LipidBuilder: A Framework To Build Realistic Models for Biological

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LipidBuilder: A Framework To Build Realistic Models for Biological Membranes Christophe Bovigny,†,‡,§,∥ Giorgio Tamò,†,‡,∥ Thomas Lemmin,†,⊥,∥ Nicolas Maïno,† and Matteo Dal Peraro*,†,‡ †

Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland ‡ Swiss Institute of Bioinformatics (SIB), CH-1015 Lausanne, Switzerland S Supporting Information *

ABSTRACT: The physical and chemical characterization of biological membranes is of fundamental importance for understanding the functional role of lipid bilayers in shaping cells and organelles, steering vesicle trafficking and promoting membrane-protein signaling. Molecular dynamics simulations stand as a powerful tool to probe the properties of membranes at atomistic level. However, the biological membrane is highly complex, and closely mimicking its physiological constitution in silico is not a straightforward task. Here, we present LipidBuilder, a framework for creating and storing models of biologically relevant phospholipid species with acyl tails of heterogeneous composition. LipidBuilder also enables the assembly of these database-stored lipids into realistic bilayers featuring asymmetric distribution on layer leaflets and concentration of given membrane constituents as defined, for example, by lipidomics experiments. The ability of LipidBuilder to assemble robust membrane models was validated by simulating membranes of homogeneous lipid composition for which experimental data are available. Furthermore, taking advantage of the extensive lipid headgroup repertoire, we assembled models of membranes of heterogeneous nature as naturally found in viral (phage PRD1), bacterial (Salmonella enterica, Laurinavicius, S.; Kakela, R.; Somerharju, P.; Bamford, D. H.; Virology 2004, 322, 328−336) and plant (Chlorella kessleri, Rezanka, T.; Podojil, M.; J. Chromatogr. 1989, 463, 397−408) organisms. These realistic membrane models were built using a near-exact lipid composition revealed from analytical chemistry experiments. We suggest LipidBuilder as a useful tool to model biological membranes of nearbiological complexity, and as a robust complement to the current efforts to characterize the biophysical properties of biological membrane using molecular simulation.

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INTRODUCTION Biological membranes are essential for living organisms, because they constitute natural barriers mediating the exchange of intra- and extracellular components. Lipid membranes are composed of two layers arranged such that the hydrophilic heads face the solvent fluids and the hydrophobic tails contact and face each other inwardly. The composition of phospholipid bilayers can vary from simple quasi-homogeneous systems to complex mixtures composed of several different lipid species as in the crowded cellular membrane environment.1 The individual lipid species are the main building blocks of any biological membrane and their physicochemical properties define structural and functional membrane organization.2−4 They play a key role in the segregation of membrane domains, the formation of nonbilayer structures and the interactions with proteins.2 Furthermore, their dynamic shape and relative concentration in the respective membrane layers have proven to be necessary for important cellular functions as in the case of vesicle trafficking.5 This applies especially during vesicle budding and fusion induced by temporary and localized curvature changes in the membrane.5,6 © 2015 American Chemical Society

In addition to the lipid head groups, the nature of the acyl chains strongly influences the membrane properties. The latter defines the membrane thickness and fluidity, and consequently the functional positioning of membrane proteins.7,8 Acyl tail unsaturation changes the packing of lipids and creates defects in the bilayer, which in turn affects the insertion of molecules and peptides.9 Moreover, the electrostatic signature of anionic lipid species, such as those present in bacterial or mitochondrial membranes, is important to define membrane shaping and protein interactions. For example, anionic cardiolipin (CL) species were found to be involved in the development of membrane curvature and related sorting, energy metabolism, pathologies10,11 and cell division.12 Because biological data on membranes and membrane associated proteins are rapidly increasing,13−16 there is a parallel growing interest to accompany the study of these biological components with molecular simulations, which have the ability to capture atomistic properties otherwise undetectReceived: August 12, 2015 Published: November 25, 2015 2491

DOI: 10.1021/acs.jcim.5b00501 J. Chem. Inf. Model. 2015, 55, 2491−2499

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Journal of Chemical Information and Modeling

Figure 1. LipidBuilder and LipidDatabase modules. Lipids structure and topology are created from a list of parametrized lipid heads and customized acyl tails, and are successively stored in a relational database.

