The authors thank Jinhui Li and Ricardo X. Ramirez for their simulation trajectories and discussions during the writing of this manuscript. O.C. was supported by the University at Buffalo Presidential Fellowship and National Institute of Health&#39;s Initiative for Maximizing Student Development Training Grant 1T32GM144920-01 awarded to Margarita L. Dubocovich (PI).

1. Building the system coordinates
<ol>
	<li>Navigate to CHARMM-GUI.org (C-GUI) using a web browser. On the top menu, navigate to Input Generator, then select Membrane Builder from the vertical options on the left side of the screen.</li>
	<li>To build a bilayer, select Bilayer Builder. 
	NOTE: First time users must activate their free account before building their first set of coordinates.</li>
	<li>Select Membrane Only System. Save the generated JOB ID to retrieve the system and continue from where you left off during the process if needed.
	<ol>
		<li>Visualize the systems during every step of the building process by clicking View Structure in the box located at the top of the page, or by downloading the resulting PDB file. Look out for missing components, mistakes in the selected input lipid species or patch size.</li>
	</ol>
	</li>
	<li>Select the components of the system.
	<ol>
		<li>Choose the Heterogeneous Lipid option, even if building a single-component bilayer; then select a Rectangular Box type.</li>
		<li>Select 45 water molecules per lipid for the hydration option; this is sufficient to ensure a fully hydrated bilayer.</li>
		<li>Set the length of XY to be based on number of lipid components. Then, select the number of lipids to be included for each lipid species determined ahead of the model. For the case study discussed in the next section, a membrane model with 600 lipids distributed symmetrically in two leaflets was built. To model the ER of eukaryotic cells a mixture of 336 DOPC, 132 DPPE, 60 CHOL, 72 POPI lipids was used for the PI model; and 330 DOPC, 126 DPPE, 54 CHOL, 66 POPI, and 24 DOPS lipids for the PI-PS model. 
		NOTE: C-GUI provides a library of lipid structures to choose from; click on the images next to the species name for its chemical structure.</li>
		<li>Type the desired number of molecules in the upper and lower leaflet in the two boxes next to the lipid name. For the case study, a symmetric membrane composition is desired - make sure there are no errors about unmatched number of lipids in the upper leaflet and lower leaflet. If asymmetry is desired, make sure the total number of lipids in each leaflet is correct. For details on building asymmetric bilayers, refer to work by Park et al.52,53.</li>
		<li>Go to the top of the lipid species list and click on Show the System Info&#160;button. Assemble components and complete the system.</li>
		<li>Select the option to include neutralizing ions using the distance-based algorithm for faster convergence54.</li>
		<li>Leave the default solution concentration of KCl at 0.15 mM. This is a&#160;typical salt concentration to render neutral the simulation box for membrane bilayers. 
		NOTE: If a different concentration is to be used, make sure to click the Calculate Solvent Composition button after editing it.</li>
	</ol>
	</li>
	<li>Select the simulation conditions and settings.
	<ol>
		<li>Select CHARMM36m as the FF option; it is commonly used for lipid and protein simulations, but the user may select other options discussed in the introduction.</li>
		<li>Select GROMACS as the MD engine to obtain sample input files in the corresponding format. 
		NOTE: GROMACS is recommended for new users because it has multiple online resources, tutorials, and forums for support. The user can select from multiple MD engines to explore options in terms of simulation performance and code syntax.</li>
		<li>Select the Constant Particle-Pressure-Temperature (NPT) ensemble, by far the most used dynamic ensemble in the simulation of lipid bilayers.</li>
		<li>Set the temperature and pressure in Kelvin and bars as 303 K and 1 bar, respectively. It is typical to set the temperature between 298 K and 310 K for the study of biological processes to ensure a bilayer in the liquid disorder state. 
		NOTE: The temperature depends on the conditions of the process to be simulated and can be altered as needed. Depending on the lipid species in the model, set the temperature to be above the transition temperature of pure lipid components prior to running the simulation.</li>
