Pentameric ligand-gated ion channels (pLGICs) can be found in numerous cell types, particularly in the postsynaptic membrane of the vertebrate nervous system, where they transduce electrochemical signals upon neurotransmitter binding (Tasneem et al., 2005). Gating in pLGICs is initiated by ligand binding in the extracellular domain (ECD), which is believed to lead to large twisting and blooming motions in the ECD, followed by rearrangements in the transmembrane domain (TMD), which eventually lead to opening of the central transmembrane pore. In addition to soluble ligands and numerous allosteric modulators, lipid components of the plasma membrane have been shown to bind to a variety of membrane proteins, potentially modifying their stability or gating (Duncan et al., 2020). For pLGICs, lipid composition was shown to influence reconstitution of functional activity in the Torpedo nicotinic acetylcholine receptor (nAChR) as early as the 1980s (Popot et al., 1981; Criado et al., 1982; Fong and McNamee, 1986; daCosta et al., 2002). Multiple members of the pLGIC family, particularly nAChRs and prokaryotic channels cloned from Gloeobacter violaceus (GLIC) and Dickeya dadantii (formerly Erwinia chrysanthemi, reflected in the channel name ELIC), have since demonstrated functional sensitivity or co-purification with membrane lipids (Thompson and Baenziger, 2020). It has been suggested that lipids interact with membrane-facing loops from the ECD, or with the four membrane-spanning helices (M1-M4) of the TMD. However, many structural details of these lipid interactions remain poorly understood (Poveda et al., 2017).

A critical challenge to elucidating lipid-protein interactions is their typically poor definition in experimental densities. Among more than 100 deposited structures in the protein data bank for GLIC crystallized under activating conditions, several lipid poses have been reported (Zhu et al., 2018; Masiulis et al., 2019; Laverty et al., 2019; Walsh et al., 2018; Gharpure et al., 2019; Rahman et al., 2020; Zhao et al., 2021). These include an outer-leaflet site on the complementary face of a single GLIC subunit, an inner-leaflet site on the principal face of a subunit, and an interfacial inner-leaflet site making contacts with two neighboring subunits (Thompson and Baenziger, 2020). In one case, an additional outer-leaflet density was built as a detergent molecule (Hu et al., 2018). However, the positions and orientations of these membrane components or mimetics vary between structures and typically include only a subset of their constituent atoms.

Another issue is the potential dependence of lipid interactions on the functional state of the membrane protein. In most members of the pLGIC family, binding of a chemical stimulus to the ECD favors a transition from an unliganded, nonconducting state to a liganded, conducting state, and in some cases to a subsequent liganded, desensitized state. Although terminology in the field may vary, conditions predicted to favor the first versus second of these states are referred to here as resting versus activating; the corresponding conformations of the TMD pore are designated closed versus open. No lipids were reported with the lone X-ray structure of GLIC under resting conditions, raising the possibility that interactions could be state-dependent, or obscured by the relatively low overall resolution of this dataset (4.0 Å) (Sauguet et al., 2014). Independent validation of phospholipid interactions in the closed as well as open states of GLIC would provide a valuable baseline for investigating such contacts in the larger family of pLGICs, including pharmacologically important effects of components such as cholesterol and neurosteroids that modulate LGICs (Thompson and Baenziger, 2020; Kim and Hibbs, 2021).

Molecular dynamics (MD) simulations offer an alternative approach to visualizing lipid-protein interactions. All-atom simulations of membrane proteins typically involve inserting an experimental structure in a simplified lipid bilayer surrounded by water and ions, all of which move freely according to physical principles encoded in the force field. Simulations on both atomistic and coarse-grained levels have recently enabled the identification of specific lipid interactions in pLGICs such as ELIC (Sridhar et al., 2021; Dietzen et al., 2022), nAChRs (Sharp and Brannigan, 2021), glycine (Dämgen and Biggin, 2021), and serotonin-3 receptors (Crnjar and Molteni, 2021), but were typically limited to shorter timescales or specific protein conformations. We recently reported free energy landscapes for GLIC gating using Markov state modeling (MSM) of 120 µs unrestrained MD simulations (Bergh et al., 2021). Because GLIC is activated by external acidification (pH < 6), resting versus activating conditions could be approximated throughout these simulations by setting titratable amino acid residues as deprotonated versus protonated, respectively. Under these two conditions, different distributions of functional states were captured, consistent with the relative stabilization of open versus closed channels upon activation. Although our previous analyses focused on conformational changes of the protein alone, the same simulation data encodes a wealth of additional features, including membrane lipids. We also recently reported the first structural data for GLIC using cryogenic electron microscopy (cryo-EM), which enabled the reconstruction of multiple new closed structures (Rovšnik et al., 2021).

