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Initial and final structures of the plasma membrane. POPC is shown in light gray, POPE in red, Sph in green, GM3 in magenta, Chol in cyan, POPS in blue and PIP 2 in yellow. (A) Side view of
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Cell membranes are complex multicomponent systems, which are highly heterogeneous in the lipid distribution and composition. To date, most molecular simulations have focussed on relatively simple lipid compositions, helping to inform our understanding of in vitro experimental studies. Here we describe on simulations of complex asymmetric plasma mem...
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... 5. Lipid organization and interactions between GM3 head groups in a 6000 lipid plasma membrane model. (A) Plasma membrane composed of 6000 lipids. The same color scheme as Fig. 1 has been applied. (B) Zoom in (see white box of approximately 12 nm in (A)) on interactions between GM3 head groups within the lipid nano-domains. GM3s are represented as magenta coloured sticks. Water beads within 5 A ̊ of GM3 have been shown in cyan and sodium ions within 5 A ̊ of GM3 are shown in yellow. The entire membrane is shown as a white surface. doi:10.1371/journal.pcbi.1003911.g005
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Glycosylphosphatidylinositols (GPIs) act as membrane anchors of many eukaryotic cell surface proteins. GPIs in various organisms have a common backbone consisting of ethanolamine phosphate (EtNP), three mannoses (Mans), one non-N-acetylated glucosamine (GlcN) and inositol phospholipid, whose structure is EtNP-6Manα-2Manα-6Manα-4GlNα-6myoInositol-P-...
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... Regression analysis of curvature distributions gives the extent of sorting for each lipid type (Fig. 4D). Consistent with our findings, other studies have also reported PIP2 and POPE enrichment in negatively curved regions and relatively curvature agnostic behavior for POPC, PS, and SM lipids (Koldsø, Shorthouse, Hélie, & Sansom, 2014). A variety of computational and experimental methods have been used to study curvature-based lipid sorting, including buckling, tethers, and pipette aspiration. ...
... The size of nanodomains in the CG model had a broad distribution of up to approximately 50 glycolipids, which were highly dynamic with individual clusters breaking apart and reforming at the microsecond time scale. Another group [75] reported that the formation of such GM3 nanoscale clusters correlated with spontaneous curvature of the membrane. The interactions between the gangliosides GM1 and GM3 were further studied using equilibrium MD simulations at both CG and atomistic levels [76]. ...
... The model host membranes were either composed completely of simple phospholipids or had the more complex lipidic mixture reported by Koldsø et al., which includes cholesterol, sphingolipids, and gangliosides. 40,41 We found the O-antigen containing OMVs to be less susceptible to deformation than those containing only rough LPS, in agreement with previous studies of flat model outer membranes. 42,43 Furthermore, we found that interaction of the OMVs with ''domain-forming'' lipids such as gangliosides and sphingomyelin enables the host membrane to begin to wrap around the O-antigen containing OMVs (Figure 2). ...
... For other C2 domain proteins like synaptotagmin and DOC2, membrane insertion by the PI(4,5)P2 binding C2 domain is required for protein activity. [47][48][49] . We therefore speculate that the V67D mutation results in a loss of C2A membrane binding and insertion, leading to the observed loss of tubulation activity. ...
Dysferlin is a large membrane protein found most prominently in striated muscle. Loss of dysferlin activity is associated with reduced exocytosis, abnormal intracellular Ca2+ and the muscle diseases limb-girdle muscular dystrophy and Miyoshi myopathy. The cytosolic region of dysferlin consists of seven C2 domains with mutations in the C2A domain at the N-terminus resulting in pathology. Despite the importance of Ca2+ and membrane binding activities of the C2A domain for dysferlin function, the mechanism of the domain remains poorly characterized. In this study we find that the C2A domain preferentially binds membranes containing PI(4,5)P2 through an interaction mediated by residues Y23, K32, K33, and R77 on the concave face of the domain. We also found that subsequent to membrane binding, the C2A domain inserts residues on the Ca2+ binding loops into the membrane. Analysis of solution NMR measurements indicate that the domain inhabits two distinct structural states, with Ca2+ shifting the population between states towards a more rigid structure with greater affinity for PI(4,5)P2. Based on our results, we propose a mechanism where Ca2+ converts C2A from a structurally dynamic, low PI(4,5)P2 affinity state to a high affinity state that targets dysferlin to PI(4,5)P2 enriched membranes through interaction with Tyr23, K32, K33, and R77. Binding also involves changes in lipid packing and insertion by the third Ca2+ binding loop of the C2 domain into the membrane, which would contribute to dysferlin function in exocytosis and Ca2+ regulation.
