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Lipid14: The Amber Lipid Force Field

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§ Department of Chemistry and Institute of Chemical Biology, Imperial College London, South Kensington SW7 2AZ, United Kingdom
San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive MC0505, La Jolla, California 92093-0505, United States
Department of Biomedicine, University of Bergen, N-5009 Bergen, Norway
± Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive MC0505, La Jolla, California 92093-0505, United States
Cite this: J. Chem. Theory Comput. 2014, 10, 2, 865–879
Publication Date (Web):January 30, 2014
https://doi.org/10.1021/ct4010307
Copyright © 2014 American Chemical Society
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Supporting Info (1)»

Abstract

The AMBER lipid force field has been updated to create Lipid14, allowing tensionless simulation of a number of lipid types with the AMBER MD package. The modular nature of this force field allows numerous combinations of head and tail groups to create different lipid types, enabling the easy insertion of new lipid species. The Lennard-Jones and torsion parameters of both the head and tail groups have been revised and updated partial charges calculated. The force field has been validated by simulating bilayers of six different lipid types for a total of 0.5 μs each without applying a surface tension; with favorable comparison to experiment for properties such as area per lipid, volume per lipid, bilayer thickness, NMR order parameters, scattering data, and lipid lateral diffusion. As the derivation of this force field is consistent with the AMBER development philosophy, Lipid14 is compatible with the AMBER protein, nucleic acid, carbohydrate, and small molecule force fields.

Introduction

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Membranes are integral components of the cell, separating intracellular compartments from the cytosol. Such membranes consist of a back-to-back arrangement of lipid molecules, driven into a bilayer structure by the hydrophobic effect, leaving the polar lipid head groups exposed to water, and bringing the nonpolar lipid tail groups together. The composition of cell membranes is complex, with constituent species including, but not limited to, saturated and unsaturated PC and PE lipids, sphingomyelin and cholesterol, which serve as a matrix in which membrane proteins may reside. (1) Cell membranes possess functions such as regulating transport in to and out of the cell and modulating the activity of membrane embedded ion channels and proteins. (2, 3)
In order to probe the many roles of membranes in the cell, membrane structures are studied experimentally using techniques such as X-ray and neutron scattering, IR/Raman, and NMR spectroscopy. (4, 5) To gain atomic-level resolution, however, membranes may also be simulated computationally using molecular dynamics (MD). The validity of results obtained using MD methods depends, to a large extent, on the potential energy function, or force field, that is used.
Membranes can be simulated using all-atom, united-atom, or coarse-grained models, (6-13) with the increasing simplicity of each representation allowing access to larger models and longer time scales, at the expense of atomic detail. All-atom models may be preferred for bilayer simulations due to their ability to reproduce NMR order parameters and easy combination with all-atom protein, nucleic acid, carbohydrate, and small molecule force fields. (14-16) One such MD software package that includes all-atom simulations is AMBER, (17, 18) which contains extensively validated protein, nucleic, carbohydrate, and small molecule force fields. The AMBER simulation code has also been ported to NVIDIA GPU cards, making simulation speeds in excess of 100 ns per day possible for systems of 25,000 to 50,000 atoms, with systems of up to 4 million atoms possible on the latest generation hardware. (19-21) Although the AMBER force field suite includes numerous different species of biological interest, parameters for the simulation of lipids have traditionally been lacking. Recently the Lipid11 framework was introduced, which is a modular lipid AMBER force field, allowing the simulation of a number of lipids via the combination of different head and tail groups. (22) Lipid11 used force field parameters predominantly taken directly from the General Amber Force Field (GAFF). (16) Although previous studies found some success in simulating lipid bilayers using GAFF parameters, (23-25) longer time scale simulations required a surface tension term in order to keep a bilayer in the correct phase at a given temperature. (22)
In this paper we draw inspiration from previous work (26) to update Lipid11 headgroup and tail group charges and parameters to enable proper tensionless simulation of lipid bilayers, thereby creating a modular AMBER lipid force field. Lennard-Jones and torsion parameters are revised, while charges are derived according to the standard AMBER convention, as was implemented in Lipid11 with minor improvements in sampling for Lipid14. As such, the resulting parameters are expected to be compatible with other force fields in the AMBER package.
We validate these parameters for a number of PC and PE lipids – six different lipid types are simulated for a total of 500 ns each, with resulting area per lipid, volume per lipid, isothermal compressibility, NMR order parameters, scattering form factors, and lipid lateral diffusion finding favorable comparison to experiment, particularly for PC lipids. We also assess the conformation of the lipid tails by examining the number of rotamers and rotamer sequences. To fully test the reproducibility of the results, a number of additional GPU and CPU runs were performed. We believe that these parameters will be transferable thus allowing the easy insertion of new lipid types into Lipid14 owing to the modular nature of the force field. The Lipid14 force field will be released with the upcoming AmberTools14. It is our intention to incorporate support for additional lipid types and other residues commonly found in bilayers, as part of the upcoming release. The derivation of these parameters will be described elsewhere.

Parameterization Strategy

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Generation of Lipid14 Parameters

Lipid14 aims to extend the Lipid11 (22) framework to allow accurate, tensionless simulation of numerous lipid types via a modular lipid force field. Bond, angle, torsion, and Lennard-Jones (LJ) parameters in Lipid11 were taken directly from the General Amber Force Field (GAFF). (16) Although bond and angle parameters should not require updating, it was expected that torsion and LJ parameters would need modification to realize this aim.
Previous work on lipid simulation with AMBER indicated that the Lennard-Jones and torsion parameters of the lipid aliphatic tails are intricately linked to the ability of the force field to reproduce experimentally observed bilayer properties. (26) Indeed, the majority of all-atom lipid force field parameterization has involved the modification of the LJ and torsion parameters used to model the aliphatic tail regions of the lipids. (7, 8, 10, 27)
Simulating a box of 144 pentadecane chains using the standard GAFF LJ and torsion parameters at constant pressure (1 atm) and temperature (298.15 K) causes the hydrocarbon chains to ‘freeze’ into a crystalline state in under 2 ns (see Figure 1). Such a scenario has previously been observed by Klauda et al. using a box of tetradecane molecules and the AMBER99 force field. (28) Consequently, the calculated density and heat of vaporization are much higher and the diffusion much lower than experiment.

Figure 1

Figure 1. A box of 144 pentadecane molecules simulated in the NPT ensemble at 298.15 K using the General Amber Force Field (16) to model the carbon chains.

In light of this, LJ and torsion parameters were modified to reproduce the experimental density (ρ) and heat of vaporization (ΔHvap) of alkanes covering a range of chain lengths. Given that both the torsion and LJ parameters affected the simulated ρ and ΔHvap, these parameters were altered simultaneously, with the CH2–CH2–CH2–CH2 torsion being fitted to ab initio data using Paramfit. (17, 29) Satisfactory agreement was found by modifying only the LJ parameters of the hydrogen atom on the alkane chains.
The modified LJ and torsion parameters were tested by examining the density ρ, heat of vaporization ΔHvap, the diffusion D, the 13C NMR T1 relaxation times, and the trans/gauche conformer populations of a selection of hydrocarbon chains.
The parameters of the ester linkage region connecting headgroup and tail residues in the lipids were then examined using methyl acetate as a model compound. The density and heat of vaporization of methyl acetate were calculated and ΔHvap found to be in poor agreement with experiment. Hence the Lennard-Jones parameters of a number of atoms in this region were also adjusted until better agreement with experiment was achieved.
Once optimal hydrocarbon and glycerol parameters were identified, attention turned to the lipid partial charges. The Lipid11 force field used a capping procedure, separating the lipid head and tail groups into ‘residues’, thus creating a modular lipid force field. (22) The standard AMBER RESP protocol (30) was used to generate partial charges from quantum mechanical (QM) optimized structures, using six different orientations of a single conformation.
This methodology was kept for Lipid14. However, in line with other all-atom lipid force field parameterizations, (10, 31) a greater number of conformations were used per residue (twenty-five headgroup conformations, fifty tail conformations), with the partial charges calculated as an average over all conformations. The head and tail group starting structures were extracted from previous in-house bilayer simulations. This allows one to obtain Boltzmann weighted charges, introducing a temperature dependence. (10, 31) The electrostatic potential (ESP) was calculated directly from the conformations extracted from a bilayer simulation, with no QM optimization performed on those structures. Charges were derived using the standard AMBER RESP protocol (at the HF/6-31G* level in gas phase). (30)
Finally, on identification of appropriate LJ parameters and calculation of lipid partial charges, torsions involving the ester linkage in the glycerol region were fitted to QM scans performed on a capped lauroyl (LA) tail moiety.

Hydrocarbon Tail Parameters

The alkane CH2–CH2–CH2–CH2 torsion potential was refitted using torsion scans performed on hexane and octane molecules using an estimation of the CCSD(T)/cc-pVQZ level of theory via the HM-IE relation (32)where the small basis set (SBS) was cc-pVDZ and the large basis set (LBS) was cc-pVQZ. Consequently molecules were optimized at the MP2/cc-pVDZ level before single-point energy calculations were performed at the CCSD(T)/cc-pVDZ and MP2/cc-pVQZ levels. In order to obtain a representative torsion fit, it was ensured that torsion scans, conducted at increments of 15°, included local minima of hexane and octane. (10, 28, 33) The Paramfit program of AmberTools13 (17, 29) was used to perform a multiple molecule weighted torsion fit, with the tgt local minima of hexane and tgttt local minima of octane given a weighting of 10, all other local minima given a weighting of 4 and cis conformers given a weighting of 0.1; all other structures were assigned a weighting of 1. These weighting values have previously given good results for alkane torsion fitting. (33) Torsions were fitted using a genetic algorithm.
Lennard-Jones parameters were modified while simultaneously refitting the torsion parameters (see Table 1) until good agreement with experiment was achieved for heats of vaporization and densities for a range of alkane chains. These properties were monitored by performing liquid phase simulations of pentane, hexane, heptane, octane, decane, tridecane, and pentadecane. The alkane trajectories also enabled the calculation of a number of other properties for comparison to experiment.
Table 1. Modification of LJ and Torsion Parameters of Alkane Chains
 LJ parametersCH2–CH2–CH2–CH2 torsion
 atom typeradius R (Å)well-depth ε (kcal mol–1)force constant PK (kcal mol–1)periodicity PNphase (deg)
Lipid11cA1.90800.10940.201180
hA1.48700.01570.252180
0.1830
Lipid14cD1.90800.10940.31121180
hL1.46000.0100–0.12332180
0.114930
–0.219940
0.217050
Initial charges for each hydrocarbon chain were generated using the standard AMBER RESP protocol (optimization and calculation of the ESP at HF/6-31G* level in gas phase). A box of 288 (pentane, hexane and heptane) or 144 (octane, decane, tridecane, and pentadecane) molecules were then simulated in the liquid phase at 298.15 K for 10 ns using updated LJ and torsion parameters. From each liquid phase simulation, fifty different chains were extracted and used for the charge calculation, with the ESP calculated directly from each structure at the HF/6-31G* level, and partial charges derived using the RESP fitting procedure. Charges were taken as an average over all fifty RESP fits.
The heat of vaporization was calculated according to (34)(1)where Epot(g) is the average molecular potential energy in the gas phase, Epot(l) is the average molecular potential energy in the liquid phase, R is the gas constant, and T is temperature. In order to calculate the heat of vaporization, two simulations were performed in a similar manner to previous work. (26) A gas phase simulation consisting of a single alkane molecule was run for 10 ns equilibration and 50 ns production under the NPT ensemble, at a temperature of 298.15 K to obtain Epot(g). A liquid phase simulation consisting of a box of either 144 or 288 chains, run under the NPT ensemble with periodic boundary conditions using particle mesh Ewald to treat long-range electrostatics (35) and a real space cutoff of 10 Å, was also performed for 60 ns with the first 10 ns removed for equilibration to obtain Epot(l). The temperature was maintained at 298.15 K using Langevin dynamics and a collision frequency of 5 ps–1. Bonds involving hydrogen were constrained using the SHAKE algorithm. (36) Pressure regulation was achieved with isotropic position scaling, a Berendsen barostat, (37) and a pressure relaxation time of 1 ps. The system was heated from 0 to 298.15 K over 20 ps, with a force constant of 20 kcal/mol/Å2 restraining the chains. This restraint was gradually decreased to 10, 5 and finally 1 kcal/mol/Å2 and the system simulated for 20 ps at each value of the force constant. The heat of vaporization was then calculated using eq 1, with results reported as block averages ± standard deviation using five equal blocks of 10 ns.
By reducing the van der Waals radius (R) and well-depth (ε) of the alkane hydrogen atom type while simultaneously correcting the torsion fit, satisfactory agreement with experiment was achieved for ρ and ΔHvap for the alkane chains under study.
Three torsion scans about the C═C double bond were also performed on a cis-5-decene molecule using the MP2:CC extrapolation method – see Figure 2. When fitting the C═C double bond torsions, the LJ parameters of the alkene hydrogen atom (Lipid14 atom type hB) were scaled until satisfactory agreement with experiment was found for ρ and ΔHvap of cis-2-hexene, cis-5-decene, and cis-7-pentadecene. Reducing both the van der Waals radius (R) from 1.459 to 1.25 and the well-depth (ε) from 0.015 to 0.007 resulted in far better agreement for the density values (ρ); however, it proved difficult to correct the heat of vaporization (ΔHvap) of cis-5-decene to that of the experimental value by only modifying the LJ parameters. This is due to the high charge on the double bond atoms. A similar problem has previously been encountered by Chiu et al. (13) and Jämbeck et al. (10)

Figure 2

Figure 2. The energy profile for rotating about selected torsions of a cis-5-decene molecule. Energy evaluated using QM and the HM-IE method (filled triangle ▲), AMBER with standard GAFF parameters (dotted line), and AMBER with Lipid14 parameters (black line). Torsion fits from the top are as follows: CH2–CH–CH–CH2, CH–CH–CH2–CH2, and CH–CH2–CH2–CH2.

The uncorrected diffusion DPBC was calculated for each alkane using the slope of a mean-square displacement (MSD) plot versus time for the centers of mass, averaged over the trajectories of each molecule via the Einstein relation(2)where nf is the number of dimensions (in this instance nf = 3), and Δr(t)2 is the distance that the molecule travels in time t. It is then possible to correct for the system size dependence of a diffusion coefficient calculated under periodic boundaries (DPBC) to yield the corrected diffusion coefficient Dcorr by adding the correction term derived by Yeh and Hummer (38)(3)where kB is Boltzmann’s constant, T is the temperature, ε = 2.837297, η is the viscosity, and L is the length of the simulation box.
The diffusion was calculated from 100 ns NVE ensemble simulations (extended from the 50 ns NPT runs). PME was used, along with a 10 Å cutoff, at a temperature of 298.15 K. In order to avoid energy and temperature drift, it was necessary to remove the center of mass motion every 500 steps (nscm = 500), make both the shake tolerance and Ewald direct sum tolerance more stringent, and reduce the time step to 1 fs. Diffusion values were then calculated by taking the slope of the linear 2–5 ns region of the MSD versus time curve and the correction calculated using experimental viscosity values. Diffusion results are reported with standard deviations calculated by block averaging, with each 100 ns run divided into five 20 ns blocks.
The trans, gauche, end gauche, double gauche, and kinked gtg′+gtg conformer populations were evaluated from the 50 ns NPT runs by classifying torsion angles as either gauche plus (g+) 0–120°, trans (t) 120–240°, or gauche minus (g–) 240–360°.
13C NMR T1 relaxation times were calculated from the NPT alkane simulations using the following formula for dipolar relaxation from the reorientation correlation functions of the CH vectors: (39)(4)
This assumes motional narrowing and an effective C–H bond length of 1.117 Å, (40) with N specifying the number of protons bonded to the carbon and μ̂ the CH vector. T1 relaxation times were calculated from simulations of alkanes of four different chain lengths. These simulations were repeated using the same NPT alkane protocol at the experimentally relevant temperature of 312 K and production runs extended to 200 ns to improve sampling.