Table 1. Head Groups Available in LipidBuilder for Constructing Customized Lipids head group

head nomenclature

abbreviation

acyl tails number

glycerophospholipid glycerophospholipid glycerophospholipid glycerophospholipid glycerophospholipid glycerophospholipid glycerophospholipid lysoglycerophospholipid lysoglycerophospholipid lysoglycerophospholipid lysoglycerophospholipid lysoglycerophospholipid miscellaneous phosphatidylinositol phosphatidylinositol phosphatidylinositol phosphatidylinositol phosphatidylinositol phosphatidylinositol phosphatidylinositol phosphatidylinositol phosphatidylinositol sphingolipid sphingolipid sphingolipid sphingolipid sphingolipid sphingolipid sterol sterol

cardiolipin protonated cardiolipin deprotonated phosphatidic acid phosphatidylcholine phosphatidylethanolamine phosphatidylglycerol phosphatidylserine phosphatidic acid phosphatidylcholine phosphatidylethanolamine phosphatidylethanolamine phosphatidylglycerol sulfate phosphatidylinositol phosphatidylinositol-3-P phosphatidylinositol-4-P phosphatidylinositol-5-P PI-3-4-5′H′-triP PI-3-4′H′-5-triP PI-3′H′-4-5-triP PI-4-5′H′-biP PI-4′H′-5-biP ceramide phosphatidic acid phosphatidylethanolamine phosphatidylglycerol phosphatidylserine sphingomyelin 5-ene sterol 5,7-diene sterol

CP CD PA PC PE PG PS PA PC PE PS PG SO I0 I3 I4 I5 I9 I8 I7 I1 I2 CE SA SE SG SS SM CH EG

4 4 2 2 2 2 2 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1

able to experiments.1,17 Taking advantage of the recent upscaling in computational power, molecular dynamics (MD) has enabled the study of membrane dynamics and energetics at atomistic resolution.18,19 Moreover, advances in the parametrization of a broad set of force fields for lipids,20−23 along with the modeling of important constituent species of the biological membrane, such as cardiolipins24,25 or lipopolysaccharides26 provide a solid and increasingly complete toolbox to allow a molecular understanding of membrane properties. In parallel, to approach larger systems for more biologically relevant time scales, coarse-grained models of various membrane constituents have shown to possess proper accuracy to study the physicochemical properties of membranes as well as their specific interactions with integral and peripheral proteins.

In fact, the composition of real biological membranes is more complex than most of the simplified models currently used in in silico and in vitro experiments. Mimicking membrane systems composed of complex mixtures of lipids as normally found in cells still constitutes a challenge for in vitro analyses and molecular simulations, which has started to be recognized and addressed by more recent investigations.27−30 To face this challenge from the computational side, a few tools have been created to facilitate the creation of membrane systems to be studied using molecular simulations. One example is the online public repository called LipidBook, built to store parametrization details about lipids.31 Its creation was stimulated by the existence of different force fields, which are used for simulation of lipid systems. This highlights the need for a simple way to gather and share lipid related data within the community. 2492