... Here, all lipids are treated identically, thus disregarding any structural differences or packing preferences between species. This approach has been used to study the physical properties of both simpler lipid mixtures [27,30] and asymmetric plasma membrane models of increasing complexity [75,76]. ...
... Here, all lipids are treated identically, thus disregarding any structural differences or packing preferences between species. This approach has been used to study the physical properties of both simpler lipid mixtures [27,30] and asymmetric plasma membrane models of increasing complexity [75,76]. An asymmetric bilayer can be built (B) with equal numbers of lipids in the two leaflets (EqN method) or (C) by ensuring that the relative packing densities (or surface areas) of the two leaflets are initially the same as in cognate symmetric bilayers (the SA method). ...
... Motivated by the asymmetry observed in biological membranes, other studies have focused on understanding the biophysics of more complex asymmetric membranes [75,76,87]. Here, the point of reference is usually the biological membrane of interest, for example, the plasma membrane (PM) of eukaryotic cells. ...
The compositional asymmetry of biological membranes has attracted significant attention over the last decade. Harboring more differences from symmetric membranes than previously appreciated, asymmetric bilayers have proven quite challenging to study with familiar concepts and techniques, leaving many unanswered questions about the reach of the asymmetry effects. One particular area of active research is the computational investigation of composition- and number-asymmetric lipid bilayers with molecular dynamics (MD) simulations. Offering a high level of detail into the organization and properties of the simulated systems, MD has emerged as an indispensable tool in the study of membrane asymmetry. However, the realization that results depend heavily on the protocol used for constructing the asymmetric bilayer models has sparked an ongoing debate about how to choose the most appropriate approach. Here we discuss the underlying source of the discrepant results and review the existing methods for creating asymmetric bilayers for MD simulations. Considering the available data, we argue that each method is well suited for specific applications and hence there is no single best approach. Instead, the choice of a construction protocol—and consequently, its perceived accuracy—must be based primarily on the scientific question that the simulations are designed to address.
... For the Chol and POPC atom-atom occupancy analysis with the protein, contact between two atoms was assumed to occur if the distance was less than a cutoff distance of 0.5 nm. 28,29 The fractional occupancy O r for residue, r is defined using, ...
Plasma membrane induced protein folding and conformational transitions play a central role in cellular homeostasis. Several transmembrane proteins are folded in the complex lipid milieu to acquire a specific structure and function. Bacterial pore forming toxins (PFTs) are proteins expressed by a large class of pathogenic bacteria that exploit the plasma membrane environment to efficiently undergo secondary structure changes, oligomerize and form transmembrane pores. Unregulated pore formation causes ion imbalance leading to cell death and infection. Determining the free energy landscape of these membrane driven transitions remains a challenging problem. Although cholesterol recognition is required for lytic activity of several proteins in the PFT family of toxins, the regulatory role of cholesterol for the α -PFT, cytolysin A expressed by E. coli is less understood. In a recent free energy computation, we have shown that the β -tongue, a critical membrane inserted motif of the ClyA toxin, has an on-pathway partially unfolded intermediate that refolds into the helix-turn-helix motif of the pore state. ¹ To understand the molecular role played by cholesterol, we have carried out string method based computations in membranes devoid of cholesterol which reveals an increase of ∼ 30 times in the free energy barrier for the loss of β -sheet secondary structure when compared with membranes containing cholesterol. Specifically the tyrosine-cholesterol interaction was found to be critical to stabilizing the unfolded intermediate. In the absence of cholesterol the membrane was found to undergo large curvature deformations in both leaflets of the membrane accompanied by bilayer thinning. Our study with the α -toxin, ClyA illustrates that cholesterol is critical to catalyzing and stabilizing the unfolded state of the β -tongue in the membrane, opening up fresh insights into cholesterol assisted unfolding of membrane proteins.