	</ol>
	</li>
	<li>Download the resulting files and transfer to the computer cluster.
	<ol>
		<li>Visualize the final system on a software of choice, such as VMD or PyMol, and inspect for proper setup. 
		&#8203;NOTE: It is good to check, for example, that there is sufficient water around the membrane such that lipids do not interact with image atoms during the simulation, and proper leaflet setup (a bilayer with no space or water in-between).</li>
	</ol>
	</li>
</ol>
2. Running MD simulations
<ol>
	<li>Upload and unzip the files from C-GUI on your computing cluster. Navigate to the Gromacs directory. Create a relaxation submission script.</li>
	<li>Follow the guidelines of the cluster for the format of a submission script.
	<ol>
		<li>Copy the commands listed until just above the # Production comment in the README file into the submission script. 
		NOTE: This default from C-GUI is a loop that runs a 6-step relaxation of the system. If a different and well-established protocol is desired, edit it to read the coordinates just built and downloaded from C-GUI.</li>
	</ol>
	</li>
	<li>Submit the relaxation script and confirm that all output files have been downloaded for all steps before moving to the production run. Upon completion, check for the following output files from GROMACS, generated during the 6 step run: *.log, *.tpr, *.gro, *.edr, *.trr / *.xtc</li>
	<li>Create a production run script.
	<ol>
		<li>Use one of the samples gmx grompp and gmx mdrun commands from any of the relaxation steps as a template.</li>
		<li>Before using the script, make sure to create a *.mdp file that contains similar simulation options as the provided step7_production.mdp file. 
		NOTE: The provided default options are standard for membrane simulations; ditances are listed in nm and time is given in picoseconds or number of steps (picoseconds / integration time step). Update the nsteps to run up to the desired simulation length (this is equal to dt * nsteps) and nst[x,v,f]out to update the data saving frequency in number of integration steps. For the case study, set the nsteps to 250,000,000 for a simulation length of 500ns (simulation time / integration step = 500,000 ps / 0.002ps), and nst[x,v,f]out to 50,000 to save data every 100 ps</li>
	</ol>
	</li>
	<li>Before running the actual simulation, run benchmark studies to determine the best use of resources.
	<ol>
		<li>Run the system for 1-2 ns using different number of computing nodes. 
		NOTE: The ER case study was submitted on the UB center for computational research (CCR) high-performance computing cluster55 for 2 ns, where the performance was tested for 1-10 nodes.</li>
		<li>Compare the performance in ns/day for each setting to determine the optimal resources for the run. It is common practice to select the number of nodes that result in 75%-80% of maximum performance.</li>
	</ol>
	</li>
	<li>Run the production run.
	<ol>
		<li>Run each system in triplicates to ensure reproducibility and conduct statistical analysis on the data.</li>
		<li>Extend the trajectory based on the benchmarks if the allowed queue time for submission on the computing cluster runs out. Use the gmx convert-tpr, then gmx mdrun commands to continue trajectory collection. 
		&#8203;NOTE: Options are described in the GROMACS documentation online (https://manual.gromacs.org/).</li>
		<li>For a membrane-only system, examine if the system has reached equilibrium by calculating the area-per-lipid over time. If it has not, extend the simulation trajectory.</li>
	</ol>
	</li>
</ol>
3. Analyzing the trajectory
<ol>
	<li>Visualize the system before running analysis to determine molecules of interest and portion of the trajectory that is intended for characterization.</li>
	<li>Compress raw trajectory files (*.trr) by changing file format to *.xtc and/or skipping frames to reduce file size and facilitate more efficient transfer to the local station for visualization and analysis. 
	NOTE: For large membrane systems, one may opt to strip water from the trajectory to further reduce the file size. This can be done with index files on GROMACS, TCL scripts on VMD, or Python libraries such as MDAnalysis and MDTraj.</li>
	<li>Perform chosen analyses during the equilibrated portion of the trajectory, as determined from the area-per-lipid timeseries. 
	NOTE: Refer to discussion for more details on typical membrane analyses and how to run them.</li>
</ol>

