Simulating Gram-Negative Bacterial Outer ... - ACS Publications

Article pubs.acs.org/JPCB

Simulating Gram-Negative Bacterial Outer Membrane: A Coarse Grain Model Huilin Ma, Flaviyan Jerome Irudayanathan, Wenjuan Jiang, and Shikha Nangia* Department of Biomedical and Chemical Engineering, Syracuse University, Syracuse, New York 13244, United States S Supporting Information *

ABSTRACT: The cell envelope of Gram-negative bacteria contains a lipopolysaccharide (LPS) rich outer membrane that acts as the first line of defense for bacterial cells in adverse physical and chemical environments. The LPS macromolecule has a negatively charged oligosaccharide domain that acts as an ionic brush, limiting the permeability of charged chemical agents through the membrane. Besides the LPS, the outer membrane has radially extending O-antigen polysaccharide chains and β-barrel membrane proteins that make the bacterial membrane physiologically unique compared to phospholipid cell membranes. Elucidating the interplay of these contributing macromolecular components and their role in the integrity of the bacterial outer membrane remains a challenge. To bridge the gap in our current understanding of the Gram-negative bacterial membrane, we have developed a coarse grained force field for outer membrane that is computationally affordable for simulating dynamical process over physiologically relevant time scales. The force field was benchmarked against available experimental and atomistic simulations data for properties such as membrane thickness, density profiles of the residues, area per lipid, gel to liquid-crystalline phase transition temperatures, and order parameters. More than 17 membrane compositions were studied with a combined simulation time of over 100 μs. A comparison of simulated structural and dynamical properties with corresponding experimental data shows that the developed force field reproduces the overall physiology of LPS rich membranes. The affordability of the developed model for long time scale simulations can be instrumental in determining the mechanistic aspects of the antimicrobial action of chemical agents as well as assist in designing antimicrobial peptides with enhanced outer membrane permeation properties.

1. INTRODUCTION

bacterial cell wall is therefore important in combating bacterial resistance. The LPS macromolecule is composed of three key domains: lipid A, core oligosaccharides, and a polymeric O-antigen group (Figure 1).22−24 Although the exact composition of LPS is highly variable among the bacterial species, each LPS domain performs specific functions. Lipid A is a phosphorylated glucosamine disaccharide unit with 5−7 acyl chains that anchor the LPS in the outer membrane and provide a scaffold for the assembly of the core and the O-antigen domains.25,26 The LPS core comprises negatively charged oligosaccharides that are peripherally attached to lipid A. The core contains a high proportion of rare sugars such as 2-keto-3-deoxyoctulosonate and L-glycero-D-manno-heptose along with common sugars such as hexoses and hexosamines.23 The presence of negative charges makes the role of counterions critical in maintaining the lamellar structure of the outer membrane by minimizing the electrostatic repulsion between adjacent core LPS molecules.27 Covalently bonded to the core oligosaccharides are the Oantigen polysaccharides, which can have a variable number of

Bacterial infections are becoming a cause of concern as Gramnegative bacteria continue to acquire resistance to the available spectrum of antibiotic drugs,1−9 causing persistent chronic infections, and contributing to escalating healthcare costs worldwide.10,11 Despite the push for new antibiotic therapies, there has been a decline in the number of newly approved drugs due to limited understanding of how Gram-negative bacteria adapt to stress stimuli by dynamically altering their complex cell envelopes.12−14 Structurally, the outer membrane of Gram-negative bacteria is highly asymmetric, comprising an inner leaflet of phospholipids and an outer leaflet of lipopolysaccharides (LPS).13,14 Embedded in the bilayer are outer membrane proteins (OMPs) that often form nonspecific pores to allow passage of small hydrophilic molecules through the membrane.15−21 The outer membrane, however, is only the first of the three layers that envelop the cytoplasm of the bacterial cell. Adjacent to the outer membrane is an intermediate periplasmic peptidoglycan layer followed by an innermost phospholipid bilayer. Together the three layers form a protective barrier for the bacterial cells against variations in temperature and toxins, making the bacteria resistant to hostile environments. Understanding the molecular structure of the © 2015 American Chemical Society

Received: July 22, 2015 Revised: September 12, 2015 Published: September 15, 2015 14668

DOI: 10.1021/acs.jpcb.5b07122 J. Phys. Chem. B 2015, 119, 14668−14682

Article

The Journal of Physical Chemistry B

Figure 1. Schematic (A) of the trilamellar Gram-negative bacteria membrane with outer membrane, intermediate peptidoglycan layer, and inner membrane along with (B) coarse grained representation of the LPS (multicolor) outer leaflet and DPPE (green) inner leaflet. Panel C is the schematic representation of the LPS structure with lipid A [LP1 (dark blue); LP2 (light blue); XYA (pink); SYB (pink)], core polysaccharide [LKO (aqua); 0KO (aqua); PH2 (aqua); 3H1 (aqua); WLL (aqua); 0GA (yellow); 6GB (yellow); 6GA (yellow); 2HA (yellow); 0GB (yellow)], and Oantigen repeat unit [GLA (purple); MAN (purple); GLC (purple); GNA (purple)].

(NMR),40 and transmission electron spectroscopy41 have been employed to investigate bacterial membranes, but detailed characterization of the outer membrane is often difficult due to the chemical heterogeneity in the membrane composition and polymorphism of the LPS macromolecule. Furthermore, bacteria have developed adaptive mechanisms to dynamically alter their response to environmental stimuli at the membrane level.42 Knowledge of the molecular level structure and dynamics of the LPS is important because the outer membrane interfaces directly with diverse chemical environments and can influence the biological activity of the bacterial cells. Molecular dynamics (MD) simulations are now increasingly employed to study chemical systems because they provide temporal behavior of each atom in the system.31,43−50 Atomistic models have been developed and validated for the outer membrane of Gram-negative bacteria that are compatible with a variety of molecular force fields, such as GROMOS,46 GLYCAM,31,45 and CHARMM36.47 Pontes et al. extended the GROMOS 45a4 force field to include for lipid A component of P. aeroginosa.46 Kirschner et al. modeled rough LPS (without O-antigen) compatible with the GLYCAM force field.45,50 Wu et al. developed a CHARMM3651 based model

repeating units depending on the bacterial serological group.28−31 Together the O-antigen and the charged core polysaccharides form a hydrophilic bacterial outer coat that interacts with the surrounding medium. Integral components of the bacterial outer membrane are the β-barrel OMPs, also called porins, which provide nonspecific channels for translocation of small hydrophilic molecules (less than 0.6 kDa) across the membrane.15−21 Although the translocation through the porins is passive, the channels can be charge and size selective based on the charge distribution in the lumen of the β-barrel scaffold. For example, Pseudomonas aeruginosa has outer membrane carboxylate channels (Occ) that are substrate-specific and are considered to be responsible for the uptake of a majority of small molecules, including antibiotics.32 There are reports where porins have been purified, reconstituted, and even engineered to analyze their selectivity and voltage gating to determine the mechanism of antibiotic translocation through the channels.33,34 Numerous experimental techniques such as neutron diffraction,35 small-angle scattering,36 Fourier transform infrared spectroscopy,37,38 differential scanning calorimetry,38 smallangle X-ray diffraction,36,39 nuclear magnetic resonance 14669