Significance
Cholesterol, an integral part of mammalian cell membranes, is necessary for activity of pathogenic toxins. Our understanding of the thermodynamic and molecular underpinnings of cholesterol-protein interactions during different stages of toxin activity is unclear. Using path based all atom molecular dynamics simulations, we illustrate lowered free energy barriers and enhanced stability of the membrane unfolded intermediate of an α -pore forming toxin (PFT) ‘ClyA’ providing insights into the increased pore formation kinetics with cholesterol. Thus, membrane cholesterol generally believed to play a passive receptor function for PFT activity is involved in a more complex regulatory role in assisting secondary structure transitions critical to PFT lytic activity. Our findings could aid in drug development strategies for mitigating PFT mediated bacterial infections.
... We focused on MOR because the high-resolution cryoelectron microscopy insights into MOR-Gi coupling provided a valuable basis for our analyses (35). The membrane models were built on the basis of previous computational studies of realistic membranes, with multiple lipid types and a high degree of compositional complexity ( Fig. 4A) (36)(37)(38)(39). The initial structures of the MOR-mGsi and MOR-mGi1 complexes were generated using "Al-phafold2-multimer," and each complex was embedded in either membrane model by using the Chemistry at Harvard Macromolecular Mechanics -Graphical User Interface (CHARMM-GUI) (see Materials and Methods). ...
... Subsequently, the two heterodimers were embedded into tailored phospholipid bilayers using CHARMM-GUI (73) to obtain the following four systems: (i) MOR-mGi1 in PM, (ii) MOR-mGsi in PM, (iii) MOR-mGi1 in Golgi-like membrane (Golgi), and (iv) MOR-mGsi in Golgi. To reproduce the properties of PM and Golgi (36,37), two different phospholipidic compositions were used (Fig. 5A). All systems were solvated using a box of TIP3P water model and a salinity of 0.15 M NaCl. ...
Intracellular G protein-coupled receptors (GPCRs) can be activated by permeant ligands, which contributes to agonist selectivity. Opioid receptors (ORs) provide a notable example, where opioid drugs rapidly activate ORs in the Golgi apparatus. Our knowledge on intracellular GPCR function remains incomplete, and it is unknown whether OR signaling in plasma membrane (PM) and Golgi apparatus differs. Here, we assess the recruitment of signal transducers to mu- and delta-ORs in both compartments. We find that Golgi ORs couple to Gαi/o probes and are phosphorylated but, unlike PM receptors, do not recruit β-arrestin or a specific Gα probe. Molecular dynamics simulations with OR-transducer complexes in bilayers mimicking PM or Golgi composition reveal that the lipid environment promotes the location-selective coupling. We then show that delta-ORs in PM and Golgi have distinct effects on transcription and protein phosphorylation. The study reveals that the subcellular location defines the signaling effects of opioid drugs.
... Subsequently, the two heterodimers were embedded into tailored phospholipid bilayers using CHARMM-GUI (75,76) in order to obtain the following four systems: 1) MOR-mGi1 in PM, 2) MOR-mGsi in PM; 3) MOR-mGi1 in Golgi-like membrane (GM); 4) MOR-mGsi in GM. To reproduce the properties of PM and GM (36)(37)(38), two different phospholipidic compositions were employed, which are detailed in Figure 5A. All systems were solvated using a box of TIP3P water model and a salinity of 0.15 M NaCl. ...