Head Group Parameters

It was found that the heat of vaporization of methyl acetate, a model compound representing the ester linkage region connecting the lipid head and tail groups, was not sufficiently close to experiment using GAFF/Lipid11 parameters. In order to correct for this discrepancy with experiment, the Lennard-Jones well-depths of the carbonyl oxygen (oC), carbonyl carbon (cC), and ester oxygen (oS) atoms were scaled until better agreement with experiment was obtained (see Table 2). The ester oxygen parameters were also applied to oxygen atoms in the phosphate region.
Table 2. Thermodynamic Properties of Methyl Acetate Simulated with GAFF/Lipid11 and Lipid14 and Comparison to Experimenta
 LJ parameters  
 atom typeradius R (Å)well-depth ε (kcal mol–1)ΔHvap (kJ mol-1)ρ (kg m-3)
Lipid11oC1.66120.21039.11 ± 0.04928.38 ± 0.09
oS1.68370.170
cC1.90800.086
Lipid14oC1.65000.14033.0 ± 0.07925.8 ± 0.05
oS1.65000.120
cC1.90800.070
Expt   32.29 (41)934.2 (41)
a

All values at 298.15 K.

The density and heat of vaporization of methyl acetate were calculated using an identical procedure as for the hydrocarbon chains, with a box of 288 methyl acetate molecules applied for the liquid phase calculation. Simulations were run for 60 ns and the final 50 ns used for sampling. Results are reported as block averages (five blocks of 10 ns).

Partial Charges

RESP fitting was performed in exactly the same manner to Lipid11, (22) using the capping groups shown in Figure 3. However a greater number of conformations were used to calculate the average charges for each unit.
Twenty-five phosphatidylcholine (PC) and twenty-five phosphatidylethanolamine (PE) head groups were extracted from previous bilayer simulations of DOPC and POPE; while fifty lauroyl (LA), myristoyl (MY), palmitoyl (PA), and oleoyl (OL) tails were extracted from bilayer simulations of DLPC, DMPC, DPPC, and DOPC, respectively. Each headgroup was then capped with a methyl group and each tail capped with an acetate moiety (Figure 3), according to the Lipid11 charge derivation methodology. (22) For each conformation, the ESP was calculated directly from each structure at the HF/6-31G* level using Gaussian 09. (42) Charges were taken as an average over all conformations for each residue. Resulting charges for the PC and PE headgroup residues and the LA, MY, PA, and OL tail group residues are detailed in the Supporting Information.

Figure 3

Figure 3. Structure and charges of Lipid11/Lipid14 headgroup and tail group caps. (22)

Head Group Torsion Fits

Two torsions involving the ester linkage region (see Figure 4) were fitted to QM data. The scans were performed on a capped lauroyl (LA) tail residue, at 15° increments using the HM-IE method with Gaussian 09. (42) These were then fitted using Paramfit (17, 29) for periodicity n = 1 to n = 5 using the genetic algorithm implemented in Paramfit.

Figure 4

Figure 4. A capped lauroyl tail group residue was used to fit the oS-cC-cD-cD and oC-cC-cD-cD torsions.

Figure 5

Figure 5. The energy profiles for rotating about selected torsions of a capped lauroyl tail group residue. Energy evaluated using QM and the HM-IE method (filled triangle ▲), AMBER with standard GAFF/Lipid11 parameters (dotted line), and AMBER with Lipid14 parameters (black line). Torsion fits from the top are oC-cC-cD-cD and oS-cC-cD-cD.

As can be seen from Figure 5 Paramfit brings the oS-cC-cD-cD and oC-cC-cD-cD torsions into substantially better agreement with the QM data.

Parameterization

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Hydrocarbon Parameters

Table 3. Thermodynamic and Dynamic Properties of a Selection of Alkane Chains Simulated Using the Updated Lipid14 Parameters and Comparison to Experimenta
 ΔHvap (kJ mol-1)ρ (kg m-3)DPBC (10-5 cm2 s-1)Dcorr (10-5 cm2 s-1)
Pentane
Lipid1423.03 ± 0.16592.45 ± 0.166.45 ± 0.567.1 ± 0.56
Expt26.43 (41)626.2 (41)5.45 (43)
Hexane
Lipid1428.54 ± 0.1636.3 ± 0.094.55 ± 0.295.02 ± 0.29
Expt31.56 (41)656, (44) 660.6 (41)4.21 (43)
Heptane
Lipid1433.37 ± 0.11667.31 ± 0.143.47 ± 0.233.85 ± 0.23
Expt36.57 (41)679.5 (41)3.12 (43)
Octane
Lipid1438.67 ± 0.31690.96 ± 0.102.11 ± 0.152.46 ± 0.15
Expt41.49 (41)698.6 (41)2.354 (45)
Decane
Lipid1449.34 ± 0.30724.47 ± 0.071.44 ± 0.151.65 ± 0.15
Expt51.42 (41)726.6 (41)1.39 (45)
Tridecane
Lipid1464.62 ± 0.27756.19 ± 0.240.48 ± 0.040.57 ± 0.04
Expt66.68 (41)756.4 (41)0.712 (45)
Pentadecane
Lipid1474.99 ± 0.39770.67 ± 0.250.30 ± 0.020.36 ± 0.02
Expt76.77 (41)768.5 (41)0.461 (45)
a

All values at 298.15 K.

The results for the alkane properties calculated using the updated torsion and LJ parameters are shown in Table 3. The heat of vaporization values increase with alkane chain length, following the experimental trend and converging with experiment as the number of carbon atoms in the chain increases. The simulation values match experiment with an RMS error of 7.67%. The simulated densities are reproduced somewhat better than ΔHvap, with an RMS error of 2.60% when compared to experiment. The diffusion values lie close to experiment and decrease with increasing chain length, following the experimental trend; however, the RMS error between simulation and experiment remains significant at 20.92%. As is also the case with the heat of vaporization results, the main source of this discrepancy is the result for the shorter alkane chains. The better agreement with experiment of these parameters at modeling longer hydrocarbon chains may arise from the use of a high number of octane structures during torsion fitting. Furthermore, this parameter set is intended for the simulation of membrane lipids, which typically contain aliphatic tails that are ten carbon atoms or greater in length.
Table 4. Thermodynamic Properties of a Selection of Alkene Chains Simulated Using the Updated Lipid14 Parameters and, Where Available, Comparison to Experiment
 ΔHvap (kJ mol-1)ρ (kg m-3)
cis-2-Hexene
Lipid1426.17 ± 0.21656.23 ± 0.13
Expt32.19 (41)683 (44)
cis-5-Decene
Lipid1445.27 ± 0.22739.19 ± 0.16
Expt42.9 (41)744.5 (41)
cis-7-Pentadecene
Lipid1469.5 ± 0.22781.44 ± 0.24
Expt775 (44)
Thermodynamic properties for a selection of alkenes calculated using the updated LJ and torsion parameters are shown in Table 4. As previously stated, properties of unsaturated chains are not as well reproduced as for alkanes due to the difficulty in tuning LJ parameters to achieve the experimental heat of vaporization, resulting in an RMS error of 13.80% for ΔHvap when compared to experiment. The density values are again better reproduced with an RMS error of 2.35%.
Table 5. Average Number of trans, gauche, End gauche (eg), Double gauche (gg), and gtg′+gtg Conformers Per Alkane Molecule and Comparison to Experiment
 transgauchet/g ratioeggggtg′+gtg
Pentane
Lipid141.200.801.490.800.13-
Hexane
Lipid141.831.171.570.830.220.14
Heptane
Lipid142.491.511.650.830.310.24
Octane
Lipid143.151.851.710.810.390.33
Decane
Lipid144.472.531.770.810.570.54
Tridecane
Lipid146.473.531.840.810.820.83
Expt (46)6.53.51.860.680.640.77
Pentadecane
Lipid147.804.201.860.811.001.02
The fractions of trans, gauche, end gauche, double gauche, and kinked gtg′+gtg conformers per molecule were computed for the selection of alkanes under study (see Table 5). Experimental data, estimated by FTIR, exists only for tridecane; (46) however, the updated Lipid14 parameters reproduce these results extremely well with an overestimation of the end gauche and double gauche conformations only. Furthermore, the population of gauche conformations per molecule falls close to the experimental value of 35% for all chains investigated (t/g ratio ∼1.86), meaning that the overpopulation of trans conformations which drives GAFF bilayer simulations into the gel phase is avoided.
The quality of the hydrocarbon parameters was further assessed by calculating the 13C NMR T1 relaxation times for heptane, decane, tridecane, and pentadecane. Similar to diffusion, this is a dynamic property; however, it depends on the rotation of the CH vector. Results are shown in Figure 6. In general the simulation values follow the same profile as the experimental data. Simulation values tend to converge with experiment upon moving further from the end carbon of the alkane chains. Although the result for pentadecane is slightly high, the overall comparison between simulation and experiment is reasonable.

Figure 6

Figure 6. Calculated 13C NMR T1 relaxation times for selected alkane chains and comparison to experiment. (47) Values at 312 K.

Lipid Bilayer Simulation

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Initial Structures

Bilayers were constructed using the CHARMM Membrane Builder GUI (48) at the relevant experimental hydration level (see Table 6) and converted to Lipid14 PDB format using the charmmlipid2amber.x script. (17) All systems used the TIP3P water model (49) and had 0.15 M KCl salt concentration added to the water layer, modeled using suitable AMBER parameters. (50)
Table 6. System Size, Hydration, Temperature, and Simulation Time for the Lipid Bilayer Systems
 no. of lipidssimulation time (ns)temp (K)waters/lipid nW
DLPC1285 × 12530331.3
DMPC1285 × 12530325.6
DPPC1285 × 12532330.1
DOPC1285 × 12530332.8
POPC1285 × 12530331
POPE1285 × 12531032

Equilibration Procedure

The full system was minimized for 10000 steps, of which the first 5000 steps used the steepest descent method and the remaining steps used the conjugate gradient method. (51)
The system was then heated from 0 K to 100 K using Langevin dynamics (52) for 5 ps at constant volume, with weak restraints on the lipid (force constant 10 kcal mol–1 Å–2).
Following this, the volume was allowed to change freely and the temperature increased to a lipid dependent value (see Table 6) with a Langevin collision frequency of γ = 1.0 ps–1, and anisotropic Berendsen regulation (37) (1 atm) with a time constant of 2 ps for 100 ps. The same weak restraint of 10 kcal mol–1 Å–2 was maintained on the lipid molecules.

Production Runs

Constant pressure and constant temperature (NPT) runs were performed on the six bilayers using the AMBER 12 package. (17) The GPU implementation of the AMBER 12 code (bugfix 21) was used to run the simulations on NVIDIA GPU cards, achieving approximately 30 ns per day for the 128-lipid bilayer systems. (17, 19) Three dimensional periodic boundary conditions with the usual minimum image convention were employed. Bonds involving hydrogen were constrained using the SHAKE algorithm, (36) allowing a 2 fs time step. Structural data was recorded every 10 ps. PME was used to treat all electrostatic interactions with a real space cutoff of 10 Å. A long-range analytical dispersion correction was applied to the energy and pressure. All simulations were performed at constant pressure of 1 atm and constant target temperature (Table 6). Temperature was controlled by the Langevin thermostat, (52) with a collision frequency of γ = 1.0 ps–1, as this method was identified as the most suitable in previous work. (25) Pressure was regulated by the anisotropic Berendsen method (37) (1 atm) with a pressure relaxation time of 1.0 ps.
Each lipid type was simulated for 125 ns with five repeats. The first 25 ns of each run was removed for equilibration, resulting in a total of 500 ns of data per lipid system, an aggregate of 3 μs of data. Results are presented as block averages over the five repeats ± standard deviation. The majority of analysis in this paper used PTRAJ or CPPTRAJ analysis routines. (17, 53)
To check stability over time of the lipid bilayer systems, the simulations were extended from 125 ns to 250 ns. Additional GPU and CPU validations were also performed, and in all cases the GPU results were consistent with CPU results (see the Supporting Information).

Validation

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Bilayer Structural Properties

