The proton-activated chloride channel (PAC) is active across a wide range of mammalian cells and is involved in acid-induced cell death and tissue injury1,2,3. PAC has recently been shown to represent a novel and evolutionarily conserved protein family4,5. Here we present two cryo-electron microscopy structures of human PAC in a high-pH resting closed state and a low-pH proton-bound non-conducting state. PAC is a trimer in which each subunit consists of a transmembrane domain (TMD), which is formed of two helices (TM1 and TM2), and an extracellular domain (ECD). Upon a decrease of pH from 8 to 4, we observed marked conformational changes in the ECD–TMD interface and the TMD. The rearrangement of the ECD–TMD interface is characterized by the movement of the histidine 98 residue, which is, after acidification, decoupled from the resting position and inserted into an acidic pocket that is about 5 Å away. Within the TMD, TM1 undergoes a rotational movement, switching its interaction partner from its cognate TM2 to the adjacent TM2. The anion selectivity of PAC is determined by the positively charged lysine 319 residue on TM2, and replacing lysine 319 with a glutamate residue converts PAC to a cation-selective channel. Our data provide a glimpse of the molecular assembly of PAC, and a basis for understanding the mechanism of proton-dependent activation.
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Capurro, V. et al. Functional analysis of acid-activated Cl− channels: properties and mechanisms of regulation. Biochim. Biophys. Acta 1848, 105–114 (2015).
Wang, H. Y., Shimizu, T., Numata, T. & Okada, Y. Role of acid-sensitive outwardly rectifying anion channels in acidosis-induced cell death in human epithelial cells. Pflugers Arch. 454, 223–233 (2007).
Sato-Numata, K., Numata, T. & Okada, Y. Temperature sensitivity of acid-sensitive outwardly rectifying (ASOR) anion channels in cortical neurons is involved in hypothermic neuroprotection against acidotoxic necrosis. Channels 8, 278–283 (2014).
Yang, J. et al. PAC, an evolutionarily conserved membrane protein, is a proton-activated chloride channel. Science 364, 395–399 (2019).
Ullrich, F. et al. Identification of TMEM206 proteins as pore of PAORAC/ASOR acid-sensitive chloride channels. eLife 8, e49187 (2019).
Osei-Owusu, J., Yang, J., Del Carmen Vitery, M., Tian, M. & Qiu, Z. PAC proton-activated chloride channel contributes to acid-induced cell death in primary rat cortical neurons. Channels 14, 53–58 (2020).
Jasti, J., Furukawa, H., Gonzales, E. B. & Gouaux, E. Structure of acid-sensing ion channel 1 at 1.9 A resolution and low pH. Nature 449, 316–323 (2007).
Noreng, S., Bharadwaj, A., Posert, R., Yoshioka, C. & Baconguis, I. Structure of the human epithelial sodium channel by cryo-electron microscopy. eLife 7, e39340 (2018).
Gonzales, E. B., Kawate, T. & Gouaux, E. Pore architecture and ion sites in acid-sensing ion channels and P2X receptors. Nature 460, 599–604 (2009).
Baconguis, I., Bohlen, C. J., Goehring, A., Julius, D. & Gouaux, E. X-ray structure of acid-sensing ion channel 1-snake toxin complex reveals open state of a Na+-selective channel. Cell 156, 717–729 (2014).
Yoder, N. & Gouaux, E. Divalent cation and chloride ion sites of chicken acid sensing ion channel 1a elucidated by X-ray crystallography. PLoS ONE 13, e0202134 (2018).
Yoder, N., Yoshioka, C. & Gouaux, E. Gating mechanisms of acid-sensing ion channels. Nature 555, 397–401 (2018).
Mansoor, S. E. et al. X-ray structures define human P2X3 receptor gating cycle and antagonist action. Nature 538, 66–71 (2016).
Vullo, S. et al. Conformational dynamics and role of the acidic pocket in ASIC pH-dependent gating. Proc. Natl Acad. Sci. USA 114, 3768–3773 (2017).