G protein-coupled receptors in intracellular organelles can be activated in response to membrane permeant ligands, which contributes to the diversity and specificity of agonist action. The opioid receptors (ORs) provide a striking example, where opioid drugs activate ORs in the Golgi apparatus within seconds of drug addition. Till date, our knowledge on the signaling of intracellular GPCRs remains incomplete and it is unknown if the downstream effects triggered by ORs in plasma membrane and Golgi apparatus differ. To address this gap, we first assess the recruitment of signal transducers to ORs in both compartments. We find that Golgi-localized ORs couple to Gai/o probes and are phosphorylated by GPCR kinases (GRK2/3), but unlike plasma membrane receptors, do not recruit b-arrestin or a specific Ga probe. Subsequent molecular dynamics simulations with OR-transducer complexes in model bilayers mimicking plasma membrane or Golgi composition reveal that the lipid environment promotes location selective coupling. Unbiased global analyses then show that OR activation in the plasma membrane and Golgi apparatus has strikingly different downstream effects on transcription and protein phosphorylation. Taken together, the study delineates OR signal transduction with unprecedented spatial resolution and reveals that the subcellular location defines the signaling effect of opioid drugs.
... In realistic models of biological membranes, lipid composition is highly heterogeneous, and lipid species are distributed asymmetrically between the hemilayers (Ingólfsson et al., 2014). Such organization has a high propensity to exhibit spontaneous local bending (Koldsø et al., 2014). Membrane curvature generally forms part of even larger lipid reorganization events for a wide range of cellular phenomena and processes, e.g., membrane fusion (di Bartolo and Masone, 2022) and fission (Lipowsky, 2022), endo and exocytosis (Tomes, 2015), cytokinesis (Schiel and Prekeris, 2013), and autophagy (Gómez-Sánchez et al., 2021). ...
The inflexible concept of membrane curvature as an independent property of lipid structures is today obsolete. Lipid bilayers behave as many-body entities with emergent properties that depend on their interactions with the environment. In particular, proteins exert crucial actions on lipid molecules that ultimately condition the collective properties of the membranes. In this review, the potential of enhanced molecular dynamics to address cell-biology problems is discussed. The cases of membrane deformation, membrane fusion, and the fusion pore are analyzed from the perspective of the dimensionality reduction by collective variables. Coupled lipid-protein interactions as fundamental determinants of large membrane remodeling events are also commented. Finally, novel strategies merging cell biology and physics are considered as future lines of research.
... Figure 9a also highlights the paradigm shift brought by Ing olfsson et al.'s simulation of a 63-component plasma membrane of realistic lipid composition. 194 That effort showcased Martini's maturity and suitability to this type of systems and kick-started an entire sub-field in simulations of realistic lipid bilayers 192,195,395,396 (of which the current complexity record, held by Corradi et al. on lipid interactions of membrane proteins, 192 is an example; Table 1, Figure 9b). These simulations also underscore the simulation size and time requirements that high complexity can entail: Ing olfsson et al. had to simulate a membrane patch of over 70 Â 70 nm 2 so that, at 0.03% mole fraction, the least represented lipid could still be present in multiple copies. ...
The Martini model, a coarse‐grained force field for molecular dynamics simulations, has been around for nearly two decades. Originally developed for lipid‐based systems by the groups of Marrink and Tieleman, the Martini model has over the years been extended as a community effort to the current level of a general‐purpose force field. Apart from the obvious benefit of a reduction in computational cost, the popularity of the model is largely due to the systematic yet intuitive building‐block approach that underlies the model, as well as the open nature of the development and its continuous validation. The easy implementation in the widely used Gromacs software suite has also been instrumental. Since its conception in 2002, the Martini model underwent a gradual refinement of the bead interactions and a widening scope of applications. In this review, we look back at this development, culminating with the release of the Martini 3 version in 2021. The power of the model is illustrated with key examples of recent important findings in biological and material sciences enabled with Martini, as well as examples from areas where coarse‐grained resolution is essential, namely high‐throughput applications, systems with large complexity, and simulations approaching the scale of whole cells.
This article is categorized under: Software > Molecular Modeling
Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods
Structure and Mechanism > Computational Materials Science
Structure and Mechanism > Computational Biochemistry and Biophysics
