The current view of biological membranes is that proteins are densely packed and often organized into functional modules that perform specific biological functions (Engelman, 2005; Nicolson, 2014). Lipids play an important role in organizing membrane proteins, and the resulting specialized membrane domains are now commonly known as lipid microdomains or lipid rafts (Karnovsky et al., 1982; Lingwood and Simons, 2010; Simons and Ikonen, 1997; Simons and Toomre, 2000; Stier and Sackmann, 1973; Vereb et al., 2003).

Lipid microdomains are enriched in cholesterol (Barenholz, 2002; Korade and Kenworthy, 2008; Simons and Ikonen, 1997) and characterized by a lipid phase that is distinct from the surrounding membrane (Ahmed et al., 1997; Schroeder et al., 1991). Cholesterol has a planar four-aromatic ring structure with a short isooctyl alkyl chain and a small 3-β-hydroxyl head group that can form a hydrogen bond with a polar group (Figure 1—figure supplement 1A). Because the aliphatic groups linked to the ring system are asymmetrically distributed, cholesterol features a smooth (or α) face and a rough (or β) face (Figure 1—figure supplement 1A). It makes up 40–90% of the lipids in eukaryotic cell membranes (Liscum and Munn, 1999; van Meer et al., 2008). Cholesterol can form non-covalent interactions with lipid acyl chains, preferentially with saturated ones (Epand and Epand, 2004; Zheng et al., 2007). As a result, the membrane can transition from a liquid-disordered to a liquid-ordered phase (de Meyer et al., 2010; Ghysels et al., 2019; Marsh, 2010; Seelig, 1977), which has been linked to the formation of lipid microdomains (Quinn and Wolf, 2009; Silvius, 2003).

Mammalian lens membranes contain a high percentage of sphingomyelin (SM) – a lipid consisting of a phosphocholine head group, a sphingosine amino alcohol, and a fatty acid (Figure 1—figure supplement 1B) – and cholesterol, lipids that are characteristic for lipid rafts (Simons and Ikonen, 1997; Zelenka, 1984). With a molar ratio of cholesterol to other phospholipids ranging from 2.02 to 2.52, the human lens core plasma membrane features the highest molar cholesterol content of all membranes found in human tissues (Fleschner and Cenedella, 1991; Zelenka, 1984). Aquaporin-0 (AQP0), a lens-specific water channel, is the most abundant membrane protein in lens membranes (over 60% of the total protein content) (Alcalá et al., 1975; Bloemendal et al., 1972), where it forms large two-dimensional (2D) arrays (Gorin et al., 1984; Kistler and Bullivant, 1980; Zampighi et al., 1982).

The propensity of AQP0 to form 2D crystals was used to determine its structure by electron crystallography (EC) of in vitro assembled 2D crystals grown with the lipid dimyristoyl phosphatidylcholine (DMPC) (Gonen et al., 2005). Later, AQP0 2D crystals were grown with a variety of different lipids (Hite et al., 2015; Hite et al., 2010). All these crystals had the same lattice constants, which were identical to those of AQP0 arrays in native lens membranes (Buzhynskyy et al., 2007). Thus, in vitro grown 2D crystals recapitulate the organization of AQP0 tetramers in 2D arrays in the native lens membrane. Interestingly, however, high-resolution EC studies, which also resolved the annular lipids (Gonen et al., 2005; Hite et al., 2010), showed that AQP0 tetramers in the 2D crystals are separated by a layer of lipids and form essentially no direct protein–protein interactions. As the crystal contacts are almost exclusively mediated by lipids, it is surprising that AQP0 2D crystals could be obtained with almost every lipid tested.

Molecular dynamics (MD) simulations further advanced our understanding of how lipids interact with AQP0. Initial simulations probing the interactions of AQP0 with lipids were carried out with pure DMPC and mixed DMPC:cholesterol membrane patches, predicting the existence of a cholesterol hotspot at the extracellular side of the AQP0 surface (O’Connor and Klauda, 2011). Later, MD simulations demonstrated that the positions of lipids around AQP0 seen in EC structures obtained from 2D crystals, in which the lipids are constrained by the protein crystal packing, are indeed representative of the localization of unconstrained lipids around individual AQP0 tetramers (Aponte-Santamaría et al., 2012). Molecular aspects, such as protein mobility and surface roughness, were identified to play key roles in defining the lipid positions (Aponte-Santamaría et al., 2012; Briones et al., 2017). Furthermore, a combination of coarse-grained and atomistic MD simulations revealed that annular lipids adopt similar positions around several members of the aquaporin family (Stansfeld et al., 2013), while mass spectrometry demonstrated the weak nature of the interaction of lipids with AQPZ (Laganowsky et al., 2014).

In vitro, AQP0 can be induced to form 2D crystals by choosing a very specific lipid-to-protein ratio (LPR) for reconstitution, but this in vitro approach does not explain why AQP0 tetramers form 2D arrays in native lens membranes, which contain an excess of lipids as well as other membrane proteins. Since lipids mediate the interactions in AQP0 2D arrays, it is likely that specific lipids play a role in the assembly of AQP0 2D arrays in the native lens membrane. As AQP0-containing lens membrane junctions are greatly enriched in cholesterol and sphingomyelin (Fleschner and Cenedella, 1991), lipids that play a key role in raft formation (Brown and London, 2000), these lipids were prime candidates to drive AQP0 array formation. To gain structural insights into how cholesterol and/or sphingomyelin may induce AQP0 to form 2D crystals in vivo and potentially to obtain a more general understanding of how these lipids can establish lipid microdomains, we determined structures of AQP0 in membranes formed by mixtures of sphingomyelin and cholesterol and performed extensive equilibrium and force-probe MD simulations.

In this study, we determined two EC structures of AQP0 in membranes formed by sphingomyelin and cholesterol. As these so-called raft lipids are the main constituents of lens membranes, they represent the natural environment of AQP0. The AQP0 crystals formed in sphingomyelin/cholesterol membranes have the same lattice constants as previous 2D crystals obtained with other lipids (Gonen et al., 2005; Hite et al., 2010) as well as the 2D arrays found in lens membranes (Buzhynskyy et al., 2007). The 2D crystals thus reflect the arrangement of AQP0 tetramers in native 2D arrays.

Our structure of AQP0_2SM:1Chol reveals seven sphingomyelins (Figure 3A), the same number of lipids also seen in structures of AQP0 in bilayers formed by DMPC (Gonen et al., 2005) and EPL (Hite et al., 2010), and these are also located in almost identical positions (Figure 3B and C). The interaction of sphingomyelins with AQP0 thus seems to be governed by the same principles that were found for phosphoglycerolipids, namely that the acyl chains locate to grooves in the protein surface and that the lipid head groups make few if any interactions with the protein (Hite et al., 2010). However, compared to the acyl chains in the EPL bilayer, those of the sphingomyelins appear to be straighter, which likely reflects the saturated nature of the sphingomyelin acyl chains. Despite this difference, the structure of the sphingomyelin bilayer is very similar to those of bilayers formed by phosphoglycerolipids, indicating that sphingomyelin by itself is unlikely the reason for AQP0 to form 2D arrays in lens membranes.

Cholesterol has a higher affinity for sphingomyelin than for phosphoglycerolipids (Niu and Litman, 2002), because the mostly saturated acyl chains of sphingomyelin can better accommodate the planar ring system of cholesterol (Epand and Epand, 2004; Zheng et al., 2007) and because the sphingomyelin backbone has two hydrogen-bond donor groups, an amide and a hydroxyl group, that make hydrogen bonding to cholesterol more effective (Róg and Pasenkiewicz-Gierula, 2006). Furthermore, the interaction of the sphingomyelin amide group with the hydroxyl group of cholesterol orients the smooth (α) face of the cholesterol toward sphingomyelin, leaving only the rough (β) face to interact with membrane proteins (Fantini and Barrantes, 2013). In the context of AQP0 2D arrays, however, these interactions do not appear to occur. While Chol1 does interact with sphingomyelin acyl chains in both structures, the additional cholesterols in the AQP0_1SM:2Chol structure seem to have little interactions with sphingomyelin acyl chains and instead seem to substitute for some of the sphingomyelin acyl chains seen in the AQP0_2SM:1Chol structure (Figure 4A and C). We also do not observe interactions between the hydroxyl head group of any cholesterols with an amide group of the sphingomyelins. While we cannot rule out that specific sphingomyelin–cholesterol interactions occur in the membrane area enclosed by four AQP0 tetramers (where we were unable to build models for the lipids due to the fourfold symmetry axis), our structures suggest that specific sphingomyelin–cholesterol interactions do not play a critical role in AQP0 array formation in the lens membrane.