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The Journal of Physical Chemistry B for Escherichia coli LPS molecule with O-antigen.47 Although these studies provide experimentally comparable structural properties in atomistic detail, they are computationally intensive and are limited to fewer than a million atoms, spanning a few nanometers in length and submicrosecond time scales. For simulating dynamical processes such as influx of nutrients through porins, antibiotic permeation into the bacterial cells, and membrane disruption action of antimicrobial peptides, the atomistic modeling approach is inadequate even with the state-of-the-art computers, primarily because the required simulation time scales are at least 2 to 3 orders of magnitude larger. An affordable alternative to computationally intensive atomistic simulations is to employ coarse grain (CG) models that can faithfully represent the intrinsic chemical structure and interactions of the underlying atomistic system. Although there are rigorous force-matching CG algorithms,52 Martini CG uses a more intuitive geometric four-to-one mapping prescription, where on average four heavy atoms (non-hydrogen) are represented as a single CG bead.53,54 The Martini force field has been parametrized for the simulation of lipids,53,55 proteins,54 carbohydrates,56 glycolipids,57 nanoparticles,58,59 surfactants,60 and polymers61−63 through benchmarking against atomistic and experimental data. In this work, we extend the Martini force field to develop a model of LPS macromolecule present in the outer membrane of Gram-negative bacteria. We adopt a mapping scheme of replacing six-ring hexoses by three CG beads in the LPS molecule. The optimized CG parameter set for lipopolysaccharides is presented after extensive benchmarking against atomistic force field data available in the published literature.45,46 Structural properties, such as area per lipid, order parameters, phase transition temperature, density profiles, and effect of counterions, have been computed and compared to available atomistic and experimental data. Furthermore, to understand how the properties of the asymmetric outer membrane may affect embedded OMPs, we simulated the OccD1 protein, an excellent representative for the porin channels found in P. aeruginosa.33 Overall, we performed coarse grained molecular dynamics simulations for over 100 μs for 17 asymmetric bilayer systems.

cautiously, can capture time-averaged properties and conformational space of atomistic systems. In view of this, the present model and force field parametrization has been benchmarked against experimental and atomistic system to capture highly averaged structural and thermodynamic properties of the bacterial membrane. The LPS molecule was benchmarked against the atomistic GLYCAM compatible force field developed by Kirschner et al.45 For a one-to-one comparison, we adopt the Kirschner et al. naming convention for each of the 20 residues in the LPS macromolecule (Table 1), representing lipid A and the core Table 1. List of Chemical Names and the Corresponding Acronyms in Lipopolysaccharide Model acronym

chemical name

LP1 LP2 PH2 3H1 WLL 2HA DPPE XYA and SYB 0KO and LKO 6GA/6GB(GAL/GBS) 0GA/0GB(GAS/GBL) GLA GLC GNA MAN

dodecanoyl acid decyl ester 3-hydroxydecanoyl acid 10:0 (3-OH) 2-(2-hydroxyethyl)-6-deoxy-D-manno-heptose L-glycero-D-manno-heptose-7-formamide 2-(2-L-alanyl)-2-deoxy-D-galactosamine 2-α-L-rhamnose 1,2-dipalmitoyl-3-phosphatidyl-ethanolamine 3-(acetyl amino)-3-deoxy-D-glucose 3-deoxy-D-manno-oct-2-ulosonic acid 6-α-D-glucose 0-β-D-glucose N-acetyl-D-glucosamine D-glucose N-acetyl-D-galactosamine D-mannose

residues. Starting from the atomistic structure, the residues were individually mapped into CG representation and the resulting beads were assigned one of the four Martini bead types: polar (P), nonpolar (N), apolar (C), and charged (Q). Further classification of these Martini beads into subtypes is discussed elsewhere.53 The CG model preserves the geometric backbone of the LPS molecule and maps the 479 atoms (without the O-antigen) into 68 CG beads, labeled as LIPO. For parametrization of the O-antigen repeating units, we adopt the E. coli O6 antigen that has been simulated using the CHARMM36 lipid and carbohydrate force field.47 Each Oantigen repeating unit is a branched pentasaccharide consisting of 3-substituted N-acetyl-D-glucosamine (GlcNAc), two β-Dmannoses (Man), one N-acetyl-α-D-galactosamine (GalNAc) in the backbone and a β-D-glucose (Glc) branched residue linked to mannose (Figure 2). The pentasaccharide repeating unit was mapped into 17 CG beads (Figure 2). In the present work, five repeating units were linked to LIPO (core + lipid A) molecule that increased the total CG beads to 153 in the O-antigen inclusive macromolecule (O-antigen + core + lipid A), labeled as OLIP. The LPS force field parameters include both bonded and nonbonded interaction terms consistent with the Martini force field. Specifically, the bonded interactions are represented by a sum of three potential energy terms, 1 1 Vbonded = Kbond(R − R bond)2 + K angle 2 2 1 2 [cos(θ ) − cos(θ0)] + K pd[1 + cos(ϕ − ϕpd)] 2

2. METHODS 2.1. Parametrization. The Martini four-to-one mapping approach has been adopted for LPS molecule to allow seamless integration with the existent Martini parameter suite for protein, lipids, and carbohydrates.53,54,56 Although the mapping approach is similar to the one published in the literature, the complexity of the LPS warranted developing systematic parametrization and optimization of several additional residues that are not available in the current Martini force field. The Martini model has the innate advantage of providing at least 2 orders of magnitude speed-up in simulation time compared to an analogous atomistic simulation, which is primarily due to the 10-fold larger time-step and reduced number of particles in the system. However, there are several approximations that need to be considered especially for the oligosaccharide domain of the LPS molecule that contains anomeric centers. Due to the loss of atomistic resolution and dihedral structure within the CG bead, there is loss of information on stereoisomers. The limitations of the CG model were noted by López et al. for Martini glycolipids.57 Despite the limitations, the CG parametrization, if performed

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Figure 2. Coarse grained mapping and bead assignments for (A) LP1, (B) LP2, (C) PO4, (D) XYA, (E) SYB, (F) 3H1, (G) LKO, (H) PH2, (I) WLL, (J) 0KO, (K) 2HA, (L) 6GA-6GB, (M) 0GA-0GB, and (N) O-antigen repeat unit. The coarse grained mapping scheme shows Martini beads of types C1 (light gray), P1 (green), P2 (yellow), P4 (orange), P5 (pink), Na (blue), N0 (purple), and Qa (dark gray) overlaid on the atomistic structure in ball and stick representation with carbon (cyan), oxygen (red), and linking bonds (pink). Hydrogen atoms are not shown for clarity.