Table 7. Average Structural Properties over Five Repeats of the Six Lipid Systems Simulated with Lipid14 and Comparison to Experiment
lipid systemarea per lipid AL2)volume per lipid VL3)isothermal area compressibility modulus KA (mNm-1)bilayer thickness DHH (Å)bilayer Luzzati thickness DB (Å)ΔDB-H = (DBDHH)/2 (Å)ratio r of terminal methyl to methylene volume
DLPC       
Lipid1463.0 ± 0.2948.9 ± 0.3281 ± 3730.4 ± 0.430.2 ± 0.1–0.1 ± 0.21.9
Expt63.2, (54)60.8 (55)991 (54)-30.8 (54)31.4 (54)0.8 (56)1.8–2.1 (57)
DMPC       
Lipid1459.7 ± 0.71050.2 ± 1.5264 ± 9034.7 ± 0.635.2 ± 0.40.3 ± 0.22.2
Expt60.6, (54)59.9 (55)1101 (4, 54)234 (58)34.4, (59) 35.3 (60)36.3, (54) 36.7, (55)0.8 (56)1.8–2.1 (57)
     36.9 (59)  
DPPC       
Lipid1462.0 ± 0.31177.3 ± 0.5244 ± 5037.9 ± 0.538.0 ± 0.20.1 ± 0.22.1
Expt63.1, (55)64.3 (61)1232 (4)231 (4)38, (62) 38.3 (4)39.0 (55, 62)0.8 (56)1.8–2.1 (57)
DOPC       
Lipid1469.0 ± 0.31249.6 ± 0.2338 ± 3137.0 ± 0.236.2 ± 0.2–0.4 ± 0.12.1
Expt67.4, (62) 72.5 (4)1303 (4)265, (58) 300, (63) 318 (64)35.3, (65) 36.7, (62, 66) 36.9, (4) 37.1 (67)35.9, (4) 36.1, (65, 67) 38.7 (62)1.0–1.7 (57)1.8–2.1 (57)
POPC       
Lipid1465.6 ± 0.51205.4 ± 0.4257 ± 4736.9 ± 0.636.8 ± 0.3–0.1 ± 0.21.9
Expt64.3, (55) 68.3 (68)1256 (68)180–330 (69)37 (68)36.8, (68) 39.1 (55)0.8 (56)1.8–2.1 (57)
POPE       
Lipid1455.5 ± 0.21138.7 ± 0.3350 ± 8142.4 ± 0.241.0 ± 0.1–0.7 ± 0.12.0
Expt56.6, (70) 59–60 (71)1180 (71)233 (70)39.5 (71)--1.8–2.1 (57)
Despite the degree of uncertainty in obtaining accurate experimental values, (72) the bilayer surface area each lipid occupies, or area per lipid, is easily calculated from membrane simulations and gives a quick indication of whether a bilayer is in the correct phase at a given temperature. The area per lipid for each system was calculated using the dimensions of the simulation box as per previous work. (22, 26) The AL for each lipid type is reported in Table 7, with all simulation values within 3% of experimental values, indicating that all the bilayers are in the correct Lα-phase. The result for POPE is closer to the older experimental AL value of 56.6 Å2 than the more recent AL value 59–60 Å2. Nevertheless, the AL should be but one of a number of properties calculated to validate a lipid force field. (73, 74)
Experimentally, the volume per lipid VL is more accurately measured and is thus a better comparison for simulation results than AL. The volume per lipid was calculated using the dimensions of the simulation box (26) and the volume of a water molecule as determined by simulating 1936 TIP3P waters in the NPT ensemble for 50 ns using an identical procedure to the bilayer simulations at the relevant temperature.
VL values for each lipid are reported in Table 7, which although systematically underestimated, are within 5% of experimental values. It is likely that the headgroup LJ parameters could be further tuned to remedy this discrepancy, as the thorough reparamaterization of the hydrocarbon chains makes it unlikely that the tail groups are causing this lack of agreement.
This intuition is confirmed when studying the lipid component volumes calculated with the SIMtoEXP software. (75) The headgroup volume of DOPC was found to be 305.41 Å3, which is below the experimental estimate of 319–331 Å3, while the hydrocarbon chain volume of 965.88 Å3 is closer to the experimental range of 972–984 Å3. (57)
The volume breakdown provided by SIMtoEXP was used to calculate the ratio r of terminal methyl to methylene volume. All lipid systems report a value of r = 1.85–2.17, within or very close to the experimental range of 1.8–2.1.
Isothermal area compressibility modulus, KA, was calculated from the fluctuation in the area per lipid. (26) In general, KA values fall close to experiment, with experimental values falling within the standard deviation of DMPC, DPPC, DOPC and POPC simulation results; however the POPE value comes out high and there is a large standard deviation in all values. Although the DOPC value is above the published experimental value of 300 mN m–1, (63) a personal communication with E. Evans revealed that this KA value has recently been revised upward to 318 mN m–1, (64) closer to the Lipid14 simulation result. This was not known prior to the lipid simulations.
In this work, the Berendsen method was used for pressure coupling, given that it is the only barostat currently available in the AMBER MD package. It has recently been shown that Berendsen pressure control is not ideal for simulations in which volume fluctuations are important, (76) thus by implementing other barostats into AMBER better KA results may potentially be achieved. This is a work in progress, the results of which will be shown elsewhere. Furthermore, larger system sizes and longer simulation times could also be investigated, as such changes have been shown to speed up the convergence of KA values. (74)
The membrane thickness was examined by calculating DHH, the peak-to-peak distance, from electron density profiles of the membranes. Again, satisfactory agreement with experiment is achieved for all lipids, though the POPE value is a little high, indicating that this system is slightly too ordered.
An alternative bilayer thickness, the Luzzati thickness DB, was calculated using the z-dimension of the simulation box and the integral of the probability distribution of the water density along the z-axis. (9, 10)DB values are found to lie close to experimental values, though the DB thicknesses for the saturated lipids are slightly underestimated. Given that DB is the distance between the points along the membrane normal at which the water density is half of its bulk value, this suggests that water is penetrating slightly too far into the hydrophobic region of the bilayer, thereby lowering the value of DB.

Ordering and Conformation of Lipid Acyl Chains

The ordering of the lipid acyl chains may be determined by calculation of the order parameter SCD. This quantity can be directly compared to experimental SCD values determined by 2H NMR or 1H–13C NMR. (77-80)SCD is a measure of the relative orientation of the C–D bonds with respect to the bilayer normal and was calculated according to(5)where θ is the angle between the bilayer normal and the vector joining Ci to its deuterium atom, and < > represents an ensemble average.

Figure 7

Figure 7. Simulation NMR order parameters for the six lipid systems and comparison to experiment. (77, 78, 80-84)

Figure 7 shows the Lipid14 order parameters with comparison to experiment. All lipid systems follow the experimental order parameter trend. The carbon-2 atom of the sn-1 and sn-2 chains display markedly different order parameters owing to the different alignment of the acyl chains in this region. Experimentally, it has been found that the SCD order parameter of the C-D bonds near the headgroup in the sn-1 chains are greater than the sn-2 chains. (85, 86) Splitting of the order parameter value of the sn-2 chain from the sn-1 chain is observed for the simulated lipid systems. The unsaturated chain of the DOPC, POPC and POPE lipids show a distinctive drop at the carbon-9 and -10 positions due to the cis double bond. The SCD values for the sn-1 chain of POPE are a little high, indicating that POPE may be slightly too ordered. In agreement with Jämbeck et al., the two chains of DOPC show differing behavior, the sn-1 chain having higher SCD values about the double bond than the sn-2 chain. (87)
The conformation of the acyl chains may be examined by analyzing the rotamers and rotamer sequences along the lipid tails and comparing results to experimental data collected by Fourier transform infrared (FTIR) spectroscopy. (88) FTIR can determine the number of trans (t) and gauche (g) conformers and sequences of t and g (end gauche eg, double gauche gg, gtg, and kinks gtg′). The lipid bilayer simulations were analyzed by denoting torsion angles φ in the acyl chains as either t (φ< −150° or φ> 150°), g- (−90°≤ φ< −30°) or g+ (30°< φ≤90°). (89) The rotamer sequence gtg correspond to g+tg+ or g-tg- while the sequence (or kinks) gtg′ corresponds to g+tg- or g-tg+.
Results are shown in Table 8 and are in general satisfactorily close to available experimental values. The discrepancies observed between simulation and experimental values of gtg′ for DLPC and DPPC may result from the experimental ambiguity in assigning gtg and gtg′ wagging modes. (90) These results also confirm that the bilayers are in the correct phase, as the gel-to-liquid phase transition is associated with an increase in the number of gauche rotamers and kink rotamer sequences. (90-92) However the eg and gg results for POPE are not in accordance with the experimental values obtained by Senak et al. using FTIR, (88) who found a marked increase in eg, gg, and gtg′+gtg values going from DPPE to DPPC because of the tighter packing of the PE lipid in the Lα phase. The present simulation values for POPE and POPC, though, are similar.
Table 8. Analysis of Rotamers and Rotamer Sequences in the Acyl Chains of the Six Lipid Systems – End gauche (eg), Double gauche (gg), Kinks (gtg′), gtg′+gtg, and Number of gauche (ng)
lipid systemeggggtggtg′+gtgng
DLPC     
Lipid140.350.440.280.522.50
Expt (93)0.450.320.88*-2.85
DMPC     
Lipid140.340.480.350.622.82
Expt (94)0.380.67-0.442.6
DPPC     
Lipid140.360.660.470.833.58
Expt0.38, (94) 0.4, (88) 0.54 (93)0.4, (88, 93) 0.57 (94)1.19 (93)a0.46, (94) 1.0 (88)2.44, (94) 3.6–4.2, (95) 3.7, (93) 3.8 (82)
DOPC     
Lipid140.360.750.370.703.93
POPC     
Lipid140.360.690.410.753.73
POPE     
Lipid140.350.600.420.733.50
Expt (88)0.050.2-0.8-
a

The gtg′ sequence may be ascribed to a gtg′+gtg sequence. (90)

Electron Density Profiles

The electron density profiles (EDP) were calculated by assuming an electron charge equal to the atomic number minus the atomic partial charge, located at the center of each atom. Profiles have also been decomposed into contributions from the following groups: water, choline (CHOL), phosphate (PO4), glycerol (GLY), carbonyl (COO), methylene (CH2), unsaturated CH═CH and terminal methyls (CH3). These profiles, shown in Figure 8, are all symmetrical, with water penetrating up to the carbonyl groups, leaving the terminal methyl groups dehydrated in agreement with experimental findings. (54, 68, 61) The electron density profiles were then utilized for the calculation of scattering form factors using the SIMtoEXP software. (75)

Figure 8

Figure 8. The total and decomposed electron density profiles for each of the six lipid bilayer systems with contributions from water, choline (CHOL), phosphate (PO4), glycerol (GLY), carbonyl (COO), methylene (CH2), unsaturated CH═CH and terminal methyls (CH3).

Scattering Form Factors

Scattering data allow direct comparison between lipid bilayer simulation and experiment, avoiding any intermediate modeling of experimental raw data. (57) X-ray and neutron scattering form factors can be computed by Fourier transformation of simulation electron density profiles and compared to experimental scattering data.
Recent work determined the areas per lipid (AX and AN) at which DOPC bilayer simulations best replicate the experimental X-ray scattering and neutron scattering data by varying the area per lipid through application of a surface tension, with the ideal situation being AX = AN = ANPT (bilayer is run in the tensionless NPT ensemble). (57) In this work we were concerned with validating the Lipid14 parameters for tensionless bilayer simulation only; thus we report the X-ray and neutron scattering form factors for ANPT only.

Figure 9

Figure 9. Simulation X-ray scattering form factors for the six lipid systems (black line) and comparison to experiment (54, 55, 62, 66, 68) (cyan circles). Inset: Simulation neutron scattering form factors at 100% D2O (black line), 70% D2O (red line), and 50% D2O (blue line) and comparison to experiment (55, 96) (black, red, and blue circles, respectively).

It can be seen from Figure 9 that there is general agreement between both the X-ray and neutron scattering form factors for all lipids for which there is experimental scattering data available, indicating that the simulated bilayers have the correct structure. In general the minima of the experimental F(q) profiles are correctly reproduced, as are the relative lobe heights.
The quantity ΔDB-H was computed from the membrane thickness values (see Table 7). Agreement with X-ray scattering data is sensitive to the value of DHH, while agreement with neutron scattering data is sensitive to the value of DB. Therefore it has been proposed that bilayer simulations should aim to replicate experimental ΔDB-H values to best achieve agreement with both types of scattering data, where ΔDB-H = (DB-DHH)/2. (57) The GROMOS united-atom lipid force field has been shown to match experiment for simulation ΔDB-H results. (56, 57) As evidenced by Table 7, Lipid14 ΔDB-H values are lower than those found by experiment, though all simulation values do maintain a large standard deviation. In fact, analysis of ΔDB-H results for two other all-atom lipid force fields, CHARMM36 (8) and Slipids (10, 87) indicates that this quantity is difficult to reproduce using all-atom models, with only the Slipids POPC result falling close to experiment. Figure 10 plots ΔDB-H values against area per lipid for CHARMM36, (56) Slipids, (10, 87) and AMBER Lipid14, displaying a downward trend in ΔDB-H with increasing area per lipid, in disagreement with the experimental trend. Results for Lipid14 are similar to CHARMM36 and most Slipids values. Improving this discrepancy with experiment for Lipid14 may further improve the comparison with scattering data; however, present simulation scattering profiles are still seen to be in satisfactory agreement with experiment.

Figure 10

Figure 10. Plot of ΔDB-H versus area per lipid AL for the three all-atom lipid force fields CHARMM36 (squares), Slipids (diamonds), and AMBER Lipid14 (circles). Values shown for DLPC (green), DMPC (magenta), DPPC (blue), DOPC (red), and POPC (orange).

Lipid Lateral Diffusion

To assess the ability of the Lipid14 parameters to reproduce dynamic lipid properties, the lipid lateral diffusion coefficient Dxy was calculated using the Einstein relation (eq 2) with two degrees of freedom (nf = 2). Diffusion coefficients were computed for each lipid as a block average over the five NPT production runs. The mean-square-displacement (MSD) curves were determined using window lengths spanning 20 ns and averaged over different time origins separated by 200 ps. The slope of this curve yields the diffusion coefficient using eq 2, with the linear 10–20 ns region used to perform the fit. Prior to the MSD calculation, the lipid coordinates were corrected to remove the artificial center of mass drift of each monolayer. (73)
Table 9. Lipid Lateral Diffusion Coefficients Calculated from NPT Runs, NVE Runs, and Experimental Values
lipid systemcalcd NPT Dxy (10-8 cm2 s-1)calcd NVE Dxy (10-8 cm2 s-1)simulation temp (K)exptl Dxy (10-8 cm2 s-1)exptl temp (K)
DLPC7.657.783038.5 (97)298
DMPC5.056.323035.95, (98) 9 (99, 100)303, 303
DPPC9.2111.9432312.5, (101) 15.2 (102)323, 323
DOPC6.489.4930311.5, (100) 17 (103)303, 308
POPC5.746.5430310.7 (100)303
POPE4.674.853105.2 (104)a305
a

Cell culture membrane containing 78% POPE at 305 K.

Results are of the same order of magnitude as experimental values; although in general they are underestimated. Unlike the bulk alkane work there is no correction term to account for collective motion which cannot be sampled using a periodic box of limited size. Accordingly, the underestimation may be a result of size effects. As highlighted by Poger et al. there is a widespread in experimental lipid lateral diffusion values in the literature, with a range of experimental techniques applied to the calculation of diffusion values. (105) Even different groups applying the same experimental technique do not necessarily yield comparable diffusion coefficients. Our calculated diffusion coefficients are nonetheless found to be in good agreement with other simulation values. (10, 23, 87, 105-107)
Given that the production runs were performed in the NPT ensemble and that temperature regulation methods such as Langevin dynamics, which randomizes particle velocities, may affect dynamic properties such as diffusion, the lipid lateral diffusion was also determined in the microcanonical (NVE) ensemble. A single production run of each lipid system was extended into the NVE ensemble for 100 ns using the same simulation settings as used for the alkane diffusion runs (see Parameterization Strategy). Resulting time averaged MSD curves are shown in Figure 11, and calculated diffusion coefficients are reported in Table 9. Although similar to the Dxy values determined from the NPT runs, the diffusion coefficients from the NVE runs are slightly higher, with the results for DPPC and DOPC showing the largest differences. This supports a recent study on the effect of temperature control on dynamic properties by Basconi et al., (108) who found that diffusion coefficients calculated from simulations applying Langevin dynamics approach the coefficients derived from NVE simulations, provided weak coupling is used for the temperature regulation.

Figure 11

Figure 11. Time averaged mean square displacement of the center of mass of the lipid molecules versus NVE simulation time.

Lipid lateral diffusion is known to occur via two regimes: fast ‘rattling’ of the lipid in the local solvation cage (109) and slower, long distance diffusion in the plane of the bilayer. The two regimes are clearly observed in the MSD versus time curves (Figure 11) and are also revealed by computing the diffusion coefficients using different time ranges of the MSD curve. Figure 12 plots the diffusion coefficient Dxy against the starting time for fitting the MSD slope. Time windows were either 100 ps long (fit starts between 10 ps and 500 ps) or 500 ps long (fit starts between 500 ps and 20 ns). (23)Dxy values decrease smoothly and then converge at a value representing the long time diffusion of lipids in the bilayer plane.

Figure 12

Figure 12. Lateral diffusion coefficients for the six lipid types calculated using different time ranges of the mean square displacement curve for the linear fit.