Cholesterol increases the order of lipid acyl chains (de Meyer and Smit, 2009; Lafleur et al., 1990), and the resulting phase transition is thought to contribute to the segregation of cholesterol-enriched membrane areas (Pandit et al., 2007). A potential mechanism by which cholesterol could induce AQP0 array formation could thus be that cholesterol bound to AQP0 would order the surrounding lipids, leading to a phase transition that leads to an initial segregation and crystallization of the AQP0/cholesterol/sphingomyelin units in the membrane.

In an attempt to assess this possibility, we looked at the B-factors of the acyl chains, which are affected, among other factors, by the mobility of the atoms. Comparison of corresponding acyl chains showed that the B-factors of the acyl chains in the AQP0_1SM:2Chol structure tend to be slightly lower than those of the acyl chains in the AQP0_2SM:1Chol structure (Figure 4—figure supplement 2), suggesting that the cholesterols could have an ordering effect on the sphingomyelin acyl chains. However, the B-factors of the acyl chains in the AQP0_EPL structure, which contain double bonds and are not constrained by cholesterol and should thus be much less ordered, are similar to those of the acyl chains in the AQP0_2SM:1Chol structure. A likely explanation is that acyl chains, including unsaturated ones, preferentially fill in grooves in the protein surface (Hite et al., 2010), which should already substantially constrain their mobility. Therefore, cholesterol bound to the protein surface may not add much additional constraints to their already restricted mobility. Cholesterol-induced phase separation thus does not seem to be a likely cause for AQP0 array formation.

The hydrophobic thickness is an important characteristic of a lipid bilayer, which depends on the length and saturation of the acyl chains of the lipids forming the bilayer. Mismatch of the hydrophobic thickness between a membrane protein and the lipid bilayer is thought to be one mechanism by which membrane proteins can cluster in a membrane (Hanulová and Weiss, 2012; Schmidt et al., 2008). One factor that affects the hydrophobic thickness of a membrane is its cholesterol content, which is related to the ordering effect of cholesterol on the lipid acyl chains (de Meyer and Smit, 2009; Lafleur et al., 1990). Change in cholesterol-induced hydrophobic thickness may thus be another potential force that drives the formation of AQP0 arrays in the lens membrane. Our measurements of the average distance between the phosphorus atoms of the phosphodiester groups and the nitrogen atoms of the amide groups of the sphingomyelins in the two leaflets (Figure 4A and B) show that both distances are larger for the bilayer with the higher cholesterol content, by ~3 Å for the phosphodiester groups and ~4 Å for the amide groups, suggesting that a higher cholesterol content indeed increases the hydrophobic thickness of the bilayer formed by the annular sphingomyelins.

In cells, sphingomyelin is predominantly found in the extracellular leaflet of the plasma membrane (Devaux, 1991), so that sphingomyelins seen in the extracellular leaflet in our two structures likely represent true positions of sphingomyelin in native AQP0 2D arrays. The two sphingomyelins in the extracellular leaflet that are seen in both sphingomyelin/cholesterol membranes are in exactly the same position (Figure 4C), establishing that the extracellular leaflet does not change its position relative to AQP0 upon thickening of the membrane with increasing cholesterol content. Sphingomyelin SM3 may be responsible for the fixed position of the extracellular leaflet. In all structures of AQP0 in different lipid bilayers, the lipid at the position equivalent to that of SM3 always has the best-defined density and the lowest B-factors (including for the acyl chains; Figure 4—figure supplement 2). The lipid at this position is always the only one whose head group makes interactions with AQP0. It is thus possible that the lipid at this position defines and locks in the position of the extracellular leaflet with respect to AQP0. As a result, the extracellular leaflet does not play a role in defining the thickness of the membrane.

The increase in hydrophobic thickness with higher cholesterol content is the result of the three sphingomyelins in the cytoplasmic leaflet moving further out from the bilayer center, which they do even though this leaflet contains only a single cholesterol (Figure 4C). The notion that an increase in hydrophobic thickness is predominantly due to movements of lipids in the cytoplasmic leaflet is consistent with the observation that these lipids are usually more mobile, i.e., have higher B-factors in crystal structure than lipids in the extracellular leaflet (Belrhali et al., 1999). Comparison of the lipid arrangement in the cytoplasmic leaflet shows that Chol4 in the AQP0_1SM:2Chol structure assumes the position of SM6 in the AQP0_2SM:1Chol structure (Figure 4A–C). The new position of the displaced SM6 in the AQP0_1SM:2Chol structure is further out from the bilayer center and potentially is the cause for similar outward movements of lipids SM5 and SM7, thus defining the new position of the cytoplasmic leaflet and increasing the hydrophobic thickness of the annular lipid bilayer. Hence, a higher cholesterol concentration of the membrane does increase the hydrophobic thickness of the lipid bilayer formed by the annular lipids surrounding AQP0 tetramers, thus creating a hydrophobic mismatch with the remaining lipid bilayer, which, in turn, would provide a driving force for AQP0 tetramers to cluster in the native lens membrane.

The cholesterols in our structures do not interact with AQP0 through consensus cholesterol-binding sites known as the Cholesterol Recognition/interaction Amino acid Consensus sequence (CRAC domain; -L/V-(X)_1-5-Y-(X)_1-5-R/K-) (Li and Papadopoulos, 1998) or the inverted CRAC domain, CARC (Baier et al., 2011). Cholesterol can also bind to membrane proteins in a fashion that does not involve a CRAC or CARC domain, as seen for example for the influenza M2 protein (Elkins et al., 2018; Elkins et al., 2017) and α-synuclein (Fantini et al., 2011). Emerging cryo-EM structures of G-protein-coupled receptors (GPCRs) also do not show a consensus motif for their interaction with cholesterol (Sarkar and Chattopadhyay, 2020; Taghon et al., 2021). Furthermore, the cholesterols interact with AQP0 through their smooth α face (Figure 4—figure supplement 1), which is not engaged in interactions with sphingomyelin as previously thought (Fantini and Barrantes, 2013), thus differing from the predominant interaction of cholesterols with GPCRs, the G-protein-gated inwardly rectifying potassium-2 (GIRK2) channel, and the Na⁺, K⁺-ATPase, which occur predominantly through the rough β face of cholesterol (Gimpl, 2016; Mathiharan et al., 2021; Shinoda et al., 2009). However, there are other cases in which cholesterol interacts with membrane proteins through its α face, such as seen for the Niemann–Pick C1-like 1 protein (Huang et al., 2020). Likely the residues on the protein surface determine whether a protein interacts with cholesterol through its α or β face. The smooth α face interacts with aromatic side chains through π-stacking (Nishio et al., 1995), whereas the β face appears to interact with hydrophobic side chains, such as leucine, valine, or isoleucine (Fantini et al., 2011).

Chol3 is the most unusual lipid in our structures, because it is located in the middle of the lipid bilayer and because it is sandwiched between two neighboring AQP tetramers. Binding sites for cholesterol in the middle of the membrane have also been described for GPCRs, and these sites have been named ‘deep binding sites’ (Genheden et al., 2017). A docking investigation with known membrane protein structures for cholesterol-binding sites revealed deep binding sites not only in GPCRs but also in ion channels and transporters (Lee, 2018). Unlike any other cholesterol in our structures, Chol3 interacts directly with two AQP0 subunits that are part of different tetramers. This interaction is different from the cholesterol-induced dimerization of GPCRs as these are mediated by interactions between two or more cholesterol molecules rather than one cholesterol directly interacting with two GPCRs (Gimpl, 2016; Hanson et al., 2008). The two adjacent AQP0 tetramers form a pocket that is ~8.5 Å wide at the position of the ring system and ~2.5 Å at the position of the acyl chain (Figure 4E). The interaction surface on the AQP0 subunit interacting with the smooth face of Chol3 encompasses 373 Ų and is formed by residues Ser106, Val103, Ala102, Ala99, and Leu21, which line the ring system, and three phenylalanine residues, Phe17, Phe18, and Phe14, which surround the acyl chain. The interaction surface on the AQP0 subunit interacting with the rough face of Chol3 is 344 Ų and is predominantly formed by residues Ile132, Ile135, Phe136, Ile210, Leu214, and Leu217 (Figure 4E). Leucine and isoleucine residues have also been found to mediate the interaction of many GPCRs with the rough β face of cholesterol (Gimpl, 2016). The hydroxyl head group of Chol3 interacts with a water molecule that may be stabilized through an interaction with Ser126 (Figure 4E). Because of its intricate interactions with two AQP0 subunits, Chol3 can likely function as a glue that can keep two tetramers together. This structural characteristic may be the reason why AQP0 can form 2D arrays in pure cholesterol membranes (Figure 1E and F).