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Journal of Chemical Information and Modeling LipidBook allows users to upload any type of parameter files related to molecules useful for the simulation of lipid membranes.31 Another need is the creation of the lipid membranes themselves, a task that is today facilitated by the CHARMM-GUI framework,32 which enables the automated creation of lipid membranes by allowing the selection of already parametrized lipids selected from the CHARMM36 force field.32−34 More recently, Wassenenaar et al. created the INSANE framework,35 which featured the building of coarsegrained membranes containing lipids of heterogeneous nature in acyl tail composition, linkers and head groups. In the same vein, MemGen36 is a newly created web service that enables the creation of heterogeneous membranes from uploaded lipid structure files regardless of type and parametrization. As an effort to move toward a sustainable atomistic modeling of large membrane systems closely mimicking conditions found in real biological membranes, we introduce here a framework called LipidBuilder. The aim is to simplify, first, the creation of tailor-made lipid models constituting a given biological membrane of interest, and then the assembly of membrane bilayers thus featuring realistic conditions, such as heterogeneous composition of the acyl tails, asymmetric distribution on layer leaflets and concentration of given membrane constituents. Therefore, this tool permits the creation and storage of customized lipids and the assembly of heterogeneous membrane bilayer from an integrated lipid database. We validated the building of the lipid models and the ability of our protocol to construct membrane systems, which are readily suitable for molecular dynamics production and analysis. The ability of LipidBuilder to create virtually any kind of customized lipid species starting from existing atomistic force field parameters allowed us to create ad hoc curated repositories for realistic biological membrane systems from the phage PRD1, Salmonella enterica, Chlorella kessleri, which lipid compositions were accurately determined by analytical chemistry experiments.37,38

Fatty acid tails can be found in many different lengths, branching levels, saturations and may also contain cyclopropane functional groups.33 To allow the users to build any specific acyl tail, the web-based chemical structure editor, Ketcher (http:// ggasoftware.com/opensource/ketcher) was incorporated into LipidBuilder. To facilitate the creation of templates and topology files, a VMD41 plugin based on the CHARMM force field was implemented. It should be noted that this plugin can also be downloaded as a standalone tool from the LipidBuilder Web site. On the basis of the SMILE structure of the acyl chains, the creation of the corresponding phospholipid is fully automated (Figure 1). First, the lipid topology is created by combining the selected phospholipid head groups, extracted from a built-in library of structures and the drawn acyl chains. Because the acyl chains have been parametrized based on a plug-and-play philosophy in the CHARMM force field, the acyl chains are generated by linking a series of alkane moieties. To describe better the cyclopropane ring, we used refined QM-based parameters.42 This cyclic moiety has frequently been neglected in the MD simulations of membranes, even though it is present in many species of bacteria and significantly increases lipid surface area.42 Finally, the psfgen algorithm43 and the generated topology file are combined to build the template PDB and PSF files of the phospholipid (Figure 1). The CHARMM-based protocol has been used here to present the capabilities of the framework, but, as mentioned, work is in progress to extend this pipeline to the other commonly used atomistic force fields. LipidDatabase Module. Once built, the lipid models are stored in a relational database implemented using the query language MySQL. Lipids can be retrieved based on characteristics ranging from their CHEBI, IUPAC or headgroup name to their acyl tail carbon length and/or saturation level. Users can also add any type of comment or additional information related to the newly built lipids. LipidPacker Module. The customized lipids present in the database are used for the creation of lipid bilayer models to be used, for instance, in MD simulation studies. First, the different lipids composing the bilayer are selected and stored in a “collection”. Such lipid collections can be shared with other members of the community or with collaborators on the server. Once the desired concentration of each lipid and the size of the system are defined, LipidPacker allows for the creation of either symmetric or asymmetric membranes patches, by assigning different lipid concentrations to either the top and bottom leaflets. To build the membrane model with a given lipid concentration, membrane size and configuration, we used a 2D bin-packing algorithm in which we approximate each lipid element in the collection as rectangles which dimensions reflect the lipid shape (Figure 2A). In this study, the bin packing algorithm was inspired by the study from.44 The algorithm featured in LipidBuilder was implemented by Montes (available at http://pollinimini.net/demos/RectanglePacker.html). It was incorporated into the Web site architecture and optimized for lipid packing. The aim the packing algorithm was to generate rapidly different random configurations for large and heterogeneous membranes systems. Because of the complexity of the packing problem, the current implementation of the algorithm was chosen as a compromise between speed and randomness. Some detrimental effects on randomness could be observed, especially on large membrane models, with a predisposition to arrange larger lipids on the diagonal of the

