the IC are not yet known. The IC receives bilateral projections from the auditory brainstem , 2008 anIn this study, we observed that lemniscal regions of the AC send bilateral projections from layers 5 and 6 to the DC of both inferior colliculi, with the majority being ipsilateral. We also observed that the LC receives only an ipsilateral projection from the AC and that this projection is derived from both layers 5 and 6. The proportion of layer 6 cells projecting to the IC is approximately 18–20% and does not differ based on IC target. Finally, we observed that the bilateral projection to the DC comprises, at least in part, individual neurons in both layers 5 and 6 that branch to innervate both DCs. Understanding the implications of these findings requires further investigation but may relate to the suspected roles of corticofugal projections in rapid acoustic escape behaviors.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.The animal study was reviewed and approved by Illinois Institutional Animal Care and Use Committee (IACUC).NV, MD, and DL analyzed the data and co-wrote the manuscript. BI performed stereotactic injections. GX and YS processed the tissue sections. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Solitary fibrous tumor (SFT) is a soft tissue tumor derived from mesenchymal cells. We report a case of a giant SFT with insulin-like growth factor 2 (IGF-2) production in the pelvis of an 85-year-old male. SFT was diagnosed in surgery for a complaint of left lower abdominal distension. Subsequent tumor recurrence and progression caused rectal passage obstruction and hypoglycemia. Low-dose radiotherapy of 15 Gy in five fractions was started five years and four months after surgery, initially for a huge tumor around the rectum to improve rectal passage obstruction. The tumor volume shrank from 1054 cc before irradiation to 449 cc at one month and 396 cc at 10 months after irradiation. He had reached 90 years old at that time. Two months after the initial irradiation, similar radiotherapy of 15 Gy in five fractions was performed for a huge tumor in the right abdominal cavity. This tumor decreased from 1874 cc before irradiation to 615 cc at two months and 556 cc at seven months after irradiation. Dexamethasone (2.5 mg) was used for paraneoplastic syndrome at the time of initial radiation but was then reduced and became unnecessary two months after the second irradiation. Acute and late adverse events were mild. The patient is alive 60 months after the first irradiation. This case suggests that low-dose radiotherapy is beneficial as palliative therapy for symptom relief in patients with SFT. Solitary fibrous tumor (SFT) is a rare soft tissue tumor derived from mesenchymal cells  that occThe patient was an 85-year-old male with a complaint of left lower abdominal distension. An examination revealed a mass lesion in the abdomen, which was diagnosed as SFT by surgery. At two years and eight months after surgery, there was a recurrence in the left paracolonic groove. The patient was followed up without treatment, but additional recurrence appeared on the right side of the bladder. Hypoglycemic symptoms also recurred at this time. The cause of hypoglycemia was thought to be paraneoplastic syndrome due to SFT, and dexamethasone was started.Due to tumor progression, rectal passage obstruction and uncontrollable hypoglycemia appeared, and radiotherapy was started at five years and four months after surgery. He had reached 90 years old at that time. Computed tomography (CT) showed a huge mass in the pelvis and right abdominal cavity, with rectal stenosis and obstruction of passage due to the tumor. It was difficult to use radiotherapy simultaneously for all lesions; thus, we decided to focus on the site of obstruction. Therefore, radiotherapy was initially performed on the huge tumor around the rectum to improve rectal passage obstruction Figure . Given tMoreover, rectal passage obstruction was resolved. Two months after the initial irradiation, radiotherapy of 15 Gy in five fractions was performed for a huge tumor in the right abdominal cavity. The volume of this tumor decreased from 1874 cc before irradiation to 615 cc at two months after irradiation and to 556 cc at seven months Figure .At the start of the first radiotherapy, 2.5 mg of dexamethasone was used for paraneoplastic syndrome. It was possible to reduce this dose gradually after the first irradiation, and dexamethasone became unnecessary two months after the second irradiation. Acute and late adverse events were mild (only grade 1 radiation enteritis occurred), and none of grade 2 or higher occurred during and after the courses of radiotherapy. The patient is still alive 60 months after the first irradiation. In the pathological findings of the first surgery Figure , hematoxHypoglycemia due to IGF-2 production from SFT is referred to as Doege-Potter syndrome . SurgicaThere are only a few reports of radiotherapy for SFT. These include a 74-year-old female with a tumor of 11 cm maximum diameter who was treated with radiotherapy of 50 Gy in 25 fractions and survived for 12 months with tumor shrinkage of 36% , a 43-yeOur case involved a huge tumor in a 90-year-old super-elderly patient, and it was difficult to administer radiotherapy exceeding 50 Gy, as used in the above reports. As far as we are aware, there is no report of such good tumor shrinkage and improvement of hypoglycemic symptoms at a low dose of about 15 Gy in five fractions. Low-dose irradiation has advantages of a low risk of adverse events and a short irradiation period of only one week, compared to the usual period of five to six weeks for 50-60 Gy irradiation . In addiIn conclusion, we experienced a case in which low-dose radiotherapy resulted in a marked reduction of the tumor and improvement of hypoglycemia in a case of SFT with IGF-2 production. This case suggests that low-dose radiotherapy should be considered as palliative therapy for symptom relief in patients with SFT.


V1.1. Both VSDs displayed the typical features of the sliding helix model; however, they greatly differed in ion-pair formation of the outer gating charges. Specifically, stabilization of the activated state enhanced the voltage dependence of activation, while stabilization of resting states slowed the kinetics. This mechanism provides a mechanistic model explaining how specific ion-pair formation in separate VSDs can realize the characteristic gating properties of voltage-gated cation channels.Voltage-gated calcium channels control key functions of excitable cells, like synaptic transmission in neurons and the contraction of heart and skeletal muscles. To accomplish such diverse functions, different calcium channels activate at different voltages and with distinct kinetics. To identify the molecular mechanisms governing specific voltage sensing properties, we combined structure modeling, mutagenesis, and electrophysiology to analyze the structures, free energy, and transition kinetics of the activated and resting states of two functionally distinct voltage sensing domains (VSDs) of the eukaryotic calcium channel Ca Communication in our body runs on electricity. Between the exterior and interior of every living cell, there is a difference in electrical charge, or voltage. Rapid changes in this so-called membrane potential activate vital biological processes, ranging from muscle contraction to communication between nerve cells.Ion channels are cellular structures that maintain membrane potential and help ‘excitable’ cells like nerve and muscle cells produce electrical impulses. They are specialized proteins that form highly specific conduction pores in the cell surface. When open, these channels let charged particles  through, rapidly altering the electrical potential between the inside and outside the cell.To ensure proper control over this process, most ion channels open in response to specific stimuli, which is known as ‘gating’. For example, voltage-gated calcium channels contain charge-sensing domains that change shape and allow the channel to open once the membrane potential reaches a certain threshold. These channels play important roles in many tissues and, when mutated, can cause severe brain or muscle disease.Although the basic principle of voltage gating is well-known, the properties of individual voltage-gated calcium channels still vary. Different family members open at different voltage levels and at different speeds. Such fine-tuning is thought to be key to their diverse roles in various parts of the body, but the underlying mechanisms are still poorly understood. Here, Fernández-Quintero, El Ghaleb et al. set out to determine how this variation is achieved.V1.1. The simulation was then used to predict the movements of the voltage sensing regions while the channel opened.The first step was to create a dynamic computer simulation showing the detailed structure of a mammalian voltage-gated calcium channel, called CaThe computer modelling experiments showed that although the voltage sensors looked superficially similar, they acted differently. The first of the four voltage sensors of the studied calcium channel controlled opening speed. This was driven by shifts in its configuration that caused oppositely charged parts of the protein to sequentially form and break molecular bonds; a process that takes time. In contrast, the fourth sensor, which set the voltage threshold at which the channel opened, did not form these sequential bonds and accordingly reacted fast. Experimental tests in muscle cells that had been engineered to produce channels with mutations in the sensors, confirmed these results.These findings shed new light on the molecular mechanisms that shape the activity of voltage-gated calcium channels. This knowledge will help us understand better how ion channels work, both in healthy tissue and in human disease. V) translate membrane depolarization into calcium influx. Thus, they contribute to cellular excitability and they couple electrical activity to fundamental cell functions like contraction of heart and skeletal muscle, secretion of neurotransmitters and hormones, and the regulation of gene expression. Together with voltage-gated sodium channels (NaV), CaVs form a structurally related ion channel superfamily with a fourfold symmetry , each containing six transmembrane helices (S1-S6). The S5 and S6 helices plus the connecting P loop of all four repeats form the central channel pore with the selectivity filter and the activation gate  simulation and Markov state modeling (MSM) combined with site-directed mutagenesis and electrophysiological analyses to identify the molecular mechanism by which individual VSDs determine the