where Kbond, Kangle, and Kpd are the force constants for the bond, angle, and dihedral interactions, Rbond is the equilibrium bonded distance, θ0 is the equilibrium angle, and ϕpd is the equilibrium dihedral angle. The nonbonded interactions are represented as a sum of Lennard-Jones potential and Coulombic energy function, qiqj ⎡⎛ σij ⎞12 ⎛ σij ⎞6 ⎤ Vnonbonded = 4εij⎢⎜ ⎟ − ⎜ ⎟ ⎥ + ⎝ r ⎠ ⎦ 4πε0εrr ⎣⎝ r ⎠

the number of hydroxyl groups in CG bead and the substitution of the hydroxyl group, bead types P1, P2, P4, and P5 were assigned. For some hexoses, anomeric carbon and hemiacetal ring oxygen atoms were assigned a nonpolar N0 bead type. Before arriving at the final mapping scheme several possible mapping schemes and starting configurations were attempted, but due to limitations in assigning correct Martini bead types, all the inconsistent schemes were eliminated. The final mapping scheme shown in Figure 2 was used to generate pseudo-CG structures of the 100 saved atomistic snapshots. Third, we computed all the bond lengths, angles, and dihedrals for the mapped pseudo-CG structures for each of the 100 snapshots, and used their average values as parameters for the initial CG LPS model. The force constants were estimated by matching the force constants from structurally similar geometries in existing Martini force field models. The topology file for CG simulations was created manually. The dihedral angle optimization required careful selection of parameters as the simulation results were very sensitive to the initial guess values. Benchmarking CG runs of single LPS molecule in standard Martini water (W) were performed iteratively by optimizing the parameter set until the percentage error, defined as

(2)

where σij is the collision diameter between i and j particles, εij is the depth of the potential well, qi denotes the charge on the bead, and εr is the relative dielectric screening constant. The CG equilibrium bond length, angle, and dihedral angle parameters were determined using a systematic three-step protocol. First, we performed an atomistic simulation for a single LPS molecule in TIP3P water64 for 100 ns and saved the geometries every 1 ns along the trajectory. The starting geometry for the atomistic simulation was obtained from the 650 ns pre-equilibrated system provided by Kirschner et al.45 Second, each of the 100 trajectory snapshots was converted into CG resolution using the mapping scheme in Figure S1. The bead types were assigned to the mapped CG structure based on the structural similarity to the existent CG parameters sets for lipids,53,55 carbohydrates,56 and glycolipids.57 The 10− 12 carbon long LP1 and LP2 chains were mapped into CG beads containing 3 or 4 carbon atoms and assigned apolar C1 bead type similar to standard Martini lipids. The charged phosphates and carboxylate groups were assigned unit negative charge with Q bead type and “a” subtype to represent the capability of these groups to accept hydrogen bonds. Based on

% error = (|⟨XCG⟩ − ⟨XAtm⟩| /⟨XAtm⟩) × 100

(3)

for all time-averaged bond distances, bond angles, and dihedral angles were reduced to less than 5% in a 500 ns trajectory (Tables S1 and S2). The errors were reduced over multiple iterations targeting problematic angles and dihedrals. For the final parameter set, the CG bond distances showed small fluctuations of 3−4% over 2 μs of simulation (Figure S2). Similarly, bond angle distribution maps showed good agree14671


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The Journal of Physical Chemistry B Table 2. Equilibrium Bond Length and Force Constant Values for the Coarse Grained LPS Model residue

bonds

Rmin (nm)

Kbond (kJ mol−1 nm−2)

residue

bonds

Rmin (nm)

Kbond (kJ mol−1 nm−2)

GBL GBL GBL GBL-HBL HAL HAL HAL HAL-GAL GAL GAL GAL GAL-WLL GAS GAS GAS GAS-GBS GBS GBS GBS GBS-WLL WLL WLL WLL WLL WLL WLL-JHI JHI JHI JHI JHI JH1-PH2 PH2 PH2 PH2 PH2 PH2 PH2 PH2 PH2-LKO LKO LKO LKO LKO LKO LKO-OKO OKO OKO OKO OKO OKO

L1−L2 L1−L3 L2−L3 L1−L4 L4−L5 L4−L6 L5−L6 L4−L9 L7−L8 L7−L9 L8−L9 L7−L19 L10−L12 L10−L11 L11−L12 L10−L14 L14−L15 L14−L13 L13−L15 L13−L19 L19−L20 L19−L16 L16−L20 L16−L17 L17−L18 L16−L22 L22−L21 L22−L23 L21−L23 L23−L24 L21−L36 L36−L37 L36−L41 L37−L40 L37−L35 L37−L38 L38−L39 L36−L35 L35−L27 L27−L25 L27−L28 L28−L29 L25−L26 L25−L28 L27−L30 L32−L30 L32−L33 L30−L33 L34−L33 L31−L30

0.368 0.291 0.298 0.321 0.274 0.302 0.268 0.33 0.286 0.372 0.275 0.352 0.369 0.281 0.33 0.341 0.301 0.365 0.282 0.365 0.319 0.234 0.323 0.287 0.227 0.308 0.272 0.306 0.334 0.387 0.393 0.267 0.309 0.293 0.259 0.316 0.289 0.258 0.262 0.248 0.258 0.216 0.239 0.216 0.349 0.238 0.242 0.21 0.284 0.233

17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000

LKO-SYB SYB SYB SYB SYB SYB SYB-LP2 LP2 LP2 SYB-LP1 LP1 LP1 LP1 LP1 LP1 LP1 SYB-XYA XYA XYA XYA XYA XYA XYA-LP1 LP1 LP1 LP1 LP1 LP1 LP1 GL GL GL GL GL GL GL GL GL GL GL GL GL GL GL GL GL GL GL GL GL

L25−L55 L55−L56 L55−L54 L54−L56 L56−L58 L54−L57 L56−L66 L66−L67 L67−L68 L57−L59 L59−L62 L59−L60 L60−L61 L62−L63 L63−L64 L64−L65 L54−L45 L45−L42 L45−L44 L42−L44 L42−L46 L42−L43 L43−L47 L47−L50 L50−L51 L51−L52 L52−L53 L47−L48 L48−L49 U1−U2 U1−U3 U2−U3 U3−U4 U3−U5 U5−U6 U5−U7 U6−U7 U7−U8 U8−U9 U8−U10 U9−U10 U10−U11 U11−U12 U11−U13 U12−U13 U13−U14 U8−U15 U15−U16 U15−U17 U16−U17

0.325 0.292 0.317 0.272 0.303 0.266 0.3 0.376 0.546 0.366 0.297 0.445 0.386 0.367 0.452 0.381 0.357 0.307 0.24 0.289 0.27 0.313 0.368 0.293 0.366 0.447 0.375 0.45 0.388 0.329 0.329 0.329 0.379 0.329 0.329 0.329 0.379 0.329 0.329 0.329 0.379 0.329 0.329 0.329 0.379 0.329 0.329 0.379 0.379 0.329