Simulating and Analyzing Lipid Bilayers Using Molecular Dynamics

The authors have no competing interests to disclose.

Experimental techniques can visualize biomolecules at high resolution using cryo-electron microscopy (cryo-EM)58, fluorescence techniques, and atomic force microscopy (AFM)59. However, it is challenging to capture the interplay and dynamics of molecular interactions that underlie biological pathways, disease pathogenesis, and therapeutic delivery at the atomic or amino acid level. Here, capabilities of MD simulations to study lipid membranes and the main steps to design, bu...

Lipids are major constituents of membranes, which provide boundaries for cells and enable intracellular compartmentalization1,2,3. Lipids are amphiphilic, with a polar head group and two hydrophobic fatty acid tails; these self-assemble into a bilayer to minimize contact of the hydrophobic chains with water3,4. Various combinations of hydrophilic head groups and hydrophobic tails result in different classes of lipids in biological membranes, such as glycerophospholipids, sphingolipids, and sterols (Figure 1)1,5,6. Glycerophospholipids are primary building blocks of eukaryotic cell membranes composed of glycerophosphate, long-chain fatty acids, and head groups of low-molecular weight7. Lipid nomenclature is based on differences in head groups; examples include phosphatidyl-choline (PC), phosphatidyl-ethanolamine (PE), phosphatidyl-serine (PS), phosphatidyl-glycerol (PG), phosphatidyl-inositol (PI), or the unmodified phosphatidic acid (PA)5,6. As for hydrophobic tails, the length and degree of saturation vary, along with the backbone structure. The possible combinations are numerous, resulting in thousands of lipid species in mammalian cells6. Changes in membrane lipid composition lead to different mechanical and structural membrane properties that impact the activity of both integral membrane proteins and peripheral proteins2,6.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65712/65712fig01.jpg" /> 
Figure 1. Representative lipid structures. Fatty acid tails are shown in blue boxes, common lipid head groups in orange, and sample backbones in purple. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/65712/65712fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Lipids are active players in cellular processes, protein activation in signaling cascades, and healthy cell homeostasis8,9. Altered lipid dynamics are the result of infection or can be markers of pathogenesis of disease10,11,12,13,14,15. As barriers for the cell, the study of membrane lipids and their role in permeation of small molecules is of relevance for drug delivery systems and membrane disruption mechanisms16,17. Chemical diversity and different ratios of lipid species across organelles, tissues, and organisms give rise to complex membrane dynamics2. It is therefore important to retain these characteristics in modelling studies of lipid bilayers, especially when the goal of a study is to examine interactions of other biomolecules with the membrane. The lipid species to consider in a model depends on the organism and cellular compartment of interest. For example, PG lipids are important for electron transfer in photosynthetic bateria18, while phosphorylated inositol lipids (PIPs) are major players in plasma membrane (PM) dynamics and signaling cascades in mammalian cells19,20. Inside the cell, the PM, endoplasmic reticulum (ER), Golgi, and mitochondrial membranes contain unique lipid abundances that influence their function. For example, the ER is the hub for lipid biogenesis and transports cholesterol out to the PM and Golgi; it contains a high lipid diversity with an abundance of PC and PE, but low sterol content, which promotes membrane fluidity21,22,23,24. In contrast, the PM incorporates hundreds and even thousands of lipid species depending on the organism25, it contains high levels of sphingolipids and cholesterol that give it a characteristic rigidity compared to other membranes in the cell24. Leaflet asymmetry should be considered for membranes like the PM, which has an outer leaflet rich in sphingomyelin, PC, and cholesterol, and an inner leaflet rich in PE, PI and PS that are important for signaling cascades24. Finally, lipid diversity also prompts the formation of micro-domains that differ in packing and internal order, known as lipid rafts24,26; these exhibit lateral asymmetry, are hypothesized to play important roles in cellular signaling26, and are hard to study due to their transient nature.
Experimental techniques such as fluoroscopy, spectroscopy, and model membrane systems like giant unilamellar vesicles (GUVs) have been used to investigate interactions of biomolecules with membranes. However, the complex and dynamic nature of the components involved is difficult to capture with experimental methods alone. For example, there are limitations on the imaging of transmembrane domains of proteins, the complexity of membranes used in such studies, and the identification of intermediate or transient states during the process of interest27,28,29. Since the advent of molecular simulation of lipid monolayers and bilayers in the 1980s29, lipid-protein systems and their interactions can now be quantified at the molecular level. Molecular dynamics (MD) simulation is a common computational technique that predicts the movement of particles based on their intermolecular forces. An additive interaction potential describes the bonded and non-bonded interactions between particles of the system30. The set of parameters used to model these interactions is termed the simulation forcefield (FF). These parameters are obtained from ab initio computations, semi-empirical, and quantum mechanical calculations, and optimized to reproduced data from X-ray and electron diffraction experiments, NMR, infrared, Raman and neutron spectroscopy, amongst other methods31.