Here, we present the first cryo-EM structure of GLIC in a closed conformation with multiple bound lipids resolved in each subunit. These were built into non-protein densities evident in the cryo-EM data, further informed by closed-state lipid occupancies independently identified and characterized from extensive MD simulations. Five distinct closed-state lipid-interaction sites per subunit covered intra- and intersubunit cavities in the inner and outer leaflets. We also compared open-state lipid occupancies from our simulations with previous open X-ray structures, identifying six possible poses. These analyses allowed prediction of novel lipid interaction features in GLIC, including a potentially state-dependent binding site at the outer subunit interface, and a potential role for lipid-tail saturation in binding at the inner leaflet, further validated through mutant simulations. In addition to casting light on selective lipid interactions with LGICs, this work demonstrates the combined power of experimental densities and molecular simulations to support the characterization of lipid binding sites in systems challenged by heterogeneous or otherwise low-resolution data.

Lipid interactions have increasingly been shown to play important roles in stabilizing and regulating membrane proteins (Phillips et al., 2009; Cordero-Morales and Vásquez, 2018). Particularly since the emergence of cryo-EM, a growing number of ion channel structures have been reported with detergents or lipids in membrane-facing regions (Thompson and Baenziger, 2020; Levental and Lyman, 2023). However, the resolution of bound lipids is typically low compared to the protein, making it challenging to define precise poses or interactions. As a result, lipids and detergents are often built with truncated heads, modeled as isolated hydrocarbon chains, or disregarded altogether to avoid overfitting. Here, we present the building of lipids in the closed state of GLIC by comparing non-protein densities in a relatively high-resolution cryo-EM map with lipid interactions sampled in MD simulations.

Our 2.9 Å cryo-EM structure represents, to our knowledge, the highest-resolution closed state of wild-type GLIC yet reported, and the first in which lipids could be confidently built. Notably, the GLIC sample was solubilized in detergent, which is typically thought to replace biological lipids. Nonetheless, the forked shape of non-protein densities associated with each channel subunit indicated the presence of phospholipids, evidently bound tightly enough to resist substitution by detergent. Similarly persistent lipids have been reported in at least three sites per subunit of open-state GLIC; here, we find evidence for five lipids per subunit in the closed state (Figure 5A). Continued improvements in membrane-protein cryo-EM may reveal the full extent of persistent lipid binding at key transmembrane sites in the larger pLGIC family.

Figure 5

Download asset Open asset

MD simulations have recently been used to identify lipid binding sites corresponding to experimental data for both prokaryotic and eukaryotic ligand-gated ion channels (Sridhar et al., 2021; Dietzen et al., 2022), including nAChRs (Sharp and Brannigan, 2021), glycine (Dämgen and Biggin, 2021), and serotonin-3A receptors (Crnjar and Molteni, 2021). In most cases, the slow rate of lateral lipid diffusion has resulted in the preferential use of coarse-grained force fields able to sample longer timescales, but that may overestimate the strength of interactions (Lamprakis et al., 2021). In addition, such studies typically apply harmonic restraints to the protein, potentially obscuring contributions of protein dynamics to lipid interactions. Indeed, the unrestrained atomistic MD simulations studied here were not expected to capture the maximal duration of stable contacts, as indicated by some interaction times approaching the full 1.7 µs trajectory (Figure 2). Nevertheless, simulations were of sufficient length to sample exchange of up to four lipids, particularly around the M4 helix. Calculation of lipid lateral diffusion coefficients resulted in average displacements at the end of simulations of 2.15 nm for lipids initially interacting with the protein surface, roughly corresponding to lipids diffusing out to the fourth lipid shell. Diffusion of bulk lipids was faster, allowing lipids originally 3.16 nm away from the protein surface to ingress the first lipid shell. This observation underscores the potential for lipid exchange events even among lipids initially distant from the protein surface. Of course, duration of exceptionally stable interactions, such as those involving T274 (Figure 2C), inevitably remain bounded by the length of our simulations. Still, diffusion metrics, supported by robust statistical analysis encompassing diverse starting conditions (500 trajectories), enable confident estimation of relative interaction times.

Sampling was further simplified by performing simulations in a uniform POPC membrane. Prior experiments have been conducted to assess the sensitivity of GLIC in varying lipid environments (Labriola et al., 2013; Carswell et al., 2015; Menny et al., 2017), indicating that GLIC remains fully functional in pure POPC bilayers. In our cryo-EM experiments, the protein was recombinantly expressed from Escherichia coli, which means that the experimental density would likely represent phosphatidylglycerol or phosphatidylethanolamine lipids. However, as the molecular identities of bound lipids could not be precisely determined, POPC lipids were built for straightforward comparison with simulation poses. While it appears that GLIC is capable of gating in a pure POPC bilayer, it remains plausible that its function could be influenced by different lipid species, especially due to the presence of multiple charged residues around the TMD/ECD interface which might interact differently with different lipid head groups. Further experiments would be needed to confirm whether the state dependence observed in simulations is also lipid-dependent. It is possible that certain types of lipids bind in one but not the other state, or that certain states are stabilized by a particular lipid type.