17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 17000 30000 30000 30000 30000 30000 30000 30000 30000 30000 30000 30000 30000 30000 30000 30000 30000 30000 30000 30000 30000 30000

ment with the atomistic data with % error of 1−4% in the average values. A representative set of angle distribution maps for CG and atomistic simulations are provided in Figure S3. Optimizing dihedral angle parameters was often difficult because 1−4 CG interactions typically span 10−12 atomic centers, which can be ill-defined if the persistence length of the underlying atomistic system is shorter than the separation between the CG centers. Further benchmarking was extended to a patch of LPS−DPPE bilayer system to ensure that

distributions obtained from single LPS molecule simulations are in good agreement with those in membrane microenvironment, and that the structural properties such as area per lipid, order parameters, and membrane thickness were consistent with the atomistic data. The parametrization of the O-antigen was performed independent of the LIPO molecule because it was not available in the GLYCAM force field used in previous atomistic simulations. The atomistic structure of a single pentasaccharide 14672


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The Journal of Physical Chemistry B Table 3. Equilibrium Angle and Force Constant Values for the Coarse Grained LPS Model angle

θ0 (deg)

Kangle (kJ mol−1)

L3−L1−L4

122

50

L3−L1−L2

52

50

L1−L3−L2

77

50

L2−L1−L4

52

50

L1−L4−L5

116

50

L1−L4−L6

132

50

L4−L5−L6

67

50

L1−L4−L9

149

50

L6−L4−L9

68

50

L4−L9−L8

111

50

L9−L8−L7

79

50

L9−L7−L19

63

50

L7−L19− L13 L10−L11− L12 L11−L12− L10 L11−L10− L14 L14−L15− L13 L14−L13− L19 L19−L20− L16 L19−L16− L17 L16−L17− L18 L16−L22− L21

140

50

73

50

47

50

105

50

77

50

83

50

42

50

99

50

95

50

L55−L54− L56 L54−L56− L66 L56−L66− L67 L66−L67− L68 L55−L56− L54 L54−L57− L59 L57−L59− L62 L57−L59− L60 L59−L60− L61 L59−L62− L63 L62−L63− L64 L54−L45− L44 L45−L44− L42 L44−L42− L46 L44−L43− L47 L43−L47− L50 L47−L50− L51 L50−L51− L52 L51−L52− L53 L47−L48− L49 U1−U2−U3

98

50

U1−U3−U2

angle

θ0 (deg)

Kangle (kJ mol−1)

59

50

101

50

134

50

167

50

68

50

63

50

72

50

169

50

165

50

94

50

156

50

107

50

70

50

51

50

93

50

72

50

118

50

123

50

164

50

170

50

60

80

60

80

angle L21−L22− L23 L22−L23− L24 L21−L36− L41 L23−L36− L41 L36−L37− L35 L36−L37− L40 L36−L35− L38 L35−L38− L39 L37−L38− L39 L35−L27− L28 L27−L28− L29 L27−L25− L26 L28−L25− L26 L28−L27− L25 L35−L27− L30 L30−L32− L33 L32−L33− L30 L33−L30− L31 L32−L33− L34 L25−L55− L56 L55−L56− L58

repeat unit was built in ChemDraw,65 and its Gromacs topology was generated using the online PRODRG server.66 The pentasaccharide unit was then simulated using the welltested CHARMM36 force field51 for polysaccharide in explicit TIP3P64 water. The equilibrated structure was used as a prototype for generating the CG structure and parameters for the entire O-antigen. The three-step protocol, described above, was employed to obtain the benchmarked CG parameters for the single repeat unit. Next, the repeat units were increased to five and several iterations of 500 ns simulations were performed in both the atomistic and CG resolutions to optimize the parameters related to the glycosidic linkages between the repeat units. Finally the optimized OLIP structure and topology was manually generated by linking the O-antigen with the core residues of LIPO. The CG particle types (Figure 2) and optimized list of parameters are provided in Tables 2−4. To make the simulations electrostatically neutral, monovalent (Na+) and divalent (Ca2+) ions were used as counterions. Although the Martini ions do not include the long-range electrostatic interactions, the parametrization implicitly includes

θ0 (deg)

Kangle (kJ mol−1)

angle

θ0 (deg)

Kangle (kJ mol−1)

70

50

U1−U3−U4

115

80

131

50

U4−U3−U5

115

80

125

50

U3−U5−U6

115

80

122

50

U6−U5−U7

60

80

58

50

U5−U6−U7

60

80

110

50

U5−U7−U6

60

80

112

50

U7−U8−U9

115

80

134

50

U7−U8−15

115

80

134

50

60

80

91

50

60

80

117

50

60

80

140

50

115

80

106

50

60

80

50

50

60

80

108

50

60

80

52

50

115

80

63

50

60

80

105

50

60

80

110

50

60

80

133

50

U9−U8− U10 U8−U9− U10 U8−U10− U9 U8−U15− U17 U15−U16− U17 U16−U15− U17 U15−U17− U16 U10−U11− U12 U12−U11− U13 U11−U12− U13 U11−U13− U12 U11−U13− U14

115

80

87

50

Table 4. Equilibrium Dihedral Angle and Force Constant Values for the LPS Molecule dihedrals

ϕpd (deg)

Kpd (kJ mol−1)

L11−L12−L14−L13 L3−L1−L4−L6 L5−L6−L9−L8 L28−L27−L30−L33 L29−L28−L25−L26 L31−L30−L33−L32 L41−L38−L36−L37 L45−L44−L42−L43 L58−L56−L54−L45 U13−U11−U10−U8 U7−U5−U3−U1 U8−U15−U16-U17

−180 −180 70 130 120 30 30 140 −180 −180 −180 120

5 5 8 6 9 8 8 5 15 5 5 10

the first hydration shell around the ion. The hydrated Na+ and Ca2+ ions were given the “Qd” bead type with integral +1 and +2 charges, respectively. No additional parametrization of Ca2+ 14673


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The Journal of Physical Chemistry B Table 5. System Details of the Membrane Simulations inner

outer

no. of

systems

DPPE

LIPOa

DPPE

OLIPb

counterions

water

ions

porin

temp (K)

time (μs)

I II III IV V VI VII VIII IX Xc

256 256 202 202 256 256 256 256 256 180

86 86 68 68 86 86 86 86 68 72

28 28 22 22 28 28 28 28 22 −

5 5 4 4 5 5 5 5 4 −

Ca Na Ca Na Ca Ca Ca Ca Ca Ca

16622 16622 17075 17075 16622 16622 16622 16622 17057 16008

364 728 296 592 364 364 364 364 296 288

n n y y n n n n y n

310 310 310 310 283 → 350 350 → 283 283 350 310 310

10 10 2 2 2 2 2 2 2 0.01

a

LIPO: Lipid includes core polysaccharides + lipid A domains. bOLIP: Lipid includes O-antigen + core polysaccharides + lipid A domains. cThe initial geometry was obtained from 650 ns pre-equilibrated system, Kirschner et al.45