Gao, C. et al. Roles of the lateral fenestration residues of the P2X4 receptor that contribute to the channel function and the deactivation effect of ivermectin. Purinergic Signal. 11, 229–238 (2015).
Lambert, S. & Oberwinkler, J. Characterization of a proton-activated, outwardly rectifying anion channel. J. Physiol. 567, 191–213 (2005).
Liechti, L. A. et al. A combined computational and functional approach identifies new residues involved in pH-dependent gating of ASIC1a. J. Biol. Chem. 285, 16315–16329 (2010).
Smith, E. S. J., Zhang, X., Cadiou, H. & McNaughton, P. A. Proton binding sites involved in the activation of acid-sensing ion channel ASIC2a. Neurosci. Lett. 426, 12–17 (2007).
Paukert, M., Chen, X., Polleichtner, G., Schindelin, H. & Gründer, S. Candidate amino acids involved in H+ gating of acid-sensing ion channel 1a. J. Biol. Chem. 283, 572–581 (2008).
Goehring, A. et al. Screening and large-scale expression of membrane proteins in mammalian cells for structural studies. Nat. Protoc. 9, 2574–2585 (2014).
Haley, E. et al. Expression and purification of the human lipid-sensitive cation channel TRPC3 for structural determination by single-particle cryo-electron microscopy. J. Vis. Exp. 143, e58754 (2019).
Mastronarde, D. N. Automated electron microscope tomography using robust prediction of specimen movements. J. Struct. Biol. 152, 36–51 (2005).
Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).
Zhang, K. Gctf: real-time CTF determination and correction. J. Struct. Biol. 193, 1–12 (2016).
Rohou, A. & Grigorieff, N. CTFFIND4: fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 192, 216–221 (2015).
Scheres, S. H. W. RELION: implementation of a Bayesian approach to cryo-EM structure determination. J. Struct. Biol. 180, 519–530 (2012).
Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).
Zivanov, J., Nakane, T. & Scheres, S. H. W. A Bayesian approach to beam-induced motion correction in cryo-EM single-particle analysis. IUCrJ 6, 5–17 (2019).
Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D 60, 2126–2132 (2004).
Drozdetskiy, A., Cole, C., Procter, J. & Barton, G. J. JPred4: a protein secondary structure prediction server. Nucleic Acids Res. 43, W389–W394 (2015).
Afonine, P. V. et al. New tools for the analysis and validation of cryo-EM maps and atomic models. Acta Crystallogr. D 74, 814–840 (2018).
Wang, R. Y. R. et al. Automated structure refinement of macromolecular assemblies from cryo-EM maps using Rosetta. eLife 5, e17219 (2016).
Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).
Goddard, T. D. et al. UCSF ChimeraX: meeting modern challenges in visualization and analysis. Protein Sci. 27, 14–25 (2018).
The PyMOL Molecular Graphics System, v.2.1. (Schrödinger, LLC, 2020).
Dolinsky, T. J., Nielsen, J. E., McCammon, J. A. & Baker, N. A. PDB2PQR: an automated pipeline for the setup of Poisson–Boltzmann electrostatics calculations. Nucleic Acids Res. 32, W665–W667 (2004).
Olsson, M. H. M. Søndergaard, C. R., Rostkowski, M. & Jensen, J. H. PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J. Chem. Theory Comput. 7, 525–537 (2011).
Lomize, M. A., Pogozheva, I. D., Joo, H., Mosberg, H. I. & Lomize, A. L. OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res. 40, D370–D376 (2012).
Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008).
Abraham, M. J. et al. Gromacs: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).
Huang, J. et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat. Methods 14, 71–73 (2017).
Hess, B. P-LINCS: a parallel linear constraint solver for molecular simulation. J. Chem. Theory Comput. 4, 116–122 (2008).
Leaver-Fay, A., Kuhlman, B. & Snoeyink, J. An adaptive dynamic programming algorithm for the side chain placement problem. Pac. Symp. Biocomput. 10, 16–27 (2005).
Yang, H. et al. TMEM16F forms a Ca2+-activated cation channel required for lipid scrambling in platelets during blood coagulation. Cell 151, 111–122 (2012).