In simulations, we were able to identify both long-lasting and highly occupied lipid sites by analyzing a large number of parallel trajectories (500 instances of each subunit). Simulation frames could further be classified into closed or open states by projection onto a previously published Markov state model of GLIC gating, which had been validated against electrophysiology recordings and structural features (Bergh et al., 2021). In particular, as simulations were initiated by placing the GLIC structures in generated POPC bilayers, it is reassuring that lipid binding sites identified in simulations appear to correspond closely to densities representing lipids bound tightly enough in the cryo-EM data to be retained during reconstitution (Figure 1—figure supplement 1C, D). The resulting occupancy plots enabled confident distinction between functional annotations while preserving the protein’s flexibility and capacity to transition between states throughout the simulations. Interestingly, the occupational densities varied remarkably little between resting and activating conditions (Figure 1—figure supplement 1), indicating state rather than pH dependence in lipid interactions, also further justifying the approach of merging closed-state GLIC cryo-EM datasets collected at different pH conditions to resolve lipids.

The three lipids identified in the inner leaflet may play primarily structural roles, as they did not vary substantially between closed and open states (Figure 5). Principal-face and intersubunit sites in our closed cryo-EM structure were largely superimposable with those in open X-ray structures, and all three sites contained comparable densities in closed- and open-state simulation frames (Figure 3A, Figure 1—figure supplement 1D). A particularly stable contact in this leaflet featured deep intercalation of a lipid tail between channel subunits (Figure 4—figure supplement 1), as represented by lipid interactions persisting over 1 µs at residue T274, the longest-lasting in the entire protein (Figure 2). A lipid in this site was also reported in previous open-state X-ray structures of GLIC (Bocquet et al., 2009; Hu et al., 2018). Removal of the sidechain at W217 substantially abbreviated interactions with its buried neighbor T274 (Figure 3), suggesting this hydrophobic contact helps to stabilize lipid penetration. Notably, the same W217A substitution was previously shown to suppress GLIC function and was considered the most impactful of several aromatic residues on the transmembrane surface (Therien and Baenziger, 2017). Interestingly, a lack of double bonds in the intercalating tail was associated with the lipid head group interacting more closely with the protein surface, indicating a possible role for saturation in overall lipid-binding modes (Figure 3). The relevance of lipid intercalation and saturation has been difficult to establish on the basis of structural data, given that resolved lipid tails are often limited to a few hydrocarbons beyond the glycerol group. An important extension of this work may be to apply similar analyses to simulations of mixed-lipid systems, albeit at substantially higher computational cost.

Lipid interactions in the outer leaflet were more variable, both between functional states and experimental methods, possibly reflecting larger conformational changes of the protein in this region. At the complementary-face site - one of the most consistently documented in previous X-ray structures - simulations indicated that lipid tails could penetrate deeper into the helical bundle upon channel opening (Figure 5B), and even further in a state associated with desensitization (Figure 4—figure supplement 2A). Interestingly, buried portions of this site have been shown to host allosteric modulators such as propofol, substituting for lipid-tail interactions (Heusser et al., 2018). State dependence was also suggested at the principal-face site, where the lipid head preferred a pose tilted in toward M3 in the closed state, but tilted out toward M4 in the open state. However, our closed cryo-EM structure was more consistent with open-state simulations, and with a detergent built in an open X-ray structure (Hu et al., 2018). Indeed, the outward orientation was also sampled in closed-state simulations, albeit with lower frequency (Figure 4—figure supplement 2D). The relevance of the inward-facing pose, possibly in the context of an alternative lipid head group, remains to be determined. The most provocative indication of state dependence was at the outer subunit interface, which did not contain strong density in either cryo-EM or simulations data for the closed state (Figure 5A). In contrast, lipid occupancy at this site was evident in open simulations (Figure 5B), including state-dependent contacts with P250, a conserved M2-M3 residue implicated in channel gating (Kaczor et al., 2022). Although lipids have yet to be resolved at this intersubunit site in GLIC, they have been reported in open-state structures of both ELIC (Petroff et al., 2022) and GluCl (Althoff et al., 2014), and in a desensitized neuronal GABA(A) receptor (Laverty et al., 2019). The state-dependent binding event at this site preceded pore opening in MSMs, where lipid binding coincided with crossing a smaller energy barrier between closed and intermediate states, followed by pore opening at the main energy barrier between intermediate and open states (Figure 4—figure supplement 3D). Further, since the P250-lipid interaction was characterized by relatively long residence times (Figure 2), it is possible this lipid interaction has a role to play in GLIC gating.

Finally, this work demonstrates the combined power of cryo-EM and MD simulations to characterize membrane-protein lipid interactions. In leveraging extensive MD simulations to build lipids for the first time into a closed-state structure of GLIC, we were also able to capture features of lipid-tail saturation and potential state dependence that were not available from structural data alone. Knowledge of such interactions may prove useful in the development of lipid-like drugs or lipid-focused treatments of diseases related to malfunction of pLGICs.