MD simulations can be used to study systems at various levels of resolution32,33,34. Systems that aim to characterize specific biomolecular interactions, hydrogen bonds, and other high-resolution details are studied with all-atom (AA) simulations. In contrast, coarse-grained (CG) simulations lump atoms into larger functional groups to reduce computational cost and examine larger scale dynamics33. Situated in between these two are united-atom (UA) simulations, where hydrogen atoms are combined with their respective heavy atoms to accelerate the computation33,35. MD simulations are a powerful tool for exploration of the dynamics of lipid membranes and their interactions with other molecules and can serve to provide molecular level mechanisms for processes of interest at the membrane interface. Additionally, MD simulations can serve to narrow down experimental targets and predict macromolecular properties of a given system based on microscopic interactions.
In brief, given a set of initial coordinates, velocities, and a set of conditions like constant temperature and pressure, positions and velocities of each particle are calculated through numerical integration of the interaction potential and Newton&#39;s Law of motion. This is repeated iteratively, thereby generating a simulation trajectory30. These calculations are performed with an MD engine; among several open-source packages, GROMACS36 is one of the most commonly used engines and the one we describe here. It also includes tools for analysis and constructing initial coordinates of systems to be simulated37. Other MD engines include NAMD38; CHARMM39, and AMBER40, which the user can select at their own discretion based on computational performance of a given system. It is critical to visualize the trajectories during the simulation as well as for analysis and interpretation of the results. A variety of tools are available; here we discuss visual molecular dynamics (VMD) that offers a wide range of features, including three dimensional (3-D) visualization with expansive drawing and coloring methods, volumetric data visualization, building, preparing, and analyzing trajectories of MD simulation systems, and trajectory-movie making with no limits on system size, if the memory is available41,42,43.
The accuracy of predicted dynamics between system components is directly influenced by the FF chosen for the propagation of the trajectory. Empirical FF parametrization efforts are pursued by few research groups. The most established and common FF for MD include CHARMM39, AMBER40, Martini44, OPLS45, and SIRAH46. The all-atom additive CHARMM36 (C36) force field47 is widely used for AA MD of membrane systems as it accurately reproduces experimental structural data. It was originally developed by the CHARMM community, and it is compatible with multiple MD engines like GROMACS and NAMD. Despite improvements across common FFs, there is a continuous effort to improve the parameter sets to allow predictions that closely reproduce experimental observables, driven by interests in particular systems of study48,49.
A challenge when simulating lipid membranes is determining the length of simulation trajectory. This is largely dependent on the metrics to be analyzed and the process that one aims to characterize. Typically, complex lipid mixtures require longer time to reach equilibrium, as more species must have enough time to diffuse on the membrane plane and reach a stable lateral organization. A simulation is said to be at equilibrium when the property of interest has reached a plateau and fluctuates about a constant value. It is common practice to obtain at least 100-200 ns of equilibrated trajectory to carry out appropriate statistical analysis on the properties and interactions of interest. It is common to run membrane-only simulations between 200-500 ns, depending on the complexity of the lipid mixture and research question. Protein-lipid interactions typically require longer simulation times, between 500-2000 ns. Some approaches to accelerate sampling and observable dynamics with membrane systems are: (i) the highly mobile membrane mimetic (HMMM) model, which replaces end carbons of lipids in the membrane with organic solvent to accelerate sampling50; and (ii) hydrogen mass repartitioning (HMR), which combines a fraction of the masses of heavy atoms within a system with those of hydrogen atoms to allow the use of a larger simulation timestep51.
The following protocol discusses a beginner-friendly approach to build, run, and analyze realistic membrane models using AA MD. Given the nature of MD simulations, multiple trajectories must be run to account for reproducibility and proper statistical analysis of the results. It is current practice to run at minimum three replicas per system of interest. Once the lipid species have been selected for the organism and process of interest, basic steps to build, run, and analyze a simulation trajectory of a membrane-only system are outlined and summarized in Figure 2.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65712/65712fig02.jpg" /> 
Figure 2. Schematic to run MD simulations. Orange boxes correspond to the three main steps described in the protocol. Underneath is the workflow of the simulation process. During system setup, the system containing the initial coordinates of a solvated membrane system is built with a system input generator like CHARMM-GUI Membrane Builder. After transferring the input files to a high-performance computing cluster, the simulation trajectory is propagated using an MD engine, such as GROMACS. Trajectory analysis can be done on the computer cluster or a local working station along with visualization. Analysis is then carried out, using either packages with built-in analysis code such as GROMACS and VMD, or by using Bash scripts or various Python libraries. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/65712/65712fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table><tbody><tr><td>Anaconda3</td><td>Anaconda Inc (Python &amp;amp; related libraries)</td><td>N/A</td><td></td></tr><tr><td>CHARMM-GUI.org</td><td>Im lab, Lehigh University</td><td>N/A</td><td></td></tr><tr><td>GROMACS</td><td>GROMACS development team</td><td>N/A</td><td></td></tr><tr><td>Linux HPC Cluster</td><td>UB CCR</td><td>N/A</td><td></td></tr><tr><td>MATLAB</td><td>MathWorks</td><td>N/A</td><td></td></tr><tr><td>VMD</td><td>Theoretical and Computational Biophysics Group</td><td>N/A</td><td></td></tr></tbody></table>