Kirschner et al.45 The topologies and parameters compatible with the GROMOS force field68 and pre-equilibrated 652 ns (at 310 K) coordinate files of LPS membrane were downloaded45 and used as input for additional benchmarking simulations. The contents of the atomistic systems are reported in Table 5. Explicit TIP3P water64 was used in the simulations. We used periodic boundary conditions and semi-isotropic pressure coupling at 310 K with a 2 fs time step in our production NPT runs.

ions was performed in the present work, and the only difference in Na+ and Ca2+ ions was the net charge on the two ions. 2.2. Simulation Details. In order to benchmark the developed force field, multiple independent simulations were performed with varying number of lipids, counterions, temperatures, simulations times, and molecular resolutions (Table 5). The compositions of the model systems in Table 5 have been chosen for the purpose of benchmarking against available atomistic simulation (System X) data and do not represent bacterial outer membrane composition of any specific bacterial species, primarily because the composition can be highly variable. In all simulations GROMACS 4.6.6 simulation package was used.67,68 Initial CG systems were prepared with lipid molecules preassembled into a 10 × 10 nm2 cross-sectional bilayer structure using a locally modified version of the Insane script developed by Wassenaar et al. for generating custom membranes.69 The systems were solvated with explicit water and charge balanced with cationic counterions (Table 5). In the initial test runs both standard (W) and polarizable (PW) Martini water70 were tested, and both showed similar properties for the bilayer assembly. To keep these simulations less expensive, only standard Martini water was used for the production runs. Energy minimization was performed using the steepest decent algorithm67 without constraints, followed by NVT simulation for 200 ns at 310 K. The temperature for each group (lipids and water) was kept constant using the velocity rescale coupling algorithm with 1 ps time constant. For the NPT equilibration step, semi-isotropic pressure coupling was applied using the Berendsen algorithm, with a pressure of 1 bar independently in the cross-section of the membrane and perpendicular to the membrane. A time constant of 2.0 ps and a compressibility of 1.0 × 10−6 bar−1 was used. The neighbor list was updated every 25 steps using a neighbor list cutoff equal to 1.4 and 1.2 nm short-range van der Waals and electrostatics cutoff. A time step of 20 fs was used in all the simulations. Long-range electrostatic interactions were computed using the shifted Coulomb potential with the shift starting at r = 0. Three dimensional periodic boundary conditions were used for the simulation box without any position or bond constraints. All coarse grained systems were prepared, energy minimized, and equilibrated in both isothermal−isochoric (NVT) and isothermal−isobaric (NPT) ensembles using the same protocols before the production MD runs, as described above. The atomistic system for the benchmarking our CG outer membranes was obtained from a previously published model by

3. ANALYSIS Structural properties such as area per lipid, membrane thickness, density profiles, order parameters, and phase transition temperatures have been computed for the CG systems and compared to the available atomistic simulation results. 3.1. Density Profiles and Area per Lipid. The structural properties of the membrane are closely related to how membrane residues and the surrounding ionic media interact with each other. To streamline the analysis, the CG beads of each residue were grouped together, and their number density was computed in 0.2 nm wide volume slices along the z-axis, normal to membrane in the xy-plane. The microstructure within the plane of the membrane is governed by the intermolecular lipid−lipid interactions and molecular packing, which is often quantified by the average area per lipid (AL) and membrane thickness. For each membrane leaflet, the AL value was computed by dividing the cross-sectional area of the membrane by the number of lipids in the leaflet. Standard utilities available in the Gromacs software suite were employed for all the quantities described above. 3.2. Lipid Order Parameters. Lipid order parameters are the metric for the orientational order of the lipids in a bilayer.71 It is a second-rank order parameter, P2 = (3/2)⟨cos2 θ⟩ − (1/ 2), where θ is the angle between the direction of the bond and the bilayer normal. The order parameter was computed for each CG bead or atom (atomistic) in the LPS and DPPE acyl chains. The order parameter values are widely used as a measure of orderliness of the acyl chains in the bilayer, and the values of 1, −0.5, and 0 indicate perfect alignment, antialignment, and a random orientation, respectively.72 3.3. Gel to Liquid-Crystalline Transformation. A characteristic feature of any lipid membrane is the phase transition temperature, Tm, where lipids undergo thermal reorganization from a relatively ordered (gel) state to fluidic state with higher degree of disorder (liquid crystalline). The Tm 14674


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Figure 3. Snapshot of outer membrane simulation at 0, 2, 4, 6, 8, and 10 μs. The color scheme of the membrane components is the same as in Figure 1. Counterions are shown in gray, and water is omitted for clarity.

Figure 4. Comparison of the partial number density of LPS residues along the membrane normal (z-axis) in coarse grained (solid line) and atomistic (dashed line) resolutions: (a) 0GB (blue), LP1 (red), and LP2 (green); (b) 6GA (yellow), 3H1 (light brown), and SYB (aqua); (c) 0GA (light purple), 6GB (orange), and XYA (gray); (d) 2HA (dark brown), WLL (dark blue), LKO (ocean green), and Ca2+ (pink); (e) 0KO (black) and PH2 (dark purple); and (f) DPPE (light gray) and water (deep red). For the CG system the density was averaged over the last 5 μs of the trajectory with 0.5 μs intervals.

the cooling process the membrane did not necessarily lead to well-ordered configuration. The heating scans, however, showed clear demarcation in the change structural properties during the phase transformation. Therefore, the Tm values were determined using the heating runs. Additional standalone simulations were performed at 283 and 350 K to compute the lipid order parameters and area per lipid data for the two phases. 3.4. Porin Protein. The OccD1 crystal structure was obtained from the Protein Data Bank (PDB ID: 4FOZ).34 The crystal structure was missing 13 residues in the N-terminal, but

values are sensitive to membrane composition and the individual lipid molecules constituting the membrane. To determine the characteristic Tm values for the model systems, we performed annealing simulations starting from wellequilibrated configurations to mimic the phase transition conditions.73 The heating scans were performed in 283−350 K temperature range with intermediate temperatures of 298, 311, 324, and 337 K over two microseconds of simulation time. The cooling scans were done in the reverse order starting from the equilibrated membrane at 350 K. Determination of Tm using cooling scans was difficult in many cases because during 14675