Cryo-EM density maps of the pentameric ligand-gated ion channel GLIC in detergent micelles have been deposited in the Electron Microscopy Data Bank under accession number EMD-15649. The deposition includes the cryo-EM sharpened and unsharpened maps, both half-maps and the mask used for final FSC calculation. Coordinates of the model have been deposited in the Protein Data Bank under accession number 8ATG. State-clustered simulation frames, computational lipid densities, parameter files, and full trajectories of mutant simulations as reported in this work can be accessed at https://doi.org/10.5281/zenodo.7058272. Previously published computational models and simulation files can be accessed at https://doi.org/10.5281/zenodo.5500174.

1. 1. Bergh C
   2. Rovsnik U
   3. Howard R
   4. Lindahl E

   (2022) Zenodo

   Discovery of lipid binding sites in a ligand-gated ion channel by integrating simulations and cryo-EM.

   https://doi.org/10.5281/zenodo.7058272
2. 1. Bergh C
   2. Rovsnik U
   3. Howard R
   4. Lindahl E

   (2022) EMDB

   ID EMD-15649. Pentameric ligand-gated ion channel GLIC with bound lipids.

   https://www.ebi.ac.uk/emdb/EMD-15649
3. 1. Bergh C
   2. Rovsnik U
   3. Howard R
   4. Lindahl E

   (2022) RCSB Protein Data Bank

   ID 8ATG. Pentameric ligand-gated ion channel GLIC with bound lipids.

   https://www.rcsb.org/structure/8ATG

1. 1. Bergh C
   2. Heusser S
   3. Howard R
   4. Lindahl E

   (2021) Zenodo

   Markov State Models of Proton- and Gate-Dependent Activation in a Pentameric Ligand-Gated Ion Channel.

   https://doi.org/10.5281/zenodo.5500174

1. 1. Abraham MJ
   2. Murtola T
   3. Schulz R
   4. Páll S
   5. Smith JC
   6. Hess B
   7. Lindahl E

   (2015) GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

   SoftwareX 1–2:19–25.

   https://doi.org/10.1016/j.softx.2015.06.001

   • Google Scholar
2. 1. Adams PD
   2. Afonine PV
   3. Bunkóczi G
   4. Chen VB
   5. Davis IW
   6. Echols N
   7. Headd JJ
   8. Hung L-W
   9. Kapral GJ
   10. Grosse-Kunstleve RW
   11. McCoy AJ
   12. Moriarty NW
   13. Oeffner R
   14. Read RJ
   15. Richardson DC
   16. Richardson JS
   17. Terwilliger TC
   18. Zwart PH

   (2010) PHENIX: a comprehensive Python-based system for macromolecular structure solution

   Acta Crystallographica. Section D, Biological Crystallography 66:213–221.

   https://doi.org/10.1107/S0907444909052925

   • PubMed
   • Google Scholar
3. 1. Althoff T
   2. Hibbs RE
   3. Banerjee S
   4. Gouaux E

   (2014) X-ray structures of GluCl in apo states reveal a gating mechanism of Cys-loop receptors

   Nature 512:333–337.

   https://doi.org/10.1038/nature13669

   • PubMed
   • Google Scholar
4. 1. Basak S
   2. Schmandt N
   3. Gicheru Y
   4. Chakrapani S

   (2017) Crystal structure and dynamics of a lipid-induced potential desensitized-state of a pentameric ligand-gated channel

   eLife 6:e23886.

   https://doi.org/10.7554/eLife.23886

   • PubMed
   • Google Scholar
5. 1. Berger O
   2. Edholm O
   3. Jähnig F

   (1997) Molecular dynamics simulations of a fluid bilayer of dipalmitoylphosphatidylcholine at full hydration, constant pressure, and constant temperature

   Biophysical Journal 72:2002–2013.

   https://doi.org/10.1016/S0006-3495(97)78845-3

   • PubMed
   • Google Scholar
6. 1. Bergh C
   2. Heusser SA
   3. Howard R
   4. Lindahl E

   (2021) Markov state models of proton- and pore-dependent activation in a pentameric ligand-gated ion channel

   eLife 10:e68369.

   https://doi.org/10.7554/eLife.68369

   • PubMed
   • Google Scholar
7. 1. Bocquet N
   2. Nury H
   3. Baaden M
   4. Le Poupon C
   5. Changeux JP
   6. Delarue M
   7. Corringer PJ

   (2009) X-ray structure of a pentameric ligand-gated ion channel in an apparently open conformation

   Nature 457:111–114.

   https://doi.org/10.1038/nature07462

   • PubMed
   • Google Scholar
8. 1. Briones R
   2. Blau C
   3. Kutzner C
   4. de Groot BL
   5. Aponte-Santamaría C

   (2019) GROmaρs: A gromacs-based toolset to analyze density maps derived from molecular dynamics simulations

   Biophysical Journal 116:4–11.

   https://doi.org/10.1016/j.bpj.2018.11.3126

   • PubMed
   • Google Scholar
9. 1. Bussi G
   2. Donadio D
   3. Parrinello M

   (2007) Canonical sampling through velocity rescaling

   The Journal of Chemical Physics 126:014101.