realistic membrane modeling using complex lipid mixtures in simulation studies

Lipids are structural building blocks of cell membranes; lipid species vary across cell organelles and across organisms. This variety results in different mechanical and structural properties in the membrane that directly impact the molecules and processes that occur at this interface. Lipid composition is dynamic and can serve to modulate cell signaling processes. Computational approaches are increasingly used to predict interactions between biomolecules and provide molecular insights to experimental observables. Molecular dynamics (MD) is a technique based on statistical mechanics that predicts the movement of atoms based on the forces that act on them. MD simulations can be used to characterize the interaction of biomolecules. Here, we briefly introduce the technique, outline practical steps for beginners who are interested in simulating lipid bilayers, demonstrate the protocol with beginner-friendly software, and discuss alternatives, challenges, and important considerations of the process. Particularly, we emphasize the relevance of using complex lipid mixtures to model a cell membrane of interest to capture the appropriate hydrophobic and mechanical environments in simulation. We also discuss some examples where membrane composition and properties modulate the interactions of bilayers with other biomolecules.

To illustrate the use of the protocol and the results that can be obtained, a comparison study for membrane models for the endoplasmic reticulum (ER) is discussed. The two models in this study were (i) the PI&#160;model, which contains the top four lipid species found in the ER, and (ii) the PI-PS model, which added the anionic phosphatidylserine (PS) lipid species. These models were later used in a study of a viral protein and how it interacts with the membrane, the interest on PS has been cited as important for the per...

Watch this Scientific Journal Video about Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches at JoVE.com

Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches 

Membrane lipid diversity in structure and composition is an important contributor to cellular processes and can be a marker of disease. Molecular dynamics simulations allow us to study membranes and their interactions with biomolecules at atomistic resolution. Here, we provide a protocol to build, run, and analyze complex membrane systems.

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies

Lipids are structural building blocks of cell membranes; lipid species vary across cell organelles and across organisms. This variety results in different ...

realistic-membrane-modeling-using-complex-lipid-mixtures-simulation

Universidad de Cartagena UDEC

Research

JoVE Journal

Biology

2.0K Views.  State University of New York at Buffalo. Membrane lipid diversity in structure and composition is an important contributor to cellular processes and can be a marker of disease. Molecular dynamics simulations allow us to study membranes and their interactions with biomolecules at atomistic resolution. Here, we provide a protocol to build, run, and analyze complex membrane systems.

Video: Author Spotlight: Advancing Cell Membrane Biophysics - Exploring Interactions and Challenges Through Experimental and Computational Approaches 

A Model Membrane Platform for Reconstituting Mitochondrial Membrane Dynamics

Mitochondrial dynamics is essential for the organelle&#8217;s diverse functions and cellular responses. The crowded, spatially complex, mitochondrial membrane is a challenging environment to distinguish regulatory factors. Experimental control of protein and lipid components can help answer specific questions of regulation. Yet, quantitative manipulation of these factors is challenging in cellular assays. To investigate the molecular mechanism of mitochondria inner-membrane fusion, we introduced an in vitro reconstitution platform that mimics the lipid environment of the mitochondrial inner-membrane. Here we describe detailed steps for preparing lipid bilayers and reconstituting mitochondrial membrane proteins. The platform allowed analysis of intermediates in mitochondrial inner-membrane fusion, and the kinetics for individual transitions, in a quantitative manner. This protocol describes the fabrication of bilayers with asymmetric lipid composition and describes general considerations for reconstituting transmembrane proteins into a cushioned bilayer. The method may be applied to study other membrane systems.

Mitochondrial fusion is an important homeostatic reaction underlying mitochondrial dynamics. Described here is an in vitro reconstitution system to study mitochondrial inner-membrane fusion that can resolve membrane tethering, docking, hemifusion, and pore opening. The versatility of this approach in exploring cell membrane systems is discussed.

Mitochondrial dynamics is essential for the organelle&#8217;s diverse functions and cellular responses. The crowded, spatially complex, mitochondrial ...

Biochemistry

Automated Lipid Bilayer Membrane Formation Using a Polydimethylsiloxane Thin Film

An artificial lipid bilayer, or black lipid membrane (BLM), is a powerful tool for studying ion channels and protein interactions, as well as for biosensor applications. However, conventional BLM formation techniques have several drawbacks and they often require specific expertise and laborious processes. In particular, conventional BLMs suffer from low formation success rates and inconsistent membrane formation time. Here, we demonstrate a storable and transportable BLM formation system with controlled thinning-out time and enhanced BLM formation rate by replacing conventionally used films (polytetrafluoroethylene, polyoxymethylene, polystyrene) to polydimethylsiloxane (PDMS). In this experiment, a porous-structured polymer such as PDMS thin film is used. In addition, as opposed to conventionally used solvents with low viscosity, the use of squalene permitted a controlled thinning-out time via slow solvent absorption by PDMS, prolonging membrane lifetime. In addition, by using a mixture of squalene and hexadecane, the freezing point of the lipid solution was increased (~16 &#176;C), in addition, membrane precursors were produced that can be indefinitely stored and readily transported. These membrane precursors have reduced BLM formation time of &#60; 1 hr and achieved a BLM formation rate of ~80%. Moreover, ion channel experiments with gramicidin A demonstrated the feasibility of the membrane system.

We demonstrate a storable, transportable lipid bilayer formation system. A lipid bilayer membrane can be formed within 1 hr with over 80% success rate when a frozen membrane precursor is brought to ambient temperature. This system will reduce laborious processes and expertise associated with ion channels.

An artificial lipid bilayer, or black lipid membrane (BLM), is a powerful tool for studying ion channels and protein interactions, as well as for ...