To gain further insights into the role cholesterol plays in the stability of AQP0 arrays, we used MD simulations to investigate the interplay between cholesterol and AQP0. The first set of simulations investigated the localization of cholesterol around a single AQP0 tetramer. Cholesterol positioned at well-defined positions, i.e., hotspots, that were in notable agreement with both the predicted positions from a previous computational study (O’Connor and Klauda, 2011) and the positions in the EC structures (Figure 5). Therefore, as it has been previously demonstrated to be the case for phospholipids (Aponte-Santamaría et al., 2012), the crystal packing in the crystallographic structures does not greatly affect the positioning of cholesterol around AQP0. In consequence, crystallographic cholesterol positions recapitulate the behavior of unconstrained cholesterol molecules around a single AQP0 tetramer. This is the case for all cholesterols except for one, the deep cholesterol, which will be discussed in detail below. Cholesterol localization around membrane proteins has been extensively studied, especially in the context of the regulation and activity of GPCRs, exchangers, and ion channels, but also aquaporins (for a comprehensive review, see Corradi et al., 2019). Interestingly, in coarse-grained MD simulations, when AQP1 was embedded in a lipid bilayer resembling the plasma membrane, cholesterol was enriched at two positions that qualitatively correspond to the lateral projections of Chol3 and Chol4 positions observed here for AQP0 (compare Figure 5 with Figure 2 of Corradi et al., 2018). This suggests that individual AQP0 and AQP1 tetramers share similarities in their cholesterol-binding fingerprints, as was already shown for phospholipids in this protein family (Stansfeld et al., 2013). Nevertheless, cholesterol was rather depleted from the Chol1 position in AQP1. The shape of the protein surface has been shown to critically define the positions where lipids non-specifically associate with AQP0 (Aponte-Santamaría et al., 2012; Briones et al., 2017) and also other proteins (e.g. ion channels [Niemelä et al., 2010] and GPCRs [Kurth et al., 2020], among many others reviewed in Corradi et al., 2019). This principle presumably applies here, too, i.e., the different surface of AQP0 at the Chol1 position, compared to that of AQP1, may result in a greater association of cholesterol over sphingolipids in this position (Figure 8—figure supplements 1 and 3).

In a second set of simulations, we attempted to understand the molecular reasons for the unusual position of deep-binding Chol3 (Figure 6). We demonstrate that neither a single AQP0 tetramer (Figure 6C and D) nor a specific bulk cholesterol concentration (Figure 6B) is sufficient to maintain annular cholesterol molecules at such a deeply inserted position. Instead, the constraint imposed by two adjacent AQP0 tetramers and the coordination of two water molecules were required (Figures 6E and 8A and Figure 8—figure supplement 1). Our data are thus consistent with the existence of deep cholesterol positions but only in a highly constrained setup that involves two AQP0 tetramers. The immediate questions raised by the existence of deep cholesterols are whether these affect the formation and/or stability of 2D AQP0 arrays.

Our simulations provide strong evidence for a direct influence of deep cholesterol molecules on the mechanical stability of the association between two adjacent AQP0 tetramers. We demonstrate that two adjacent tetramers can withstand larger lateral detachment forces when their interface contains deep cholesterol molecules (Figure 7C–F). Crystal structures of several membrane proteins already revealed that cholesterol can mediate crystal packing interactions (Crnjar and Molteni, 2021; Khunweeraphong et al., 2020; Wu et al., 2014). Moreover, the presence of cholesterol has been reported to influence membrane–protein oligomerization, a feature mainly explored for GPCRs (Corradi et al., 2019; Gahbauer and Böckmann, 2016). We expand on these data, by providing evidence of cholesterol—directly—engaging in the stronger attachment of two membrane proteins. We attribute this to cholesterol facilitating the formation of transient protein–protein contacts (Figure 8A–D) together with providing higher surface complementarity when sandwiched between two AQP0 tetramers (Figure 8E and F). Cardiolipin is a phospholipid that has been implicated in stabilizing transient α-helical membrane protein oligomers (Gupta et al., 2017). More specifically, cardiolipin has been described to serve as a ‘glue’ for the cytochrome components of the respiratory chain in mitochondrial membranes (Arnarez et al., 2013; Zhang et al., 2002). Interestingly, cardiolipin also impacts the resistance of AQPZ, the bacterial homolog of AQP0, to unfolding (Laganowsky et al., 2014). In addition, lipids have been recently shown to mediate protein assembly in the outer membrane of bacteria (Webby et al., 2022). Analogous to these systems and based on our EC structures and MD simulations, we propose that deep cholesterol increases the mechanical stability of associated AQP0 tetramers. Although we provide solid evidence here that deep cholesterol imparts mechanical stabilization, free energy calculations would be required to obtain the full picture of thermodynamic stabilization. However, such free energy calculations are challenging for lipids, due to the chemical complexity and poor convergence involved (Wang et al., 2021), and are thus beyond the scope of the current work.

Our structures of AQP0 arrays in sphingomyelin bilayers with low and high cholesterol content and the results from the MD simulations allow us to propose a model for cholesterol-induced array formation (Figure 9). In a lipid bilayer with a low cholesterol concentration, cholesterol may interact with AQP0 predominantly through the most specific cholesterol-binding site, the one occupied by Chol1, but this interaction would be transient and would not have a meaningful effect on the organization of the surrounding lipid molecules (sphingomyelin in our AQP0_2SM:1Chol structure) (Figure 9A). An increase in cholesterol concentration will cause more cholesterols in the extracellular leaflet to bind to AQP0. In addition, cholesterol will also enrich in the cytoplasmic leaflet and associate with AQP0. This interaction will shift the annular lipids in this leaflet further away from the membrane center and create a hydrophobic mismatch between the shell of annular lipids and the surrounding lipid bilayer (Figure 9B). Driven by the force to minimize membrane tension induced by hydrophobic mismatch, AQP0 tetramers will cluster. Cholesterols in between the tetramers can diffuse deeper into the membrane and associate with deep binding sites on AQP0, thus stabilizing the interaction between adjacent tetramers (Figure 9C). Alternatively, it may also be possible that cholesterol first interacts with a deep binding site on one AQP0 but is only stabilized as the second AQP0 traps it in position. As each AQP0 tetramer has four deep cholesterol-binding sites, the avidity effect would result in the formation and stabilization of large 2D arrays as those seen in native lens membranes (Figure 9D).

Figure 9

Download asset Open asset

This model is specific for the formation of AQP0 arrays in lens membranes, but we speculate that similar principles may underlie the organization of lipid rafts. AQP0 may be special in that it forms tetramers and thus has four deep cholesterol-binding sites, so that the avidity effect allows it to form much larger domains than seen for typical lipid rafts (Simons and Ikonen, 1997; Zelenka, 1984). In addition, it is presumably not common that the same protein has surfaces that can interact with both the smooth α and the rough β face of cholesterol. However, cholesterol would have the potential to mediate the association of any protein with an α face-interacting surface with any other protein that features a β face-interacting surface. Thus, while hydrophobic mismatch and phase separation may be driving forces that bring proteins into close proximity, cholesterol may be the actual glue that increases the time they remain associated.

Electron diffraction data have been deposited in PDB under the accession codes 8SJY and 8SJX.Molecular dynamics simulations data have been deposited on GitHub (copy archived at Chiu et al., 2024).

1. 1. Chiu PL
   2. Walz T

   (2024) Protein Data Bank

   ID 8SJY. Structure of lens aquaporin-0 array in sphingomyelin/cholesterol bilayer (1SM:2Chol).

   https://www.rcsb.org/structure/8SJY
2. 1. Chiu PL
   2. Walz T

   (2024)

   Protein Data Bank

   Structure of lens aquaporin-0 array in sphingomyelin/cholesterol bilayer (2SM:1Chol).

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 LW
   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. Ahmed SN
   2. Brown DA
   3. London E

   (1997) On the origin of sphingolipid/cholesterol-rich detergent-insoluble cell membranes: physiological concentrations of cholesterol and sphingolipid induce formation of a detergent-insoluble, liquid-ordered lipid phase in model membranes

   Biochemistry 36:10944–10953.

   https://doi.org/10.1021/bi971167g

   • PubMed
   • Google Scholar
4. 1. Alcalá J
   2. Lieska N
   3. Maisel H

   (1975) Protein composition of bovine lens cortical fiber cell membranes

   Experimental Eye Research 21:581–595.

   https://doi.org/10.1016/0014-4835(75)90040-8

   • PubMed
   • Google Scholar
5. 1. Amadei A
   2. Linssen AB
   3. Berendsen HJ

   (1993) Essential dynamics of proteins

   Proteins 17:412–425.

   https://doi.org/10.1002/prot.340170408

   • PubMed
   • Google Scholar
6. 1. Aponte-Santamaría C
   2. Briones R
   3. Schenk AD
   4. Walz T
   5. de Groot BL

   (2012) Molecular driving forces defining lipid positions around aquaporin-0

   PNAS 109:9887–9892.

   https://doi.org/10.1073/pnas.1121054109

   • PubMed
   • Google Scholar
7. 1. Arnarez C
   2. Mazat JP
   3. Elezgaray J
   4. Marrink SJ
   5. Periole X

   (2013) Evidence for cardiolipin binding sites on the membrane-exposed surface of the cytochrome bc1

   Journal of the American Chemical Society 135:3112–3120.

   https://doi.org/10.1021/ja310577u

   • PubMed
   • Google Scholar
8. 1. Baier CJ
   2. Fantini J
   3. Barrantes FJ

   (2011) Disclosure of cholesterol recognition motifs in transmembrane domains of the human nicotinic acetylcholine receptor

   Scientific Reports 1:69.

   https://doi.org/10.1038/srep00069

   • PubMed
   • Google Scholar
9. 1. Barenholz Y

   (2002) Cholesterol and other membrane active sterols: from membrane evolution to “rafts.”