Conclusions

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The Lipid14 force field represents a significant advancement for the simulation of phospoholipids in the AMBER MD package. Hydrocarbon parameters have been refined, resulting in good reproduction of thermodynamic and dynamic properties for a number of simple carbon chains, thus we can be confident that the hydrocarbon region of the lipid membrane is correctly represented. Head group parameters have also been updated, with the final parameter set finding good agreement with experiment for a range of properties, including the area per lipid, volume per lipid, bilayer thickness, NMR order parameters, scattering data, and lipid lateral diffusion, without applying a surface tension in the simulations. Crucially, the experimental raw data that requires no empirical input to derive, namely the NMR order parameters and scattering data, are well reproduced. Results for POPE however indicate that PE lipids may require further attention, as the order parameter results for POPE indicate that it remains somewhat artificially ordered in comparison to experiment. Results from five GPU repeats and CPU runs are seen to be consistent (these additional results are provided in the Supporting Information), with a number of tests performed on GPUs using both different starting structures and extending production runs to 250 ns. Future improvements may involve further refinement of parameters in order to address the underestimation of the volume per lipid and bilayer Luzzati thickness values in addition to PE lipid types.
Although the present study only concerns the validation of Lipid14 for the simulation of three saturated and three unsaturated lipids, the modular nature of the Lipid14 force field allows for a number of different lipids to be constructed from headgroup and tail group ‘building blocks’ and for the easy insertion of new lipid species into the force field. The Lipid14 charge derivation follows the usual AMBER convention, making this force field compatible with other AMBER potentials, such as the General Amber Force Field (16) and the ff99SB protein force field. (14) As such, the interaction of other species, such as small molecules or proteins, with lipid membranes can be studied in AMBER using the Lipid14 force field.

Supporting Information

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Details of the Lipid14 atom types, partial charges, and force field parameters. Also included are the bilayer results for additional GPU and CPU runs. This material is available free of charge via the Internet at http://pubs.acs.org.

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Authors
    • Ian R. Gould - Department of Chemistry and Institute of Chemical Biology, Imperial College London, South Kensington SW7 2AZ, United Kingdom Email: [email protected]
    • Ross C. Walker - San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive MC0505, La Jolla, California 92093-0505, United StatesDepartment of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive MC0505, La Jolla, California 92093-0505, United States Email: [email protected]
  • Authors
    • Callum J. Dickson - Department of Chemistry and Institute of Chemical Biology, Imperial College London, South Kensington SW7 2AZ, United Kingdom
    • Benjamin D. Madej - San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive MC0505, La Jolla, California 92093-0505, United StatesDepartment of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive MC0505, La Jolla, California 92093-0505, United States
    • Åge A. Skjevik - San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive MC0505, La Jolla, California 92093-0505, United StatesDepartment of Biomedicine, University of Bergen, N-5009 Bergen, Norway
    • Robin M. Betz - San Diego Supercomputer Center, University of California San Diego, 9500 Gilman Drive MC0505, La Jolla, California 92093-0505, United States
    • Knut Teigen - Department of Biomedicine, University of Bergen, N-5009 Bergen, Norway
  • Notes
    The authors declare no competing financial interest.

Acknowledgment

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We are very grateful to Dr. Hannes Loeffler of the Science and Technology Facilities Council, UK, for writing and maintaining the modified PTRAJ/CPPTRAJ routines which were used for much of the analysis in this work. C.J.D. wishes to thank the Institute of Chemical Biology, UK Biotechnology and Biological Sciences Research Council (BBSRC) and GlaxoSmithKline for the award of a studentship, and the High Performance Computing centre of Imperial College London for the provision of computing time. B.D.M. would like to acknowledge funding for this work provided by the NIH Molecular Biophysics Training Grant (T32 GM008326) and the NVIDIA Graduate Fellowship Program. R.M.B. was supported by a grant from the University of California Institute for Mexico and the United States (UC MEXUS) and the Consejo Nacional de Ciencia y Tecnología de México (CONACYT) (R.C.W.). We acknowledge the support of the Strategic Programme for International Research and Education (SPIRE) and the Meltzer Foundation for travel grants provided to Å.A.S. The Norwegian Metacenter for Computational Science (NOTUR) is acknowledged for allocation of computational resources. This work was supported by NSF SI2-SSE grants (NSF-1047875 and 1148276) to R.C.W. and by the University of California (UC Lab 09-LR-06-117793) grant to R.C.W. R.C.W. also acknowledges funding through the NSF XSEDE program and through a fellowship from NVIDIA, Inc. Additional computer time was provided by the San Diego Supercomputer Center and by XSEDE and TG-CHG13W10 to R.C.W.

References

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This article references 109 other publications.