Bioengineering

Crystallizing Membrane Proteins for Structure Determination using Lipidic Mesophases

A detailed protocol for crystallizing membrane proteins by using lipidic mesophases is described. This method has variously been referred to as the lipidic cubic phase or in meso method. The method has been shown to be quite versatile in that it has been used to solve X-ray crystallographic structures of prokaryotic and eukaryotic proteins, proteins that are monomeric, homo- and hetero-multimeric, chromophore-containing and chromophore-free, and alpha-helical and beta-barrel proteins. Recent successes using in meso crystallization are the human engineered beta2-adrenergic and adenosine A2a G protein-coupled receptors. Protocols are presented for reconstituting the membrane protein into the monoolein-based mesophase, and for setting up crystallizations in the manual mode. Additional steps in the overall process, such as crystal harvesting, are to be addressed in future video articles. The time required to prepare the protein-loaded mesophase and to set up a crystallization plate manually is about one hour.

Herein is described the procedure implemented in the Caffrey Membrane Structural and Functional Biology Group to set up manually crystallization trials of membrane proteins in lipidic mesophases.

A detailed protocol for crystallizing membrane proteins by using lipidic mesophases is described. This method has variously been referred to as the ...

Assembly of Cell Mimicking Supported and Suspended Lipid Bilayer Models for the Study of Molecular Interactions

Model cell membranes are a useful screening tool with applications ranging from early drug discovery to toxicity studies. The cell membrane is a crucial protective barrier for all cell types, separating the internal cellular components from the extracellular environment. These membranes are composed largely of a lipid bilayer, which contains outer hydrophilic head groups and inner hydrophobic tail groups, along with various proteins and cholesterol. The composition and structure of the lipids themselves play a crucial role in regulating biological function, including interactions between cells and the cellular microenvironment, which may contain pharmaceuticals, biological toxins, and environmental toxicants. In this study, methods to formulate uni-lipid and multi-lipid supported and suspended cell mimicking lipid bilayers are described. Previously, uni-lipid phosphatidylcholine (PC) lipid bilayers as well as multi-lipid placental trophoblast-inspired lipid bilayers were developed for use in understanding molecular interactions. Here, methods for achieving both types of bilayer models will be presented. For cell mimicking multi-lipid bilayers, the desired lipid composition is first determined via lipid extraction from primary cells or cell lines followed by liquid chromatography-mass spectrometry (LC-MS). Using this composition, lipid vesicles are fabricated using a thin-film hydration and extrusion method and their hydrodynamic diameter and zeta potential are characterized. Supported and suspended lipid bilayers can then be formed using quartz crystal microbalance with dissipation monitoring (QCM-D) and on a porous membrane for use in a parallel artificial membrane permeability assay (PAMPA), respectively. The representative results highlight the reproducibility and versatility of in vitro cell membrane lipid bilayer models. The methods presented can aid in rapid, facile assessment of the interaction mechanisms, such as permeation, adsorption, and embedment, of various molecules and macromolecules with a cell membrane, helping in the screening of drug candidates and prediction of potential cellular toxicity.

This protocol describes the formation of cell mimicking uni-lipid and multi-lipid vesicles, supported lipid bilayers, and suspended lipid bilayers. These in vitro models can be adapted to incorporate a variety of lipid types and can be used to investigate various molecule and macromolecule interactions.

Model cell membranes are a useful screening tool with applications ranging from early drug discovery to toxicity studies. The cell membrane is a crucial ...

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Here, we present methods for the development of assays to query potentially clinically significant nonsynonymous changes using in vivo complementation in zebrafish. Zebrafish (Danio rerio) are a useful animal system due to their experimental tractability; embryos are transparent to enable facile viewing, undergo rapid development ex vivo, and can be genetically manipulated.1 These aspects have allowed for significant advances in the analysis of embryogenesis, molecular processes, and morphogenetic signaling. Taken together, the advantages of this vertebrate model make zebrafish highly amenable to modeling the developmental defects in pediatric disease, and in some cases, adult-onset disorders. Because the zebrafish genome is highly conserved with that of humans (~70% orthologous), it is possible to recapitulate human disease states in zebrafish. This is accomplished either through the injection of mutant human mRNA to induce dominant negative or gain of function alleles, or utilization of morpholino (MO) antisense oligonucleotides to suppress genes to mimic loss of function variants. Through complementation of MO-induced phenotypes with capped human mRNA, our approach enables the interpretation of the deleterious effect of mutations on human protein sequence based on the ability of mutant mRNA to rescue a measurable, physiologically relevant phenotype. Modeling of the human disease alleles occurs through microinjection of zebrafish embryos with MO and/or human mRNA at the 1-4 cell stage, and phenotyping up to seven days post fertilization (dpf). This general strategy can be extended to a wide range of disease phenotypes, as demonstrated in the following protocol. We present our established models for morphogenetic signaling, craniofacial, cardiac, vascular integrity, renal function, and skeletal muscle disorder phenotypes, as well as others.