characteristic voltage dependence and kinetics of current activation. Our structure models of the activated and resting states of CaV1.1  is positioned next to the pore domain (S5-S6) of the adjacent repeat in a clockwise orientation (VAb (Cα root-mean-square deviation [RMSD] of 2.7 Å)  , which ivailable . While aes in S4 . Only VS S4 , whereas in VSDs II and IV, the innermost gating charge is a lysine residue (K4) instead of an arginine (R4) in VSDs I and III. Note that lysine forms only a single interaction with a negative countercharge in the INC as opposed to two formed by arginine. Together these differences in the ion pairs formed by the gating charges indicate that the activated state of VSDs I and III is considerably more stabilized than that of VSDs II and IV.As predicted by the sliding helix model , the gatr center . HoweverV1.1 merely represent a snapshot depicting the endpoint of the voltage sensing process. However, because high-resolution structures of resting states of CaV channels are lacking, the molecular details of the steps leading up to VSD activation are still elusive. Exploiting the potential of structure modeling to fill this gap  to obtain unbiased trajectories, which were then projected in a time-lagged independent component analysis (tICA), representing the slowest reaction coordinates. Finally, the kinetic coordinate system provided by the tICA allows calculation of thermodynamics and kinetics by an MSM . Relying on a simple transition state theory model , the functional importance of which for splicing-dependent regulation of the voltage dependence of activation had been shown previously  . R2 moveeviously . Consisty  . ConsistThe direct comparison of VSD I and the two splice variants of VSD IV in the activated and resting states demonstrates striking differences between VSDs I and IV in the extent of ion-pair formation in the ENC . As showV1.1e  in the rabbit GFP-CaV1.1e , expressV1.1e . The strchannels . Immunofchannels . In contchannels . As hypochannels , thus id reduced .If the comparably long transition times for WT VSD I determined by MSM related to the experimentally determined activation kinetics, then MSM of the E87A/E90A mutant channel should result in rapid transition times. This was indeed the case! The transition times of VSD I on activation and deactivation of the E87A/E90A mutant were more than 50 times faster than those of the WT VSD  and 2. TV1.1 activation, we wondered whether these two properties are mechanistically linked to each other or separable? Our structural model predicts that E87 interacts with R1 in resting state 3, and with R1 and R2 in the activated state . In contrast, on deactivation the channel gate closes when the first essential VSD transits from the activated state into resting state 2  (VAb/NaV1.7 chimera stabilized in a deactivated state by toxin binding (Cα RMSD of 1.9 Å)  and probably define the essence of the voltage sensing mechanism.Overall, the structures of the lowest energy states of Caix model  and of pix model . Notablyf 1.7 Å) . Our resf 1.9 Å) . Thus, tV1.1, as well as between the two splice variants of VSD IV. Although the outer gating charges of both VSDs established such ion pairs, they used partners in different transmembrane segments (IS2 vs. IVS3). In both VSDs, the examined ion-pair partners of R1 proved to be crucial for stabilizing the voltage sensor in the activated state, as their mutations consistently resulted in a shift of the voltage dependence of activation to more depolarized voltages  and cholesterol in a 3:1 ratio, using the CHARMM-GUI Membrane Builder . However, from our calculations we see no indications of the existence of such completely different pathways, which are kinetically accessible. Thus, to reconstruct the transition kinetics and to improve the sampling efficiency, we clustered the Umbrella sampling trajectories applying the program implemented in the AMBER suite cpptraj .To build the Markov state model, we used the Cα coordinates of the respective S4 transmembrane helix, defined 100 microstates using the k-means clustering algorithm and applied a lag time of 10 ns. The sampling efficiency and the reliability of the Markov state model  can be evaluated with the Chapman-Kolmogorov test , by usinTo calculate the 1D free energy barriers V1.1e WT was previously described  were PCR-amplified with overlapping primers introducing the point mutation A > C at position nt 260 and/or the point mutation A > G at position nt 269  in separate PCR reactions using GFP-CaV1.1e-WT as template. The two separate PCR products were then used as templates for a final PCR reaction with flanking primers to connect the nucleotide sequences. This fragment was then SalI/EcoRI digested and cloned into the respective sites of GFP-CaV1.1e WT. Sequence integrity of the newly generated constructs was confirmed by sequencing .Cloning procedure for GFP-Caescribed . For betition R1 . To gene SOE-PCR . BrieflyV1 subunit 4 days after plating using FuGENE-HD transfection reagent (Promega). After 7–8 days in culture, transfected myotubes showing GFP fluorescence were analyzed by electrophysiology or fixed for immunolabeling after 9–10 days in culture. The auxiliary calcium channel subunits α2δ−1, β1a, and γ1, along with the STAC3 protein and ryanodine receptor, are endogenously expressed by GLT myotubes, enabling functional membrane incorporation of the channel constructs in the triad junction.Myoblasts of the dysgenic (mdg/mdg) cell line GLT were cultured as previously described in Paraformaldehyde-fixed cultures were immunolabeled as previously described  with rab2, 10 HEPES, 0.1 Cs-EGTA, and 2 Mg-ATP (pH 7.4 with CsOH). The extracellular bath solution contained (mM) 10 CaCl2, 145 tetraethylammonium chloride, and 10 HEPES (pH 7.4 with tetra-ethylammonium hydroxide). Data acquisition and command potentials were controlled by pCLAMP software ; analysis was performed using Clampfit 10.7 (Axon Instruments) and SigmaPlot 12.0 (SPSS Science) software. The current-voltage dependence was fitted according tomax is the maximum conductance of the L-type calcium currents, Vrev is the extrapolated reversal potential of the calcium current, V1/2 is the potential for half-maximal conductance, and k is the slope. The conductance was calculated using G = (− I * 1000)/(Vrev − V), and its voltage dependence was fitted according to a Boltzmann distribution:Calcium currents were recorded with the whole-cell patch clamp technique in voltage clamp mode using an Axopatch 200A amplifier (Axon Instruments). Patch pipettes  had resistances between 1.5 and 3.5 MΩ when filled with (mM) 145 Cs-aspartate, 2 MgClV1.1e were always recorded in parallel with the WT CaV1.1e in cells of the same passage to obtain the best controls for statistical comparison. Consequently, the values for WT controls vary slightly between conditions. The means, standard errors (SE), and p-values were calculated using the Student’s t-test, two-tailed, with significance criteria *p<0.05, **p<0.01, and ***p<0.001. Two-way repeated measures ANOVA, with the Holm Sidak post hoc test, was used to calculate p-values of deactivation.All four experimental groups were analyzed in transiently transfected cells from three to five independent cell passages. The E87A/E90A, E87A, and E90A variants of Ca In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:Here Fernandez-Quintero et al demonstrate how changes in acidic residues in the voltage sensors of skeletal muscle Cav1.1 calcium channels can control their function. The atomistic simulations and Markov State Model developed here describe voltage-sensor movements from resting to activated states, revealing distinct kinetic barriers in different domains. This is backed by mutagenesis studies which show differentially regulated channel function for mutants of one domain that controls slow activation. The results provide new insight into voltage-gated ion channel function.Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Richard Aldrich as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Vladimir Yarov-Yarovoy (Reviewer #2).Thank you for submitting your article "Structural determinants of voltage-gating properties in calcium channels" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at Summary:V1.1 may control kinetics and voltage dependence. The molecular models of VSDs I and IV describe a sliding helix mechanism, but with different activation barriers, such that VSD I appears to control slow activation of CaV1.1.Your manuscript has been evaluated by three reviewers who find the results interesting. The models demonstrate how changes in the acidic residues in the voltage sensors of skeletal muscle CaEssential revisions:The reviewers are concerned about the heavy reliance on simulations that are not well described and lack measures of reliability. Better description of the conceptual basis of the methods for non-MD expects and much more precise definition of the methods for MD-experts are needed so that the readers can accept that the models are reasonable representations of the gating motions studied.eLife. This is particularly important as trajectories of the order of 100 ns need to represent events that actually take place on 100 microsecond or longer time scales in physiology, such that simulations could be merely anecdotal, incomplete, dependent on starting models or dependent on the choices made in enhanced sampling techniques. Currently, the description leaves the reader to guess what has been done, unsure of the impacts those decisions might have on the findings, as well as the strengths and weaknesses of the approaches. Thus, as well as detailed responses to the comments of the reviewers, it is essential that the simulation methods be better described and justified, with results supplemented by rigorous proofs of sampling , convergence  and reproducibility , along with quantitative comparisons to experiment.The reviewers criticise the lack of statistical reliability tests, with absence of measures of error, convergence and reproducibility, each of which are required for publication in Reviewer #1:V1.1 to explain its slow kinetics and right shifted V dependence in terms of the roles of distinct structures and ion pair interactions in VSDs I and IV, backed by electrophysiology. In particular, the results suggest interactions specific to subunit chemistry can drastically affect activation. This is backed by mutagenesis and electrophysiology seeing special roles played by residues, such as E87 affecting V dependence while E90 affects rate, representing important information on how subunits of a CaV control voltage gating. The study is well written and presents a unified story to explain