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MATERIALS AND METHODS The LipidBuilder framework is implemented in a web service available at http://lipidbuilder.epfl.ch (online since 29/05/ 2013) and is divided over three main modules, which outline the successive steps necessary for the assembly of membrane models from a customized lipids database. The first module takes care of the creation of new lipid species (LipidBuilder). The database for storing lipids constitutes the second module (LipidDatabase). The third module relates to the creation of membrane bilayer models (LipidPacker). The CHARMM36 parametrization was chosen in this first version for describing lipids because of its widespread use in the community and its extensive use in simulations of lipid membranes.32 However, our protocol is not limited to a specific force field and, as planned for future development, can be easily updated to host parametrization sets from other common lipid force fields such as SLipid,20 Amber Lipid1121 or GROMOS.22,39,40 LipidBuilder Module. This module represents the major novelty of our framework, allowing to design in few steps specific acyl tails for realistic membrane models. The creation of new lipids is divided into two parts. First, the category of lipid headgroup is chosen. From this category, a list of lipid families parametrized in CHARMM36 can be selected (Figure 1). The current library features the parametrized head groups of glycerophospholipids, phosphatidylinositols, sphingolipids and sterols (Table 1). 2493


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proposed equilibration protocol were in line with other membrane building protocols, as it was adequate to equilibrate membrane physical properties in around 10 ns (Figure S1). We think, however, that this equilibration time can be further decreased by further optimizing the bin-packing algorithm. The protocol for the initial minimization and equilibration of the membrane system is provided to download after membrane building, and we plan to include this minimization procedure directly on the server in the future. A NAMD-based protocol (see below) has been used here to present the capabilities of the framework, but work is in progress to provide a similar protocol compatible with other commonly used MD engines, such as Amber and Gromacs. Molecular Dynamics Simulations for Validation and Analysis. We first wanted to test and validate the ability of LipidBuilder to generate suitable initial membrane models that can be further studied by MD simulations. Therefore, following the LipidBuilder procedure, we built four 64 × 64 Å membrane patches composed of single species of phospholipid dioyloleoly phosphatidylcholine (DOPC), palmitoyloleoly phosphatidylcholine (POPC), and palmitoyloleoly phosphatidyl-ethanolamine (POPE) and dipalmitoyl- phosphatidylcholine (DPPC) (Figure 3). These four phospholipid species have been extensively studied in silico, as well as in vitro.45,46 They are therefore ideal for validating the modeling capabilities of LipidBuilder. After the validation of these test models, we used the LipidBuilder framework for the creation of three biologically relevant membrane systems composed of lipids which concentration matches the ones obtained by chromatography and MS experiments.37,38 Specifically, we built three distinct model systems of 120 × 120 Å representing heterogeneous membranes as found in viral phage PRD1,37 bacterial S. enterica37 and plant C. kessleri38 organisms (Figure 4). All systems were solvated in a 25 Å padding water box, guaranteeing at least 50 water molecules per lipid. All MD simulations were performed using NAMD 2.947 engine, with CHARMM36.33 TIP3P water48 parametrization was used to describe the water molecules. The periodic electrostatic interactions were computed using particle-mesh Ewald (PME) summation with a grid spacing smaller than 1 Å.49 A constant temperature was imposed by using Langevin dynamics50 with a damping coefficient of 1.0 ps (i.e., 303 K for the POPC, DOPC, POPE systems, 323 K for the DPPC system, and of 310.15 K for organism systems). Constant pressure of 1 atm was maintained with Langevin piston dynamics51 with 200 fs decay period and 50 fs time constant. First, three cycles of simulated annealing were performed. The systems were subsequently gradually heated from their base temperature to 400 K in 80 ps and then gradually cooled down back to their base temperature in 160 ps. The systems were then equilibrated for 5 ns at their respective temperature. Free molecular dynamics were performed with a 2 fs integration time step using the RATTLE algorithm applied to all bonds. Total MD simulation time lasted 100 and 60 ns for the pure and mixed membranes models respectively (Figures 3 and 4) after the initial 5 ns long equilibration. Area per lipid (SA) and membrane thicknesses were measured during MD simulations of the four test case membranes models for validation purposes. The pure bilayers SA was calculated by dividing the squared box length by the number of phospholipids per leaflet.17,32 Voronoi decomposition was used on mixed lipid bilayers to estimate the