   Progress in Lipid Research 41:1–5.

   https://doi.org/10.1016/s0163-7827(01)00016-9

   • PubMed
   • Google Scholar
10. 1. Belrhali H
    2. Nollert P
    3. Royant A
    4. Menzel C
    5. Rosenbusch JP
    6. Landau EM
    7. Pebay-Peyroula E

    (1999) Protein, lipid and water organization in bacteriorhodopsin crystals: A molecular view of the purple membrane at 1.9 A resolution

    Structure 7:909–917.

    https://doi.org/10.1016/s0969-2126(99)80118-x

    • PubMed
    • Google Scholar
11. 1. Berendsen HJC
    2. Postma JPM
    3. van Gunsteren WF
    4. DiNola A
    5. Haak JR

    (1984) Molecular dynamics with coupling to an external bath

    The Journal of Chemical Physics 81:3684–3690.

    https://doi.org/10.1063/1.448118

    • Google Scholar
12. 1. Bloemendal H
    2. Zweers A
    3. Vermorken F
    4. Dunia I
    5. Benedetti EL

    (1972) The plasma membranes of eye lens fibres

    Biochemical and Structural Characterization. Cell Differ 1:91–106.

    https://doi.org/10.1016/0045-6039(72)90032-2

    • Google Scholar
13. 1. Briones R
    2. Aponte-Santamaría C
    3. de Groot BL

    (2017) Localization and ordering of lipids around aquaporin-0: protein and lipid mobility effects

    Frontiers in Physiology 8:124.

    https://doi.org/10.3389/fphys.2017.00124

    • PubMed
    • Google Scholar
14. 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
15. 1. Brooks BR
    2. Brooks CL
    3. Mackerell AD
    4. Nilsson L
    5. Petrella RJ
    6. Roux B
    7. Won Y
    8. Archontis G
    9. Bartels C
    10. Boresch S
    11. Caflisch A
    12. Caves L
    13. Cui Q
    14. Dinner AR
    15. Feig M
    16. Fischer S
    17. Gao J
    18. Hodoscek M
    19. Im W
    20. Kuczera K
    21. Lazaridis T
    22. Ma J
    23. Ovchinnikov V
    24. Paci E
    25. Pastor RW
    26. Post CB
    27. Pu JZ
    28. Schaefer M
    29. Tidor B
    30. Venable RM
    31. Woodcock HL
    32. Wu X
    33. Yang W
    34. York DM
    35. Karplus M

    (2009) CHARMM: the biomolecular simulation program

    Journal of Computational Chemistry 30:1545–1614.

    https://doi.org/10.1002/jcc.21287

    • Google Scholar
16. 1. Brown DA
    2. London E

    (2000) Structure and function of sphingolipid- and cholesterol-rich membrane rafts

    The Journal of Biological Chemistry 275:17221–17224.

    https://doi.org/10.1074/jbc.R000005200

    • PubMed
    • Google Scholar
17. 1. Brünger AT
    2. Adams PD
    3. Clore GM
    4. DeLano WL
    5. Gros P
    6. Grosse-Kunstleve RW
    7. Jiang JS
    8. Kuszewski J
    9. Nilges M
    10. Pannu NS
    11. Read RJ
    12. Rice LM
    13. Simonson T
    14. Warren GL

    (1998) Crystallography & NMR system: A new software suite for macromolecular structure determination

    Acta Crystallographica. Section D, Biological Crystallography 54:905–921.

    https://doi.org/10.1107/s0907444998003254

    • PubMed
    • Google Scholar
18. 1. Burley SK
    2. Petsko GA

    (1985) Aromatic-aromatic interaction: a mechanism of protein structure stabilization

    Science 229:23–28.

    https://doi.org/10.1126/science.3892686

    • PubMed
    • Google Scholar
19. 1. Buzhynskyy N
    2. Hite RK
    3. Walz T
    4. Scheuring S

    (2007) The supramolecular architecture of junctional microdomains in native lens membranes

    EMBO Reports 8:51–55.

    https://doi.org/10.1038/sj.embor.7400858

    • PubMed
    • Google Scholar
20. 1. Chiu PL
    2. Li X
    3. Li Z
    4. Beckett B
    5. Brilot AF
    6. Grigorieff N
    7. Agard DA
    8. Cheng Y
    9. Walz T

    (2015) Evaluation of super-resolution performance of the K2 electron-counting camera using 2D crystals of aquaporin-0

    Journal of Structural Biology 192:163–173.

    https://doi.org/10.1016/j.jsb.2015.08.015

    • PubMed
    • Google Scholar
21. Software

    1. Chiu PL
    2. Orjuela JD
    3. de Groot BL
    4. Aponte-Santamaría C
    5. Walz T

    (2024) Molecular dynamics (MD) simulations data of “structure and dynamics of cholesterol-mediated aquaporin-0 arrays and implications for lipid rafts.”, version swh:1:rev:0b4dba381e355c288d7a80b5b1de17c6879bb799

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:38897a675a6d8e20330985fd2574c23494bafbfd;origin=https://github.com/MPTG-CBP/aqp0-chol;visit=swh:1:snp:127fce03d35319c0093aa23e1a6ffa7a20b9498b;anchor=swh:1:rev:0b4dba381e355c288d7a80b5b1de17c6879bb799
22. 1. Connolly ML

    (1983) Analytical molecular surface calculation

    Journal of Applied Crystallography 16:548–558.

    https://doi.org/10.1107/S0021889883010985

    • Google Scholar
23. 1. Corradi V
    2. Mendez-Villuendas E
    3. Ingólfsson HI
    4. Gu RX
    5. Siuda I
    6. Melo MN
    7. Moussatova A
    8. DeGagné LJ
    9. Sejdiu BI
    10. Singh G
    11. Wassenaar TA
    12. Delgado Magnero K
    13. Marrink SJ
    14. Tieleman DP

    (2018) Lipid-protein interactions are unique fingerprints for membrane proteins

    ACS Central Science 4:709–717.

    https://doi.org/10.1021/acscentsci.8b00143

    • PubMed
    • Google Scholar
24. 1. Corradi V
    2. Sejdiu BI
    3. Mesa-Galloso H
    4. Abdizadeh H
    5. Noskov SY
    6. Marrink SJ
    7. Tieleman DP

    (2019) Emerging diversity in lipid-protein interactions

    Chemical Reviews 119:5775–5848.

    https://doi.org/10.1021/acs.chemrev.8b00451

    • PubMed
    • Google Scholar
25. 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
26. 1. Darden T
    2. York D
    3. Pedersen L

    (1993) Particle mesh Ewald: AnN⋅log(N) method for Ewald sums in large systems

    The Journal of Chemical Physics 98:10089–10092.

    https://doi.org/10.1063/1.464397

    • Google Scholar
27. 1. de Meyer F
    2. Smit B

    (2009) Effect of cholesterol on the structure of a phospholipid bilayer

    PNAS 106:3654–3658.

    https://doi.org/10.1073/pnas.0809959106

    • PubMed
    • Google Scholar
28. 1. de Meyer FJM
    2. Benjamini A
    3. Rodgers JM
    4. Misteli Y
    5. Smit B

    (2010) Molecular simulation of the DMPC-cholesterol phase diagram

    The Journal of Physical Chemistry. B 114:10451–10461.

    https://doi.org/10.1021/jp103903s

    • PubMed
    • Google Scholar
29. 1. Devaux PF

    (1991) Static and dynamic lipid asymmetry in cell membranes

    Biochemistry 30:1163–1173.

    https://doi.org/10.1021/bi00219a001

    • PubMed
    • Google Scholar
30. 1. Doktorova M
    2. Kučerka N
    3. Kinnun JJ
    4. Pan J
    5. Marquardt D
    6. Scott HL
    7. Venable RM
    8. Pastor RW
    9. Wassall SR
    10. Katsaras J
    11. Heberle FA

    (2020) Molecular structure of sphingomyelin in fluid phase bilayers determined by the joint analysis of small-angle neutron and X-ray scattering data