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Here, we present a systematic approach for developing physiologically relevant, sensitive and specific in vivo assays for interpreting variation in human pathology. Transient genetic manipulation via microinjection of WT and mutant human mRNA and morpholino (MO) antisense oligonucleotides harness the tractability of the developing zebrafish embryo to rapidly assay pathogenic mutations, especially, but not exclusively, in the context of human developmental disorders.

Here,  we present methods for the development of assays to query potentially clinically  significant nonsynonymous changes using in  vivo complementation ...

Reconstitution of a Kv Channel into Lipid Membranes for Structural and Functional Studies

To study the lipid-protein interaction in a reductionistic fashion, it is necessary to incorporate the membrane proteins into membranes of well-defined lipid composition. We are studying the lipid-dependent gating effects in a prototype voltage-gated potassium (Kv) channel, and have worked out detailed procedures to reconstitute the channels into different membrane systems. Our reconstitution procedures take consideration of both detergent-induced fusion of vesicles and the fusion of protein/detergent micelles with the lipid/detergent mixed micelles as well as the importance of reaching an equilibrium distribution of lipids among the protein/detergent/lipid and the detergent/lipid mixed micelles. Our data suggested that the insertion of the channels in the lipid vesicles is relatively random in orientations, and the reconstitution efficiency is so high that no detectable protein aggregates were seen in fractionation experiments. We have utilized the reconstituted channels to determine the conformational states of the channels in different lipids, record electrical activities of a small number of channels incorporated in planar lipid bilayers, screen for conformation-specific ligands from a phage-displayed peptide library, and support the growth of 2D crystals of the channels in membranes. The reconstitution procedures described here may be adapted for studying other membrane proteins in lipid bilayers, especially for the investigation of the lipid effects on the eukaryotic voltage-gated ion channels.

Procedures for complete reconstitution of a prototype voltage-gated potassium channel into lipid membranes are described. The reconstituted channels are suitable for biochemical assays, electrical recordings, ligand screening and electron crystallographic studies. These methods may have general applications to the structural and functional studies of other membrane proteins.

To study the lipid-protein interaction in a reductionistic fashion, it is necessary to incorporate the membrane proteins into membranes of well-defined ...

Green Fluorescent Protein-based Expression Screening of Membrane Proteins in Escherichia coli

The production of recombinant membrane proteins for structural and functional studies remains technically challenging due to low levels of expression and the inherent instability of many membrane proteins once solubilized in detergents. A protocol is described that combines ligation independent cloning of membrane proteins as GFP fusions with expression in Escherichia coli detected by GFP fluorescence. This enables the construction and expression screening of multiple membrane protein/variants to identify candidates suitable for further investment of time and effort. The GFP reporter is used in a primary screen of expression by visualizing GFP fluorescence following SDS polyacrylamide gel electrophoresis (SDS-PAGE). Membrane proteins that show both a high expression level with minimum degradation as indicated by the absence of free GFP, are selected for a secondary screen. These constructs are scaled and a total membrane fraction prepared and solubilized in four different detergents. Following ultracentrifugation to remove detergent-insoluble material, lysates are analyzed by fluorescence detection size exclusion chromatography (FSEC). Monitoring the size exclusion profile by GFP fluorescence provides information about the mono-dispersity and integrity of the membrane proteins in different detergents. Protein: detergent combinations that elute with a symmetrical peak with little or no free GFP and minimum aggregation are candidates for subsequent purification. Using the above methodology, the heterologous expression in E. coli of SED (shape, elongation, division, and sporulation) proteins from 47 different species of bacteria was analyzed. These proteins typically have ten transmembrane domains and are essential for cell division. The results show that the production of the SEDs orthologues in E. coli was highly variable with respect to the expression levels and integrity of the GFP fusion proteins. The experiment identified a subset for further investigation.

Green Fluorescent Protein-based Expression Screening of Membrane Proteins in Escherichia coli

A streamlined approach to screening for the expression of recombinant membrane proteins in Escherichia coli based on fusion to green fluorescent protein is presented.

The production of recombinant membrane proteins for structural and functional studies remains technically challenging due to low levels of expression and ...