   https://doi.org/10.1063/1.2408420

   • PubMed
   • Google Scholar
10. 1. Carswell CL
    2. Sun J
    3. Baenziger JE

    (2015) Intramembrane aromatic interactions influence the lipid sensitivities of pentameric ligand-gated ion channels

    The Journal of Biological Chemistry 290:2496–2507.

    https://doi.org/10.1074/jbc.M114.624395

    • PubMed
    • Google Scholar
11. 1. Cordero-Morales JF
    2. Vásquez V

    (2018) How lipids contribute to ion channel function, a fat perspective on direct and indirect interactions

    Current Opinion in Structural Biology 51:92–98.

    https://doi.org/10.1016/j.sbi.2018.03.015

    • PubMed
    • Google Scholar
12. 1. Criado M
    2. Eibl H
    3. Barrantes FJ

    (1982) Effects of lipids on acetylcholine receptor. Essential need of cholesterol for maintenance of agonist-induced state transitions in lipid vesicles

    Biochemistry 21:3622–3629.

    https://doi.org/10.1021/bi00258a015

    • PubMed
    • Google Scholar
13. 1. Crnjar A
    2. Molteni C

    (2021) Cholesterol content in the membrane promotes key lipid-protein interactions in a pentameric serotonin-gated ion channel

    Biointerphases 15:061018.

    https://doi.org/10.1116/6.0000561

    • PubMed
    • Google Scholar
14. 1. daCosta CJB
    2. Ogrel AA
    3. McCardy EA
    4. Blanton MP
    5. Baenziger JE

    (2002) LIPID-protein interactions at the NICOTINIC acetylcholine receptor a functional coupling between nicotinic receptors and phosphatidic acid-containing lipid bilayers

    The Journal of Biological Chemistry 277:201–208.

    https://doi.org/10.1074/jbc.M108341200

    • PubMed
    • Google Scholar
15. 1. Dämgen MA
    2. Biggin PC

    (2021) State-dependent protein-lipid interactions of a pentameric ligand-gated ion channel in a neuronal membrane

    PLOS Computational Biology 17:e1007856.

    https://doi.org/10.1371/journal.pcbi.1007856

    • PubMed
    • Google Scholar
16. 1. Dietzen NM
    2. Arcario MJ
    3. Chen LJ
    4. Petroff JT
    5. Moreland KT
    6. Krishnan K
    7. Brannigan G
    8. Covey DF
    9. Cheng WW

    (2022) Polyunsaturated fatty acids inhibit a pentameric ligand-gated ion channel through one of two binding sites

    eLife 11:e74306.

    https://doi.org/10.7554/eLife.74306

    • PubMed
    • Google Scholar
17. 1. Duncan AL
    2. Song W
    3. Sansom MSP

    (2020) Lipid-dependent regulation of ion channels and g protein-coupled receptors: insights from structures and simulations

    Annual Review of Pharmacology and Toxicology 60:31–50.

    https://doi.org/10.1146/annurev-pharmtox-010919-023411

    • PubMed
    • Google Scholar
18. 1. Emsley P
    2. Cowtan K

    (2004) Coot: model-building tools for molecular graphics

    Acta Crystallographica. Section D, Biological Crystallography 60:2126–2132.

    https://doi.org/10.1107/S0907444904019158

    • PubMed
    • Google Scholar
19. 1. Fong TM
    2. McNamee MG

    (1986) Correlation between acetylcholine receptor function and structural properties of membranes

    Biochemistry 25:830–840.

    https://doi.org/10.1021/bi00352a015

    • PubMed
    • Google Scholar
20. 1. Gharpure A
    2. Teng J
    3. Zhuang Y
    4. Noviello CM
    5. Walsh RM
    6. Cabuco R
    7. Howard RJ
    8. Zaveri NT
    9. Lindahl E
    10. Hibbs RE

    (2019) Agonist selectivity and ion permeation in the α3β4 ganglionic nicotinic receptor

    Neuron 104:501–511.

    https://doi.org/10.1016/j.neuron.2019.07.030

    • PubMed
    • Google Scholar
21. 1. Goddard TD
    2. Huang CC
    3. Meng EC
    4. Pettersen EF
    5. Couch GS
    6. Morris JH
    7. Ferrin TE

    (2018) UCSF ChimeraX: Meeting modern challenges in visualization and analysis

    Protein Science 27:14–25.

    https://doi.org/10.1002/pro.3235

    • PubMed
    • Google Scholar
22. Conference

    1. Gowers RJ
    2. Linke M
    3. Barnoud J
    4. Reddy TJE
    5. Melo MN
    6. Seyler SL
    7. Domański J
    8. Dotson DL
    9. Buchoux S
    10. Kenney IM
    11. Beckstein O

    (2016) MDAnalysis: A python package for the rapid analysis of molecular dynamics simulations

    Python in Science Conference. pp. 98–105.

    https://doi.org/10.25080/Majora-629e541a-00e

    • Google Scholar
23. 1. Heusser SA
    2. Lycksell M
    3. Wang X
    4. McComas SE
    5. Howard RJ
    6. Lindahl E