kinetics and V dependence. The models are used in MD simulations to examine thermodynamics and kinetics of the voltage-sensing transitions, but they are not well described and reliability tests appear non-existent, as discussed more below. A more involved examination of sampling, reproducibility and errors is needed to trust those results. Without this it is hard to judge if they are even qualitatively consistent with the electrophysiology to provide insight into voltage sensing.This manuscript represents a study of the voltage sensor movements in homology models of CaAlthough given a fairly wordy description in the methods, the MD approaches are not well described and I am left unsure of what was actually done. Umbrella sampling was first used to "overcome high barriers". But details of the Umbrella Sampling are not described. I assume this was 1D sampling, but what is the reaction coordinate? On page 18 the authors talk about "pulling S4 down" with K = 80 kcal/mol*rad*2. What does that even mean having an angular restraint? Likewise, the authors mention some torsion restraint on S4, but it is not clear what it was actually acting on and its purpose. Was this the restraint for each window? How many windows and how was the coordinate defined? Was each of many windows run for 100ns or was that just subsequent sampling in a few macrostates? Did the windows overlap well and was the preliminary free energy profile from this Umbrella Sampling (not provided) converged? As this Umbrella Sampling set the stage for all of the MD results, how it was carried out is critical. Importantly, as the authors admit the barriers are high and timescales long for S4 movements, if not captured adequately by the chosen reaction coordinate may lead to far-from-equilibrium starting points for subsequent simulations, tICA analysis and MSM. The authors then took clusters and simulated different "activation states" for 100ns each. Does that mean activated states, or activation states between activated and resting states? How were these states actually identified? We would need to see those defined states from the Umbrella simulations and how they were chosen. Are final results depending on such decisions?eLife readers will not understand this, and it is vague in that it does not specify the actual method, but some example method "k-means clustering" without explaining what it means. They then say "To create a more understandable model, a kinetic clustering of a relevant set of microstates to so-called macrostates, can be performed, which are larger aggregates that correspond to the free energy wells ." Again stating an example method called PCCA+ clustering. Was this what was used, and what does it mean? Then, MSM is used to extract thermodynamics and kinetics. Details of this analysis, its reliability and reproducibility are completely lacking.The methods description following this stage is vague. The authors say on page 19 that they "discretize the obtained conformational space into so-called microstates, grouping together conformations of the system that can exchange rapidly ." Most In Figure 2 and later figures, the authors choose to plot sampling and identify states in the dominant two tICA vectors. What do these vectors actually mean physically? How do we know only 2 such order parameters characterise the slowest coordinates in the activation transition? Subsequent panels in Figure 2 suggest an activation/deactivation reaction coordinate that is visualised as a sliding S4, presumably due to changing structures in the macrostates. How does concerted movement between minima in tICA1,2 translate to this S4 sliding movement? Those two order parameters would need visualisation to become meaningful to the reader.Throughout the paper the data is presented without any errors. e.g. What are the error bars in transition mean first passage times in Figure 2? This is particularly important for barriers and the exponentially sensitive rates. Figure 2d suggests activation barriers of 60 kcal/mol. Is that even possible? If one were to estimate a rate for hopping over such barriers, it would not be microseconds (more likely millennia)! How reliable are those barriers (and estimated rates)? Analysis to calculate errors and prove evidence for reproducibility of the results is missing.Regarding these barriers, I have concerns about sampling of interconversion of macrostates to capture the MFPT. e.g. Looking at Figure 2a, it seems that there is no sampling between them in the chosen TICA1-2 space, and this raises doubt about the free energies and rates. Regarding apparent lack of overlap between sampling in macrostates , what is the origin of this? This may come down to the Umbrella Sampling used to get starting points for subsequent 100ns simulations for MSM analysis. Some reaction coordinate (unspecified) was presumed for Umbrella Sampling and pulling along that direction may have yielded patches of sampling such that the minima found in the MSM are artifacts of the starting Umbrella trajectory (sequence of windows). Would the same results emerge from a different initial starting method?In Figure 2 panel A, the scale goes to 10kT whereas the predicted barriers are much higher . These are given different units, and 60 kcal/mol is of order 100kT, being on a different order to the scale of the free energy maps. n.b. On page 9 the authors now refer to barriers of 50 kJ/mol (a third different unit of energy and again different magnitude). This of order 10-15 kcal/mol and appears inconsistent with the above data. Units again on page 10 in kJ/mol appear wrong. I do not trust the predictions based on the data shown. We would need to be convinced of good sampling of transitions both back and forth between macrostates, such that the MFPT estimates have high accuracy, with propagated errors to rates estimated. Regarding rate estimates: How is it that the predicted barriers between successive macrostates change by 10 kcal/mol (e.g. sequence of barriers in panel D), but rates between macrostates (e.g. in panel B) remain of the same order of magnitude (1ms)? This makes no sense to me. Perhaps the barriers in panel D and other similar graphs are not based on any calculated data?The authors write that "although the absolute times derived from MSM may not correspond to the actual transition times of the VSD upon physiological activation and deactivation, relative differences between transition times provide meaningful information when compared between different VSDs or functionally different mutants". Sources of error in absolute times  is required. Regarding reliability of relative rates, how do we know to believe the above statement? Besides model and force field dependence, proof of error and reproducibility of what was sampled is needed.On page 11 the authors state that "additionally formed ion-pairs in the restingB T) of the order of 10-14. Yet the rates change between just micro and millisecond. Again, perhaps the representation of the barriers in the figures are not actual computed barriers? Related, on page 13 the authors write that their result "substantiates the reliability and predictive value of the kinetic analysis of our MD simulation". A much more quantitative comparison than this is needed to demonstrate reliability.states of VSD I are paralleled by a remarkable increase in the energy barriers and the state transition times, suggesting that the number and strength of interactions between the gating charges and the ENC transiently formed in the resting states determine the slow activation kinetics of VSD I." It is suggested here that those changed interactions change the barriers, as indicated in Fig2D-H by 20 kcal/mol. Increased barrier like that means a prefactor (assuming similar order of friction) of exp: E1950-9. doi: 10.1073/pnas.1406161111.1. Discuss potential role of other interactions involving hydrophobic and polar residues in stabilizing specific VSD states besides the ion-pair interactions identified in this study. Also note that residue size and hydrophobicity may also contribute to differences in VSD kinetics – see Jérôme J. Lacroix et al. (2014) Moving gating charges through the gating pore in a K2. A large part of the study is computational modeling to generated models of VSDs in different resting states and run MD simulations to compute transition kinetics. However, to prove the rigor of the approach authors need to describe essential details and related results in supplemental figures and/or text. Specifically:a. What was the reaction coordinate in the Umbrella sampling?b. How many windows used and how well they overlapped?c. How did the authors decide if the Umbrella sampling has generated enough data for the following steps?d. The author mentioned for clustering the Umbrella sampling trajectory "… resulting in a large number of clusters". How many clusters exactly, given all of these clusters were subsequently simulated for 100 ns?e. How was the time lag of 10 ns chosen for the tlCA analysis in the MSM modeling?f. The authors should show an implied timescales (ITS) plot as a function of different lag times. Was it convergent?g. The authors should also show the results of the Chapman-Kolmogorov test to ensure the dynamics observed is Markovian so that the results can be meaningful.3. Discuss accuracy of absolute activation and deactivation times estimated by MSM – how far away they are from the actual transition times observed experimentally?4. An assumption in the computational modeling part of this study is that transitions between VSD states can happen at equilibrium. However, this rarely happens in reality. The activation or deactivation can be considered as a non-equilibrium process by which the electric field exerts force on gating charges to trigger conformational changes in VSDs. Authors should discuss strength and weaknesses of chosen computational modeling methods.5. Note that while the experimental testing is in agreement with observations form computations models, it is not a direct validation of accuracy of the computational prediction. Given the largely unchanged conformation of the rest of the channel except S4, the computational modeling at its best can only infer S4 gating charges movement in individual VSD in a specific physiological state of the channel. The experiments, on the other hand, only showed macroscopic view of the channel activation, in which the contribution of the gating charges movement, VSDs cooperation, and pore coupling is complex. The authors need to discuss these limitations.6. The authors studied activation and deactivation kinetics of the gating charges movement using the computational modeling approaches. Discuss why only activation