  1. 1
    van Meer, G.; Voelker, D. R.; Feigenson, G. W. Membrane lipids: where they are and how they behave Nat. Rev. Mol. Cell Biol. 2008, 9 (2) 112 124
  2. 2
    Lodish, H.; Berk, A.; Kaiser, C. A.; Scott, M. P.; Bretscher, A.; Ploegh, H.; Matsudaira, P. Molecular Cell Biology,6th ed.; W. H. Freeman: New York, 2007.
  3. 3
    Phillips, R.; Ursell, T.; Wiggins, P.; Sens, P. Emerging roles for lipids in shaping membrane-protein function Nature 2009, 459 (7245) 379 385
  4. 4
    Nagle, J. F.; Tristram-Nagle, S. Structure of lipid bilayers Biochim. Biophys. Acta 2000, 1469 (3) 159 195
  5. 5
    Katsaras, J.; Gutberlet, T. Lipid bilayers: Structure and interactions; Springer-Verlag: Berlin, 2001.
  6. 6
    Tieleman, D. P.; Marrink, S. J.; Berendsen, H. J. A computer perspective of membranes: molecular dynamics studies of lipid bilayer systems Biochim. Biophys. Acta 1997, 1331, 235 270
  7. 7
    Berger, O.; Edholm, O.; Jähnig, F. Molecular dynamics simulations of a fluid bilayer of dipalmitoylphosphatidylcholine at full hydration, constant pressure, and constant temperature Biophys. J. 1997, 72, 2002 2013
  8. 8
    Klauda, J. B.; Venable, R. M.; Freites, J. A.; O’Connor, J. W.; Tobias, D. J.; Mondragon-Ramirez, C.; Vorobyov, I.; MacKerell, A. D.; Pastor, R. W. Update of the CHARMM all-atom additive force field for lipids: Validation on Six lipid types J. Phys. Chem. B 2010, 114 (23) 7830 7843
  9. 9
    Poger, D.; Van Gunsteren, W. F.; Mark, A. E. A new force field for simulating phosphatidylcholine bilayers J. Comput. Chem. 2010, 31 (6) 1117 1125
  10. 10
    Jämbeck, J. P. M.; Lyubartsev, A. P. Derivation and systematic validation of a refined all-atom force field for phosphatidylcholine lipids J. Phys. Chem. B 2012, 116 (10) 3164 3179
  11. 11
    Marrink, S. J.; Risselada, H. J.; Yefimov, S.; Tieleman, D. P.; de Vries, A. H. The MARTINI force field: Coarse grained model for biomolecular simulations J. Phys. Chem. B 2007, 111 (27) 7812 7824
  12. 12
    Orsi, M.; Essex, J. W. The ELBA force field for coarse-grain modeling of lipid membranes PLoS One 2011, 6 (12) e28637
  13. 13
    Chiu, S.-W.; Pandit, S. A.; Scott, H. L.; Jakobsson, E. An improved united atom force field for simulation of mixed lipid bilayers J. Phys. Chem. B 2009, 113 (9) 2748 2763
  14. 14
    Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Comparison of multiple Amber force fields and development of improved protein backbone parameters Proteins: Struct., Funct., Bioinf. 2006, 65 (3) 712 725
  15. 15
    Kirschner, K. N.; Yongye, A. B.; Tschampel, S. M.; González-Outeiriño, J.; Daniels, C. R.; Foley, B. L.; Woods, R. J. GLYCAM06: A generalizable biomolecular force field. Carbohydrates J. Comput. Chem. 2008, 29 (4) 622 655
  16. 16
    Wang, J.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A. Development and testing of a general amber force field J. Comput. Chem. 2004, 25, 1157 1174
  17. 17
    Case, D. A.; Darden, T. A.; Cheatham, T. E., III; Simmerling, C. L.; Wang, J.; Duke, R. E.; Luo, R.; Walker, R. C.; Zhang, W.; Merz, K. M.; Roberts, B.; Hayik, S.; Roitberg, A.; Seabra, G.; Swails, J.; Goetz, A. W.; Kolossváry, I.; Wong, K. F.; Paesani, F.; Vanicek, J.; Wolf, R. M.; Liu, J.; Wu, X.; Brozell, S. R.; Steinbrecher, T.; Gohlke, H.; Cai, Q.; Ye, X.; Wang, J.; Hsieh, M.-J.; Cui, G.; Roe, D. R.; Mathews, D. H.; Seetin, M. G.; Salomon-Ferrer, R.; Sagui, C.; Babin, V.; Luchko, T.; Gusarov, S.; Kovalenko, A.; Kollman, P. A.AMBER 12; University of California: San Francisco, 2012.
  18. 18
    Salomon-Ferrer, R.; Case, D. A.; Walker, R. C. An overview of the Amber biomolecular simulation package Wiley Interdiscip. Rev.: Comput. Mol. Sci. 2013, 3 (2) 198 210
  19. 19
    Götz, A. W.; Williamson, M. J.; Xu, D.; Poole, D.; Le Grand, S.; Walker, R. C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized Born J. Chem. Theory Comput. 2012, 8 (5) 1542 1555
  20. 20
    Salomon-Ferrer, R.; Götz, A. W.; Poole, D.; Le Grand, S.; Walker, R. C. Routine microsecond molecular dynamics simulations with Amber on GPUs. 2. Explicit solvent particle mesh Ewald J. Chem. Theory Comput. 2013, 9 (9) 3878 3888
  21. 21
    Le Grand, S.; Götz, A. W.; Walker, R. C. SPFP: Speed without compromise—A mixed precision model for GPU accelerated molecular dynamics simulations Comput. Phys. Commun. 2013, 184 (2) 374 380
  22. 22
    Skjevik, Å. A.; Madej, B. D.; Walker, R. C.; Teigen, K. LIPID11: A modular framework for lipid simulations using Amber J. Phys. Chem. B 2012, 116 (36) 11124 11136
  23. 23
    Siu, S. W.; Vacha, R.; Jungwirth, P.; Bockmann, R. A. Biomolecular simulations of membranes: physical properties from different force fields J. Chem. Phys. 2008, 128 (12) 125103
  24. 24
    Jójárt, B.; Martinek, T. A. Performance of the general amber force field in modeling aqueous POPC membrane bilayers J. Comput. Chem. 2007, 28 (12) 2051 2058
  25. 25
    Rosso, L.; Gould, I. R. Structure and dynamics of phospholipid bilayers using recently developed general all-atom force fields J. Comput. Chem. 2008, 29 (1) 24 37
  26. 26
    Dickson, C. J.; Rosso, L.; Betz, R. M.; Walker, R. C.; Gould, I. R. GAFFlipid: A general Amber force field for the accurate molecular dynamics simulation of phospholipid Soft Matter 2012, 8, 9617 9627
  27. 27
    Siu, S. W. I.; Pluhackova, K.; Böckmann, R. A. Optimization of the OPLS-AA force field for long hydrocarbons J. Chem. Theory Comput. 2012, 8 (4) 1459 1470
  28. 28
    Klauda, J. B.; Brooks, B. R.; MacKerell, A. D.; Venable, R. M.; Pastor, R. W. An ab initio study on the torsional surface of alkanes and its effect on molecular simulations of alkanes and a DPPC bilayer J. Phys. Chem. B 2005, 109 (11) 5300 5311
  29. 29
    Betz, R. M.; Walker, R. C. Paramfit: Optimization of potential energy function parameters for molecular dynamics. Manuscript in preparation.
  30. 30
    Bayly, C. I.; Cieplak, P.; Cornell, W.; Kollman, P. A. A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP model J. Phys. Chem. 1993, 97 (40) 10269 10280
  31. 31
    Sonne, J.; Jensen, M. Ø.; Hansen, F. Y.; Hemmingsen, L.; Peters, G. H. Reparameterization of all-atom dipalmitoylphosphatidylcholine lipid parameters enables simulation of fluid bilayers at zero tension Biophys. J. 2007, 92 (12) 4157 4167
  32. 32
    Klauda, J. B.; Garrison, S. L.; Jiang, J.; Arora, G.; Sandler, S. I. HM-IE: Quantum chemical hybrid methods for calculating interaction energies J. Phys. Chem. A 2003, 108 (1) 107 112
  33. 33
    Davis, J. E.; Warren, G. L.; Patel, S. Revised charge equilibration potential for liquid alkanes J. Phys. Chem. B 2008, 112 (28) 8298 8310
  34. 34
    Wang, J.; Hou, T. Application of molecular dynamics simulations in molecular property prediction. 1. density and heat of vaporization J. Chem. Theory Comput. 2011, 7 (7) 2151 2165
  35. 35
    Darden, T.; York, D.; Pedersen, L. Particle mesh Ewald: An N-log(N) method for Ewald sums in large systems J. Chem. Phys. 1993, 98 (12) 10089 10092
  36. 36
    Ryckaert, J.-P.; Ciccotti, G.; Berendsen, H. J. C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes J. Comput. Phys. 1977, 23 (3) 327 341
  37. 37
    Berendsen, H. J. C.; Postma, J. P. M.; van Gunsteren, W. F.; DiNola, A.; Haak, J. R. Molecular dynamics with coupling to an external bath J. Chem. Phys. 1984, 81 (8) 3684 3690
  38. 38
    Yeh, I.-C.; Hummer, G. System-size dependence of diffusion coefficients and viscosities from molecular dynamics simulations with periodic boundary conditions J. Phys. Chem. B 2004, 108 (40) 15873 15879
  39. 39
    Lipari, G.; Szabo, A. Effect of librational motion on fluorescence depolarization and nuclear magnetic resonance relaxation in macromolecules and membranes Biophys. J. 1980, 30 (3) 489 506
  40. 40
    Ottiger, M.; Bax, A. Determination of Relative N–HN, N–C′, Cα–C′, and Cα–Hα effective bond lengths in a protein by NMR in a dilute liquid crystalline phase J. Am. Chem. Soc. 1998, 120 (47) 12334 12341
  41. 41
    Haynes, W. M. CRC Handbook of Chemistry and Physics, 93rd ed.; CRC Press: Boca Raton, FL, 2012–2013.
  42. 42
    Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G. A.; Nakatsuji, H.; Caricato, M.; Li, X.; Hratchian, H. P.; Izmaylov, A. F.; Bloino, J.; Zheng, G.; Sonnenberg, J. L.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Montgomery, J. A., Jr.; Peralta, J. E.; Ogliaro, F.; Bearpark, M.; Heyd, J. J.; Brothers, E.; Kudin, K. N.; Staroverov, V. N.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Rega, N.; Millam, N. J.; Klene, M.; Knox, J. E.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Zakrzewski, V. G.; Voth, G. A.; Salvador, P.; Dannenberg, J. J.; Dapprich, S.; Daniels, A. D.; Farkas, Ö.; Foresman, J. B.; Ortiz, J. V.; Cioslowski, J.; Fox, D. J.Gaussian 09, Revision A.1; Gaussian Inc.: Wallingford, CT, 2009.
  43. 43
    Douglass, D. C.; McCall, D. W. Diffusion in paraffin hydrocarbons J. Phys. Chem. 1958, 62 (9) 1102 1107
  44. 44
    Yaws, C. L. Yaws’ Handbook of Physical Properties for Hydrocarbons and Chemicals. http://www.knovel.com/web/portal/browse/display?_EXT_KNOVEL_DISPLAY_bookid=2147 (accessed February 19, 2013) .
  45. 45
    Tofts, P. S.; Lloyd, D.; Clark, C. A.; Barker, G. J.; Parker, G. J. M.; McConville, P.; Baldock, C.; Pope, J. M. Test liquids for quantitative MRI measurements of self-diffusion coefficient in vivo Magn. Reson. Med. 2000, 43 (3) 368 374
  46. 46
    Holler, F.; Callis, J. B. Conformation of the hydrocarbon chains of sodium dodecyl sulfate molecules in micelles: an FTIR study J. Phys. Chem. 1989, 93 (5) 2053 2058
  47. 47
    Lyerla, J. R.; McIntyre, H. M.; Torchia, D. A. A 13C nuclear magnetic resonance study of alkane motion Macromolecules 1974, 7 (1) 11 14
  48. 48
    Jo, S.; Lim, J. B.; Klauda, J. B.; Im, W. CHARMM-GUI membrane builder for mixed bilayers and its application to yeast membranes Biophys. J. 2009, 97 (1) 50 58
  49. 49
    Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. Comparison of simple potential functions for simulating liquid water J. Chem. Phys. 1983, 79 (2) 926 935
  50. 50
    Joung, I. S.; Cheatham, T. E. Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations J. Phys. Chem. B 2008, 112 (30) 9020 9041
  51. 51
    Press, W. H.; Teukolsky, S. A.; Vetterling, W. T.; Flannery, B. P. Numerical Recipes: The Art of Scientific Computing, 3rd ed. ed.; Cambridge University Press: New York, 2007.
  52. 52
    Pastor, R.; Brooks, B.; Szabo, A. An analysis of the accuracy of Langevin and molecular dynamics algorithms Mol. Phys. 1988, 65 (6) 1409 1419
  53. 53
    Roe, D. R.; Cheatham, T. E. PTRAJ and CPPTRAJ: Software for processing and analysis of molecular dynamics trajectory data J. Chem. Theory Comput. 2013, 9 (7) 3084 3095
  54. 54
    Kučerka, N.; Liu, Y.; Chu, N.; Petrache, H. I.; Tristram-Nagle, S.; Nagle, J. F. Structure of fully hydrated fluid phase DMPC and DLPC lipid bilayers using X-ray scattering from oriented multilamellar arrays and from unilamellar vesicles Biophys. J. 2005, 88 (4) 2626 2637
  55. 55
    Kučerka, N.; Nieh, M.-P.; Katsaras, J. Fluid phase lipid areas and bilayer thicknesses of commonly used phosphatidylcholines as a function of temperature Biochim. Biophys. Acta 2011, 1808 (11) 2761 2771
  56. 56
    Nagle, J. F. Introductory lecture: Basic quantities in model biomembranes Faraday Discuss. 2013, 161 (0) 11 29
  57. 57
    Braun, A. R.; Sachs, J. N.; Nagle, J. F. Comparing simulations of lipid bilayers to scattering data: The GROMOS 43A1-S3 force field J. Phys. Chem. B 2013, 117 (17) 5065 5072
  58. 58
    Rawicz, W.; Olbrich, K. C.; McIntosh, T.; Needham, D.; Evans, E. Effect of chain length and unsaturation on elasticity of lipid bilayers Biophys. J. 2000, 79 (1) 328 339
  59. 59
    Petrache, H. I.; Tristram-Nagle, S.; Nagle, J. F. Fluid phase structure of EPC and DMPC bilayers Chem. Phys. Lipids 1998, 95 (1) 83 94
  60. 60
    Klauda, J. B.; Kučerka, N.; Brooks, B. R.; Pastor, R. W.; Nagle, J. F. Simulation-based methods for interpreting X-ray data from lipid bilayers Biophys. J. 2006, 90 (8) 2796 2807
  61. 61
    Kučerka, N.; Tristram-Nagle, S.; Nagle, J. F. Closer look at structure of fully hydrated fluid phase DPPC bilayers Biophys. J. 2006, 90 (11) L83 L85
  62. 62
    Kučerka, N.; Nagle, J. F.; Sachs, J. N.; Feller, S. E.; Pencer, J.; Jackson, A.; Katsaras, J. Lipid bilayer structure determined by the simultaneous analysis of neutron and X-ray scattering data Biophys. J. 2008, 95 (5) 2356 2367
  63. 63
    Evans, E.; Rawicz, W.; Smith, B. A. Concluding remarks back to the future: mechanics and thermodynamics of lipid biomembranes Faraday Discuss. 2013, 161 (0) 591 611
  64. 64
    Evans, E.Personal Communication - DOPC isothermal compressibility modulus from X-ray data at 293 K; 2014.
  65. 65
    Tristram-Nagle, S.; Petrache, H. I.; Nagle, J. F. Structure and interactions of fully hydrated dioleoylphosphatidylcholine bilayers Biophys. J. 1998, 75 (2) 917 925
  66. 66
    Pan, J.; Tristram-Nagle, S.; Kučerka, N.; Nagle, J. F. Temperature dependence of structure, bending rigidity, and bilayer interactions of dioleoylphosphatidylcholine bilayers Biophys. J. 2008, 94 (1) 117 124
  67. 67
    Liu, Y.; Nagle, J. F. Diffuse scattering provides material parameters and electron density profiles of biomembranes Phys. Rev. E 2004, 69 (4) 040901
  68. 68
    Kučerka, N.; Tristram-Nagle, S.; Nagle, J. F. Structure of fully hydrated fluid phase lipid bilayers with monounsaturated chains J. Membr. Biol. 2006, 208 (3) 193 202
  69. 69
    Binder, H.; Gawrisch, K. Effect of unsaturated lipid chains on dimensions, molecular order and hydration of membranes J. Phys. Chem. B 2001, 105 (49) 12378 12390
  70. 70
    Rand, R. P.; Parsegian, V. A. Hydration forces between phospholipid bilayers Biochim. Biophys. Acta 1989, 988 (3) 351 376
  71. 71
    Rappolt, M.; Hickel, A.; Bringezu, F.; Lohner, K. Mechanism of the lamellar/inverse hexagonal phase transition examined by high resolution X-ray diffraction Biophys. J. 2003, 84 (5) 3111 3122
  72. 72
    Nagle, J. F.; Tristram-Nagle, S. Lipid bilayer structure Curr. Opin. Struct. Biol. 2000, 10 (4) 474 480
  73. 73
    Anézo, C.; de Vries, A. H.; Höltje, H.-D.; Tieleman, D. P.; Marrink, S.-J. Methodological issues in lipid bilayer simulations J. Phys. Chem. B 2003, 107 (35) 9424 9433
  74. 74
    Poger, D.; Mark, A. E. On the validation of molecular dynamics simulations of saturated and cis-monounsaturated phosphatidylcholine lipid bilayers: A comparison with experiment J. Chem. Theory Comput. 2009, 6 (1) 325 336
  75. 75
    Kučerka, N.; Katsaras, J.; Nagle, J. Comparing membrane simulations to scattering experiments: Introducing the SIMtoEXP software J. Membr. Biol. 2010, 235 (1) 43 50
  76. 76
    Shirts, M. R. Simple quantitative tests to validate sampling from thermodynamic ensembles J. Chem. Theory Comput. 2012, 9 (2) 909 926
  77. 77
    Seelig, J.; Waespe-Sarcevic, N. Molecular order in cis and trans unsaturated phospholipid bilayers Biochemistry 1978, 17 (16) 3310 3315
  78. 78
    Perly, B.; Smith, I. C. P.; Jarrell, H. C. Acyl chain dynamics of phosphatidylethanolamines containing oleic acid and dihydrosterculic acid: deuteron NMR relaxation studies Biochemistry 1985, 24 (17) 4659 4665
  79. 79
    Lafleur, M.; Bloom, M.; Eikenberry, E. F.; Gruner, S. M.; Han, Y.; Cullis, P. R. Correlation between lipid plane curvature and lipid chain order Biophys. J. 1996, 70 (6) 2747 2757
  80. 80
    Warschawski, D.; Devaux, P. Order parameters of unsaturated phospholipids in membranes and the effect of cholesterol: a 1H-13C solid-state NMR study at natural abundance Eur. Biophys. J. 2005, 34 (8) 987 96
  81. 81
    Petrache, H. I.; Dodd, S. W.; Brown, M. F. Area per lipid and acyl length distributions in fluid phosphatidylcholines determined by (2)H NMR spectroscopy Biophys. J. 2000, 79 (6) 3172 92
  82. 82
    Douliez, J. P.; Léonard, A.; Dufourc, E. J. Restatement of order parameters in biomembranes: calculation of C-C bond order parameters from C-D quadrupolar splittings Biophys. J. 1995, 68 (5) 1727 1739
  83. 83
    Aussenac, F.; Laguerre, M.; Schmitter, J.-M.; Dufourc, E. J. Detailed structure and dynamics of bicelle phospholipids using selectively deuterated and perdeuterated labels. 2H NMR and molecular mechanics study Langmuir 2003, 19 (25) 10468 10479
  84. 84
    Shaikh, S. R.; Brzustowicz, M. R.; Gustafson, N.; Stillwell, W.; Wassall, S. R. Monounsaturated PE does not phase-separate from the lipid raft molecules sphingomyelin and cholesterol: Role for polyunsaturation? Biochemistry 2002, 41 (34) 10593 10602
  85. 85
    Hitchcock, P. B.; Mason, R.; Thomas, K. M.; Shipley, G. G. Structural chemistry of 1,2 dilauroyl-DL-phosphatidylethanolamine: Molecular conformation and intermolecular packing of phospholipids Proc. Natl. Acad. Sci. U.S.A. 1974, 71 (8) 3036 3040
  86. 86
    Seelig, A.; Seelig, J. Bilayers of dipalmitoyl-3-sn-phosphatidylcholine: Conformational differences between the fatty acyl chains Biochim. Biophys. Acta 1975, 406 (1) 1 5
  87. 87
    Jämbeck, J. P. M.; Lyubartsev, A. P. An extension and further validation of an all-atomistic force field for biological membranes J. Chem. Theory Comput. 2012, 8 (8) 2938 2948
  88. 88
    Senak, L.; Davies, M. A.; Mendelsohn, R. A quantitative IR study of hydrocarbon chain conformation in alkanes and phospholipids: CH2 wagging modes in disordered bilayer and HII phases J. Phys. Chem. 1991, 95 (6) 2565 2571
  89. 89
    Moss, G. P. Basic terminology of stereochemistry Pure Appl. Chem. 1996, 68 (12) 2193 2222
  90. 90
    Cates, D. A.; Strauss, H. L.; Snyder, R. G. Vibrational modes of liquid n-alkanes: Simulated isotropic raman spectra and band progressions for C5H12-C20H42 and C16D34 J. Phys. Chem. 1994, 98 (16) 4482 4488
  91. 91
    Snyder, R. G.; Strauss, H. L.; Elliger, C. A. C-H stretching modes and the structure of n-alkyl chains. 1. Long, disordered chains J. Phys. Chem. 1982, 86, 5145 5150
  92. 92
    Mendelsohn, R.; Senak, L. Quantitative determination of conformational disorder in biological membranes by FTIR spectroscopy. In Biomolecular spectroscopy; Clark, R. J. H.; Hester, R. E., Eds.; Wiley: New York, 1993; pp 339 380.
  93. 93
    Casal, H. L.; McElhaney, R. N. Quantitative determination of hydrocarbon chain conformational order in bilayers of saturated phosphatidylcholines of various chain lengths by Fourier transform infrared spectroscopy Biochemistry 1990, 29 (23) 5423 5427
  94. 94
    Tuchtenhagen, J.; Ziegler, W.; Blume, A. Acyl chain conformational ordering in liquid-crystalline bilayers: comparative FT-IR and 2H-NMR studies of phospholipids differing in headgroup structure and chain length Eur. Biophys. J. 1994, 23 (5) 323 335
  95. 95
    Mendelsohn, R.; Davies, M. A.; Brauner, J. W.; Schuster, H. F.; Dluhy, R. A. Quantitative determination of conformational disorder in the acyl chains of phospholipid bilayers by infrared spectroscopy Biochemistry 1989, 28 (22) 8934 8939
  96. 96
    Kučerka, N.; Gallová, J.; Uhríková, D.; Balgavý, P.; Bulacu, M.; Marrink, S.-J.; Katsaras, J. Areas of Monounsaturated Diacylphosphatidylcholines Biophys. J. 2009, 97 (7) 1926 1932
  97. 97
    Ratto, T. V.; Longo, M. L. Obstructed diffusion in phase-separated supported lipid bilayers: A combined atomic force microscopy and fluorescence recovery after photobleaching approach Biophys. J. 2002, 83 (6) 3380 3392
  98. 98
    Almeida, P. F. F.; Vaz, W. L. C.; Thompson, T. E. Lateral diffusion in the liquid phases of dimyristoylphosphatidylcholine/cholesterol lipid bilayers: a free volume analysis Biochemistry 1992, 31 (29) 6739 6747
  99. 99
    Orädd, G.; Lindblom, G.; Westerman, P. W. Lateral diffusion of cholesterol and dimyristoylphosphatidylcholine in a lipid bilayer measured by pulsed field gradient NMR spectroscopy Biophys. J. 2002, 83 (5) 2702 2704
  100. 100
    Filippov, A.; Orädd, G.; Lindblom, G. Influence of cholesterol and water content on phospholipid lateral diffusion in bilayers Langmuir 2003, 19 (16) 6397 6400
  101. 101
    Vaz, W. L. C.; Clegg, R. M.; Hallmann, D. Translational diffusion of lipids in liquid crystalline phase phosphatidylcholine multibilayers. A comparison of experiment with theory Biochemistry 1985, 24 (3) 781 786
  102. 102
    Scheidt, H. A.; Huster, D.; Gawrisch, K. Diffusion of cholesterol and its precursors in lipid membranes studied by 1H pulsed field gradient magic angle spinning NMR Biophys. J. 2005, 89 (4) 2504 2512
  103. 103
    Kuśba, J.; Li, L.; Gryczynski, I.; Piszczek, G.; Johnson, M.; Lakowicz, J. R. Lateral diffusion coefficients in membranes measured by resonance energy transfer and a new algorithm for diffusion in two dimensions Biophys. J. 2002, 82 (3) 1358 1372
  104. 104
    Jin, A. J.; Edidin, M.; Nossal, R.; Gershfeld, N. L. A singular state of membrane lipids at cell growth temperatures Biochemistry 1999, 38 (40) 13275 13278
  105. 105
    Poger, D.; Mark, A. E. Lipid bilayers: The effect of force field on ordering and dynamics J. Chem. Theory Comput. 2012, 8 (11) 4807 4817
  106. 106
    Wohlert, J.; Edholm, O. Dynamics in atomistic simulations of phospholipid membranes: Nuclear magnetic resonance relaxation rates and lateral diffusion J. Chem. Phys. 2006, 125 (20) 204703
  107. 107
    Wang, Y.; Markwick, P. R. L.; de Oliveira, C. A. F.; McCammon, J. A. Enhanced lipid diffusion and mixing in accelerated molecular dynamics J. Chem. Theory Comput. 2011, 7 (10) 3199 3207
  108. 108
    Basconi, J. E.; Shirts, M. R. Effects of temperature control algorithms on transport properties and kinetics in molecular dynamics simulations J. Chem. Theory Comput. 2013, 9 (7) 2887 2899
  109. 109
    König, S.; Bayerl, T. M.; Coddens, G.; Richter, D.; Sackmann, E. Hydration dependence of chain dynamics and local diffusion in L-alpha-dipalmitoylphosphtidylcholine multilayers studied by incoherent quasi-elastic neutron scattering Biophys. J. 1995, 68 (5) 1871 1880