    (2018) Allosteric potentiation of a ligand-gated ion channel is mediated by access to a deep membrane-facing cavity

    PNAS 115:10672–10677.

    https://doi.org/10.1073/pnas.1809650115

    • PubMed
    • Google Scholar
24. 1. Hu H
    2. Ataka K
    3. Menny A
    4. Fourati Z
    5. Sauguet L
    6. Corringer PJ
    7. Koehl P
    8. Heberle J
    9. Delarue M

    (2018) Electrostatics, proton sensor, and networks governing the gating transition in GLIC, a proton-gated pentameric ion channel

    PNAS 115:E12172–E12181.

    https://doi.org/10.1073/pnas.1813378116

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

    (1996) VMD: visual molecular dynamics

    Journal of Molecular Graphics 14:33–38.

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

    • PubMed
    • Google Scholar
26. 1. Jakobi AJ
    2. Wilmanns M
    3. Sachse C

    (2017) Model-based local density sharpening of cryo-EM maps

    eLife 6:e27131.

    https://doi.org/10.7554/eLife.27131

    • PubMed
    • Google Scholar
27. 1. Kaczor PT
    2. Michałowski MA
    3. Mozrzymas JW

    (2022) Alpha1 proline 277 residues regulate gabaar gating through M2-M3 loop interaction in the interface region

    ACS Chemical Neuroscience 21:3044–3056.

    https://doi.org/10.1021/acschemneuro.2c00401

    • Google Scholar
28. 1. Kim S
    2. Chen J
    3. Cheng T
    4. Gindulyte A
    5. He J
    6. He S
    7. Li Q
    8. Shoemaker BA
    9. Thiessen PA
    10. Yu B
    11. Zaslavsky L
    12. Zhang J
    13. Bolton EE

    (2021) PubChem in 2021: new data content and improved web interfaces

    Nucleic Acids Research 49:D1388–D1395.

    https://doi.org/10.1093/nar/gkaa971

    • PubMed
    • Google Scholar
29. 1. Kim JJ
    2. Hibbs RE

    (2021) Direct structural insights into GABAA receptor pharmacology

    Trends in Biochemical Sciences 46:502–517.

    https://doi.org/10.1016/j.tibs.2021.01.011

    • Google Scholar
30. 1. Kimanius D
    2. Dong L
    3. Sharov G
    4. Nakane T
    5. Scheres SHW

    (2021) New tools for automated cryo-EM single-particle analysis in RELION-4.0

    The Biochemical Journal 478:4169–4185.

    https://doi.org/10.1042/BCJ20210708

    • PubMed
    • Google Scholar
31. 1. Labriola JM
    2. Pandhare A
    3. Jansen M
    4. Blanton MP
    5. Corringer PJ
    6. Baenziger JE

    (2013) Structural sensitivity of a prokaryotic pentameric ligand-gated ion channel to its membrane environment

    The Journal of Biological Chemistry 288:11294–11303.

    https://doi.org/10.1074/jbc.M113.458133

    • PubMed
    • Google Scholar
32. 1. Lamprakis C
    2. Andreadelis I
    3. Manchester J
    4. Velez-Vega C
    5. Duca JS
    6. Cournia Z

    (2021) Evaluating the efficiency of the martini force field to study protein dimerization in aqueous and membrane environments

    Journal of Chemical Theory and Computation 17:3088–3102.

    https://doi.org/10.1021/acs.jctc.0c00507

    • PubMed
    • Google Scholar
33. 1. Laverty D
    2. Desai R
    3. Uchański T
    4. Masiulis S
    5. Stec WJ
    6. Malinauskas T
    7. Zivanov J
    8. Pardon E
    9. Steyaert J
    10. Miller KW
    11. Aricescu AR

    (2019) Cryo-EM structure of the human α1β3γ2 GABAA receptor in a lipid bilayer

    Nature 565:516–520.

    https://doi.org/10.1038/s41586-018-0833-4

    • Google Scholar
34. 1. Levental I
    2. Lyman E

    (2023) Regulation of membrane protein structure and function by their lipid nano-environment

    Nature Reviews. Molecular Cell Biology 24:107–122.

    https://doi.org/10.1038/s41580-022-00524-4

    • PubMed
    • Google Scholar
35. 1. Masiulis S
    2. Desai R
    3. Uchański T
    4. Serna Martin I
    5. Laverty D
    6. Karia D
    7. Malinauskas T
    8. Zivanov J
    9. Pardon E
    10. Kotecha A
    11. Steyaert J
    12. Miller KW
    13. Aricescu AR

    (2019) GABA_A receptor signalling mechanisms revealed by structural pharmacology

    Nature 565:454–459.

    https://doi.org/10.1038/s41586-018-0832-5

    • PubMed
    • Google Scholar
36. 1. Menny A
    2. Lefebvre SN
    3. Schmidpeter PA
    4. Drège E
    5. Fourati Z
    6. Delarue M
    7. Edelstein SJ
    8. Nimigean CM
    9. Joseph D
    10. Corringer P-J

    (2017) Identification of a pre-active conformation of a pentameric channel receptor

    eLife 6:e23955.

    https://doi.org/10.7554/eLife.23955

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

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

    Journal of Computational Chemistry 32:2319–2327.