Zhang, Y. & Skolnick, J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 33, 2302–2309 (2005).
We thank G. Zhao and X. Meng for support with data collection at the David Van Andel Advanced Cryo-Electron Microscopy Suite; the HPC team of VARI for computational support; and D. Nadziejka for technical editing. W.L. is supported by National Institutes of Health (NIH) grants R56HL144929 and R01NS112363; Z.Q. is supported by a McKnight Scholar Award, a Klingenstein-Simon Scholar Award, a Sloan Research Fellowship in Neuroscience and NIH grants R35GM124824 and R01NS118014; Z.R. is supported by an American Heart Association (AHA) postdoctoral fellowship (grant 20POST35120556); J.O.-O. is supported by an AHA predoctoral fellowship (grant 18PRE34060025); and J.D. is supported by a McKnight Scholar Award, a Klingenstein-Simon Scholar Award, a Sloan Research Fellowship in Neuroscience, a Pew Scholar in the Biomedical Sciences and NIH grant R01NS111031.
The authors declare no competing interests.
Peer review information Nature thanks Lily Jan, Stephan Kellenberger and Kenton Swartz for their contribution to the peer review of this work.
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Extended data figures and tables
a, Fluorescence size-exclusion chromatography (FSEC) of PAC–GFP solubilized in GDN detergent. b, SDS–PAGE gel of purified PAC–GFP protein after metal affinity chromatography. The uncropped source gel of the image can be found in Supplementary Fig. 1a. The gel was repeated three times from different batches of purification and similar results were obtained. c, SEC profile of PAC in MSP3D1 nanodiscs. d, A deglycosylation assay of PAC–GFP with or without PNGase F treatment. The GFP and far-red signal (Alexa 488 and Alexa 680) of the gel was detected and merged using ChemiDoc imaging system (BioRad). The uncropped source gel of the image can be found in Supplementary Fig. 1b. The deglycosylation assay was repeated twice with similar results. e, F196 mediates intersubunit interactions by forming a cation-π interaction with Arg237′ and hydrophobic interactions with Tyr267′ and Phe282′ from the adjacent subunit. The two subunits are in green and blue. f, FSEC traces of GFP-tagged wild-type PAC and the F196 mutant solubilized using GDN detergent. The peak position of F196A is shifted and is broader compared to the wild type, suggesting that F196A interferes with the proper assembly of PAC. g, The whole-cell current density of wild-type PAC and PAC(F196A) recorded at pH 4.6 with a holding potential of 100 mV. The centre error bar represents mean and s.e.m. Two-tailed unpaired t-test was used to determine the difference in current density between F196A and the wild type (P = 3.09 ×10−6). D’Agostino & Pearson omnibus test was performed to check the normality of the data (P values are 0.846 and 0.349 for wild type (n = 10) and F196A (n = 11), respectively). ***denotes P < 0.001.
a, A total of 16,733 raw movies stacks were collected and processed with motion correction, CTF estimation and particle picking. Particles were subjected to two rounds of 2D classification and a 3D classification run to obtain a homogeneous particle set. To further sort out conformational heterogeneity, we attempted to subtract and classify (1) particles without nanodiscs and (2) the ECD of PAC (residues 72–317) by using a mask. Subsequent refinement allowed us to obtain a map at 3.60-Å resolution for the entire PAC protein and 3.36-Å resolution for the ECD. b, Representative micrograph, 2D class averages, Fourier shell correlation (FSC) curves and angular distribution of particles used for 3D reconstruction for the pH8-PAC dataset. The gold-standard 0.143 threshold was used to determine map resolution based on the FSC curve. The threshold for model versus map correlation was 0.5 to determine the resolution.