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and Systems I−IX is the absence of DPPE in the outer leaflet of the CG membrane, which becomes evident in the density profile curves for DPPE and water. As a consequence, in the atomistic system the width of the DPPE curve is 2.5 nm with its peak centered at the z-axis value of the inner leaflet, whereas in CG System I, the width of the curve is 4.1 nm, reflecting the presence of DPPE in both leaflets (Figure 4f). To benchmark this further, we created 7 additional systems with pure inner DPPE leaflet and varying outer leaflet (LPS:DPPE) composition ratio from (10:90) to (100:0) in the presence of counterions. The LPS (OLIP + LIPO) and DPPE composition of the outer leaflet for each of the seven systems is provided in Table 6. Density profile comparison of these systems (Figure

because they are part of the signal peptide sequence of the OccD Porin and contribute toward proper sorting of the porin to the outer membrane, these are naturally removed during post-translational modifications, and therefore they were ignored in the present simulations. Hence they are not significant toward the current study where the protein is characterized in the outer membrane in its functional form. The hydrophobic thickness of the protein was computed to be 2.04 ± 0.4 nm with tilt angle of 5 ± 1° using the PPM server.74 The protein was coarse grained using standard Martini force field parameters, and its secondary structure was maintained using the Elnedyn elastic network.75 The CG protein was embedded in the acyl chains of the outer and inner leaflets to match the hydrophobic thickness of the protein. To maintain the position of the β-barrel in the membrane, the protein backbone was position restrained along the z-direction during energy minimization to remove bad contacts during membrane equilibration. These constraints were removed in the production MD runs. The temperature for each group (lipids, protein, and water) was kept constant using the velocity rescale coupling algorithm with a time constant of 1 ps. For the production MD runs, semi-isotropic pressure coupling was applied using the Parrinello−Rahman algorithm.76 Analysis of the protein was done post-simulation in both coarse grained and reversed mapped atomistic forms.

Table 6. Phase Transition Temperature and Area per Lipid for System with Variable LPS Composition of the Outer Leaflet inner

4. RESULTS AND DISCUSSION 4.1. Comparison to Atomistic Membrane Density Profiles. The simulated sets of LPS−DPPE membranes demonstrate robustness in modeling key structural and dynamical properties of bacterial outer membrane. System I (Table 5), a representative model system with Ca2+ counterions, was simulated for 10 μs and analyzed for properties discussed below. Figure 3 shows the lamellar bilayer assembly with uniform membrane thickness throughout the simulation. The converged AL values for LPS and DPPE for the CG system are 1.49 and 0.51 nm2, respectively (Figure S4), which is in good agreement with the data for atomistic membrane.45 The O-antigen sugar residues were found to be flexible compared to the core residues that were anchored by the cations. A more detailed density analysis of the residues along the membrane normal shows minimal change in the lamellar assembly of the membrane. Furthermore, average density profiles of membrane residues were compared to the atomistic System X, and they show excellent agreement in the peak positions for most of the residues. For example, in Figure 4a, the innermost residues (LP1 and LP2) and the outermost saccharide (0GB) show perfect peak alignment, confirming the average length of the LIPO molecule to be 3.4 nm. A majority of the LPS residues (Figure 4b−e) show ±0.1 nm peak alignment for the CG and atomistic systems. The difference in the 3H1 peaks is ±0.3 nm, which is higher than the other residues, but it does not affect the overall length of the LPS molecule. In Figure 4d, unlike the atomistic system, a Ca2+ ion peak is observed at 2 nm for the CG system due to the periodic boundary conditions, because some counterions moved closer to the inner leaflet of the membrane during the 10 μs long simulation. It is indeed possible that counterions would be observed in the atomistic simulations as well, if these simulations could be affordably performed for the same length of time. One notable difference between the atomistic (preequilibrated system obtained from Kirschner et al.) system

LPS (%)

DPPE

10 20 30 50 70 90 100

196 196 196 196 196 196 196

outer

LIPO DPPE 13 31 52 57 84 105 94

176 156 137 57 30 12 0

OLIP

water

Ca2+

Tm (K)

area per lipid DPPE (nm2)

6 7 6 5 5 3 5

18230 17753 17366 17390 16700 16296 16530

76 152 232 248 356 432 396

346 336 320 312 301 293 288

0.55 0.61 0.66 0.58 0.67 0.74 0.70

S5) shows a larger width for systems with DPPE in both leaflets. In all systems (Figure S5) the CG water density profile is slightly shifted, but closely follows the atomistic profile throughout the length of the box. As expected, in the 100:0 system, due to the lack of DPPE in the outer leaflet, the DPPE density profile shows very good agreement with the atomistic data and only at ±0.3 nm variation in the widths (Figure S6). 4.2. Phase Transition Temperatures. Four systems with the same composition, labeled V−VIII (Table 5), were simulated to determine the effect of temperature on the membrane stability and to evaluate the membrane phase transition temperature (Tm). Constant temperature simulations as well as heating and cooling runs were performed using the annealing protocols described in the Methods section. Figure 5 shows snapshots of the DPPE and LP1 and LP2 groups of the membrane in the ordered crystalline phase at T = 283 K and in disordered state at T = 350 K. To determine the Tm, separate heating (283 → 350 K) and cooling (350 → 283 K) cycles were performed, and the AL values were calculated for the entire temperature range. At 283 K the DPPE area per lipid is 0.53 nm2, which gradually increases (Figure 5c) until about 325 K, followed by a definitive change in membrane characteristics caused by the highly cooperative rearrangement of the lipid tails. At 350 K the AL increases to 0.57 nm2. A linear fit to the area per lipid data in the high and low temperature regimes resulted in intersecting slopes at the 314 K (Figure 5c), which is in excellent agreement with the experimental Tm value of 310 K for E. coli membrane.37 The increase in the temperature caused a change in the membrane thickness (Figure 5d) and the order parameter of the lipid tails by more than 0.2 unit (Figure 5e). At 283 K, the order parameter values are higher (0.65−0.75), but at higher temperature, the tails are more fluid and the order parameters drop to values as low as 0.45. Considering that CG 14676


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Figure 5. Thermal phase transition of the LPS−DPPE outer membrane from (a) crystalline phase at 283 K to (b) fluid phase at 350 K along with (c) AL curves for LPS (primary y-axis, purple) and DPPE (secondary y-axis, green), (d) membrane thickness (green 283−310 K; orange 310−350 K), and (e) average order parameter values for coarse grained LPS (primary y-axis, purple) and DPPE (secondary y-axis, green) acyl chains at 283 K (dashed) and 350 K (solid) lines.