Figure 2. LipidPacker module. (A) 2D representation of the membrane surface produced by the bin-rectangular packing algorithm. Each colored rectangular box represents a different lipid species enclosed in the relative surface area. (B) The corresponding 3D model of packed membrane bilayer based on the 2D map is obtained by transferring lipid coordinates from an internal structural database (color-coded lipids as in panel A).

membrane. Nevertheless, diverse random configurations can be created within seconds even for very large membrane models, with a very high packing efficiency. In a nutshell, this algorithm arranges simple rectangles while minimizing the space between them inside a rectangular container of predefined dimensions (Figure 2A). At the core of the algorithm is computed the creation of binary trees that uses recursions to find the best packing of rectangles. After the packing of rectangles representative of each lipid structure for one leaflet, the coordinates of each packed rectangle are extracted and the membrane bilayer is assembled by simple rotation and translations of symmetric or asymmetric leaflets (Figure 2B). The LipidPacker interface displays the efficiency of the packing of rectangles with a percentage describing the space occupied by the packed rectangles inside the container. The percentage of fitted rectangles is also shown. This is used as a quantitative measure to indicate the similarity between the concentration of lipids after packing and the initial user specified lipid concentration. Because of the fact that the packing algorithm arranges lipids solely on a horizontal plane, an average overlapping distance between lipid tails of opposite leaflet was chosen at ∼10 Å when assembling the two lipid leaflets. Subsequently, minor clashes between the acyl tails of the leaflets are present in the newly created membrane models before minimization. This artifact arose as a structural compromise to generate systems with a minimal amount of steric clashes, while at the same time minimizing vacuum space between the leaflets. As a result, this design choice generated in most of the cases systems that are slightly overpacked. However, in all membrane systems constructed by LipidBuilder, these clashes were resolved in less than 200 ps (Figure S1). Furthermore, the optimized building procedure followed by the 2494


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Figure 3. Structural and biophysical properties of homogeneous lipid systems. (A) From left to right are represented the POPE, POPC, DOPC and DPPC equilibrated bilayer models solvated in TIP3P water in gray. (B) The time evolution of their respective area per lipid is reported for MD simulations of 100 ns. See also Table 2 for structural properties of these systems.

surface area of individual lipids.52 The membrane thickness was estimated by measuring the phosphorus to phosphorus distance between leaflets. The validation process was achieved through systematic comparisons with previous estimated20 and available experimental data.

used by the LipidPacker module and the following equilibration protocol is able to produce initial starting configurations of membrane systems that can subsequently be used for MD production and investigation (Figure 3). Building Repositories for Realistic Membrane Models. Studies featuring the creation of complex lipids assembled into biological membranes have shown to more accurately describe the natural composition and behavior of the bilayer. This was for instance illustrated in the study by Klauda et al.42 in which an Escherichia coli membrane was simulated using lipids with quantum mechanically refined cyclic moieties. In the same vein, we used LipidBuilder to create and assemble membranes of fully parametrized lipids with different acyl tail lengths and saturation levels. This unique property of our tool allows to build and store in curated repositories the models for three realistic heterogeneous membrane systems for the phage PRD1, bacterium S. enterica and plant C. kessleri. The composition and concentrations of these realistic membrane models are based on experimentally determined concentrations taken as an estimate of the phospholipid fractions reported by Laurinavicius et al.37 for the PRD1 and S. enterica and Rezanka and Podojil38 for C. kessleri. The exact lipid composition and concentrations of these systems is reported in Tables S2, S3 and S4, and can be accessed directly on the LipidBuilder online interface in the collections listed under the name “Curated membranes”. When bilayers from these databases were assembled, similar to the case of the pure membranes, the minor atomistic clashes present in the initial models were quickly resolved during the minimization of the systems. Due to the fact that no experimental data is currently available in the literature for the physicochemical properties of these three membrane models, except for their lipid unsaturation level and concentration,37,38 the properties obtained from the MD analysis could not be directly validated by comparison with experimental data. Nevertheless, the area per lipid relative to each lipid species was computed for all the three systems (Tables S2, S3 and S4), and the average global area per lipid of each system showed similar convergence behavior as in the