a, A total of 26,689 raw movie stacks were collected and processed with motion correction, CTF estimation and particle picking. Two rounds of 2D classification were performed to clean up junk particles. Subsequently, particles belonging to the 2D class averages with clear features were subjected to three rounds of 3D classification. The initial 3D classification was conducted by using the pH8-PAC map low-pass filter set to 50 Å as the reference. No symmetry operator was imposed in this step. After refinement with C3 symmetry, a 5.8-Å-resolution map for pH4-PAC was obtained. Subsequently, the second 3D classification job was conducted by using the 5.8-Å map as the reference and the low-pass filter set to 50 Å. We imposed C3 symmetry at this step to increase the classification efficiency. This allowed us to obtain a map at 4.6 Å after refinement. Finally, a third 3D classification job was launched by using the 4.6-Å pH4-PAC map as the reference and the low-pass filter set to 7 Å. The C3 symmetry was also imposed. This classification pushed the resolution of the pH4-PAC map to 4.2 Å. In an effort to obtain a more homogeneous particle set, we subtracted the ECD of the pH4-PAC map (residues 72–317) and classified the refined particles without image alignment. In the end, we obtained a reconstruction of the pH4-PAC map at 3.73-Å resolution and a pH4-PAC ECD map at 3.66-Å resolution. b, Representative micrograph, 2D class averages, Fourier shell correlation (FSC) curves and angular distribution of particles used for 3D reconstruction for the pH4-PAC dataset. The gold-standard 0.143 threshold was used to determine map resolution based on the FSC curve. The threshold for model versus map correlation was 0.5 to determine the resolution.
Extended Data Fig. 4 Local-resolution cryo-EM maps, representative densities of cryo-EM maps and domain organization of human PAC.
a, The local resolution of the pH8-PAC map. A non-sliced (left) and a sliced (right) view of the map viewed parallel to the membrane are shown. The unit for the colour key is Å. b, Representative densities of several secondary structural elements of pH8-PAC. The atomic model is overlaid with the density to show the side chain information. c, The local resolution of the pH4-PAC map. A non-sliced (left) and a sliced (right) view of the map viewed parallel to the membrane are shown. The unit for the colour key is Å. d, Representative densities of several secondary structural elements of pH4-PAC. The atomic model is overlaid with the density to show the side chain information. e, The pH8-PAC single subunit viewed parallel to the membrane. The wrist, palm, thumb, finger and β-ball domains are highlighted. f, The pH4-PAC single subunit viewed in the same orientation as the right image of panel e. g, Domain organization of PAC. Clusters of secondary structure that form the palm, finger, thumb and β-ball domains are labelled.
a–d, Structural comparison of human PAC (a, c) with chicken ASIC1a (b, d) viewed parallel to the membrane (a, b) and from the extracellular side (c, d). The acidic pocket of human PAC and chicken ASIC1a are in different locations. e, Overlay of the pH8-PAC (blue) and pH4-PAC (red) single subunit with the chicken ASIC1a (green) subunit. The ECD of ASICa is composed of a β-sheet core and the exterior helical structure. Although the β-sheet core shares high similarity with the human PAC structure, the chicken ASIC1a TMD is organized differently from that of the human PAC.
Sequence alignment of PAC homologues (from human, frog (XENLA) and zebrafish (DANRE)) and chicken ASIC1. The ASIC1 sequence is aligned with PAC based on the structural alignment using TMalign45. Secondary structural (SS) elements of PAC are labelled at the top, whereas the SS elements of ASIC1 are indicated at the bottom. Cysteine residues mediating disulfide bonds in the extracellular domain of PAC are marked with yellow dots. Putative N-linked glycosylation sites of PAC are highlighted with green dots. Lys319 of PAC is marked with red dots. The pre-TM2 helix observed in the pH4-PAC structure is indicated with a red frame. PAC lacks the α1, α2, α3, α4 and α5 helices that form the ECD exterior helical structure in chicken ASIC1a, whereas the αA and αB helices are unique to PAC.