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

    • PubMed
    • Google Scholar
38. 1. Nury H
    2. Van Renterghem C
    3. Weng Y
    4. Tran A
    5. Baaden M
    6. Dufresne V
    7. Changeux JP
    8. Sonner JM
    9. Delarue M
    10. Corringer PJ

    (2011) X-ray structures of general anaesthetics bound to a pentameric ligand-gated ion channel

    Nature 469:428–431.

    https://doi.org/10.1038/nature09647

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

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

    Journal of Applied Physics 52:7182–7190.

    https://doi.org/10.1063/1.328693

    • Google Scholar
40. 1. Petroff JT
    2. Dietzen NM
    3. Santiago-McRae E
    4. Deng B
    5. Washington MS
    6. Chen LJ
    7. Trent Moreland K
    8. Deng Z
    9. Rau M
    10. Fitzpatrick JAJ
    11. Yuan P
    12. Joseph TT
    13. Hénin J
    14. Brannigan G
    15. Cheng WWL

    (2022) Open-channel structure of a pentameric ligand-gated ion channel reveals a mechanism of leaflet-specific phospholipid modulation

    Nature Communications 13:7017.

    https://doi.org/10.1038/s41467-022-34813-5

    • PubMed
    • Google Scholar
41. 1. Phillips R
    2. Ursell T
    3. Wiggins P
    4. Sens P

    (2009) Emerging roles for lipids in shaping membrane-protein function

    Nature 459:379–385.

    https://doi.org/10.1038/nature08147

    • PubMed
    • Google Scholar
42. 1. Popot JL
    2. Cartaud J
    3. Changeux JP

    (1981) Reconstitution of a functional acetylcholine receptor

    European Journal of Biochemistry 118:203–214.

    https://doi.org/10.1111/j.1432-1033.1981.tb06388.x

    • Google Scholar
43. 1. Poveda JA
    2. Marcela Giudici A
    3. Lourdes Renart M
    4. Morales A
    5. González-Ros JM

    (2017) Towards understanding the molecular basis of ion channel modulation by lipids: Mechanistic models and current paradigms

    Biochimica et Biophysica Acta (BBA) - Biomembranes 1859:1507–1516.

    https://doi.org/10.1016/j.bbamem.2017.04.003

    • Google Scholar
44. 1. Rahman MM
    2. Teng J
    3. Worrell BT
    4. Noviello CM
    5. Lee M
    6. Karlin A
    7. Stowell MHB
    8. Hibbs RE

    (2020) Structure of the native muscle-type nicotinic receptor and inhibition by snake venom toxins

    Neuron 106:952–962.

    https://doi.org/10.1016/j.neuron.2020.03.012

    • PubMed
    • Google Scholar
45. 1. Rohou A
    2. Grigorieff N

    (2015) CTFFIND4: Fast and accurate defocus estimation from electron micrographs

    Journal of Structural Biology 192:216–221.

    https://doi.org/10.1016/j.jsb.2015.08.008

    • PubMed
    • Google Scholar
46. 1. Rovšnik U
    2. Zhuang Y
    3. Forsberg BO
    4. Carroni M
    5. Yvonnesdotter L
    6. Howard RJ
    7. Lindahl E

    (2021) Dynamic closed states of a ligand-gated ion channel captured by cryo-EM and simulations

    Life Science Alliance 4:e202101011.

    https://doi.org/10.26508/lsa.202101011

    • PubMed
    • Google Scholar
47. 1. Sauguet L
    2. Poitevin F
    3. Murail S
    4. Van Renterghem C
    5. Moraga-Cid G
    6. Malherbe L
    7. Thompson AW
    8. Koehl P
    9. Corringer PJ
    10. Baaden M
    11. Delarue M

    (2013) Structural basis for ion permeation mechanism in pentameric ligand-gated ion channels

    The EMBO Journal 32:728–741.

    https://doi.org/10.1038/emboj.2013.17

    • PubMed
    • Google Scholar
48. 1. Sauguet L
    2. Shahsavar A
    3. Poitevin F
    4. Huon C
    5. Menny A
    6. Nemecz À
    7. Haouz A
    8. Changeux JP
    9. Corringer PJ
    10. Delarue M

    (2014) Crystal structures of a pentameric ligand-gated ion channel provide a mechanism for activation

    PNAS 111:966–971.

    https://doi.org/10.1073/pnas.1314997111

    • Google Scholar
49. 1. Scherer MK
    2. Trendelkamp-Schroer B
    3. Paul F
    4. Pérez-Hernández G
    5. Hoffmann M
    6. Plattner N
    7. Wehmeyer C
    8. Prinz JH
    9. Noé F

    (2015) PyEMMA 2: a software package for estimation, validation, and analysis of markov models

    Journal of Chemical Theory and Computation 11:5525–5542.