    The Journal of Physical Chemistry B 124:5186–5200.

    https://doi.org/10.1021/acs.jpcb.0c03389

    • Google Scholar
31. 1. Eisenhaber F
    2. Lijnzaad P
    3. Argos P
    4. Sander C
    5. Scharf M

    (1995) The double cubic lattice method: Efficient approaches to numerical integration of surface area and volume and to dot surface contouring of molecular assemblies

    Journal of Computational Chemistry 16:273–284.

    https://doi.org/10.1002/jcc.540160303

    • Google Scholar
32. 1. Elkins MR
    2. Williams JK
    3. Gelenter MD
    4. Dai P
    5. Kwon B
    6. Sergeyev IV
    7. Pentelute BL
    8. Hong M

    (2017) Cholesterol-binding site of the influenza M2 protein in lipid bilayers from solid-state NMR

    PNAS 114:12946–12951.

    https://doi.org/10.1073/pnas.1715127114

    • Google Scholar
33. 1. Elkins MR
    2. Sergeyev IV
    3. Hong M

    (2018) Determining cholesterol binding to membrane proteins by cholesterol¹³C labeling in yeast and dynamic nuclear polarization NMR

    Journal of the American Chemical Society 140:15437–15449.

    https://doi.org/10.1021/jacs.8b09658

    • Google Scholar
34. 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
35. 1. Engelman DM

    (2005) Membranes are more mosaic than fluid

    Nature 438:578–580.

    https://doi.org/10.1038/nature04394

    • PubMed
    • Google Scholar
36. 1. Epand RM
    2. Epand RF

    (2004) Non-raft forming sphingomyelin-cholesterol mixtures

    Chemistry and Physics of Lipids 132:37–46.

    https://doi.org/10.1016/j.chemphyslip.2004.09.012

    • PubMed
    • Google Scholar
37. 1. Essmann U
    2. Perera L
    3. Berkowitz ML
    4. Darden T
    5. Lee H
    6. Pedersen LG

    (1995) A smooth particle mesh Ewald method

    The Journal of Chemical Physics 103:8577–8593.

    https://doi.org/10.1063/1.470117

    • Google Scholar
38. 1. Fantini J
    2. Carlus D
    3. Yahi N

    (2011) The fusogenic tilted peptide (67-78) of α-synuclein is a cholesterol binding domain

    Biochimica et Biophysica Acta 1808:2343–2351.

    https://doi.org/10.1016/j.bbamem.2011.06.017

    • PubMed
    • Google Scholar
39. 1. Fantini J
    2. Barrantes FJ

    (2013) How cholesterol interacts with membrane proteins: an exploration of cholesterol-binding sites including CRAC, CARC, and tilted domains

    Frontiers in Physiology 4:31.

    https://doi.org/10.3389/fphys.2013.00031

    • PubMed
    • Google Scholar
40. 1. Fleschner CR
    2. Cenedella RJ

    (1991)

    Lipid composition of lens plasma membrane fractions enriched in fiber junctions

    Journal of Lipid Research 32:45–53.

    • PubMed
    • Google Scholar
41. 1. Gahbauer S
    2. Böckmann RA

    (2016) Membrane-mediated oligomerization of g protein coupled receptors and Its Implications for GPCR Function

    Frontiers in Physiology 7:494.

    https://doi.org/10.3389/fphys.2016.00494

    • PubMed
    • Google Scholar
42. 1. Genheden S
    2. Essex JW
    3. Lee AG

    (2017) G protein coupled receptor interactions with cholesterol deep in the membrane

    Biochimica et Biophysica Acta. Biomembranes 1859:268–281.

    https://doi.org/10.1016/j.bbamem.2016.12.001

    • PubMed
    • Google Scholar
43. 1. Ghysels A
    2. Krämer A
    3. Venable RM
    4. Teague WE
    5. Lyman E
    6. Gawrisch K
    7. Pastor RW

    (2019) Permeability of membranes in the liquid ordered and liquid disordered phases

    Nature Communications 10:5616.

    https://doi.org/10.1038/s41467-019-13432-7

    • PubMed
    • Google Scholar
44. 1. Gimpl G

    (2016) Interaction of G protein coupled receptors and cholesterol

    Chemistry and Physics of Lipids 199:61–73.

    https://doi.org/10.1016/j.chemphyslip.2016.04.006

    • PubMed
    • Google Scholar
45. 1. Gipson B
    2. Zeng X
    3. Stahlberg H

    (2007a) 2dx_merge: data management and merging for 2D crystal images

    Journal of Structural Biology 160:375–384.

    https://doi.org/10.1016/j.jsb.2007.09.011

    • PubMed
    • Google Scholar
46. 1. Gipson B
    2. Zeng X
    3. Zhang ZY
    4. Stahlberg H

    (2007b) 2dx--user-friendly image processing for 2D crystals

    Journal of Structural Biology 157:64–72.

    https://doi.org/10.1016/j.jsb.2006.07.020

    • PubMed
    • Google Scholar
47. 1. Gonen T
    2. Sliz P
    3. Kistler J
    4. Cheng Y
    5. Walz T

    (2004) Aquaporin-0 membrane junctions reveal the structure of a closed water pore

    Nature 429:193–197.

    https://doi.org/10.1038/nature02503

    • PubMed
    • Google Scholar
48. 1. Gonen T
    2. Cheng Y
    3. Sliz P
    4. Hiroaki Y
    5. Fujiyoshi Y
    6. Harrison SC
    7. Walz T

    (2005) Lipid-protein interactions in double-layered two-dimensional AQP0 crystals

    Nature 438:633–638.

    https://doi.org/10.1038/nature04321

    • PubMed
    • Google Scholar
49. 1. Gorin MB
    2. Yancey SB
    3. Cline J
    4. Revel JP
    5. Horwitz J

    (1984) The major intrinsic protein (MIP) of the bovine lens fiber membrane: characterization and structure based on cDNA cloning

    Cell 39:49–59.

    https://doi.org/10.1016/0092-8674(84)90190-9

    • PubMed
    • Google Scholar
50. 1. Gu RX
    2. Baoukina S
    3. Tieleman DP

    (2019) Cholesterol flip-flop in heterogeneous membranes

    Journal of Chemical Theory and Computation 15:2064–2070.

    https://doi.org/10.1021/acs.jctc.8b00933

    • PubMed
    • Google Scholar
51. 1. Gupta K
    2. Donlan JAC
    3. Hopper JTS
    4. Uzdavinys P
    5. Landreh M
    6. Struwe WB
    7. Drew D
    8. Baldwin AJ
    9. Stansfeld PJ
    10. Robinson CV

    (2017) The role of interfacial lipids in stabilizing membrane protein oligomers

    Nature 541:421–424.

    https://doi.org/10.1038/nature20820

    • PubMed
    • Google Scholar
52. 1. Gyobu N
    2. Tani K
    3. Hiroaki Y
    4. Kamegawa A
    5. Mitsuoka K
    6. Fujiyoshi Y

    (2004) Improved specimen preparation for cryo-electron microscopy using a symmetric carbon sandwich technique

    Journal of Structural Biology 146:325–333.

    https://doi.org/10.1016/j.jsb.2004.01.012

    • PubMed
    • Google Scholar
53. 1. Hanson MA
    2. Cherezov V
    3. Griffith MT
    4. Roth CB
    5. Jaakola VP
    6. Chien EYT
    7. Velasquez J
    8. Kuhn P
    9. Stevens RC

    (2008) A specific cholesterol binding site is established by the 2.8 A structure of the human beta2-adrenergic receptor

    Structure 16:897–905.

    https://doi.org/10.1016/j.str.2008.05.001

    • PubMed
    • Google Scholar
54. 1. Hanulová M
    2. Weiss M

    (2012) Protein sorting and membrane-mediated interactions

    Biophysical Reviews 4:117–124.

    https://doi.org/10.1007/s12551-012-0069-8

    • PubMed
    • Google Scholar
55. 1. Hess B
    2. Bekker H
    3. Berendsen HJC
    4. Fraaije JGEM

    (1997) LINCS: A linear constraint solver for molecular simulations

    Journal of Computational Chemistry 18:1463–1472.

    https://doi.org/10.1002/(sici)1096-987x(199709)18:123.0.co;2-h

    • Google Scholar
56. 1. Hirai T
    2. Mitsuoka K
    3. Kidera A
    4. Fujiyoshi Y

    (2007) Simulation of charge effects on density maps obtained by high-resolution electron crystallography

    Journal of Electron Microscopy 56:131–140.

    https://doi.org/10.1093/jmicro/dfm019

    • PubMed
    • Google Scholar
57. 1. Hite RK
    2. Li Z
    3. Walz T

    (2010) Principles of membrane protein interactions with annular lipids deduced from aquaporin-0 2D crystals