Microperfusion Technique to Investigate Regulation of Microvessel Permeability in Rat Mesentery

Experiments to measure the permeability properties of individually perfused microvessels provide a bridge between investigation of molecular and cellular mechanisms regulating vascular permeability in cultured endothelial cell monolayers and the functional exchange properties of whole microvascular beds. A method to cannulate and perfuse venular microvessels of rat mesentery and measure the hydraulic conductivity of the microvessel wall is described. The main equipment needed includes an intravital microscope with a large modified stage that supports micromanipulators to position three different microtools: (1) a beveled glass micropipette to cannulate and perfuse the microvessel; (2) a glass micro-occluder to transiently block perfusion and enable measurement of transvascular water flow movement at a measured hydrostatic pressure, and (3) a blunt glass rod to stabilize the mesenteric tissue at the site of cannulation. The modified Landis micro-occlusion technique uses red cells suspended in the artificial perfusate as markers of transvascular fluid movement, and also enables repeated measurements of these flows as experimental conditions are changed and hydrostatic and colloid osmotic pressure difference across the microvessels are carefully controlled. Measurements of hydraulic conductivity first using a control perfusate, then after re-cannulation of the same microvessel with the test perfusates enable paired comparisons of the microvessel response under these well-controlled conditions. Attempts to extend the method to microvessels in the mesentery of mice with genetic modifications expected to modify vascular permeability were severely limited because of the absence of long straight and unbranched microvessels in the mouse mesentery, but the recent availability of the rats with similar genetic modifications using the CRISPR/Cas9 technology is expected to open new areas of investigation where the methods described herein can be applied.

The modified Landis technique enables paired measurement of the hydraulic conductivity of individual microvessels in the mesentery of normal and genetically modified rats under control and test conditions using microperfusion techniques. It provides a convenient method to evaluate mechanisms that regulate microvessel permeability and transvascular exchange under physiological conditions.

Experiments to measure the permeability properties of individually perfused microvessels provide a bridge between investigation of molecular and cellular ...

Pulling Membrane Nanotubes from Giant Unilamellar Vesicles

The reshaping of the cell membrane is an integral part of many cellular phenomena, such as endocytosis, trafficking, the formation of filopodia, etc. Many different proteins associate with curved membranes because of their ability to sense or induce membrane curvature. Typically, these processes involve a multitude of proteins making them too complex to study quantitatively in the cell. We describe a protocol to reconstitute a curved membrane in vitro, mimicking a curved cellular structure, such as the endocytic neck. A giant unilamellar vesicle (GUV) is used as a model of a cell membrane, whose internal pressure and surface tension are controlled with micropipette aspiration. Applying a point pulling force on the GUV using optical tweezers creates a nanotube of high curvature connected to a flat membrane. This method has traditionally been used to measure the fundamental mechanical properties of lipid membranes, such as bending rigidity. In recent years, it has been expanded to study how proteins interact with membrane curvature and the way they affect the shape and the mechanics of membranes. A system combining micromanipulation, microinjection, optical tweezers, and confocal microscopy allows measurement of membrane curvature, membrane tension, and the surface density of proteins, concurrently. From these measurements, many important mechanical and morphological properties of the protein-membrane system can be inferred. In addition, we lay out a protocol of creating GUVs in the presence of physiological salt concentration, and a method of quantifying the surface density of proteins on the membrane from fluorescence intensities of labeled proteins and lipids.

Many proteins in the cell sense and induce membrane curvature. We describe a method to pull membrane nanotubes from lipid vesicles to study the interaction of proteins or any curvature-active molecule with curved membranes in vitro.

The reshaping of the cell membrane is an integral part of many cellular phenomena, such as endocytosis, trafficking, the formation of filopodia, etc. Many ...

Ground State Depletion Super-resolution Imaging in Mammalian Cells

Advances in fluorescent microscopy and cell biology are intimately correlated, with the enhanced ability to visualize cellular events often leading to dramatic leaps in our understanding of how cells function. The development and availability of super-resolution microscopy has considerably extended the limits of optical resolution from ~250-20 nm. Biologists are no longer limited to describing molecular interactions in terms of colocalization within a diffraction limited area, rather it is now possible to visualize the dynamic interactions of individual molecules. Here, we outline a protocol for the visualization and quantification of cellular proteins by ground-state depletion microscopy&#160;for fixed cell imaging. We provide examples from two different membrane proteins, an element of the endoplasmic reticulum translocon, sec61&#946;, and a plasma membrane-localized voltage-gated L-type Ca2+ channel (CaV1.2). Discussed are the specific microscope parameters, fixation methods, photo-switching buffer formulation, and pitfalls and challenges of image processing.

This article outlines a protocol for the detection of one or more plasma and/or intracellular membrane proteins using Ground State Depletion (GSD) super-resolution microscopy in mammalian cells. Here, we discuss the benefits and considerations of using such approaches for the visualization and quantification of cellular proteins.

Advances in fluorescent microscopy and cell biology are intimately correlated, with the enhanced ability to visualize cellular events often leading to ...

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies

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Green Fluorescent Protein-based Expression Screening of Membrane Proteins in Escherichia coli

Microperfusion Technique to Investigate Regulation of Microvessel Permeability in Rat Mesentery

Pulling Membrane Nanotubes from Giant Unilamellar Vesicles

Ground State Depletion Super-resolution Imaging in Mammalian Cells