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RESULTS AND DISCUSSION LipidBuilder Validation Based on Homogeneous Membrane Systems. Following the construction and assignation of lipid concentrations, the systems obtained using LipidBuilder consisted of the pure POPC, POPE, DOPC and DPPC membranes containing 81, 75, 66 and 79 lipids per leaflet, respectively. Minor clashes between the acyl tails of the leaflets were initially detected and quickly resolved during the short minimization that preceded the equilibration of the membrane (Figure S1). The area per lipid (SA) was measured in the four membrane systems20,53 and the systematic comparison with data obtained experimentally revealed a very good agreement with MD simulations (Table 2). Comparison between our models and the equivalent pure membranes simulated using CHARMM36 in previous studies33,54,55 showed a very good agreement as well. The resulting thickness of the DOPC membrane was only slightly higher than the one obtained experimentally. The thickness value measured for the pure POPE membrane (42.48 ± 0.50 Å2) was compared to the one (41.1 Å2) obtained from the pure POPE bilayer model by Jambeck and Lyubartsev20 and was found to be very similar. These values were in the same range as those obtained by Klauda et al.,33 Janosi et al.,54 Caffrey and Hogan,61 Jambeck and Lyubarstev20 and Kucerka46 who used the CHARMM36 and SLipids force field, respectively, to simulate the pure POPC, POPE, DOPC and DPPC membrane models. Thus, the results obtained demonstrate the ability of LipidBuilder to generate phospholipid bilayer models with characteristics that can be readily studied by common MD simulations. Although the purpose of these simulations was not to test the ability of MD to reproduce correctly lipid bilayers properties (which was extensively done in all the previously mentioned works33,54,55), these cases testify that the general procedure 2495


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Figure 4. Structural and biophysical properties of heterogeneous lipid systems. (A) From left to right are represented top and side views of the membrane models for phage PRD1, S. enterica and C. kessleri solvated in TIP3P water in gray. (B) The time evolution of their respective global area per lipid is reported for MD simulations of 60 ns. See also Figures S2, S3 and S4 for the structural composition of these models.

Table 2. Area per Lipid (SA) and Membrane Thickness of the POPC, POPE and DOPC Test Systems (Figure 3) Obtained from MD Simulations of Systems Built Using LipidBuildera LipidBuidler area per lipid (Å) POPC (16:0/18:1) POPE (16:0/18:1) DOPC (18:1/18:1) DPPC (18:1/18:1)

64.89 56.36 68.66 61.41

± ± ± ±

1.1 0.79 1.1 1.31

exp. data

CHARMM36

thickness (Å)

area per lipid (Å)

thickness (Å)

area per lipid (Å)

± ± ± ±

64.3 ± 1.3 56.6 67.4 63.1

37 n/a 35.3 39

64.7 ± 0.2 59.2 ± 0.3 68.3 62.9 ± 0.3

38.78 42.48 38.3 39.52

0.57 0.50 0.55 0.70

SLipids

thickness (Å)

area per lipid (Å)

thickness (Å)

39.1 ± 0.6 n/a 38.5 ± 0.025 n/a

64.6 ± 0.004 56.3 ± 0.004 68.0 ± 0.005 n/a

36.5 41.1 36.6 n/a

a

These properties are compared to the available data from CHARMM3633,54,55 and SLipids20 based MD simulations and experimentally derived data.46

then readily used to assemble membrane models that are therefore more faithfully matching realistic membrane compositions, such as those measured by current large-scale lipidomics efforts. In its current version our protocol features the heads of several lipid families including the glycerophospholipid, phosphatidylinositol, sphingolipid and sterol head groups parametrized within the CHARMM force field. However, it is important to point out that other head moieties (already parametrized within existing force fields) can be further added, ranging from phospholipids to other lipids, such as lipopolysaccharides.26 This is possible due to the architecture of the framework, which was designed with the aim of simplifying the creation and customization process of any lipid species parametrized for general atomistic force fields. Another feature of our framework, which will be more and more interesting with the accumulation of the chemical knowledge on the composition of biological membranes, is the possibility to link automatically data related to the lipids stored in the library to current lipidomics repositories, such as LIPID MAPS,60 LIPIDBANK (https://lipidbank.jp), LIPIDAT61 or