    The EMBO Journal 29:1652–1658.

    https://doi.org/10.1038/emboj.2010.68

    • PubMed
    • Google Scholar
58. 1. Hite RK
    2. Chiu PL
    3. Schuller JM
    4. Walz T

    (2015) Effect of lipid head groups on double-layered two-dimensional crystals formed by aquaporin-0

    PLOS ONE 10:e0117371.

    https://doi.org/10.1371/journal.pone.0117371

    • PubMed
    • Google Scholar
59. 1. Hoover WG

    (1985) Canonical dynamics: Equilibrium phase-space distributions

    Physical Review. A, General Physics 31:1695–1697.

    https://doi.org/10.1103/physreva.31.1695

    • PubMed
    • Google Scholar
60. 1. Huang CS
    2. Yu X
    3. Fordstrom P
    4. Choi K
    5. Chung BC
    6. Roh SH
    7. Chiu W
    8. Zhou M
    9. Min X
    10. Wang Z

    (2020) Cryo-EM structures of NPC1L1 reveal mechanisms of cholesterol transport and ezetimibe inhibition

    Science Advances 6:eabb1989.

    https://doi.org/10.1126/sciadv.abb1989

    • PubMed
    • Google Scholar
61. 1. Jo S
    2. Kim T
    3. Iyer VG
    4. Im W

    (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM

    Journal of Computational Chemistry 29:1859–1865.

    https://doi.org/10.1002/jcc.20945

    • PubMed
    • Google Scholar
62. 1. Jo S
    2. Rui H
    3. Lim JB
    4. Klauda JB
    5. Im W

    (2010) Cholesterol flip-flop: insights from free energy simulation studies

    The Journal of Physical Chemistry. B 114:13342–13348.

    https://doi.org/10.1021/jp108166k

    • PubMed
    • Google Scholar
63. 1. Karnovsky MJ
    2. Kleinfeld AM
    3. Hoover RL
    4. Klausner RD

    (1982) The concept of lipid domains in membranes

    The Journal of Cell Biology 94:1–6.

    https://doi.org/10.1083/jcb.94.1.1

    • PubMed
    • Google Scholar
64. 1. Keyvanloo A
    2. Shaghaghi M
    3. Zuckermann MJ
    4. Thewalt JL

    (2018) The phase behavior and organization of sphingomyelin/cholesterol membranes: ADeuterium NMR Study

    Biophysical Journal 114:1344–1356.

    https://doi.org/10.1016/j.bpj.2018.01.024

    • PubMed
    • Google Scholar
65. 1. Khunweeraphong N
    2. Mitchell-White J
    3. Szöllősi D
    4. Hussein T
    5. Kuchler K
    6. Kerr ID
    7. Stockner T
    8. Lee JY

    (2020) Picky ABCG5/G8 and promiscuous ABCG2 - a tale of fatty diets and drug toxicity

    FEBS Letters 594:4035–4058.

    https://doi.org/10.1002/1873-3468.13938

    • PubMed
    • Google Scholar
66. 1. Kistler J
    2. Bullivant S

    (1980) Lens gap junctions and orthogonal arrays are unrelated

    FEBS Letters 111:73–78.

    https://doi.org/10.1016/0014-5793(80)80764-2

    • PubMed
    • Google Scholar
67. 1. Korade Z
    2. Kenworthy AK

    (2008) Lipid rafts, cholesterol, and the brain

    Neuropharmacology 55:1265–1273.

    https://doi.org/10.1016/j.neuropharm.2008.02.019

    • PubMed
    • Google Scholar
68. 1. Kumar VV

    (1991) Complementary molecular shapes and additivity of the packing parameter of lipids

    PNAS 88:444–448.

    https://doi.org/10.1073/pnas.88.2.444

    • PubMed
    • Google Scholar
69. 1. Kurth M
    2. Lolicato F
    3. Sandoval-Perez A
    4. Amaya-Espinosa H
    5. Teslenko A
    6. Sinning I
    7. Beck R
    8. Brügger B
    9. Aponte-Santamaría C

    (2020) Cholesterol localization around the metabotropic glutamate receptor 2

    The Journal of Physical Chemistry. B 124:9061–9078.

    https://doi.org/10.1021/acs.jpcb.0c05264

    • PubMed
    • Google Scholar
70. 1. Lafleur M
    2. Bloom M
    3. Cullis PR

    (1990) Lipid polymorphism and hydrocarbon order

    Biochemistry and Cell Biology = Biochimie et Biologie Cellulaire 68:1–8.

    https://doi.org/10.1139/o90-001

    • PubMed
    • Google Scholar
71. 1. Laganowsky A
    2. Reading E
    3. Allison TM
    4. Ulmschneider MB
    5. Degiacomi MT
    6. Baldwin AJ
    7. Robinson CV

    (2014) Membrane proteins bind lipids selectively to modulate their structure and function

    Nature 510:172–175.

    https://doi.org/10.1038/nature13419

    • PubMed
    • Google Scholar
72. 1. Lee J
    2. Cheng X
    3. Jo S
    4. MacKerell AD
    5. Klauda JB
    6. Im W

    (2016) CHARMM-GUI input generator for NAMD, Gromacs, Amber, Openmm, and CHARMM/OpenMM simulations using the CHARMM36 additive force field

    Biophysical Journal 110:641a.

    https://doi.org/10.1016/j.bpj.2015.11.3431

    • Google Scholar
73. 1. Lee AG

    (2018) A database of predicted binding sites for cholesterol on membrane proteins, deep in the membrane

    Biophysical Journal 115:522–532.

    https://doi.org/10.1016/j.bpj.2018.06.022

    • PubMed
    • Google Scholar
74. 1. Li H
    2. Papadopoulos V

    (1998) Peripheral-type benzodiazepine receptor function in cholesterol transport: Identification of a putative cholesterol recognition/interaction amino acid sequence and consensus pattern

    Endocrinology 139:4991–4997.

    https://doi.org/10.1210/endo.139.12.6390

    • PubMed
    • Google Scholar
75. 1. Li X
    2. Mooney P
    3. Zheng S
    4. Booth CR
    5. Braunfeld MB
    6. Gubbens S
    7. Agard DA
    8. Cheng Y

    (2013) Electron counting and beam-induced motion correction enable near-atomic-resolution single-particle cryo-EM

    Nature Methods 10:584–590.

    https://doi.org/10.1038/nmeth.2472

    • PubMed
    • Google Scholar
76. 1. Lingwood D
    2. Simons K

    (2010) Lipid rafts as a membrane-organizing principle

    Science 327:46–50.

    https://doi.org/10.1126/science.1174621

    • PubMed
    • Google Scholar
77. 1. Liscum L
    2. Munn NJ

    (1999) Intracellular cholesterol transport

    Biochimica et Biophysica Acta 1438:19–37.

    https://doi.org/10.1016/s1388-1981(99)00043-8

    • PubMed
    • Google Scholar
78. 1. MacKerell AD
    2. Bashford D
    3. Bellott M
    4. Dunbrack RL
    5. Evanseck JD
    6. Field MJ
    7. Fischer S
    8. Gao J
    9. Guo H
    10. Ha S
    11. Joseph-McCarthy D
    12. Kuchnir L
    13. Kuczera K
    14. Lau FTK
    15. Mattos C
    16. Michnick S
    17. Ngo T
    18. Nguyen DT
    19. Prodhom B
    20. Reiher WE
    21. Roux B
    22. Schlenkrich M
    23. Smith JC
    24. Stote R
    25. Straub J
    26. Watanabe M
    27. Wiórkiewicz-Kuczera J
    28. Yin D
    29. Karplus M

    (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins

    The Journal of Physical Chemistry B 102:3586–3616.

    https://doi.org/10.1021/jp973084f

    • Google Scholar
79. 1. Marino KA
    2. Prada-Gracia D
    3. Provasi D
    4. Filizola M

    (2016) Impact of lipid composition and receptor conformation on the spatio-temporal organization of μ-opioid receptors in a multi-component plasma membrane model

    PLOS Computational Biology 12:e1005240.

    https://doi.org/10.1371/journal.pcbi.1005240

    • Google Scholar
80. 1. Marsh D

    (2010) Liquid-ordered phases induced by cholesterol: a compendium of binary phase diagrams

    Biochimica et Biophysica Acta (BBA) - Biomembranes 1798:688–699.

    https://doi.org/10.1016/j.bbamem.2009.12.027

    • Google Scholar
81. 1. Mathiharan YK
    2. Glaaser IW
    3. Zhao Y
    4. Robertson MJ
    5. Skiniotis G
    6. Slesinger PA

    (2021) Structural insights into GIRK2 channel modulation by cholesterol and PIP2

    Cell Reports 36:109619.