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  24. Yoshiki Ishii, Nobuyuki Matubayasi, Hitoshi Washizu. Nonpolarizable Force Fields through the Self-Consistent Modeling Scheme with MD and DFT Methods: From Ionic Liquids to Self-Assembled Ionic Liquid Crystals. The Journal of Physical Chemistry B 2022, 126 (24) , 4611-4622. https://doi.org/10.1021/acs.jpcb.2c02782
  25. Mengrong Li, Yiqiong Bao, Ran Xu, Honggui La, Jingjing Guo. Critical Extracellular Ca2+ Dependence of the Binding between PTH1R and a G-Protein Peptide Revealed by MD Simulations. ACS Chemical Neuroscience 2022, 13 (11) , 1666-1674. https://doi.org/10.1021/acschemneuro.2c00176
  26. She Zhang, Jeff P. Thompson, Junchao Xia, Anthony T. Bogetti, Forrest York, A. Geoffrey Skillman, Lillian T. Chong, David N. LeBard. Mechanistic Insights into Passive Membrane Permeability of Drug-like Molecules from a Weighted Ensemble of Trajectories. Journal of Chemical Information and Modeling 2022, 62 (8) , 1891-1904. https://doi.org/10.1021/acs.jcim.1c01540
  27. Gaurav Sharma, Kenneth M. Merz. Mechanism of Zinc Transport through the Zinc Transporter YiiP. Journal of Chemical Theory and Computation 2022, 18 (4) , 2556-2568. https://doi.org/10.1021/acs.jctc.1c00927
  28. Chris J. Malajczuk, Sławomir S. Stachura, James O. Hendry, Ricardo L. Mancera. Redefining the Molecular Interplay between Dimethyl Sulfoxide, Lipid Bilayers, and Dehydration. The Journal of Physical Chemistry B 2022, 126 (13) , 2513-2529. https://doi.org/10.1021/acs.jpcb.2c00353
  29. Ruibin Liang, Amirhossein Bakhtiiari. Effects of Enzyme–Ligand Interactions on the Photoisomerization of a Light-Regulated Chemotherapeutic Drug. The Journal of Physical Chemistry B 2022, 126 (12) , 2382-2393. https://doi.org/10.1021/acs.jpcb.1c10819
  30. Callum J. Dickson, Ross C. Walker, Ian R. Gould. Lipid21: Complex Lipid Membrane Simulations with AMBER. Journal of Chemical Theory and Computation 2022, 18 (3) , 1726-1736. https://doi.org/10.1021/acs.jctc.1c01217
  31. James Andrews, Estela Blaisten-Barojas. Distinctive Formation of PEG-Lipid Nanopatches onto Solid Polymer Surfaces Interfacing Solvents from Atomistic Simulation. The Journal of Physical Chemistry B 2022, 126 (7) , 1598-1608. https://doi.org/10.1021/acs.jpcb.1c07490
  32. Weiwei Xue, Tingting Fu, Shengzhe Deng, Fengyuan Yang, Jingyi Yang, Feng Zhu. Molecular Mechanism for the Allosteric Inhibition of the Human Serotonin Transporter by Antidepressant Escitalopram. ACS Chemical Neuroscience 2022, 13 (3) , 340-351. https://doi.org/10.1021/acschemneuro.1c00694
  33. Chris J. Malajczuk, Blake I. Armstrong, Sławomir S. Stachura, Ricardo L. Mancera. Mechanisms of Interaction of Small Hydroxylated Cryosolvents with Dehydrated Model Cell Membranes: Stabilization vs Destruction. The Journal of Physical Chemistry B 2022, 126 (1) , 197-216. https://doi.org/10.1021/acs.jpcb.1c07769
  34. Daniel Pulido, Verònica Casadó-Anguera, Marc Gómez-Autet, Natàlia Llopart, Estefanía Moreno, Nil Casajuana-Martin, Sergi Ferré, Leonardo Pardo, Vicent Casadó, Miriam Royo. Heterobivalent Ligand for the Adenosine A2A–Dopamine D2 Receptor Heteromer. Journal of Medicinal Chemistry 2022, 65 (1) , 616-632. https://doi.org/10.1021/acs.jmedchem.1c01763
  35. Min-Kang Hsieh, Yalun Yu, Jeffery B. Klauda. All-Atom Modeling of Complex Cellular Membranes. Langmuir 2022, 38 (1) , 3-17. https://doi.org/10.1021/acs.langmuir.1c02084
  36. Callum J Dickson, Viktor Hornak, Jose S Duca. Relative Binding Free-Energy Calculations at Lipid-Exposed Sites: Deciphering Hot Spots. Journal of Chemical Information and Modeling 2021, 61 (12) , 5923-5930. https://doi.org/10.1021/acs.jcim.1c01147
  37. Satoshi Ono, Matthew R. Naylor, Chad E. Townsend, Chieko Okumura, Okimasa Okada, Hsiau-Wei Lee, R. Scott Lokey. Cyclosporin A: Conformational Complexity and Chameleonicity. Journal of Chemical Information and Modeling 2021, 61 (11) , 5601-5613. https://doi.org/10.1021/acs.jcim.1c00771
  38. Sabahuddin Ahmad, Christoph Heinrich Strunk, Stephan N. Schott-Verdugo, Karl-Erich Jaeger, Filip Kovacic, Holger Gohlke. Substrate Access Mechanism in a Novel Membrane-Bound Phospholipase A of Pseudomonas aeruginosa Concordant with Specificity and Regioselectivity. Journal of Chemical Information and Modeling 2021, 61 (11) , 5626-5643. https://doi.org/10.1021/acs.jcim.1c00973
  39. Gert-Jan Bekker, Mitsugu Araki, Kanji Oshima, Yasushi Okuno, Narutoshi Kamiya. Accurate Binding Configuration Prediction of a G-Protein-Coupled Receptor to Its Antagonist Using Multicanonical Molecular Dynamics-Based Dynamic Docking. Journal of Chemical Information and Modeling 2021, 61 (10) , 5161-5171. https://doi.org/10.1021/acs.jcim.1c00712
  40. Daria B. Kokh, Rebecca C. Wade. G Protein-Coupled Receptor–Ligand Dissociation Rates and Mechanisms from τRAMD Simulations. Journal of Chemical Theory and Computation 2021, 17 (10) , 6610-6623. https://doi.org/10.1021/acs.jctc.1c00641
  41. Laura Jenkins, Sara Marsango, Sarah Mancini, Zobaer Al Mahmud, Angus Morrison, Stuart P. McElroy, Kirstie A. Bennett, Matt Barnes, Andrew B. Tobin, Irina G. Tikhonova, Graeme Milligan. Discovery and Characterization of Novel Antagonists of the Proinflammatory Orphan Receptor GPR84. ACS Pharmacology & Translational Science 2021, 4 (5) , 1598-1613. https://doi.org/10.1021/acsptsci.1c00151
  42. Hiromi Nakai, Toshiaki Takemura, Junichi Ono, Yoshifumi Nishimura. Quantum-Mechanical Molecular Dynamics Simulations on Secondary Proton Transfer in Bacteriorhodopsin Using Realistic Models. The Journal of Physical Chemistry B 2021, 125 (39) , 10947-10963. https://doi.org/10.1021/acs.jpcb.1c06231
  43. Kazuhiro J. Fujimoto, Takumi Minoda, Takeshi Yanai. Spectral Tuning Mechanism of Photosynthetic Light-Harvesting Complex II Revealed by Ab Initio Dimer Exciton Model. The Journal of Physical Chemistry B 2021, 125 (37) , 10459-10470. https://doi.org/10.1021/acs.jpcb.1c04457
  44. Gaurav Sharma, Kenneth M. Merz. Formation of the Metal-Binding Core of the ZRT/IRT-like Protein (ZIP) Family Zinc Transporter. Biochemistry 2021, 60 (36) , 2727-2738. https://doi.org/10.1021/acs.biochem.1c00415
  45. Tetsuo Komori, Heri Satria, Kyohei Miyamura, Ai Ito, Magoto Kamiya, Ayumi Sumino, Takakazu Onishi, Kazuaki Ninomiya, Kenji Takahashi, Jared L. Anderson, Takuya Uto, Kosuke Kuroda. Essential Requirements of Biocompatible Cellulose Solvents. ACS Sustainable Chemistry & Engineering 2021, 9 (35) , 11825-11836. https://doi.org/10.1021/acssuschemeng.1c03438
  46. Daniel Becker, Prasad V. Bharatam, Holger Gohlke. F/G Region Rigidity is Inversely Correlated to Substrate Promiscuity of Human CYP Isoforms Involved in Metabolism. Journal of Chemical Information and Modeling 2021, 61 (8) , 4023-4030. https://doi.org/10.1021/acs.jcim.1c00558
  47. Christopher Faulkner, Nora H. de Leeuw. Predicting the Membrane Permeability of Fentanyl and Its Analogues by Molecular Dynamics Simulations. The Journal of Physical Chemistry B 2021, 125 (30) , 8443-8449. https://doi.org/10.1021/acs.jpcb.1c05438
  48. Pavel Janoš, Alessandra Magistrato. All-Atom Simulations Uncover the Molecular Terms of the NKCC1 Transport Mechanism. Journal of Chemical Information and Modeling 2021, 61 (7) , 3649-3658. https://doi.org/10.1021/acs.jcim.1c00551
  49. Olaia Martí-Marí, Belén Martínez-Gualda, Sofía de la Puente-Secades, Alberto Mills, Ernesto Quesada, Rana Abdelnabi, Liang Sun, Arnaud Boonen, Sam Noppen, Johan Neyts, Dominique Schols, María-José Camarasa, Federico Gago, Ana San-Félix. Double Arylation of the Indole Side Chain of Tri- and Tetrapodal Tryptophan Derivatives Renders Highly Potent HIV-1 and EV-A71 Entry Inhibitors. Journal of Medicinal Chemistry 2021, 64 (14) , 10027-10046. https://doi.org/10.1021/acs.jmedchem.1c00315
  50. Thomas Durek, Quentin Kaas, Andrew M. White, Joachim Weidmann, Abdullah Ahmad Fuaad, Olivier Cheneval, Christina I. Schroeder, Simon J. de Veer, Anita Dellsén, Torben Österlund, Niklas Larsson, Laurent Knerr, Udo Bauer, Alleyn T. Plowright, David J. Craik. Melanocortin 1 Receptor Agonists Based on a Bivalent, Bicyclic Peptide Framework. Journal of Medicinal Chemistry 2021, 64 (14) , 9906-9915. https://doi.org/10.1021/acs.jmedchem.1c00095
  51. Humanath Poudel, David M. Leitner. Activation-Induced Reorganization of Energy Transport Networks in the β2 Adrenergic Receptor. The Journal of Physical Chemistry B 2021, 125 (24) , 6522-6531. https://doi.org/10.1021/acs.jpcb.1c03412
  52. Dehui Zhang, David A. Perrey, Ann M. Decker, Tiffany L. Langston, Vijayakumar Mavanji, Danni L. Harris, Catherine M. Kotz, Yanan Zhang. Discovery of Arylsulfonamides as Dual Orexin Receptor Agonists. Journal of Medicinal Chemistry 2021, 64 (12) , 8806-8825. https://doi.org/10.1021/acs.jmedchem.1c00841
  53. Jeffery B. Klauda. Considerations of Recent All-Atom Lipid Force Field Development. The Journal of Physical Chemistry B 2021, 125 (22) , 5676-5682. https://doi.org/10.1021/acs.jpcb.1c02417
  54. Goli Yamini, Subbarao Kanchi, Nnanya Kalu, Sanaz Momben Abolfath, Stephen H. Leppla, K. Ganapathy Ayappa, Prabal K. Maiti, Ekaterina M. Nestorovich. Hydrophobic Gating and 1/f Noise of the Anthrax Toxin Channel. The Journal of Physical Chemistry B 2021, 125 (21) , 5466-5478. https://doi.org/10.1021/acs.jpcb.0c10490
  55. Gao Tu, Tingting Fu, Fengyuan Yang, Jingyi Yang, Zhao Zhang, Xiaojun Yao, Weiwei Xue, Feng Zhu. Understanding the Polypharmacological Profiles of Triple Reuptake Inhibitors by Molecular Simulation. ACS Chemical Neuroscience 2021, 12 (11) , 2013-2026. https://doi.org/10.1021/acschemneuro.1c00127
  56. Junhao Li, Yue Chen, Yun Tang, Weihua Li, Yaoquan Tu. Homotropic Cooperativity of Midazolam Metabolism by Cytochrome P450 3A4: Insight from Computational Studies. Journal of Chemical Information and Modeling 2021, 61 (5) , 2418-2426. https://doi.org/10.1021/acs.jcim.1c00266
  57. Shun Yokoi, Ayori Mitsutake. Molecular Dynamics Simulations for the Determination of the Characteristic Structural Differences between Inactive and Active States of Wild Type and Mutants of the Orexin2 Receptor. The Journal of Physical Chemistry B 2021, 125 (17) , 4286-4298. https://doi.org/10.1021/acs.jpcb.0c10985
  58. Jin Cheng, Maozi Chen, Siyi Wang, Tianjian Liang, Hui Chen, Chih-Jung Chen, Zhiwei Feng, Xiang-Qun Xie. Binding Characterization of Agonists and Antagonists by MCCS: A Case Study from Adenosine A2A Receptor. ACS Chemical Neuroscience 2021, 12 (9) , 1606-1620. https://doi.org/10.1021/acschemneuro.1c00082
  59. Xinghang Yuan, Di Zhang, Shengjun Mao, Qiantao Wang. Filling the Gap in Understanding the Mechanism of GABAAR and Propofol Using Computational Approaches. Journal of Chemical Information and Modeling 2021, 61 (4) , 1889-1901. https://doi.org/10.1021/acs.jcim.0c01290
  60. Ruibin Liang, Jimmy K. Yu, Jan Meisner, Fang Liu, Todd J. Martinez. Electrostatic Control of Photoisomerization in Channelrhodopsin 2. Journal of the American Chemical Society 2021, 143 (14) , 5425-5437. https://doi.org/10.1021/jacs.1c00058
  61. Rafael Santana Nunes, Diogo Vila-Viçosa, Paulo J. Costa. Halogen Bonding: An Underestimated Player in Membrane–Ligand Interactions. Journal of the American Chemical Society 2021, 143 (11) , 4253-4267. https://doi.org/10.1021/jacs.0c12470
  62. Yalun Yu, Andreas Krämer, Richard M. Venable, Andrew C. Simmonett, Alexander D. MacKerell, Jr., Jeffery B. Klauda, Richard W. Pastor, Bernard R. Brooks. Semi-automated Optimization of the CHARMM36 Lipid Force Field to Include Explicit Treatment of Long-Range Dispersion. Journal of Chemical Theory and Computation 2021, 17 (3) , 1562-1580. https://doi.org/10.1021/acs.jctc.0c01326