kinetics have been studied experimentally.7. Can authors provide computational ion-pair energy calculations to support VSD state-stabilization hypotheses?8. Provide in the Supplementary Information example scripts and command lines used for Rosetta homology modeling, MD simulations, and MSM calculations. Such scripts should contain enough information such that someone else could independently run and validate author's data.9. Provide confidence intervals for experimental data.V1.1 models as text files in the Supplementary Information.10. Provide PDB coordinates of Rosetta CaReviewer #3:V1.1 calcium channels can control their function. These calcium channels serve two physiological functions with different kinetics and voltage dependence: excitation-contraction coupling triggered by conformational coupling with the ryanodine receptor in the sarcoplasmic reticulum and calcium entry triggered by opening of their pore. Voltage-driven movement of the S4 segments in their voltage sensors initiate both functions. Molecular models are developed here for the function of VSD I and VSD IV describing the transmembrane movements of their S4 segments from resting to activated states. In each case, the molecular models fit a sliding helix mechanism of VSD function in which the S4 segments exchange ion pair partners between the extracellular negative cluster and the intracellular negative cluster of counterions in the voltage sensor. The kinetic barriers for movement of the S4 segment in VSD IV were much lower than VSD I, suggesting that VSD I controls the slow activation of the calcium conductance of CaV1.1 channels. Introducing mutations in two negatively charged amino acid residues in the extracellular negative cluster of VSD I (E87A and E90A) showed that they differentially regulate channel function. E87A positively shifted the voltage dependence of activation, whereas E90A gave a smaller positive shift in activation and slowed the rate of activation by four-fold.Fernandez-Quintero et al. show how changes in the negatively charged amino acid residues in the voltage sensor of skeletal muscle CaV1.1 channel provides an interesting experimental model. The results presented here are an important advance, as they provide a detailed new molecular model of VSD function and they show how a conserved molecular feature of VSDs, the negatively charged amino acid residues in the extracellular negative cluster, control the voltage dependence and rate of activation of VSD I.Understanding how molecular specializations in VSDs control ion channel function is an important goal of molecular physiology. Because of its unique functional properties, the CaSpecific Comments:Line 33. It is an overstatement to say calcium channels control most functions in excitable cells. Many hormone receptors and other regulators act independently of them. It seems more accurate to say that calcium channels control most functions that are triggered by action potentials in excitable cells, or something similar.Lines 137-142. More description of the conceptual basis for these methods for non-MD expects would be good here. It will be important for general readers to understand why they should accept these models as good representations of corresponding functional states.Line 142. Figure 6 is cited out of order here. Perhaps it should be a Supplementary Figure.Lines 139-142. Trajectories of 100 ns raise the concern that they may not accurately represent events that actually take place on the 100 usec to msec time scales in physiology. The rationale for use of Umbrella sampling to bridge these time scales should be made more clear.Lines 120-121. Are there corresponding differences among the VSD's in interactions of their gating charges with the intracellular negative cluster, as indicated here for the extracellular interactions? Do you expect that these intracellular interactions also contribute to control of kinetics and voltage dependence?Lines 158-159 and Figure 2. How were activated and resting states designated? Is there functional evidence to back up the designations of Resting State 2 and Resting State 3 as resting vs. active? Can one have confidence that states generated by thermal perturbation in MD analysis will reflect the same states that result from voltage stimulation?Lines 163-164. A "shifting stretch" of 3-10 helix. Are all of the gating charges included in this 3-10 helical segment or only the central ones? Does S4 remain in 3-10 conformation after passing through the HCS? Are these conformations similar in VSD IV?In addition to the S4 segment and its gating charge position and interactions, the previous resting state structure of the prokaryotic sodium channel revealed new conformations of the intracellular end of the S4 segment and the S4-S5 linker. Are these conformations observed in these molecular models of the resting state?Figure 3. In most of the manuscript, the order of states begins with Activated and proceeds to Resting, presumably because that is where the MD runs start. However, it feels backwards to me. This is especially true in Figure 3 where changing the order to Resting>Activated from left to right will give a much more intuitive reading of the changes of ion pair partners in the color code.Lines 253-254. Here the paired mutations cause a 50-fold increase in the rate of activation in the MSM model, whereas the actual mutation only causes a 4-fold the experimental results. What accounts for this large discrepancy?Figure 5. There is a larger dispersion of WT data for activation rate in Figure 4D and in Figure 5O and Q. Why are these data more variable?Lines 400-402. Please justify the forces used: 80 and 50 kcal/mol*rad2eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Richard Aldrich as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Vladimir Yarov-Yarovoy (Reviewer #2); William Catterall (Reviewer #3).Thank you for submitting your article "Structural determinants of voltage-gating properties in calcium channels" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential Revisions:Reviewer #1:Your manuscript has been seen by all of the original reviewers, who agree that many of the issues have been resolved. However, there remains some concern about the possible dependence of sampling for the MSM model on the original umbrella sampling, and that this may have been too easily dismissed by the authors. There is still need for clarification and justification of methods, and improved illustration of tests. However, although umbrella sampling simulations may not have reached equilibrium and may still influence the subsequent dynamics to some degree, this does not necessarily preclude a valid MSM, especially for a process that is fairly well constrained (such as in this case where there are strong interactions between the S4 and other VSD residues during S4 translocation), there is sufficient MD sampling achieved after the umbrella sampling, the sampling is validated with Chapman-Kolmogorov tests, and the data is supported by experimental measurements. Thus, although ideally data would be provided that proves the final results do not depend on the initially pulling procedure (redoing with a different starting procedure/path), we do not insist on this here. We do, however, ask that: 1. the possibility that results may depend on those choices not be dismissed; 2. remaining questions and comments from the reviewers regarding clarity of methods and results be addressed in the revised manuscript; and 3. please provide the requested PDB models.The authors have addressed some concerns, but the major critique regarding the possible dependence of results on the initial umbrella sampling and the description of methods have not been fully resolved. The authors did not seem to understand the concern expressed about the possible dependence of results on the initial umbrella sampling path. We know that the authors analysed MD with their MSM after US, but the point is that all sampling commenced with a pulling of the S4 along a predetermined path using umbrella sampling, done so because of the long timescale of sampling S4 movements, and one cannot just expect the subsequent MD not to be biased by that initial pulling.1. The authors admit that the umbrella sampling was used to seed the subsequent MD because otherwise the long timescales of S4 movement may not be reached, but then dismiss the possibility that the subsequent MD may remain trapped in the vicinity of that umbrella sampling path? The authors further dismiss this possible dependence by saying that this is because of their inherent statistical checks, which does not prove much to me. Statistical analysis on the subsequent MD may show reproduction of that MD by the MSM, but may still be restricted to their sampling close to the initial umbrella sampling path. I note that the cartoon in Figure S10 illustrates  that the sampling from MD looks very similar in pattern to the original umbrella sampling, highlighting the concern. To say that all MSM analysis of the MD after umbrella sampling was independent of the initial US because it came after the umbrella sampling is missing the point. One could instead model initial S4 movement along an extremely different pathway  and then go off sampling a different statistically reliable set of sampled states, if those different initial pathways are kinetically separated.2. The methods section still does not explain all approaches clearly. What is the mysterious "dihedral torsion restraint" on S4, why was it needed and what was its purpose? Is it because the pulling of S4 caused the helix to lose secondary structure? What atom selections did it involve? The authors merely write that it was used to "minimise local artifacts"! Details of the umbrella sampling and its reliability remain unclear. The 1D umbrella sampling was along a translocation coordinate, presumed parallel to the helix, and how do we know such a pulling was successful? I note that S4 was pulled with US away from equilibrium, at 1 Å per 20ns . If this trajectory is meant to yield states near equilibrium to seed the subsequent MD , then it is essential that convergence of the umbrella sampling itself is demonstrated . If the authors were to redo the umbrella sampling, pulling in the opposite direction, I would expect significant hysteresis. If the pulling never reached equilibrium, why should we have confidence that the subsequent MD used for MSM analysis was not influenced by this?3. Regarding the tests in Figure S4. This figure is poorly described in its caption. The purpose, approach and what are each of the panels for the Chapman-Kolmogorov tests should be properly described. Moreover, the figure has tiny information at low