    https://doi.org/10.1016/j.celrep.2021.109619

    • Google Scholar
82. 1. McCoy AJ
    2. Grosse-Kunstleve RW
    3. Adams PD
    4. Winn MD
    5. Storoni LC
    6. Read RJ

    (2007) Phaser crystallographic software

    Journal of Applied Crystallography 40:658–674.

    https://doi.org/10.1107/S0021889807021206

    • PubMed
    • Google Scholar
83. 1. McGaughey GB
    2. Gagné M
    3. Rappé AK

    (1998) pi-Stacking interactions: alive and well in proteins

    The Journal of Biological Chemistry 273:15458–15463.

    https://doi.org/10.1074/jbc.273.25.15458

    • PubMed
    • Google Scholar
84. 1. Mercadante D
    2. Gräter F
    3. Daday C

    (2018) CONAN: a tool to decode dynamical information from molecular interaction maps

    Biophysical Journal 114:1267–1273.

    https://doi.org/10.1016/j.bpj.2018.01.033

    • PubMed
    • Google Scholar
85. 1. Miyamoto S
    2. Kollman PA

    (1992) Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models

    Journal of Computational Chemistry 13:952–962.

    https://doi.org/10.1002/jcc.540130805

    • Google Scholar
86. 1. Moriarty NW
    2. Grosse-Kunstleve RW
    3. Adams PD

    (2009) electronic Ligand Builder and Optimization Workbench (eLBOW): a tool for ligand coordinate and restraint generation

    Acta Crystallographica. Section D, Biological Crystallography 65:1074–1080.

    https://doi.org/10.1107/S0907444909029436

    • PubMed
    • Google Scholar
87. 1. Nicolson GL

    (2014) The fluid-mosaic model of membrane structure: still relevant to understanding the structure, function and dynamics of biological membranes after more than 40 years

    Biochimica et Biophysica Acta 1838:1451–1466.

    https://doi.org/10.1016/j.bbamem.2013.10.019

    • PubMed
    • Google Scholar
88. 1. Niemelä P
    2. Hyvönen MT
    3. Vattulainen I

    (2004) Structure and dynamics of sphingomyelin bilayer: insight gained through systematic comparison to phosphatidylcholine

    Biophysical Journal 87:2976–2989.

    https://doi.org/10.1529/biophysj.104.048702

    • PubMed
    • Google Scholar
89. 1. Niemelä PS
    2. Miettinen MS
    3. Monticelli L
    4. Hammaren H
    5. Bjelkmar P
    6. Murtola T
    7. Lindahl E
    8. Vattulainen I

    (2010) Membrane proteins diffuse as dynamic complexes with lipids

    Journal of the American Chemical Society 132:7574–7575.

    https://doi.org/10.1021/ja101481b

    • PubMed
    • Google Scholar
90. 1. Nishio M
    2. Umezawa Y
    3. Hirota M
    4. Takeuchi Y

    (1995) The CH/π interaction: significance in molecular recognition

    Tetrahedron 51:8665–8701.

    https://doi.org/10.1016/0040-4020(94)01066-9

    • Google Scholar
91. 1. Niu SL
    2. Litman BJ

    (2002) Determination of membrane cholesterol partition coefficient using a lipid vesicle-cyclodextrin binary system: effect of phospholipid acyl chain unsaturation and headgroup composition

    Biophysical Journal 83:3408–3415.

    https://doi.org/10.1016/S0006-3495(02)75340-X

    • PubMed
    • Google Scholar
92. 1. Nosé S
    2. Klein ML

    (1983) Constant pressure molecular dynamics for molecular systems

    Molecular Physics 50:1055–1076.

    https://doi.org/10.1080/00268978300102851

    • Google Scholar
93. 1. Nosé S

    (1984) A molecular dynamics method for simulations in the canonical ensemble

    Molecular Physics 52:255–268.

    https://doi.org/10.1080/00268978400101201

    • Google Scholar
94. 1. O’Connor JW
    2. Klauda JB

    (2011) Lipid membranes with a majority of cholesterol: applications to the ocular lens and aquaporin 0

    The Journal of Physical Chemistry. B 115:6455–6464.

    https://doi.org/10.1021/jp108650u

    • PubMed
    • Google Scholar
95. 1. Páll S
    2. Hess B

    (2013) A flexible algorithm for calculating pair interactions on SIMD architectures

    Computer Physics Communications 184:2641–2650.

    https://doi.org/10.1016/j.cpc.2013.06.003

    • Google Scholar
96. 1. Páll S
    2. Zhmurov A
    3. Bauer P
    4. Abraham M
    5. Lundborg M
    6. Gray A
    7. Hess B
    8. Lindahl E

    (2020) Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS

    The Journal of Chemical Physics 153:134110.

    https://doi.org/10.1063/5.0018516

    • PubMed
    • Google Scholar
97. 1. Pandit SA
    2. Khelashvili G
    3. Jakobsson E
    4. Grama A
    5. Scott HL

    (2007) Lateral organization in lipid-cholesterol mixed bilayers

    Biophysical Journal 92:440–447.

    https://doi.org/10.1529/biophysj.106.093864

    • Google Scholar
98. 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
99. 1. Pettersen EF
    2. Goddard TD
    3. Huang CC
    4. Couch GS
    5. Greenblatt DM
    6. Meng EC
    7. Ferrin TE

    (2004) UCSF Chimera--a visualization system for exploratory research and analysis

    Journal of Computational Chemistry 25:1605–1612.

    https://doi.org/10.1002/jcc.20084

    • PubMed
    • Google Scholar
100. 1. Quinn PJ
     2. Wolf C

     (2009) The liquid-ordered phase in membranes

     Biochimica et Biophysica Acta (BBA) - Biomembranes 1788:33–46.

     https://doi.org/10.1016/j.bbamem.2008.08.005

     • Google Scholar
101. 1. Raguz M
     2. Mainali L
     3. Widomska J
     4. Subczynski WK

     (2011) The immiscible cholesterol bilayer domain exists as an integral part of phospholipid bilayer membranes

     Biochimica et Biophysica Acta 1808:1072–1080.

     https://doi.org/10.1016/j.bbamem.2010.12.019

     • PubMed
     • Google Scholar
102. 1. Róg T
     2. Pasenkiewicz-Gierula M

     (2006) Cholesterol-Sphingomyelin Interactions: a molecular dynamics simulation study

     Biophysical Journal 91:3756–3767.

     https://doi.org/10.1529/biophysj.106.080887

     • Google Scholar
103. 1. Sarkar P
     2. Chattopadhyay A

     (2020) Cholesterol interaction motifs in G protein-coupled receptors: Slippery hot spots?

     Wiley Interdisciplinary Reviews. Systems Biology and Medicine 12:e1481.

     https://doi.org/10.1002/wsbm.1481

     • PubMed
     • Google Scholar
104. 1. Schenk AD
     2. Philippsen A
     3. Engel A
     4. Walz T

     (2013) A pipeline for comprehensive and automated processing of electron diffraction data in IPLT

     Journal of Structural Biology 182:173–185.

     https://doi.org/10.1016/j.jsb.2013.02.017

     • PubMed
     • Google Scholar
105. 1. Schmidt U
     2. Guigas G
     3. Weiss M

     (2008) Cluster formation of transmembrane proteins due to hydrophobic mismatching

     Physical Review Letters 101:128104.

     https://doi.org/10.1103/PhysRevLett.101.128104

     • Google Scholar
106. 1. Schroeder F
     2. Jefferson JR
     3. Kier AB
     4. Knittel J
     5. Scallen TJ
     6. Wood WG
     7. Hapala I

     (1991) Membrane cholesterol dynamics: cholesterol domains and kinetic pools

     Experimental Biology and Medicine 196:235–252.

     https://doi.org/10.3181/00379727-196-43185

     • Google Scholar
107. Conference

     1. Seabold S
     2. Perktold J

     (2010) Statsmodels: Econometric and Statistical Modeling with Python

     Python in Science Conference.

     https://doi.org/10.25080/Majora-92bf1922-011

     • Google Scholar
108. 1. Seelig J

     (1977) Deuterium magnetic resonance: theory and application to lipid membranes

     Quarterly Reviews of Biophysics 10:353–418.

     https://doi.org/10.1017/S0033583500002948

     • Google Scholar
109. 1. Shinoda T
     2. Ogawa H
     3. Cornelius F
     4. Toyoshima C

     (2009) Crystal structure of the sodium-potassium pump at 2.4 A resolution

     Nature 459:446–450.

     https://doi.org/10.1038/nature07939

     • PubMed
     • Google Scholar
110. 1. Silvius JR

     (2003) Role of cholesterol in lipid raft formation: lessons from lipid model systems

     Biochimica et Biophysica Acta 1610:174–183.

     https://doi.org/10.1016/s0005-2736(03)00016-6

     • PubMed
     • Google Scholar
111. 1. Simons K
     2. Ikonen E

     (1997) Functional rafts in cell membranes

     Nature 387:569–572.