Figure 6. Comparison of the partial number density of LPS residues along the membrane normal (z-axis) in coarse grained model with Na+ (solid line) and Ca2+ (dashed line) counterions: (a) 2HA (pink), WLL (light blue), and LKO (olive green); (b) 6GA (yellow), 3H1 (light green), and SYB (light blue); (c) 0GA (purple), 6GB (orange), and XYA (gray); (d) 0GB (blue), LP1 (dark red), and LP2 (green); (e) 0KO (sky blue), PH2 (dark purple), Na+ (solid light purple), and Ca2+ (dashed light purple); and (f) DPPE (gray) and water (deep red). The density was averaged over the last 5 μs of the trajectory with 0.5 μs intervals.

representations do not have the atomistic resolution of each carbon−carbon bond order parameter, the computed values are

in good agreement with the experimental data for ordered (0.70 ± 0.05) and disordered (0.25 ± 0.5) phases.77 14677


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Figure 7. Snapshot of β-barrel OccD1 porin embedded in the bacterial outer membrane system (after 2 μs of the simulation) and structural analysis results are shown. (a) The porin (ribbons, magenta) spans the inner leaflet (green) and outer leaflet (yellow) of the membrane. The O-antigen domain (gray) and counterions (black) are shown; water is omitted for clarity. In panel b, water molecules (aqua) and porin are shown; membrane and ions are omitted for clarity. In panel c, the root-mean-square deviation of the protein backbone (blue) is provided. In panel d, average diameter fluctuations of the OccD1 porin diameter at the top (light and dark green), middle (light and dark orange), and bottom (pink and maroon) of the pore are shown.

membrane residues in the Na+ ion system possibly because of single unit charge on the sodium ion that caused it to smear over a larger cross-section of the membrane. Repeating the simulation for another pair of systems (III and IV) with a different membrane composition yielded no significant difference in the observed trends for the two cations. 4.4. Effect of Membrane Composition. To determine the effect of LPS composition on the Tm in the model membranes, change in the AL values was monitored (Figure S8) during the heating and cooling annealing simulations for all 7 systems (Table 6). Increasing the outer leaflet LPS composition had significant influence on decreasing the phase transition values from 346 to 290 K, indicating a less ordered packing of the LPS molecules and increased area per lipid for the membrane. Bacterial species exhibit chemical heterogeneity in membrane composition that results in significant variation in membrane properties, especially in phase transition temperatures and fluidity. The phase transition temperatures can vary by ±15 deg depending on the proportion of lipids with long and saturated acyl chains. FT-IR spectroscopy and differential scanning calorimetry study on a diverse set of Gram-negative bacteria reported Tm values ranging between 301 and 315 K.37 The variation in composition can be genetic in origin or a consequence of physiological conditions. Direct correlation of Tm values with the growth temperature was observed in E. coli

4.3. Role of Counterions. The results show that cationic counterions assist in maintaining the structural integrity of the membrane, primarily by intercalating between the negatively charged groups of the neighboring LPS molecules. The screening of the negative charges (phosphate groups linked to WLL, 0KO, LKO residues) is done more effectively by a divalent Ca2+ ion as compared to monovalent Na+ cation mainly because of the amount of charge on the counterion and the hydration.50 This has been experimentally verified using a wide range of techniques including small-angle scattering, Fourier transform infrared spectroscopy, small-angle X-ray diffraction, and transmission electron spectroscopy. The postsimulation analysis of Systems I and II after 10 μs showed that the membrane remained lamellar in the presence of both monovalent and divalent counterions, but the thickness of the membrane changed slightly. In case of Na+ was 6.1 ± 0.2 nm whereas in presence of Ca2+ ions it increased to 6.6 ± 0.4 nm (Figure S7). To rule out any bias due to the starting configuration, System I was prepared by randomly deleting half of the Na+ ions in System II and reassigning the remaining Na+ ions as Ca2+ ions to maintain electroneutrality of the system. Electron density profiles of membrane residues (Figure 6) show alignment of peaks for most membrane residues within ±0.4 nm, which could be due to 0.5 nm difference in the average value of the membrane thickness in the two systems. Additionally, broader density distributions were observed for 14678


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Figure 8. Partial number densities of System III (after 2 μs of the simulation) containing OccD1 porin in the outer leaflet of LPS and inner leaflet of DPPE. Panels show (a) protein position in the LPS−DPPE lipid domain, (b) number density of water in the simulation box, and (c) top view of lipid number density in the membrane. The color scheme shows low (blue) to high (red) number density in each panel. The dashed lines show the vertical alignment of the LPS−DPPE leaflets in the simulation box for reference.

System III at 310 K the lateral diffusion coefficient of OccD1 protein was found to be (0.12 ± 0.05) × 10−7 cm2 s−1, which is similar to that of LPS (0.12 ± 0.07) × 10−7 cm2 s−1 lipid, but both of which are 1 order of magnitude smaller than the DPPE diffusion coefficient value of (3.95 ± 0.45) × 10−7 cm2 s−1 in the inner leaflet (Table S3). The slow diffusing bulkier LPS molecules with 5 acyl chains restrict the diffusion of DPPE lipids in the outer leaflet (0.21 ± 0.10) × 10−7 cm2 s−1 as well. No significant difference in the diffusion coefficients was observed in System IV with Na+ counterions.

lipid extracts using electron spin resonance labeled technique.78,79 More interestingly, bacteria evade external physicochemical stress stimuli by readjusting the membrane fluidity simply by altering their membrane composition.42,79 4.5. Porin Channel. The stability of OccD1 porin embedded in outer membrane assembly was monitored in multiple CG systems for over 2 μs (Figure 7) of simulation time. For detailed analysis the protein was reverse mapped into atomistic representation.80 The overall structural stability of the pore scaffold is assessed by the root-mean-square deviation (RMSD) of the protein backbone (Figure 7). The RMSD fluctuations were small with a 0.28 nm average over the entire length of the trajectory (Figure 7c). The uptake and transport of substrate through the pore is highly charge selective,33 and water was observed in the lumen of the pore (Figure 7b) . The pore diameter was measured by assigning multiple diametrically opposite residues at the bottom (Phe7 and Ser235; Asp205 and Lys394), top (Tyr369 and Val97; Thr92 and Thr185), and middle (Phe308 and Arg399; Val313 and Thr131) of the pore. Figure 7d shows the average pore diameters at all three locations during the trajectory. The top and the middle of the pore lumen showed ±0.2 nm fluctuation about the average, while the bottom of the pore had more pronounced fluctuations of ±0.6 nm (Figure 7d). These fluctuations, however, did not affect the average pore diameter. The number density map (Figure 8) shows equilibrated position of the OccD1 protein embedded in the membrane. The hydrophobic thickness of the β-barrel matches with the width of the acyl chains in the DPPE−LPS interface (Figure 8a), and the cytosolic loops of the protein are below the plane of the polar DPPE lipid heads. A portion of βbarrel protein embedded in the LPS acyl chains and the top of the porin interacts with the column of water (Figures 8b). The number density of lipids distinctly shows an unobstructed top view of the pore lumen (Figure 8c). Additionally long time diffusive behavior of the membrane was estimated for Systems III and IV that differ in the charge of the counterion. For