  63. Abhishek Sirohiwal, Frank Neese, Dimitrios A. Pantazis. How Can We Predict Accurate Electrochromic Shifts for Biochromophores? A Case Study on the Photosynthetic Reaction Center. Journal of Chemical Theory and Computation 2021, 17 (3) , 1858-1873. https://doi.org/10.1021/acs.jctc.0c01152
  64. Brian H. Morrow, Judith A. Harrison. Evaluating the Ability of Selected Force Fields to Simulate Hydrocarbons as a Function of Temperature and Pressure Using Molecular Dynamics. Energy & Fuels 2021, 35 (5) , 3742-3752. https://doi.org/10.1021/acs.energyfuels.0c03363
  65. Jovan Damjanovic, Jiayuan Miao, He Huang, Yu-Shan Lin. Elucidating Solution Structures of Cyclic Peptides Using Molecular Dynamics Simulations. Chemical Reviews 2021, 121 (4) , 2292-2324. https://doi.org/10.1021/acs.chemrev.0c01087
  66. Hanne S. Antila, Tiago M. Ferreira, O. H. Samuli Ollila, Markus S. Miettinen. Using Open Data to Rapidly Benchmark Biomolecular Simulations: Phospholipid Conformational Dynamics. Journal of Chemical Information and Modeling 2021, 61 (2) , 938-949. https://doi.org/10.1021/acs.jcim.0c01299
  67. Shiji Zhao, Andrew J. Schaub, Shiou-Chuan Tsai, Ray Luo. Development of a Pantetheine Force Field Library for Molecular Modeling. Journal of Chemical Information and Modeling 2021, 61 (2) , 856-868. https://doi.org/10.1021/acs.jcim.0c01384
  68. Deborah Thomas, Vicente Rubio, Vijaya Iragavarapu, Esther Guzman, Oliver B. Pelletier, Shahriar Alamgir, Qi Zhang, Maciej J. Stawikowski. Solvatochromic and pH-Sensitive Fluorescent Membrane Probes for Imaging of Live Cells. ACS Chemical Neuroscience 2021, 12 (4) , 719-734. https://doi.org/10.1021/acschemneuro.0c00732
  69. Zack Jarin, James Newhouse, Gregory A. Voth. Coarse-Grained Force Fields from the Perspective of Statistical Mechanics: Better Understanding of the Origins of a MARTINI Hangover. Journal of Chemical Theory and Computation 2021, 17 (2) , 1170-1180. https://doi.org/10.1021/acs.jctc.0c00638
  70. Loknath Dhar, Saddam Hossain, Md Sajjadur Rahman, Shamshad B. Quraishi, Koushik Saha, Farzana Rahman, Mir Tamzid Rahman. Adsorption Mechanism of Methylene Blue by Graphene Oxide-Shielded Mg–Al-Layered Double Hydroxide From Synthetic Wastewater. The Journal of Physical Chemistry A 2021, 125 (4) , 954-965. https://doi.org/10.1021/acs.jpca.0c09124
  71. Siddhartha Banerjee, Mohtadin Hashemi, Karen Zagorski, Yuri L. Lyubchenko. Cholesterol in Membranes Facilitates Aggregation of Amyloid β Protein at Physiologically Relevant Concentrations. ACS Chemical Neuroscience 2021, 12 (3) , 506-516. https://doi.org/10.1021/acschemneuro.0c00688
  72. Ipsita Basu, Prabal K. Maiti. Insight into the Mechanism of Carrier-Mediated Delivery of siRNA in the Cell Membrane Using MD Simulation. Langmuir 2021, 37 (1) , 266-277. https://doi.org/10.1021/acs.langmuir.0c02871
  73. Matti Javanainen, Wei Hua, Ondrej Tichacek, Pauline Delcroix, Lukasz Cwiklik, Heather C. Allen. Structural Effects of Cation Binding to DPPC Monolayers. Langmuir 2020, 36 (50) , 15258-15269. https://doi.org/10.1021/acs.langmuir.0c02555
  74. Matheus Henrique Reis, Deborah Antunes, Lucianna H. S. Santos, Ana Carolina Ramos Guimarães, Ernesto Raul Caffarena. Shared Binding Mode of Perrottetinene and Tetrahydrocannabinol Diastereomers inside the CB1 Receptor May Incentivize Novel Medicinal Drug Design: Findings from an in Silico Assay. ACS Chemical Neuroscience 2020, 11 (24) , 4289-4300. https://doi.org/10.1021/acschemneuro.0c00547
  75. Mara Silber, Manuel Hitzenberger, Martin Zacharias, Claudia Muhle-Goll. Altered Hinge Conformations in APP Transmembrane Helix Mutants May Affect Enzyme–Substrate Interactions of γ-Secretase. ACS Chemical Neuroscience 2020, 11 (24) , 4426-4433. https://doi.org/10.1021/acschemneuro.0c00640
  76. Christine Robinson, Valeria Gradinati, Fatima Hamid, Carly Baehr, Bethany Crouse, Saadyah Averick, Marina Kovaliov, Danni Harris, Scott Runyon, Federico Baruffaldi, Mark LeSage, Sandra Comer, Marco Pravetoni. Therapeutic and Prophylactic Vaccines to Counteract Fentanyl Use Disorders and Toxicity. Journal of Medicinal Chemistry 2020, 63 (23) , 14647-14667. https://doi.org/10.1021/acs.jmedchem.0c01042
  77. Cecilie Søderlund Kofod, Salvatore Prioli, Mick Hornum, Jacob Kongsted, Peter Reinholdt. Computational Characterization of Novel Malononitrile Variants of Laurdan with Improved Photophysical Properties for Sensing in Membranes. The Journal of Physical Chemistry B 2020, 124 (43) , 9526-9534. https://doi.org/10.1021/acs.jpcb.0c06011
  78. Michael A. Johnston, Andrew Ian Duff, Richard L. Anderson, William C. Swope. Model for the Simulation of the CnEm Nonionic Surfactant Family Derived from Recent Experimental Results. The Journal of Physical Chemistry B 2020, 124 (43) , 9701-9721. https://doi.org/10.1021/acs.jpcb.0c06132
  79. Carmen Esposito, Shuzhe Wang, Udo E. W. Lange, Frank Oellien, Sereina Riniker. Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates. Journal of Chemical Information and Modeling 2020, 60 (10) , 4730-4749. https://doi.org/10.1021/acs.jcim.0c00525
  80. Zhiwei Feng, Tianjian Liang, Siyi Wang, Maozi Chen, Tianling Hou, Jack Zhao, Hui Chen, Yuehan Zhou, Xiang-Qun Xie. Binding Characterization of GPCRs-Modulator by Molecular Complex Characterizing System (MCCS). ACS Chemical Neuroscience 2020, 11 (20) , 3333-3345. https://doi.org/10.1021/acschemneuro.0c00457
  81. Abhishek Sirohiwal, Frank Neese, Dimitrios A. Pantazis. Protein Matrix Control of Reaction Center Excitation in Photosystem II. Journal of the American Chemical Society 2020, 142 (42) , 18174-18190. https://doi.org/10.1021/jacs.0c08526
  82. Violetta Burns, Blake Mertz. Using Simulation to Understand the Role of Titration on the Stability of a Peptide–Lipid Bilayer Complex. Langmuir 2020, 36 (41) , 12272-12280. https://doi.org/10.1021/acs.langmuir.0c02038
  83. Didem Sanver, Amin Sadeghpour, Michael Rappolt, Florent Di Meo, Patrick Trouillas. Structure and Dynamics of Dioleoyl-Phosphatidylcholine Bilayers under the Influence of Quercetin and Rutin. Langmuir 2020, 36 (40) , 11776-11786. https://doi.org/10.1021/acs.langmuir.0c01484
  84. Alessio Prunotto, Guillermo Bahr, Lisandro J. González, Alejandro J. Vila, Matteo Dal Peraro. Molecular Bases of the Membrane Association Mechanism Potentiating Antibiotic Resistance by New Delhi Metallo-β-lactamase 1. ACS Infectious Diseases 2020, 6 (10) , 2719-2731. https://doi.org/10.1021/acsinfecdis.0c00341
  85. Hyeonuk Woo, Sang-Jun Park, Yeol Kyo Choi, Taeyong Park, Maham Tanveer, Yiwei Cao, Nathan R. Kern, Jumin Lee, Min Sun Yeom, Tristan I. Croll, Chaok Seok, Wonpil Im. Developing a Fully Glycosylated Full-Length SARS-CoV-2 Spike Protein Model in a Viral Membrane. The Journal of Physical Chemistry B 2020, 124 (33) , 7128-7137. https://doi.org/10.1021/acs.jpcb.0c04553
  86. Mounika Gosika, Vasumathi Velachi, M. Natália D. S. Cordeiro, Prabal K. Maiti. Covalent Functionalization of Graphene with PAMAM Dendrimer and Its Implications on Graphene’s Dispersion and Cytotoxicity. ACS Applied Polymer Materials 2020, 2 (8) , 3587-3600. https://doi.org/10.1021/acsapm.0c00596
  87. Conner M. Winkeljohn, Benjamin Himberg, Juan M. Vanegas. Balance of Solvent and Chain Interactions Determines the Local Stress State of Simulated Membranes. The Journal of Physical Chemistry B 2020, 124 (32) , 6963-6971. https://doi.org/10.1021/acs.jpcb.0c03937
  88. Yue Zhang, Hong-Xing Zhang, Qing-Chuan Zheng. In Silico Study of Membrane Lipid Composition Regulating Conformation and Hydration of Influenza Virus B M2 Channel. Journal of Chemical Information and Modeling 2020, 60 (7) , 3603-3615. https://doi.org/10.1021/acs.jcim.0c00329
  89. Jayesh Arun Bafna, Eulàlia Sans-Serramitjana, Silvia Acosta-Gutiérrez, Igor V. Bodrenko, Daniel Hörömpöli, Anne Berscheid, Heike Brötz-Oesterhelt, Mathias Winterhalter, Matteo Ceccarelli. Kanamycin Uptake into Escherichia coli Is Facilitated by OmpF and OmpC Porin Channels Located in the Outer Membrane. ACS Infectious Diseases 2020, 6 (7) , 1855-1865. https://doi.org/10.1021/acsinfecdis.0c00102
  90. Christopher Faulkner, David Santos-Carballal, David F. Plant, Nora H. de Leeuw. Atomistic Molecular Dynamics Simulations of Propofol and Fentanyl in Phosphatidylcholine Lipid Bilayers. ACS Omega 2020, 5 (24) , 14340-14353. https://doi.org/10.1021/acsomega.0c00813
  91. Pallavi Banerjee, Reinhard Lipowsky, Mark Santer. Coarse-Grained Molecular Model for the Glycosylphosphatidylinositol Anchor with and without Protein. Journal of Chemical Theory and Computation 2020, 16 (6) , 3889-3903. https://doi.org/10.1021/acs.jctc.0c00056
  92. Nozomu Kamiya, Megumi Kayanuma, Hideaki Fujitani, Keiko Shinoda. A New Lipid Force Field (FUJI). Journal of Chemical Theory and Computation 2020, 16 (6) , 3664-3676. https://doi.org/10.1021/acs.jctc.9b01195
  93. Tobias Klein, Frances D. Lenahan, Manuel Kerscher, Michael H. Rausch, Ioannis G. Economou, Thomas M. Koller, Andreas P. Fröba. Characterization of Long Linear and Branched Alkanes and Alcohols for Temperatures up to 573.15 K by Surface Light Scattering and Molecular Dynamics Simulations. The Journal of Physical Chemistry B 2020, 124 (20) , 4146-4163. https://doi.org/10.1021/acs.jpcb.0c01740
  94. Christopher Lockhart, Amy K. Smith, Dmitri K. Klimov. Three Popular Force Fields Predict Consensus Mechanism of Amyloid β Peptide Binding to the Dimyristoylgylcerophosphocholine Bilayer. Journal of Chemical Information and Modeling 2020, 60 (4) , 2282-2293. https://doi.org/10.1021/acs.jcim.0c00096
  95. Dinh Quoc Huy Pham, Pawel Krupa, Hoang Linh Nguyen, Giovanni La Penna, Mai Suan Li. Computational Model to Unravel the Function of Amyloid-β Peptides in Contact with a Phospholipid Membrane. The Journal of Physical Chemistry B 2020, 124 (16) , 3300-3314. https://doi.org/10.1021/acs.jpcb.0c00771
  96. Beihong Ji, Shuhan Liu, Xibing He, Viet Hoang Man, Xiang-Qun Xie, Junmei Wang. Prediction of the Binding Affinities and Selectivity for CB1 and CB2 Ligands Using Homology Modeling, Molecular Docking, Molecular Dynamics Simulations, and MM-PBSA Binding Free Energy Calculations. ACS Chemical Neuroscience 2020, 11 (8) , 1139-1158. https://doi.org/10.1021/acschemneuro.9b00696
  97. Rilei Yu, Jiayi Wang, Lok-Yan So, Peta J. Harvey, Juan Shi, Jiazhen Liang, Qin Dou, Xiao Li, Xiayi Yan, Yen-Hua Huang, Qingliang Xu, Quentin Kaas, Ho-Yin Chow, Kwok-Yin Wong, David J. Craik, Xiao-Hua Zhang, Tao Jiang, Yan Wang. Enhanced Activity against Multidrug-Resistant Bacteria through Coapplication of an Analogue of Tachyplesin I and an Inhibitor of the QseC/B Signaling Pathway. Journal of Medicinal Chemistry 2020, 63 (7) , 3475-3484. https://doi.org/10.1021/acs.jmedchem.9b01563
  98. Franccesca Fornasier, Lucas M. P. Souza, Felipe R. Souza, Franceline Reynaud, Andre S. Pimentel. Lipophilicity of Coarse-Grained Cholesterol Models. Journal of Chemical Information and Modeling 2020, 60 (2) , 569-577. https://doi.org/10.1021/acs.jcim.9b00830
  99. Vanesa Racigh, Agustín Ormazábal, Juliana Palma, Gustavo Pierdominici-Sottile. Positively Charged Residues in the Head Domain of P2X4 Receptors Assist the Binding of ATP. Journal of Chemical Information and Modeling 2020, 60 (2) , 923-932. https://doi.org/10.1021/acs.jcim.9b00856
  100. Anita Wnętrzak, Anna Chachaj-Brekiesz, Jan Kobierski, Katarzyna Karwowska, Aneta D. Petelska, Patrycja Dynarowicz-Latka. Unusual Behavior of the Bipolar Molecule 25-Hydroxycholesterol at the Air/Water Interface—Langmuir Monolayer Approach Complemented with Theoretical Calculations. The Journal of Physical Chemistry B 2020, 124 (6) , 1104-1114. https://doi.org/10.1021/acs.jpcb.9b10938
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  • Abstract