homogeneous membrane case (Figure 4). Taken together, these results indicate how LipidBuilder is able to generate suitable initial models of complex membrane systems that can be investigated using MD simulations. In addition to the phage PRD1, S. enterica and C. kessleri systems, which are directly simulated in this study, we have curated, based on available literature data, other lipid repositories stored under the “Curated membranes” menu: namely membrane models for the E. coli inner membrane, the mouse endoplasmic reticulum and rat mitochondrial inner membrane. Precisely, the exact lipid composition and concentration of the fore-mentioned systems as reported in the respective analytical chemistry experiments,56−59 were stored as collections of lipids and can already be freely used to construct membrane models of desired size.

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CONCLUSIONS We presented here a web-based framework, LipidBuilder that permits one to customize quickly and flexibly the chemical features of the acyl tails of lipid models. These lipid models are 2496


Journal of Chemical Information and Modeling SwissLipids (http://swisslipids.org), to name a few of the existing resources. Because of the ability of LipidBuilder to create quickly membrane models of virtually any size and composition, the framework can help one to investigate more efficiently the biophysical properties of these membranes. LipidBuilder can create membrane systems, which can be sampled by atomistic MD simulations in the aim of producing equilibrium distributions of lipids mixtures, which, however, are not yet easily at reach, typically requiring relaxing times in the order of several microseconds. As a remedy to this omnipresent sampling problem, LipidBuilder could be in principle extended to build membrane systems using currently available coarsegrained models in order to model larger systems to be simulated for longer time scales. LipidBuilder can be useful, as analyses of this kind can solely be undertaken on molecular membrane models, which closely mimic the nature of phospholipid bilayers and accurately depict the complexity of their lipids constituents. We thus suggest LipidBuilder as a complement to the current efforts to characterize in detail the biophysical properties of biological membrane using computational approaches.

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ACKNOWLEDGMENTS

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REFERENCES

We thank M. Pelle and M. Audagnotto for helpful discussions and suggestions to improve LipidBuilder web interface. T.L. acknowledges support form the Swiss National Foundation of Science (grant 148914), M.D.P. acknowledges support from Swiss National Foundation of Science (grants 200021_135450 and 200020_157153). To allow potential reviewers to test LipidBuilder in a completely anonymous way, we created a demo login account (http://lipidbuilder.epfl.ch/demo) in the main menu, which can be used to explore the complete functionalities of our computational tool.