resolution such that it is hard to see what is being shown in each of the small panels.4. The mix up of energy units was unfortunate. I still am not confident all units are right. Free energies are now in kJ/mol but I note constraints are still specified in kcal/mol. The pulling of S4 by residues R1, R2 and R3 uses a constraint of 80 kcal/mol/A^2. If correct (not kJ/mol), this is an extremely tight constraint. Assuming equilibrium and applying equipartition theorem, this restraint would correspond to a standard deviation of less than 0.1 Å, such that windows spaced at 1 Å cannot overlap at all . I suggest overlap can only be obtained as a result of the non-equilibrium pulling periods of the trajectory. The umbrella sampling was therefore used only as a non-equilibrium pull and not to establish an equilibrium distribution prior to unbiased MD.Reviewer #2:V1.1 channel models. I recommend to accept this manuscript for publication in eLife once the authors will provide PDB coordinates of Rosetta models of VSDI and VSDIV only – no need to provide the full CaV1.1 channel models.Authors addressed all of my comments, except providing PDB coordinates of CaReviewer #3:The authors have provided comprehensive new information on their methods and have further validated their conclusions with statistical summaries. They have responded effectively to my comments, and I now recommend acceptance for publication. Essential revisions:The reviewers are concerned about the heavy reliance on simulations that are not well described and lack measures of reliability. Better description of the conceptual basis of the methods for non-MD expects and much more precise definition of the methods for MD-experts are needed so that the readers can accept that the models are reasonable representations of the gating motions studied.The reviewers criticise the lack of statistical reliability tests, with absence of measures of error, convergence and reproducibility, each of which are required for publication in eLife. This is particularly important as trajectories of the order of 100 ns need to represent events that actually take place on 100 microsecond or longer time scales in physiology, such that simulations could be merely anecdotal, incomplete, dependent on starting models or dependent on the choices made in enhanced sampling techniques. Currently, the description leaves the reader to guess what has been done, unsure of the impacts those decisions might have on the findings, as well as the strengths and weaknesses of the approaches. Thus, as well as detailed responses to the comments of the reviewers, it is essential that the simulation methods be better described and justified, with results supplemented by rigorous proofs of sampling , convergence  and reproducibility , along with quantitative comparisons to experiment.We thank the editors and reviewers for their exceptionally thorough review of our article. We are equally grateful for your overall encouraging assessment of our study, as for numerous valuable questions and recommendation. In compliance with the above-listed essential revisions, we re-wrote the description of our modeling approach in the Results section, to better describe the conceptual basis of our modeling approach, comprehensible also for the non-MD experts. Moreover, in the Materials and methods we provide more details and explanations so that the experts will be able to understand and possibly reproduce the study. In the Supplements we now also provide additional figures and tables showing the confidence intervals and the reliability tests for the Markov-state models.Specifically:Sampling and convergence: Implied time scale plots and Chapman-Kolmogorov-tests demonstrate the validity of our MSM. This and the observed connectivity imply rigorous sampling.Reproducibility/ dependence on Umbrella sampling: We clarified that our MD simulations and the parameters deduced from it do not depend on the initial Umbrella sampling.Quantitative comparison of modeled and experimental time constants are discussed in the text.Importantly, the reviewer´s critique revealed a labeling error of the 1D free energy barrier estimation , which explains the inconsistencies of our data noted by reviewer #1. Correcting this error eliminated these inconsistencies and thus resolved several of the points of criticism raised by that reviewer. We apologize for this mistake and for the misunderstandings it caused when reviewing our paper.Finally, in sum the reviewers provided a sizable list of comments and suggestions, which for the most part were implemented in the revised manuscript. For details, please see the point-to-point reply below.Reviewer #1:V1.1 to explain its slow kinetics and right shifted V dependence in terms of the roles of distinct structures and ion pair interactions in VSDs I and IV, backed by electrophysiology. In particular, the results suggest interactions specific to subunit chemistry can drastically affect activation. This is backed by mutagenesis and electrophysiology seeing special roles played by residues, such as E87 affecting V dependence while E90 affects rate, representing important information on how subunits of a CaV control voltage gating. The study is well written and presents a unified story to explain kinetics and V dependence. The models are used in MD simulations to examine thermodynamics and kinetics of the voltage-sensing transitions, but they are not well described and reliability tests appear non-existent, as discussed more below. A more involved examination of sampling, reproducibility and errors is needed to trust those results. Without this it is hard to judge if they are even qualitatively consistent with the electrophysiology to provide insight into voltage sensing.This manuscript represents a study of the voltage sensor movements in homology models of CaWe appreciate the reviewer´s overall positive assessment and we agree that the original manuscript was in want of more detailed description of the modeling methods and information about sampling, reproducibility and errors of the results. We addressed this point making substantial revisions, which are described in detail below.Although given a fairly wordy description in the methods, the MD approaches are not well described and I am left unsure of what was actually done. Umbrella sampling was first used to "overcome high barriers". But details of the Umbrella Sampling are not described. I assume this was 1D sampling, but what is the reaction coordinate? On page 18 the authors talk about "pulling S4 down" with K = 80 kcal/mol*rad*2. What does that even mean having an angular restraint? Likewise, the authors mention some torsion restraint on S4, but it is not clear what it was actually acting on and its purpose. Was this the restraint for each window? How many windows and how was the coordinate defined? Was each of many windows run for 100ns or was that just subsequent sampling in a few macrostates? Did the windows overlap well and was the preliminary free energy profile from this Umbrella Sampling (not provided) converged? As this Umbrella Sampling set the stage for all of the MD results, how it was carried out is critical. Importantly, as the authors admit the barriers are high and timescales long for S4 movements, if not captured adequately by the chosen reaction coordinate may lead to far-from-equilibrium starting points for subsequent simulations, tICA analysis and MSM. The authors then took clusters and simulated different "activation states" for 100ns each. Does that mean activated states, or activation states between activated and resting states? How were these states actually identified? We would need to see those defined states from the Umbrella simulations and how they were chosen. Are final results depending on such decisions?V1.1 solved in the depolarized state. Experimental details including the reaction coordinates and the justification of torsional restraints are now described in the Materials and methods (pages 2021).To address this criticism, we extended the methods section so that it becomes clear what exactly has been done. Umbrella sampling was used exclusively to generate a potential pathway of the downward movement of the S4. This is necessary to compensate for the force of the membrane potential gradients lacking in our model, because it is based on the starting structure of CaImportantly, all other results, including the free energy surfaces, do not rely on the Umbrella sampling, as we used this pathway only to generate starting structures for classical molecular dynamics simulations in an extended region of the phase space . The states are derived from the Markov State Model  and are reliable within its statistical significance (see new Table S1). Therefore, the presented/analyzed structures are independent of the initial Umbrella sampling. This important point has now been made clear in the description of the approach in the Methods section .The methods description following this stage is vague. The authors say on page 19 that they "discretize the obtained conformational space into so-called microstates, grouping together conformations of the system that can exchange rapidly ." Most eLife readers will not understand this, and it is vague in that it does not specify the actual method, but some example method "k-means clustering" without explaining what it means. They then say "To create a more understandable model, a kinetic clustering of a relevant set of microstates to so-called macrostates, can be performed, which are larger aggregates that correspond to the free energy wells ." Again stating an example method called PCCA+ clustering. Was this what was used, and what does it mean? Then, MSM is used to extract thermodynamics and kinetics. Details of this analysis, its reliability and reproducibility are completely lacking.We are grateful to the reviewer for these comments and questions and addressed them as follows: We extended the Methods section, adding explanations for all the applied algorithms . We also included the Chapman-Kolmogorov plots and implied timescale plots , which are algorithms to estimate the reliability of the Markov-state models.In Figure 2 and later figures, the authors choose to plot sampling and identify states in the dominant two tICA vectors. What do these vectors actually mean physically? How do we know only 2 such order parameters characterise the slowest coordinates in the activation transition? Subsequent panels in Figure 2 suggest an activation/deactivation reaction coordinate that is visualised as a sliding S4, presumably due to changing structures in the macrostates. How does concerted movement between minima in tICA1,2 translate to this S4 