a, A representative whole-cell current trace of PAC in wild-type HEK293 cells upon extracellular acidification at pH 4.6 and pH 4.0 with a holding potential at 100 mV. Substantial desensitization was observed during the prolonged exposure to the pH 4.0 solution (position 4 versus position 3), but not to the pH 4.6 solution (position 2 versus position 1). b, Quantification of PAC desensitization (pH 4.6 (n = 12) and pH 4.0 (n = 11) as shown in a. Activation and desensitization currents are normalized to the initial PAC currents. The x axis numbers correspond to the red marker location in a. Each data point is represented by a solid dot. The mean and s.e.m. are represented by the bar graph. c, Representative whole-cell current-voltage traces of PAC at the beginning (position 3 in a) and the end (position 4 in a) of pH 4.0 treatment. d, Reversal potential of PAC at the beginning and the end of pH 4.6 and pH 4.0 treatment, respectively (n = 9). Two-tailed paired t-test was used to determine significance (P values are 0.361 and 0.077 for pH 4.6 and pH 4.0, respectively). D’Agostino & Pearson omnibus test was performed to check the normality of the data (P values are 0.673 and 0.335 for pH 4.6 and pH 4.0 conditions, respectively). NS indicates P > 0.05. e, Whole-cell patch-clamp recording configuration with 50 mM NaCl pipette solution and 150 mM bath solutions (scheme depicted on the left). This creates the concentration gradient necessary to observe any potential PAC current at 0 mV. Owing to the small amplitude of endogenous PAC current at 0 mV, we transfected PAC cDNA in PAC knockout HEK293 cells. The representative whole-cell current trace of PAC upon acidification at 0 mV is shown on the right. Location 1 and 3 represent initial activation of PAC immediately after acidic buffer treatment. Location 2 and 4 represent desensitized PAC after prolonged acidic buffer treatment. f, The desensitized currents (position 2 and 4 in e) are normalized to the initial PAC currents (position 1 and 3 in e). The desensitized data currents are represented by the normalized average ± s.e.m.
a, The reversal potential (Vrev) of wild-type PAC, PAC(K325E) and PAC(K329E) at 150 mM NaCl (black) or 15 mM NaCl (red) in the bath solution (internal solution contains 150 mM NaCl). The bar graph represents the mean and s.e.m. (n = 16 (wild type), n = 8 (K325E) and n = 6 (K329E)). Individual data points are shown as dots. The same data points for the wild type were also used in Fig. 3i for comparison with K319E. b, The relative Cl−/Na+ permeability for wild-type PAC (n = 16), and K325E (n = 8) and K329E (n = 6) mutants calculated from the pH-5-induced current at 100 mV. The centre and error bar represent the mean and s.e.m of the permeability ratio. Individual data points are shown as solid dots. The same data points for the wild type were also used in Fig. 3j for comparison with K319E. The average PCl/PNa permeability values are indicated for each construct. c, The current density of wild-type PAC (n = 10), and K325E (n = 10) and K329E mutants (n = 10) at pH 4.6 with a holding potential of 100 mV. The bar graph shows the average normalized current density ± s.e.m. One-way ANOVA with Bonferroni post-hoc test was used to determine the significance (P values are 0.832 and 0.416 for K325E and K329E, respectively). D’Agostino & Pearson omnibus test was performed to check the normality of the data (P values are 0.255, 0.153 and 0.293 for the wild type and K325E and K329E mutants, respectively). NS indicates P > 0.05. d, The pH dose–response curve of wild-type PAC, PAC(K325E) and PAC(K329E). The currents are normalized to those at pH 4.6 (n = 8 (wild-type PAC), n = 6 PAC(K325E) and n = 7 (PAC(K329E)). The currents at different pH are represented by the average normalized currents ± s.e.m. A nonlinear fitting to a sigmoidal dose–response curve is generated for each construct. e, Representative whole-cell patch-clamp recording at pH 5.0 with 150 mM NaCl pipette solution and 150 mM (black) or 15 mM NaCl (red) bath solutions. The current–voltage relationship of wild-type (left), K325E (middle) and K329E (right) PAC in two different bath solutions are plotted. The same wild-type traces were also shown in Fig. 3j (left) for comparison with K319E. f, i, The pH8-PAC and pH4-PAC extracellular fenestration viewed from the extracellular side (left) and parallel to the membrane (right), respectively. Residues forming the fenestration are shown in sticks, including three negatively charged residues (Asp91, Glu94 and Glu250) for pH8-PAC and two positively charged residues (Arg93 and Lys294) for pH4-PAC. g, j, Radius of the fenestration tunnel, estimated by CAVER v.3.0, for pH8-PAC (g) and pH4-PAC (j). The horizontal line marks the smallest radius along the tunnel. The residues lining the fenestration tunnel are marked. h, k, Fenestration water-density plot for pH8-PAC (h) and pH4-PAC (k) from a 100-ns MD simulation. Water molecules in the Z range of the side fenestration site are projected to the X/Y plane and are shown as a 2D histogram.