(1) Tieleman, D. P.; Marrink, S. J.; Berendsen, H. J. A Computer Perspective of Membranes: Molecular Dynamics Studies of Lipid Bilayer Systems. Biochim. Biophys. Acta, Rev. Biomembr. 1997, 1331, 235−305. (2) Simons, K.; Ikonen, E. Functional Rafts in Cell Membranes. Nature 1997, 387, 569−572. (3) Roux, B.; Schulten, K. Computational Studies of Membrane Channels. Structure 2004, 12, 1343−1394. (4) Schlame, M.; Ren, M. The Role of Cardiolipin in the Structural Organization of Mitochondrial Membranes. Biochim. Biophys. Acta, Biomembr. 2009, 1788, 2080−2083. (5) McMahon, H. T.; Gallop, J. L. Membrane Curvature and Mechanisms of Dynamic Cell Membrane Remodelling. Nature 2005, 438, 590−596. (6) Bansal, S.; Mittal, A. Extracting Curvature Preferences of Lipids Assembled in Flat Bilayers Shows Possible Kinetic Windows for Genesis of Bilayer Asymmetry and Domain Formation in Biological Membranes. J. Membr. Biol. 2013, 246 (7), 557−570. (7) Kimelberg, H. K.; Papahadjopoulos, D. Effects of Phospholipid Acyl Chain Fluidity, Phase Transitions, and Cholesterol on (Na++ K +)-Stimulated Adenosine Triphosphatase. J. Biol. Chem. 1974, 249, 1071−1080. (8) Engelman, D. M. Membranes are more Mosaic than Fluid. Nature 2005, 438, 578−580. (9) Litman, B. J.; Lewis, E. N.; Levin, I. W. Packing Characteristics of Highly Unsaturated Bilayer Lipids: Raman Spectroscopic Studies of Multilamellar Phosphatidylcholine Dispersions. Biochemistry 1991, 30, 313−322. (10) Houtkooper, R. H.; Vaz, F. M. Cardiolipin, the Heart of Mitochondrial Metabolism. Cell. Mol. Life Sci. 2008, 65, 2493−2999. (11) Nichols-Smith, S.; Kuhl, T. Electrostatic Interactions Between Model Mitochondrial Membranes. Colloids Surf., B 2005, 41, 121−127. (12) Pierucci, O. Phospholipid Synthesis During the Cell Division Cycle of Escherichia coli. J. Bacteriol. 1979, 138, 453−513. (13) Matsumoto, K.; Kusaka, J.; Nishibori, A.; Hara, H. Lipid Domains in Bacterial membranes. Mol. Microbiol. 2006, 61, 1110− 1117. (14) Lewis, R. N.; Zweytick, D.; Pabst, G.; Lohner, K.; McElhaney, R. N. Calorimetric, x-ray Diffraction, and Spectroscopic Studies of the Thermotropic Phase Behavior and Organization of Tetramyristoyl Cardiolipin Membranes. Biophys. J. 2007, 92, 3166−3243. (15) Rog, T.; Martinez-Seara, H.; Munck, N.; Oresic, M.; Karttunen, M.; Vattulainen, I. Role of Cardiolipins in the Inner Mitochondrial Membrane: Insight Gained through Atom-Scale Simulations. J. Phys. Chem. B 2009, 113, 3413−3422. (16) Sabzali, J. Mitochondrial Permeability Transition: Known Phenomenon with Unknown Molecular Identity. Anat. Phys. 2012, 2 (3), 1−2. (17) Heller, H.; Schaefer, M.; Schulten, K. Molecular-Dynamics Simulation of a Bilayer of 200 Lipids in the Gel and in the LiquidCrystal Phases. J. Phys. Chem. 1993, 97, 8343−8360. (18) Kapla, J.; Wohlert, J.; Stevensson, B.; Engstrom, O.; Widmalm, G.; Maliniak, A. Molecular Dynamics Simulations of Membrane-Sugar Interactions. J. Phys. Chem. B 2013, 117, 6667−6673.

ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jcim.5b00501. List of MD simulations for pure POPC, POPE, DOPC and DPPC lipids, composition and properties of S. enterica membrane, composition and properties of the Phage PRD1 membrane, composition and properties of Chlorella spp. membrane, evolution of total energies (ETOT) of the pure membrane (POPC, POPE, DOPC and DPPC) systems through the initial molecular dynamics minimization, chemical structure of lipids composing the phage PRD1 membrane system, chemical structure of lipids composing the S. enterica membrane and chemical structure of lipids composing the Chlorella spp. membrane, (PDF).

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AUTHOR INFORMATION

Corresponding Author

*Matteo Dal Peraro. E-mail: matteo.dalperaro@epfl.ch. Phone: +41 21 693 1861. Present Addresses §

Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, Quartier Sorge, Batiment Genopode, CH-1015 Lausanne, Switzerland ⊥ Department of Pharmaceutical Chemistry, University of California−San Francisco, San Francisco, California 94143 Author Contributions ∥

These authors contributed equally. C.B., G.T., T.L. and M.D.P. designed research; C.B., G.T. and T.L. implemented the computational tool; N.M. contributed with the curated membranes; all authors analyzed data; C.B., G.T., T.L. and M.D.P. wrote the paper. Notes

The authors declare no competing financial interest. 2497


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