sliding movement? Those two order parameters would need visualisation to become meaningful to the reader.In the revised manuscript we also included a detailed explanation of tIC vectors. The first two vectors correspond to the kinetically slowest directions in cartesian coordinate space and thus capture the majority of the slow movements . The S4 sliding movement is primarily represented by tIC1, whereas tIC2 corresponds more to reorganization processes along this movement. A schematic structure showing the two motions represented by the tIC1 and tIC2 coordinates is now included in Figure S10.Throughout the paper the data is presented without any errors. e.g. What are the error bars in transition mean first passage times in Figure 2? This is particularly important for barriers and the exponentially sensitive rates. Figure 2d suggests activation barriers of 60 kcal/mol. Is that even possible? If one were to estimate a rate for hopping over such barriers, it would not be microseconds (more likely millennia)! How reliable are those barriers (and estimated rates)? Analysis to calculate errors and prove evidence for reproducibility of the results is missing.As requested, we now show the confidence intervals for the calculated mean first passage times in the Supporting Methods to show the statistical relevance of our results (Table S1). We calculated the barriers in Figure 2d based on the obtained mean first passage times, to visualize the results in a different way.For estimating the energy barriers we used the transition state theory, the equation of which is now included also in the Supplementary Material. Unfortunately, we had specified the wrong units for the estimated 1D free energy barriers. The results were illustrated in kJ/mol, but had been mislabeled as kcal/mol in the original manuscript. We apologize for this mistake. This error has now been corrected in all plots  and throughout the text.Regarding these barriers, I have concerns about sampling of interconversion of macrostates to capture the MFPT. e.g. Looking at Figure 2a, it seems that there is no sampling between them in the chosen TICA1-2 space, and this raises doubt about the free energies and rates. Regarding apparent lack of overlap between sampling in macrostates , what is the origin of this? This may come down to the Umbrella Sampling used to get starting points for subsequent 100ns simulations for MSM analysis. Some reaction coordinate (unspecified) was presumed for Umbrella Sampling and pulling along that direction may have yielded patches of sampling such that the minima found in the MSM are artifacts of the starting Umbrella trajectory (sequence of windows). Would the same results emerge from a different initial starting method?Indeed, in Figure 2a there is sampling between the macrostates, which is a prerequisite for the construction of the MSM. The sampling is validated by the inherent statistical checks in the course of MSM building. Thus, consistent results would emerge from different initial starting methods.In Figure 2 panel A, the scale goes to 10kT whereas the predicted barriers are much higher . These are given different units, and 60 kcal/mol is of order 100kT, being on a different order to the scale of the free energy maps. n.b. On page 9 the authors now refer to barriers of 50 kJ/mol (a third different unit of energy and again different magnitude). This of order 10-15 kcal/mol and appears inconsistent with the above data. Units again on page 10 in kJ/mol appear wrong. I do not trust the predictions based on the data shown. We would need to be convinced of good sampling of transitions both back and forth between macrostates, such that the MFPT estimates have high accuracy, with propagated errors to rates estimated. Regarding rate estimates: How is it that the predicted barriers between successive macrostates change by 10 kcal/mol (e.g. sequence of barriers in panel D), but rates between macrostates (e.g. in panel B) remain of the same order of magnitude (1ms)? This makes no sense to me. Perhaps the barriers in panel D and other similar graphs are not based on any calculated data?This inconsistency resulted from the unit error stated above. We corrected the units of the tIC plots to kJ/mol. Consequently, it is now apparent that the free energies presented in the tIC surfaces are in the same order of magnitude.The authors write that "although the absolute times derived from MSM may not correspond to the actual transition times of the VSD upon physiological activation and deactivation, relative differences between transition times provide meaningful information when compared between different VSDs or functionally different mutants". Sources of error in absolute times  is required. Regarding reliability of relative rates, how do we know to believe the above statement? Besides model and force field dependence, proof of error and reproducibility of what was sampled is needed.We totally agree with the reviewer and now provide the confidence intervals for all transition times and also the reliability checks for the underlying Markov-state models .On page 11 the authors state that "additionally formed ion-pairs in the restingstates of VSD I are paralleled by a remarkable increase in the energy barriers and the state transition times, suggesting that the number and strength of interactions between the gating charges and the ENC transiently formed in the resting states determine the slow activation kinetics of VSD I." It is suggested here that those changed interactions change the barriers, as indicated in Fig2D-H by 20 kcal/mol. Increased barrier like that means a prefactor (assuming similar order of friction) of exp of the order of 10^-14. Yet the rates change between just micro and millisecond. Again, perhaps the representation of the barriers in the figures are not actual computed barriers? Related, on page 13 the authors write that their result "substantiates the reliability and predictive value of the kinetic analysis of our MD simulation". A much more quantitative comparison than this is needed to demonstrate reliability.Again, this inconsistency arose from our mislabeling of the units. This has been corrected and the results are now in line with the timescales and the free energy plots. Obviously, as pointed out by the reviewer, the largest errors have to be expected for kinetics due to the exponential dependency on barriers.Reviewer #2:Manuscript by Monica Fernández-Quintero et al. describes study of molecular mechanism of voltage-gated calcium channel gating using experimental and computational modeling approaches. Authors identified specific ion-pair interactions stabilizing either activated or resting states of voltage sensing domains I and IV in eukaryotic voltage-gated calcium channel. Overall, the results are promising and provide evidence towards evolutionary functional conservation of the sliding helix hypothesis. This study provides key structural insights into voltage-dependence and kinetics of current activation of eukaryotic voltage-gated calcium channels.V channel voltage sensor. Proc Natl Acad Sci U S A, 111(19): E1950-9. doi: 10.1073/pnas.1406161111.1. Discuss potential role of other interactions involving hydrophobic and polar residues in stabilizing specific VSD states besides the ion-pair interactions identified in this study. Also note that residue size and hydrophobicity may also contribute to differences in VSD kinetics – see Jérôme J. Lacroix et al. (2014) Moving gating charges through the gating pore in a KWe agree with the reviewer that the movement of S4 through the VSD involves many more interactions than the ion-pair interactions of the ENC studied here. This fact now is explicitly acknowledged in our description of the ionic interactions 2. A large part of the study is computational modeling to generated models of VSDs in different resting states and run MD simulations to compute transition kinetics. However, to prove the rigor of the approach authors need to describe essential details and related results in supplemental figures and/or text. Specifically:a. What was the reaction coordinate in the Umbrella sampling?We extended the methods section, describing the applied protocol in detail (pages 20-21)b. How many windows used and how well they overlapped?c. How did the authors decide if the Umbrella sampling has generated enough data for the following steps?The Umbrella sampling was used exclusively to generate a potential pathway of the downward movement of the S4. This pathway provided the starting structures for classical MD simulations, but did not define states. Consequently, all subsequent steps and analyses, like the free energy surfaces, are independent of the data generated by Umbrella sampling. This important fact has now been clarified at several places in the article. .d. The author mentioned for clustering the Umbrella sampling trajectory "… resulting in a large number of clusters". How many clusters exactly, given all of these clusters were subsequently simulated for 100 ns?The number of clusters was between 46 and 56 resulting in an aggregated simulation time of about 5 µs for all variants.e. How was the time lag of 10 ns chosen for the tlCA analysis in the MSM modeling?f. The authors should show an implied timescales (ITS) plot as a function of different lag times. Was it convergent?g. The authors should also show the results of the Chapman-Kolmogorov test to ensure the dynamics observed is Markovian so that the results can be meaningful.In the revised manuscript we provide a more detailed description of the used methods and in the Supplements we provide the implied timescale plots and the ChapmanKolmogorov test, as requested.3. Discuss accuracy of absolute activation and deactivation times estimated by MSM – how far away they are from the actual transition times observed experimentally?V1.1 are not known and probably impossible to measure directly in skeletal muscle cells. Thus, any further discussion of the differences and/or similarities of the transition times derived from MD/MSM would be highly speculative. Therefore, we suffice it to say that, “Because the values calculated in our model are obtained in the absence of the force provided by changes in the electric field, the absolute times derived from MSM may not correspond to the actual transition times of the VSD upon physiological activation and deactivation”. (line 187)To our knowledge activation and deactivation kinetics of individual VSD of Ca4. An assumption in the computational modeling part of this study is that transitions between VSD states can happen at equilibrium. However, this rarely happens in reality. The activation or deactivation can be considered as a non-equilibrium