     https://doi.org/10.1038/42408

     • PubMed
     • Google Scholar
112. 1. Simons K
     2. Toomre D

     (2000) Lipid rafts and signal transduction

     Nature Reviews. Molecular Cell Biology 1:31–39.

     https://doi.org/10.1038/35036052

     • PubMed
     • Google Scholar
113. 1. Stansfeld PJ
     2. Jefferys EE
     3. Sansom MSP

     (2013) Multiscale simulations reveal conserved patterns of lipid interactions with aquaporins

     Structure 21:810–819.

     https://doi.org/10.1016/j.str.2013.03.005

     • Google Scholar
114. 1. Stier A
     2. Sackmann E

     (1973) Spin labels as enzyme substrates Heterogeneous lipid distribution in liver microsomal membranes

     Biochimica et Biophysica Acta (BBA) - Biomembranes 311:400–408.

     https://doi.org/10.1016/0005-2736(73)90320-9

     • Google Scholar
115. 1. Taghon GJ
     2. Rowe JB
     3. Kapolka NJ
     4. Isom DG

     (2021) Predictable cholesterol binding sites in GPCRs lack consensus motifs

     Structure 29:499–506.

     https://doi.org/10.1016/j.str.2021.01.004

     • PubMed
     • Google Scholar
116. 1. Valpuesta JM
     2. Carrascosa JL
     3. Henderson R

     (1994) Analysis of electron microscope images and electron diffraction patterns of thin crystals of phi 29 connectors in ice

     Journal of Molecular Biology 240:281–287.

     https://doi.org/10.1006/jmbi.1994.1445

     • PubMed
     • Google Scholar
117. 1. van Meer G
     2. Voelker DR
     3. Feigenson GW

     (2008) Membrane lipids: where they are and how they behave

     Nature Reviews. Molecular Cell Biology 9:112–124.

     https://doi.org/10.1038/nrm2330

     • PubMed
     • Google Scholar
118. 1. Vereb G
     2. Szöllosi J
     3. Matkó J
     4. Nagy P
     5. Farkas T
     6. Vigh L
     7. Mátyus L
     8. Waldmann TA
     9. Damjanovich S

     (2003) Dynamic, yet structured: The cell membrane three decades after the Singer-Nicolson model

     PNAS 100:8053–8058.

     https://doi.org/10.1073/pnas.1332550100

     • PubMed
     • Google Scholar
119. 1. Virtanen P
     2. Gommers R
     3. Oliphant TE
     4. Haberland M
     5. Reddy T
     6. Cournapeau D
     7. Burovski E
     8. Peterson P
     9. Weckesser W
     10. Bright J
     11. Walt SJ
     12. Brett M
     13. Wilson J
     14. Millman KJ
     15. Mayorov N
     16. Nelson ARJ
     17. Jones E
     18. Kern R
     19. Larson E
     20. Carey CJ
     21. Polat İ
     22. Feng Y
     23. Moore EW
     24. VanderPlas J
     25. Laxalde D
     26. Perktold J
     27. Cimrman R
     28. Henriksen I
     29. Quintero EA
     30. Harris CR
     31. Archibald AM
     32. Ribeiro AH
     33. Pedregosa F
     34. Mulbregt P

     (2020) SciPy 1.0 Contributors

     Nature Methods 17:261–272.

     https://doi.org/10.1038/s41592-019-0686-2

     • Google Scholar
120. Preprint

     1. Wang Y
     2. Lyons JA
     3. Timcenko M
     4. Kümmerer F
     5. de Groot BL
     6. Nissen P
     7. Gapsys V
     8. Lindorff-Larsen K

     (2021) Substrate transport and specificity in a phospholipid flippase

     bioRxiv.

     https://doi.org/10.1101/2020.06.24.169771

     • Google Scholar
121. 1. Webby MN
     2. Oluwole AO
     3. Pedebos C
     4. Inns PG
     5. Olerinyova A
     6. Prakaash D
     7. Housden NG
     8. Benn G
     9. Sun D
     10. Hoogenboom BW
     11. Kukura P
     12. Mohammed S
     13. Robinson CV
     14. Khalid S
     15. Kleanthous C

     (2022) Lipids mediate supramolecular outer membrane protein assembly in bacteria

     Science Advances 8:eadc9566.

     https://doi.org/10.1126/sciadv.adc9566

     • PubMed
     • Google Scholar
122. 1. Winn MD
     2. Ballard CC
     3. Cowtan KD
     4. Dodson EJ
     5. Emsley P
     6. Evans PR
     7. Keegan RM
     8. Krissinel EB
     9. Leslie AGW
     10. McCoy A
     11. McNicholas SJ
     12. Murshudov GN
     13. Pannu NS
     14. Potterton EA
     15. Powell HR
     16. Read RJ
     17. Vagin A
     18. Wilson KS

     (2011) Overview of the CCP4 suite and current developments

     Acta Crystallographica. Section D, Biological Crystallography 67:235–242.

     https://doi.org/10.1107/S0907444910045749

     • PubMed
     • Google Scholar
123. 1. Wolf MG
     2. Hoefling M
     3. Aponte-Santamaría C
     4. Grubmüller H
     5. Groenhof G

     (2010) g_membed: efficient insertion of a membrane protein into an equilibrated lipid bilayer with minimal perturbation

     Journal of Computational Chemistry 31:2169–2174.

     https://doi.org/10.1002/jcc.21507

     • PubMed
     • Google Scholar
124. 1. Wu H
     2. Wang C
     3. Gregory KJ
     4. Han GW
     5. Cho HP
     6. Xia Y
     7. Niswender CM
     8. Katritch V
     9. Meiler J
     10. Cherezov V
     11. Conn PJ
     12. Stevens RC

     (2014) Structure of a class C GPCR metabotropic glutamate receptor 1 bound to an allosteric modulator

     Science 344:58–64.

     https://doi.org/10.1126/science.1249489

     • Google Scholar
125. 1. Yesylevskyy SO

     (2007) ProtSqueeze: simple and effective automated tool for setting up membrane protein simulations

     Journal of Chemical Information and Modeling 47:1986–1994.

     https://doi.org/10.1021/ci600553y

     • PubMed
     • Google Scholar
126. 1. Zampighi G
     2. Simon SA
     3. Robertson JD
     4. McIntosh TJ
     5. Costello MJ

     (1982) On the structural organization of isolated bovine lens fiber junctions

     The Journal of Cell Biology 93:175–189.

     https://doi.org/10.1083/jcb.93.1.175

     • PubMed
     • Google Scholar
127. 1. Zelenka PS

     (1984) Lens lipids

     Current Eye Research 3:1337–1359.

     https://doi.org/10.3109/02713688409007421

     • PubMed
     • Google Scholar
128. 1. Zhang M
     2. Mileykovskaya E
     3. Dowhan W

     (2002) Gluing the respiratory chain together: cardiolipin is required for supercomplex formation in the inner mitochondrial membrane

     The Journal of Biological Chemistry 277:43553–43556.

     https://doi.org/10.1074/jbc.C200551200

     • PubMed
     • Google Scholar
129. 1. Zheng L
     2. McQuaw CM
     3. Ewing AG
     4. Winograd N

     (2007) Sphingomyelin/phosphatidylcholine and cholesterol interactions studied by imaging mass spectrometry

     Journal of the American Chemical Society 129:15730–15731.

     https://doi.org/10.1021/ja0741675

     • PubMed
     • Google Scholar

Author details

1. Po-Lin Chiu

   Department of Cell Biology, Harvard Medical School, Boston, United States

   Present address

   School of Molecular Sciences, Biodesign Center for Applied Structural Discovery, Arizona State University, Tempe, United States

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Juan D Orjuela

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8608-7650

2. Juan D Orjuela

   1. Max Planck Tandem Group in Computational Biophysics, Universidad de los Andes, Bogotá, Colombia
   2. Biomedical Engineering Department, Universidad de los Andes, Bogotá, Colombia

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Po-Lin Chiu

   Competing interests

   No competing interests declared

3. Bert L de Groot

   Computational Biomolecular Dynamics Group, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany

   Contribution

   Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3570-3534

4. Camilo Aponte Santamaría

   1. Max Planck Tandem Group in Computational Biophysics, Universidad de los Andes, Bogotá, Colombia
   2. Molecular Biomechanics Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing – original draft, Writing – review and editing

   For correspondence

   camilo.aponte@h-its.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8427-6965

5. Thomas Walz

   Department of Cell Biology, Harvard Medical School, Boston, United States

   Present address

   Laboratory of Molecular Electron Microscopy, The Rockefeller University, New York, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing – original draft, Writing – review and editing

   For correspondence

   twalz@rockefeller.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2606-2835

• 837

  views

• 69

  downloads

• 2

  citations

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