5. CONCLUSIONS We have developed a parameter set for lipopolysaccharide enriched outer membrane of Gram-negative bacteria in the coarse grained representation using the standard bead types of the Martini force field. The model includes a complete set of LPS domains: lipid A, core, and O-antigen repeating units. The bonded and nonbonded parameters were optimized for individual structural components of LPS molecule and benchmarked against atomistic model. Structural and thermodynamics properties such as the area per lipid headgroup, order parameters, density distributions, and melting transition temperature agree well with the available atomistic and experimental data. Furthermore, outer membrane OccD1 porin channel was simulated in the model LPS membrane to examine the robustness of the model for lipid−protein interactions. Our study confirms the role of positively charged counterions in maintaining the lamellar structure of the membrane via the electrostatic stabilization of the negatively charged LPS core. The studies also demonstrate the sensitivity of the phase transition temperatures to the lipid composition of the outer membrane. Membranes rich in lipopolysaccharide concentration showed lower melting points, higher area per lipid, and higher disorder compared to membranes with simple phospholipids. The LPS parameters can be used to model most Gram-negative bacterial outer membranes that differ in 14679


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(10) Graves, N.; Weinhold, D.; Tong, E.; Birrell, F.; Doidge, S.; Ramritu, P.; Halton, K.; Lairson, D.; Whitby, M. Effect of HealthcareAcquired Infection on Length of Hospital Stay and Cost. Infect. Control Hosp. Epidemiol. 2007, 28, 280−292. (11) Roberts, R. R.; Scott, R. D., II; Hota, B.; Kampe, L. M.; Abbasi, F.; Schabowski, S.; Ahmad, I.; Ciavarella, G. G.; Cordell, R.; Solomon, S. L.; et al. Costs Attributable to Healthcare-Acquired Infection in Hospitalized Adults and a Comparison of Economic Methods. Med. Care 2010, 48, 1026−1035. (12) Delcour, A. H. Outer Membrane Permeability and Antibiotic Resistance. Biochim. Biophys. Acta, Proteins Proteomics 2009, 1794, 808−816. (13) Costerton, J. W.; Ingram, J. M.; Cheng, K.-J. Structure and Function of the Cell Envelope of Gram-Negative Bacteria. Bacteriol. Rev. 1974, 38, 87−110. (14) Lugtenberg, B.; Vanalphen, L. Molecular Architecture and Functioning of the Outer-Membrane of Escherichia-Coli and Other Gram-Negative Bacteria. Biochim. Biophys. Acta, Rev. Biomembr. 1983, 737, 51−115. (15) Decad, G. M.; Nikaido, H. Outer Membrane of Gram-Negative Bacteria. XII. Molecular-Sieving Function of Cell-Wall. J. Bacteriol. 1976, 128, 325−336. (16) Benz, R.; Bauer, K. Permeation of Hydrophilic Molecules through the Outer-Membrane of Gram-Negative Bacteria - Review on Bacterial Porins. Eur. J. Biochem. 1988, 176, 1−19. (17) Nikaido, H. Porins and Specific Channels of Bacterial Outer Membranes. Mol. Microbiol. 1992, 6, 435−442. (18) Nikaido, H. Porins and Specific Diffusion Channels in Bacterial Outer Membranes. J. Biol. Chem. 1994, 269, 3905−3908. (19) Koebnik, R.; Locher, K. P.; Van Gelder, P. Structure and Function of Bacterial Outer Membrane Proteins: Barrels in a Nutshell. Mol. Microbiol. 2000, 37, 239−253. (20) Voulhoux, R.; Bos, M. P.; Geurtsen, J.; Mols, M.; Tommassen, J. Role of a Highly Conserved Bacterial Protein in Outer Membrane Protein Assembly. Science 2003, 299, 262−265. (21) Pages, J. M.; James, C. E.; Winterhalter, M. The Porin and the Permeating Antibiotic: A Selective Diffusion Barrier in Gram-Negative Bacteria. Nat. Rev. Microbiol. 2008, 6, 893−903. (22) Caroff, M.; Karibian, D.; Cavaillon, J.-M.; Haeffner-Cavaillon, N. Structural and Functional Analyses of Bacterial Lipopolysaccharides. Microbes Infect. 2002, 4, 915−926. (23) Erridge, C.; Bennett-Guerrero, E.; Poxton, I. R. Structure and Function of Lipopolysaccharides. Microbes Infect. 2002, 4, 837−851. (24) Caroff, M.; Karibian, D. Structure of Bacterial Lipopolysaccharides. Carbohydr. Res. 2003, 338, 2431−2447. (25) Zahringer, U.; Lindner, B.; Rietschel, E. T. Molecular-Structure of Lipid-a, the Endotoxic Center of Bacterial Lipopolysaccharides. Adv. Carbohydr. Chem. Biochem. 1994, 50, 211−276. (26) Raetz, C. R. H.; Reynolds, C. M.; Trent, M. S.; Bishop, R. E. Lipid A Modification Systems in Gram-Negative Bacteria. Annu. Rev. Biochem. 2007, 76, 295−329. (27) Kučerka, N.; Papp-Szabo, E.; Nieh, M.-P.; Harroun, T. A.; Schooling, S. R.; Pencer, J.; Nicholson, E. A.; Beveridge, T. J.; Katsaras, J. Effect of Cations on the Structure of Bilayers Formed by Lipopolysaccharides Isolated from Pseudomonas Aeruginosa PAO1. J. Phys. Chem. B 2008, 112, 8057−8062. (28) Knirel, Y. A.; Kochetkov, N. K. The Structure of Lipopolysaccharides of Gram-Negative Bacteria. III. The Structure of O-Antigens - A Review. Biochemistry (Moscow) 1994, 59, 1325−1383. (29) Feldman, M. F.; Wacker, M.; Hernandez, M.; Hitchen, P. G.; Marolda, C. L.; Kowarik, M.; Morris, H. R.; Dell, A.; Valvano, M. A.; Aebi, M. Engineering N-Linked Protein Glycosylation with Diverse O Antigen Lipopolysaccharide Structures in Escherichia Coli. Proc. Natl. Acad. Sci. U. S. A. 2005, 102, 3016−3021. (30) Stenutz, R.; Weintraub, A.; Widmalm, G. The Structures of Escherichia Coli O-Polysaccharide Antigens. FEMS Microbiol. Rev. 2006, 30, 382−403. (31) Kang, Y.; Barbirz, S.; Lipowsky, R.; Santer, M. Conformational Diversity of O-Antigen Polysaccharides of the Gram-Negative

compositions, number of O-antigen repeat units, chemotypes, and porins. Additionally, the model also provides an excellent starting point for molecular level investigations of antimicrobial action of peptides, mechanistic aspects of antibiotic uptake, and small molecule permeation through outer membrane porin channels. In conclusion, the developed model makes study of bacterial membrane dynamics feasible with a molecular level precision for events that occur over microsecond time scales.

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ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpcb.5b07122. Mapping of individual LPS residues, benchmarking data, comparison of AL values, effect of ions on outer membrane thickness, determination of Tm values, density profiles, and diffusion coefficients of membrane components (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors thank XSEDE supercomputing facility for providing computational resources for part of the simulations presented here. We also thank the National Science Foundation, EFRI-MIKS, Grant 1137186, and Syracuse University for the financial support of this project.

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REFERENCES

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