process by which the electric field exerts force on gating charges to trigger conformational changes in VSDs. Authors should discuss strength and weaknesses of chosen computational modeling methods.This important limitation has now been explicitly stated in the sentence quoted at the end of the previous paragraph. (line 187)5. Note that while the experimental testing is in agreement with observations form computations models, it is not a direct validation of accuracy of the computational prediction. Given the largely unchanged conformation of the rest of the channel except S4, the computational modeling at its best can only infer S4 gating charges movement in individual VSD in a specific physiological state of the channel. The experiments, on the other hand, only showed macroscopic view of the channel activation, in which the contribution of the gating charges movement, VSDs cooperation, and pore coupling is complex. The authors need to discuss these limitations.We agree, this is an obvious but important issue. Therefore, we have now added a brief discussion of this limitation and its consequences to the interpretation of our MD/MSM results vis-à-vis channel gating data. (lines 275-283)6. The authors studied activation and deactivation kinetics of the gating charges movement using the computational modeling approaches. Discuss why only activation kinetics have been studied experimentally.To address this issue based on hard data, we conducted additional experiments and analyzed the deactivation kinetics of WT VSD I and the E87A and E90A mutants . As expected, in all cases, the deactivation kinetics were fast and in the range of the activation kinetics observed in the fast constructs . Fast deactivation is expected even in the slowly activating constructs, because closure of the gate is determined by the first (fastest) VSD moving from the activated state into resting state 3, rather than by the slow VSD I, which is rate limiting only during activation. This is now discussed at the end of the Results section (lines 312-325).7. Can authors provide computational ion-pair energy calculations to support VSD state-stabilization hypotheses?We performed LIE calculations  to calculate the energies of electrostatic interactions of the S4 helix with the other parts of the voltage sensors for the macrostate representatives of the two VSDs and the mutants. We provided a table in the Supporting information (Table S3).8. Provide in the Supplementary Information example scripts and command lines used for Rosetta homology modeling, MD simulations, and MSM calculations. Such scripts should contain enough information such that someone else could independently run and validate author's data.We provide a more detailed description of our approach in the Methods section as well as the input command lines for the loop modelling and the MD simulations . For the Markov-state models we used the protocol presented in the PYEMMA tutorials  including all validation tests.9. Provide confidence intervals for experimental data.All our patch-clamp data already showed descriptive statistics (scatter plots with mean and SEM). For the MSM and transition kinetics we now also provided reliability tests and confidence intervals in the Supplements .V1.1 models as text files in the Supplementary Information.10. Provide PDB coordinates of Rosetta CaV1.1 in this paper, because it also contains critical information on parts of the channel, which are not relevant here but important for independent ongoing work.We chose not to provide the PDB coordinates of the complete CaReviewer #3:V1.1 calcium channels can control their function. These calcium channels serve two physiological functions with different kinetics and voltage dependence: excitation-contraction coupling triggered by conformational coupling with the ryanodine receptor in the sarcoplasmic reticulum and calcium entry triggered by opening of their pore. Voltage-driven movement of the S4 segments in their voltage sensors initiate both functions. Molecular models are developed here for the function of VSD I and VSD IV describing the transmembrane movements of their S4 segments from resting to activated states. In each case, the molecular models fit a sliding helix mechanism of VSD function in which the S4 segments exchange ion pair partners between the extracellular negative cluster and the intracellular negative cluster of counterions in the voltage sensor. The kinetic barriers for movement of the S4 segment in VSD IV were much lower than VSD I, suggesting that VSD I controls the slow activation of the calcium conductance of CaV1.1 channels. Introducing mutations in two negatively charged amino acid residues in the extracellular negative cluster of VSD I (E87A and E90A) showed that they differentially regulate channel function. E87A positively shifted the voltage dependence of activation, whereas E90A gave a smaller positive shift in activation and slowed the rate of activation by four-fold.Fernandez-Quintero et al. show how changes in the negatively charged amino acid residues in the voltage sensor of skeletal muscle CaV1.1 channel provides an interesting experimental model. The results presented here are an important advance, as they provide a detailed new molecular model of VSD function and they show how a conserved molecular feature of VSDs, the negatively charged amino acid residues in the extracellular negative cluster, control the voltage dependence and rate of activation of VSD I.Understanding how molecular specializations in VSDs control ion channel function is an important goal of molecular physiology. Because of its unique functional properties, the CaSpecific Comments:Line 33. It is an overstatement to say calcium channels control most functions in excitable cells. Many hormone receptors and other regulators act independently of them. It seems more accurate to say that calcium channels control most functions that are triggered by action potentials in excitable cells, or something similar.We replaced “most functions” with “key functions” which certainly can be maintained, considering the examples given .Lines 137-142. More description of the conceptual basis for these methods for non-MD expects would be good here. It will be important for general readers to understand why they should accept these models as good representations of corresponding functional states.In order to make the modeling approach more accessible to non-MD experts, we amended the description of our approach in the Results section in as simple terms as possible (lines 140148). Much additional detail and the conceptual basis of the methods have now been provided in the Material and Methods and in the Supplements (lines 445-483). Furthermore, in response to the comments of reviewer #2, we included brief discussions of some of the limitations of this approach, when comparing it with channel gating data.Line 142. Figure 6 is cited out of order here. Perhaps it should be a Supplementary Figure.As suggested, we moved former Figure 6 to the supplements Figure S10 and adjusted the citation accordingly.Lines 139-142. Trajectories of 100 ns raise the concern that they may not accurately represent events that actually take place on the 100 usec to msec time scales in physiology. The rationale for use of Umbrella sampling to bridge these time scales should be made more clear.In the revised manuscript we provide a discussion of the different activation time constants observed in the model and experimentally (lines 275-283). In the Methods section we also extended the rational for using Umbrella sampling .Lines 120-121. Are there corresponding differences among the VSD's in interactions of their gating charges with the intracellular negative cluster, as indicated here for the extracellular interactions? Do you expect that these intracellular interactions also contribute to control of kinetics and voltage dependence?Strictly speaking, we do not know, because we have not examined this question directly.V, NaV, and KV channels. Moreover, our model of the activated state does not indicate similar differences. Therefore, we consider the interactions in the INC  as the common  mechanism of voltage-sensing, whereas our present results suggest that the ion-pair interactions in the ENC, which differ even between the VSDs of one pseudo-heterotetrameric channel, determine the specific gating properties.However, the ion-pair interactions in the INC are highly conserved across VSDs of all CaLines 158-159 and Figure 2. How were activated and resting states designated? Is there functional evidence to back up the designations of Resting State 2 and Resting State 3 as resting vs. active? Can one have confidence that states generated by thermal perturbation in MD analysis will reflect the same states that result from voltage stimulation?The states were designated by MSM and represent the structures at free energy wells. The sampling is validated by the inherent statistical checks in the course of MSM building. Thus, assuming statistical significance, consistent results would emerge from different initial starting methods, such as voltage simulations. However, this clearly would be beyond the scope of the present study. The quality of MSM can now be assessed in the provided implied time scale plots and the Chapman-Kolmogorov plots .VAb and a NaVAb/NaV1.7 VSD II chimera. Therefore, the termAs to the designation and credibility of the resting states, it is reassuring that the states resulting from our simulation correspond with the conceptual models based on the sequential movement of S4 through the charge-transfer center, as well as with the recently solved resting state structures of Na“resting states” seems a very reasonable assumption.Lines 163-164. A "shifting stretch" of 3-10 helix. Are all of the gating charges included in this 3-10 helical segment or only the central ones? Does S4 remain in 3-10 conformation after passing through the HCS? Are these conformations similar in VSD IV?10 helix content between the two VSDs and showing the respective gating charges.We added a figure in the Supporting Information  comparing the 3In addition to the S4 segment and its gating charge position and interactions, the previous resting state structure of the prokaryotic sodium channel revealed new conformations of the intracellular end of the S4 segment and the S4-S5 linker. Are these conformations observed in these molecular models of the resting state?To accomplish computing of the MD simulations, we analyzed the isolated voltage-sensing domains without the S4-S5 linker. Analyzing the mechanical transduction of the VSD motion to opening and closing the channel gate will require MD simulations of the