a, pKa prediction of titratable residues for the pH8 and pH4 structures of human PAC. The mean and error bar (standard deviation) are calculated based on 1,000 fixed-backbone rotamer ensembles generated from each structure (see Methods). b, SDS gel of GFP-tagged wild-type PAC, PAC(H98C/Q296C) and PAC(H98S/Q296S). A dimeric band is observed for the H98C/Q296C mutant, but not for the wild type and the H98S/Q296S mutant. The unedited source gel of the image can be found in Supplementary Fig. 1c. The gel was independently repeated twice with similar results. c, The FSEC profile of GFP-tagged wild-type PAC, PAC(H98C/Q296C) and PAC(H98S/Q296S) solubilized using GDN detergent. d, The whole-cell current density of wild-type PAC, PAC(H98C/Q296C) and PAC(H98S/Q296S) recorded at pH 5.0 at 100 mV. The bar graph shows the average current density (nA/pF) ± s.e.m. Each individual data point represents a cell (n = 8 (wild type), n = 10 (H98C/Q296C) and n = 12 (H98S/Q296S)). Two-tailed unpaired t-test was used to determine the difference in current density compared to the wild type (P values are 1.08 × 10−6 for H98C/Q296C and 0.321 for H98S/Q296S). D’Agostino & Pearson omnibus test was performed to check the normality of the data (P values are 0.328, 0.154 and 0.727 for the wild type and the H98C/Q296C and H98S/Q296S mutants, respectively). e, The pH dose–response curve of wild-type PAC and PAC(H98S/Q296S). The currents are normalized to those at pH 4.6 (n = 5 (wild-type PAC); n = 6 (PAC(H98S/Q296S)). A nonlinear fitting to a sigmoidal dose–response curve is generated for each construct. Bar plot shows the mean ± s.e.m. f, The pH50 of wild-type PAC and PAC(H98S/Q296S) estimated from the pH dose–response curve. The centre and bar represent the estimated pH50 and s.e.m. from the nonlinear fitting in e. Two-tailed Mann–Whitney test was used to determine the significance (P = 0.0087). g, The proposed pH-sensing mechanism for PAC. At high pH, the deprotonated His98 residue is surrounded by Gln296, Ser102 and Iso298, and TM1 pairs with TM2 from the same subunit. At low pH, the protonated His98 residue undergoes a conformational change and moves into an acidic pocket. As a result, TM1 dissociates from the resting interface and rotates to interact with TM2 of the adjacent subunit. For all panels, NS indicates P > 0.05, ** denotes a P value between 0.01 and 0.001 and *** denotes P < 0.001; n represents measurements from biologically independent cells.
Supplementary Figure 1: The raw gel images. a, The marker and the PAC–GFP lane are cropped to make Extended Data Fig. 1b. b, The Extended Data Fig. 1d is made by cropping the marker, and PAC–GFP lanes w/o PNGase F treatment. c, The Extended Fig. 9b is made by cropping the GFP-tagged WT, H98C/Q296C, H98S/Q296S lanes. The brightness of the image is adjusted globally to increase contrast but without biasing the data. Both gels in (b) and (c) are imaged by detecting the GFP (480 nm) and far red (680nm) signal.
A video showing the conformational change between pH8–PAC and pH4–PAC.
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Ruan, Z., Osei-Owusu, J., Du, J. et al. Structures and pH-sensing mechanism of the proton-activated chloride channel. Nature 588, 350–354 (2020). https://doi.org/10.1038/s41586-020-2875-7
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