    https://doi.org/10.1021/acs.jctc.5b00743

    • PubMed
    • Google Scholar
50. 1. Sejdiu BI
    2. Tieleman DP

    (2021) ProLint: a web-based framework for the automated data analysis and visualization of lipid-protein interactions

    Nucleic Acids Research 49:W544–W550.

    https://doi.org/10.1093/nar/gkab409

    • PubMed
    • Google Scholar
51. 1. Sharp L
    2. Brannigan G

    (2021) Spontaneous lipid binding to the nicotinic acetylcholine receptor in a native membrane

    The Journal of Chemical Physics 154:185102.

    https://doi.org/10.1063/5.0046333

    • PubMed
    • Google Scholar
52. 1. Sridhar A
    2. Lummis SCR
    3. Pasini D
    4. Mehregan A
    5. Brams M
    6. Kambara K
    7. Bertrand D
    8. Lindahl E
    9. Howard RJ
    10. Ulens C

    (2021) Regulation of a pentameric ligand-gated ion channel by a semiconserved cationic lipid-binding site

    The Journal of Biological Chemistry 297:100899.

    https://doi.org/10.1016/j.jbc.2021.100899

    • PubMed
    • Google Scholar
53. 1. Tasneem A
    2. Iyer LM
    3. Jakobsson E
    4. Aravind L

    (2005) Identification of the prokaryotic ligand-gated ion channels and their implications for the mechanisms and origins of animal Cys-loop ion channels

    Genome Biology 6:1–12.

    https://doi.org/10.1186/gb-2004-6-1-r4

    • PubMed
    • Google Scholar
54. 1. Terwilliger TC
    2. Ludtke SJ
    3. Read RJ
    4. Adams PD
    5. Afonine PV

    (2020) Improvement of cryo-EM maps by density modification

    Nature Methods 17:923–927.

    https://doi.org/10.1038/s41592-020-0914-9

    • PubMed
    • Google Scholar
55. 1. Therien JPD
    2. Baenziger JE

    (2017) Pentameric ligand-gated ion channels exhibit distinct transmembrane domain archetypes for folding/expression and function

    Scientific Reports 7:450.

    https://doi.org/10.1038/s41598-017-00573-2

    • PubMed
    • Google Scholar
56. 1. Thompson MJ
    2. Baenziger JE

    (2020) Structural basis for the modulation of pentameric ligand-gated ion channel function by lipids

    Biochimica et Biophysica Acta. Biomembranes 1862:183304.

    https://doi.org/10.1016/j.bbamem.2020.183304

    • PubMed
    • Google Scholar
57. 1. Walsh RM
    2. Roh SH
    3. Gharpure A
    4. Morales-Perez CL
    5. Teng J
    6. Hibbs RE

    (2018) Structural principles of distinct assemblies of the human α4β2 nicotinic receptor

    Nature 557:261–265.

    https://doi.org/10.1038/s41586-018-0081-7

    • PubMed
    • Google Scholar
58. 1. Zhao Y
    2. Liu S
    3. Zhou Y
    4. Zhang M
    5. Chen H
    6. Eric Xu H
    7. Sun D
    8. Liu L
    9. Tian C

    (2021) Structural basis of human α7 nicotinic acetylcholine receptor activation

    Cell Research 31:713–716.

    https://doi.org/10.1038/s41422-021-00509-6

    • PubMed
    • Google Scholar
59. 1. Zhu S
    2. Noviello CM
    3. Teng J
    4. Walsh RM
    5. Kim JJ
    6. Hibbs RE

    (2018) Structure of a human synaptic GABAA receptor

    Nature 559:67–72.

    https://doi.org/10.1038/s41586-018-0255-3

    • Google Scholar
60. 1. Zivanov J
    2. Nakane T
    3. Scheres SHW

    (2019) A Bayesian approach to beam-induced motion correction in cryo-EM single-particle analysis

    IUCrJ 6:5–17.

    https://doi.org/10.1107/S205225251801463X

    • PubMed
    • Google Scholar

Author details

1. Cathrine Bergh

   Science for Life Laboratory & Swedish e-Science Research Center, Department of Applied Physics, KTH Royal Institute of Technology, Stockholm, Sweden

   Contribution

   Conceptualization, Data curation, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7540-5887

2. Urška Rovšnik

   Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden

   Contribution

   Conceptualization, Data curation, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

3. Rebecca Howard

   1. Science for Life Laboratory & Swedish e-Science Research Center, Department of Applied Physics, KTH Royal Institute of Technology, Stockholm, Sweden
   2. Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden

   Contribution

   Supervision, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

4. Erik Lindahl

   1. Science for Life Laboratory & Swedish e-Science Research Center, Department of Applied Physics, KTH Royal Institute of Technology, Stockholm, Sweden
   2. Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden

   Contribution

   Supervision, Funding acquisition, Project administration, Writing – review and editing

   For correspondence

   erik@kth.se

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2734-2794

• 3,059

  views

• 198

  downloads

• 2

  citations

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