complete channel. This is an important goal that shall be tackled in a next step.Figure 3. In most of the manuscript, the order of states begins with Activated and proceeds to Resting, presumably because that is where the MD runs start. However, it feels backwards to me. This is especially true in Figure 3 where changing the order to Resting>Activated from left to right will give a much more intuitive reading of the changes of ion pair partners in the color code.We agree that from a physiological standpoint depicting the states from resting to activating would make more sense. However, as the reviewer stated, the available high-resolution structures of VSD are in the (activated) up-state and this is where our simulations start. Accordingly, also the expected accuracy of the modeled states will be highest in the resting state 3. Therefore, we prefer to present the states in this order and, for consistency reasons, stick to this order in Figure 3 as well.Lines 253-254. Here the paired mutations cause a 50-fold increase in the rate of activation in the MSM model, whereas the actual mutation only causes a 4-fold the experimental results. What accounts for this large discrepancy?On the one hand, we only considered the individual voltage-sensing domains and on the other hand any errors in the values of kinetics would be potentiated due to their exponential dependency on the energy barriers. We also provided errors on the transition timescales. We added a paragraph discussing that aspect.At least in part, these differences in magnitude result from the fact that MD/MSM determines the kinetics of a single VSD, whereas the current properties reflect the concerted action of the entire channel, the gating of which is differentially determined by four VSDs and the transduction of their action to the channel gate. This has now been clarified in the Results (line 275-283)Figure 5. There is a larger dispersion of WT data for activation rate in Figure 4D and in Figure 5O and Q. Why are these data more variable?Cell-to-cell variations arise from many factors including differences in the size and differentiation of the reconstituted myotubes and from the expression efficiency. This is normal. Therefore, we perform our recordings with matched controls from the same cell passages and transfections.Lines 400-402. Please justify the forces used: 80 and 50 kcal/mol*rad280 and 50 kcal/mol*rad2 was determined by trial and error to allow efficient sampling while minimally distorting the system. In the revised manuscript (Methods section) we give a justification of the applied forces.Essential Revisions:Your manuscript has been seen by all of the original reviewers, who agree that many of the issues have been resolved. However, there remains some concern about the possible dependence of sampling for the MSM model on the original umbrella sampling, and that this may have been too easily dismissed by the authors. There is still need for clarification and justification of methods, and improved illustration of tests. However, although umbrella sampling simulations may not have reached equilibrium and may still influence the subsequent dynamics to some degree, this does not necessarily preclude a valid MSM, especially for a process that is fairly well constrained (such as in this case where there are strong interactions between the S4 and other VSD residues during S4 translocation), there is sufficient MD sampling achieved after the umbrella sampling, the sampling is validated with Chapman-Kolmogorov tests, and the data is supported by experimental measurements. Thus, although ideally data would be provided that proves the final results do not depend on the initially pulling procedure (redoing with a different starting procedure/path), we do not insist on this here. We do, however, ask that: 1. the possibility that results may depend on those choices not be dismissed; 2. remaining questions and comments from the reviewers regarding clarity of methods and results be addressed in the revised manuscript; and 3. please provide the requested PDB models.In the revised manuscript we addressed all three points requested above.Reviewer #1:The authors have addressed some concerns, but the major critique regarding the possible dependence of results on the initial umbrella sampling and the description of methods have not been fully resolved. The authors did not seem to understand the concern expressed about the possible dependence of results on the initial umbrella sampling path. We know that the authors analysed MD with their MSM after US, but the point is that all sampling commenced with a pulling of the S4 along a predetermined path using umbrella sampling, done so because of the long timescale of sampling S4 movements, and one cannot just expect the subsequent MD not to be biased by that initial pulling.We do not claim that this approach is unbiased. However, as nicely summarized in the “Essential Revisions” paragraph above, this does not preclude a valid MD simulation and MSM, because the experimentally conditions are solidly based on published structure-function data of VSD action, validated by appropriate tests as well as by the mutagenesis experiments shown in the article.1. The authors admit that the umbrella sampling was used to seed the subsequent MD because otherwise the long timescales of S4 movement may not be reached, but then dismiss the possibility that the subsequent MD may remain trapped in the vicinity of that umbrella sampling path? The authors further dismiss this possible dependence by saying that this is because of their inherent statistical checks, which does not prove much to me. Statistical analysis on the subsequent MD may show reproduction of that MD by the MSM, but may still be restricted to their sampling close to the initial umbrella sampling path. I note that the cartoon in Figure S10 illustrates  that the sampling from MD looks very similar in pattern to the original umbrella sampling, highlighting the concern. To say that all MSM analysis of the MD after umbrella sampling was independent of the initial US because it came after the umbrella sampling is missing the point. One could instead model initial S4 movement along an extremely different pathway  and then go off sampling a different statistically reliable set of sampled states, if those different initial pathways are kinetically separated.We explain (there is nothing to “admit”) that to overcome the timescale limitation of MD simulations in the absence of the driving membrane potential, some kind of enhanced sampling technique (i.e. Umbrella sampling) is necessary to restrict the MD simulation of the VSD to the general vicinity determined by the probable path of the S4 helix relative to the other helices. It was not our intension to “dismiss” the possibility that different starting points could result in models with extremely different pathways. We searched the manuscript carefully for statements that might suggest that. However, nowhere in our manuscript do we claim that all MD/MSM analysis after Umbrella sampling was independent of the initial US. We only write that no states were pre-defined based on US. To make this point absolutely clear, we further amended our explanations in the Methods section accordingly (lines 454 – 465).We also clarify that the schematic figures in Figure S2  are exemplary figures and not data from the present modeling. (line 762)2. The methods section still does not explain all approaches clearly. What is the mysterious "dihedral torsion restraint" on S4, why was it needed and what was its purpose? Is it because the pulling of S4 caused the helix to lose secondary structure? What atom selections did it involve? The authors merely write that it was used to "minimise local artifacts"! Details of the umbrella sampling and its reliability remain unclear. The 1D umbrella sampling was along a translocation coordinate, presumed parallel to the helix, and how do we know such a pulling was successful? I note that S4 was pulled with US away from equilibrium, at 1 Å per 20ns . If this trajectory is meant to yield states near equilibrium to seed the subsequent MD , then it is essential that convergence of the umbrella sampling itself is demonstrated . If the authors were to redo the umbrella sampling, pulling in the opposite direction, I would expect significant hysteresis. If the pulling never reached equilibrium, why should we have confidence that the subsequent MD used for MSM analysis was not influenced by this?We replace the term “dihedral torsion restraint” by a more detailed explanation of the applied backbone restraint, i.e., ϕ torsion angle, also describing the magnitude and an explanation of why this was important.Concerning convergence, we now explicitly state that US does not result in equilibrium distributions because of insufficient overlap between the individual sampling windows, as we did not aim at a converged sampling from the US procedure. (lines 449-459)3. Regarding the tests in Figure S4. This figure is poorly described in its caption. The purpose, approach and what are each of the panels for the Chapman-Kolmogorov tests should be properly described. Moreover, the figure has tiny information at low resolution such that it is hard to see what is being shown in each of the small panels.We amended the figure legend to describe the panels and we increased the font size in the figure. (lines 771-775)2. If correct (not kJ/mol), this is an extremely tight constraint. Assuming equilibrium and applying equipartition theorem, this restraint would correspond to a standard deviation of less than 0.1 Å, such that windows spaced at 1 Å cannot overlap at all . I suggest overlap can only be obtained as a result of the non-equilibrium pulling periods of the trajectory. The umbrella sampling was therefore used only as a non-equilibrium pull and not to establish an equilibrium distribution prior to unbiased MD.4. The mix up of energy units was unfortunate. I still am not confident all units are right. Free energies are now in kJ/mol but I note constraints are still specified in kcal/mol. The pulling of S4 by residues R1, R2 and R3 uses a constraint of 80 kcal/mol/Å2). We tested the applied parameters to avoid unfolding of the S4 helix but to have enough force to induce the downward movement of the S4. As correctly stated by the reviewer and as mentioned above, there is indeed insufficient overlap between the individual sampling windows for US to result in an equilibrium distribution as we do not aim at a converged sampling from the US procedure. This has now been explicitly stated. (lines 454-459)The unit and magnitude of the constraint are correct . (https://doi.org/10.5061/dryad.hhmgqnkfd).