    Figure 1

    Figure 1. A box of 144 pentadecane molecules simulated in the NPT ensemble at 298.15 K using the General Amber Force Field (16) to model the carbon chains.

    Figure 2

    Figure 2. The energy profile for rotating about selected torsions of a cis-5-decene molecule. Energy evaluated using QM and the HM-IE method (filled triangle ▲), AMBER with standard GAFF parameters (dotted line), and AMBER with Lipid14 parameters (black line). Torsion fits from the top are as follows: CH2–CH–CH–CH2, CH–CH–CH2–CH2, and CH–CH2–CH2–CH2.

    Figure 3

    Figure 3. Structure and charges of Lipid11/Lipid14 headgroup and tail group caps. (22)

    Figure 4

    Figure 4. A capped lauroyl tail group residue was used to fit the oS-cC-cD-cD and oC-cC-cD-cD torsions.

    Figure 5

    Figure 5. The energy profiles for rotating about selected torsions of a capped lauroyl tail group residue. Energy evaluated using QM and the HM-IE method (filled triangle ▲), AMBER with standard GAFF/Lipid11 parameters (dotted line), and AMBER with Lipid14 parameters (black line). Torsion fits from the top are oC-cC-cD-cD and oS-cC-cD-cD.

    Figure 6

    Figure 6. Calculated 13C NMR T1 relaxation times for selected alkane chains and comparison to experiment. (47) Values at 312 K.

    Figure 7

    Figure 7. Simulation NMR order parameters for the six lipid systems and comparison to experiment. (77, 78, 80-84)

    Figure 8

    Figure 8. The total and decomposed electron density profiles for each of the six lipid bilayer systems with contributions from water, choline (CHOL), phosphate (PO4), glycerol (GLY), carbonyl (COO), methylene (CH2), unsaturated CH═CH and terminal methyls (CH3).

    Figure 9

    Figure 9. Simulation X-ray scattering form factors for the six lipid systems (black line) and comparison to experiment (54, 55, 62, 66, 68) (cyan circles). Inset: Simulation neutron scattering form factors at 100% D2O (black line), 70% D2O (red line), and 50% D2O (blue line) and comparison to experiment (55, 96) (black, red, and blue circles, respectively).

    Figure 10

    Figure 10. Plot of ΔDB-H versus area per lipid AL for the three all-atom lipid force fields CHARMM36 (squares), Slipids (diamonds), and AMBER Lipid14 (circles). Values shown for DLPC (green), DMPC (magenta), DPPC (blue), DOPC (red), and POPC (orange).

    Figure 11

    Figure 11. Time averaged mean square displacement of the center of mass of the lipid molecules versus NVE simulation time.

    Figure 12

    Figure 12. Lateral diffusion coefficients for the six lipid types calculated using different time ranges of the mean square displacement curve for the linear fit.

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    This article references 109 other publications.

    1. 1
      van Meer, G.; Voelker, D. R.; Feigenson, G. W. Membrane lipids: where they are and how they behave Nat. Rev. Mol. Cell Biol. 2008, 9 (2) 112 124
    2. 2
      Lodish, H.; Berk, A.; Kaiser, C. A.; Scott, M. P.; Bretscher, A.; Ploegh, H.; Matsudaira, P. Molecular Cell Biology,6th ed.; W. H. Freeman: New York, 2007.
    3. 3
      Phillips, R.; Ursell, T.; Wiggins, P.; Sens, P. Emerging roles for lipids in shaping membrane-protein function Nature 2009, 459 (7245) 379 385
    4. 4
      Nagle, J. F.; Tristram-Nagle, S. Structure of lipid bilayers Biochim. Biophys. Acta 2000, 1469 (3) 159 195
    5. 5
      Katsaras, J.; Gutberlet, T. Lipid bilayers: Structure and interactions; Springer-Verlag: Berlin, 2001.
    6. 6
      Tieleman, D. P.; Marrink, S. J.; Berendsen, H. J. A computer perspective of membranes: molecular dynamics studies of lipid bilayer systems Biochim. Biophys. Acta 1997, 1331, 235 270
    7. 7
      Berger, O.; Edholm, O.; Jähnig, F. Molecular dynamics simulations of a fluid bilayer of dipalmitoylphosphatidylcholine at full hydration, constant pressure, and constant temperature Biophys. J. 1997, 72, 2002 2013
    8. 8
      Klauda, J. B.; Venable, R. M.; Freites, J. A.; O’Connor, J. W.; Tobias, D. J.; Mondragon-Ramirez, C.; Vorobyov, I.; MacKerell, A. D.; Pastor, R. W. Update of the CHARMM all-atom additive force field for lipids: Validation on Six lipid types J. Phys. Chem. B 2010, 114 (23) 7830 7843
    9. 9
      Poger, D.; Van Gunsteren, W. F.; Mark, A. E. A new force field for simulating phosphatidylcholine bilayers J. Comput. Chem. 2010, 31 (6) 1117 1125
    10. 10
      Jämbeck, J. P. M.; Lyubartsev, A. P. Derivation and systematic validation of a refined all-atom force field for phosphatidylcholine lipids J. Phys. Chem. B 2012, 116 (10) 3164 3179
    11. 11
      Marrink, S. J.; Risselada, H. J.; Yefimov, S.; Tieleman, D. P.; de Vries, A. H. The MARTINI force field: Coarse grained model for biomolecular simulations J. Phys. Chem. B 2007, 111 (27) 7812 7824
    12. 12
      Orsi, M.; Essex, J. W. The ELBA force field for coarse-grain modeling of lipid membranes PLoS One 2011, 6 (12) e28637
    13. 13
      Chiu, S.-W.; Pandit, S. A.; Scott, H. L.; Jakobsson, E. An improved united atom force field for simulation of mixed lipid bilayers J. Phys. Chem. B 2009, 113 (9) 2748 2763
    14. 14
      Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Comparison of multiple Amber force fields and development of improved protein backbone parameters Proteins: Struct., Funct., Bioinf. 2006, 65 (3) 712 725
    15. 15
      Kirschner, K. N.; Yongye, A. B.; Tschampel, S. M.; González-Outeiriño, J.; Daniels, C. R.; Foley, B. L.; Woods, R. J. GLYCAM06: A generalizable biomolecular force field. Carbohydrates J. Comput. Chem. 2008, 29 (4) 622 655
    16. 16
      Wang, J.; Wolf, R. M.; Caldwell, J. W.; Kollman, P. A.; Case, D. A. Development and testing of a general amber force field J. Comput. Chem. 2004, 25, 1157 1174
    17. 17
      Case, D. A.; Darden, T. A.; Cheatham, T. E., III; Simmerling, C. L.; Wang, J.; Duke, R. E.; Luo, R.; Walker, R. C.; Zhang, W.; Merz, K. M.; Roberts, B.; Hayik, S.; Roitberg, A.; Seabra, G.; Swails, J.; Goetz, A. W.; Kolossváry, I.; Wong, K. F.; Paesani, F.; Vanicek, J.; Wolf, R. M.; Liu, J.; Wu, X.; Brozell, S. R.; Steinbrecher, T.; Gohlke, H.; Cai, Q.; Ye, X.; Wang, J.; Hsieh, M.-J.; Cui, G.; Roe, D. R.; Mathews, D. H.; Seetin, M. G.; Salomon-Ferrer, R.; Sagui, C.; Babin, V.; Luchko, T.; Gusarov, S.; Kovalenko, A.; Kollman, P. A.AMBER 12; University of California: San Francisco, 2012.
    18. 18
      Salomon-Ferrer, R.; Case, D. A.; Walker, R. C. An overview of the Amber biomolecular simulation package Wiley Interdiscip. Rev.: Comput. Mol. Sci. 2013, 3 (2) 198 210
    19. 19
      Götz, A. W.; Williamson, M. J.; Xu, D.; Poole, D.; Le Grand, S.; Walker, R. C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized Born J. Chem. Theory Comput. 2012, 8 (5) 1542 1555
    20. 20
      Salomon-Ferrer, R.; Götz, A. W.; Poole, D.; Le Grand, S.; Walker, R. C. Routine microsecond molecular dynamics simulations with Amber on GPUs. 2. Explicit solvent particle mesh Ewald J. Chem. Theory Comput. 2013, 9 (9) 3878 3888
    21. 21
      Le Grand, S.; Götz, A. W.; Walker, R. C. SPFP: Speed without compromise—A mixed precision model for GPU accelerated molecular dynamics simulations Comput. Phys. Commun. 2013, 184 (2) 374 380
    22. 22
      Skjevik, Å. A.; Madej, B. D.; Walker, R. C.; Teigen, K. LIPID11: A modular framework for lipid simulations using Amber J. Phys. Chem. B 2012, 116 (36) 11124 11136
    23. 23
      Siu, S. W.; Vacha, R.; Jungwirth, P.; Bockmann, R. A. Biomolecular simulations of membranes: physical properties from different force fields J. Chem. Phys. 2008, 128 (12) 125103
    24. 24
      Jójárt, B.; Martinek, T. A. Performance of the general amber force field in modeling aqueous POPC membrane bilayers J. Comput. Chem. 2007, 28 (12) 2051 2058
    25. 25
      Rosso, L.; Gould, I. R. Structure and dynamics of phospholipid bilayers using recently developed general all-atom force fields J. Comput. Chem. 2008, 29 (1) 24 37
    26. 26
      Dickson, C. J.; Rosso, L.; Betz, R. M.; Walker, R. C.; Gould, I. R. GAFFlipid: A general Amber force field for the accurate molecular dynamics simulation of phospholipid Soft Matter 2012, 8, 9617 9627
    27. 27
      Siu, S. W. I.; Pluhackova, K.; Böckmann, R. A. Optimization of the OPLS-AA force field for long hydrocarbons J. Chem. Theory Comput. 2012, 8 (4) 1459 1470
    28. 28
      Klauda, J. B.; Brooks, B. R.; MacKerell, A. D.; Venable, R. M.; Pastor, R. W. An ab initio study on the torsional surface of alkanes and its effect on molecular simulations of alkanes and a DPPC bilayer J. Phys. Chem. B 2005, 109 (11) 5300 5311
    29. 29
      Betz, R. M.; Walker, R. C. Paramfit: Optimization of potential energy function parameters for molecular dynamics. Manuscript in preparation.
    30. 30
      Bayly, C. I.; Cieplak, P.; Cornell, W.; Kollman, P. A. A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP model J. Phys. Chem. 1993, 97 (40) 10269 10280
    31. 31
      Sonne, J.; Jensen, M. Ø.; Hansen, F. Y.; Hemmingsen, L.; Peters, G. H. Reparameterization of all-atom dipalmitoylphosphatidylcholine lipid parameters enables simulation of fluid bilayers at zero tension Biophys. J. 2007, 92 (12) 4157 4167
    32. 32
      Klauda, J. B.; Garrison, S. L.; Jiang, J.; Arora, G.; Sandler, S. I. HM-IE: Quantum chemical hybrid methods for calculating interaction energies J. Phys. Chem. A 2003, 108 (1) 107 112
    33. 33
      Davis, J. E.; Warren, G. L.; Patel, S. Revised charge equilibration potential for liquid alkanes J. Phys. Chem. B 2008, 112 (28) 8298 8310
    34. 34
      Wang, J.; Hou, T. Application of molecular dynamics simulations in molecular property prediction. 1. density and heat of vaporization J. Chem. Theory Comput. 2011, 7 (7) 2151 2165
    35. 35
      Darden, T.; York, D.; Pedersen, L. Particle mesh Ewald: An N-log(N) method for Ewald sums in large systems J. Chem. Phys. 1993, 98 (12) 10089 10092
    36. 36
      Ryckaert, J.-P.; Ciccotti, G.; Berendsen, H. J. C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes J. Comput. Phys. 1977, 23 (3) 327 341
    37. 37
      Berendsen, H. J. C.; Postma, J. P. M.; van Gunsteren, W. F.; DiNola, A.; Haak, J. R. Molecular dynamics with coupling to an external bath J. Chem. Phys. 1984, 81 (8) 3684 3690
    38. 38
      Yeh, I.-C.; Hummer, G. System-size dependence of diffusion coefficients and viscosities from molecular dynamics simulations with periodic boundary conditions J. Phys. Chem. B 2004, 108 (40) 15873 15879
    39. 39
      Lipari, G.; Szabo, A. Effect of librational motion on fluorescence depolarization and nuclear magnetic resonance relaxation in macromolecules and membranes Biophys. J. 1980, 30 (3) 489 506
    40. 40
      Ottiger, M.; Bax, A. Determination of Relative N–HN, N–C′, Cα–C′, and Cα–Hα effective bond lengths in a protein by NMR in a dilute liquid crystalline phase J. Am. Chem. Soc. 1998, 120 (47) 12334 12341
    41. 41
      Haynes, W. M. CRC Handbook of Chemistry and Physics, 93rd ed.; CRC Press: Boca Raton, FL, 2012–2013.
    42. 42
      Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G. A.; Nakatsuji, H.; Caricato, M.; Li, X.; Hratchian, H. P.; Izmaylov, A. F.; Bloino, J.; Zheng, G.; Sonnenberg, J. L.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Montgomery, J. A., Jr.; Peralta, J. E.; Ogliaro, F.; Bearpark, M.; Heyd, J. J.; Brothers, E.; Kudin, K. N.; Staroverov, V. N.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Rega, N.; Millam, N. J.; Klene, M.; Knox, J. E.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Zakrzewski, V. G.; Voth, G. A.; Salvador, P.; Dannenberg, J. J.; Dapprich, S.; Daniels, A. D.; Farkas, Ö.; Foresman, J. B.; Ortiz, J. V.; Cioslowski, J.; Fox, D. J.Gaussian 09, Revision A.1; Gaussian Inc.: Wallingford, CT, 2009.
    43. 43
      Douglass, D. C.; McCall, D. W. Diffusion in paraffin hydrocarbons J. Phys. Chem. 1958, 62 (9) 1102 1107
    44. 44
      Yaws, C. L. Yaws’ Handbook of Physical Properties for Hydrocarbons and Chemicals. http://www.knovel.com/web/portal/browse/display?_EXT_KNOVEL_DISPLAY_bookid=2147 (accessed February 19, 2013) .
    45. 45
      Tofts, P. S.; Lloyd, D.; Clark, C. A.; Barker, G. J.; Parker, G. J. M.; McConville, P.; Baldock, C.; Pope, J. M. Test liquids for quantitative MRI measurements of self-diffusion coefficient in vivo Magn. Reson. Med. 2000, 43 (3) 368 374
    46. 46
      Holler, F.; Callis, J. B. Conformation of the hydrocarbon chains of sodium dodecyl sulfate molecules in micelles: an FTIR study J. Phys. Chem. 1989, 93 (5) 2053 2058
    47. 47
      Lyerla, J. R.; McIntyre, H. M.; Torchia, D. A. A 13C nuclear magnetic resonance study of alkane motion Macromolecules 1974, 7 (1) 11 14
    48. 48
      Jo, S.; Lim, J. B.; Klauda, J. B.; Im, W. CHARMM-GUI membrane builder for mixed bilayers and its application to yeast membranes Biophys. J. 2009, 97 (1) 50 58
    49. 49
      Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. Comparison of simple potential functions for simulating liquid water J. Chem. Phys. 1983, 79 (2) 926 935
    50. 50
      Joung, I. S.; Cheatham, T. E. Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations J. Phys. Chem. B 2008, 112 (30) 9020 9041
    51. 51
      Press, W. H.; Teukolsky, S. A.; Vetterling, W. T.; Flannery, B. P. Numerical Recipes: The Art of Scientific Computing, 3rd ed. ed.; Cambridge University Press: New York, 2007.
    52. 52
      Pastor, R.; Brooks, B.; Szabo, A. An analysis of the accuracy of Langevin and molecular dynamics algorithms Mol. Phys. 1988, 65 (6) 1409 1419
    53. 53
      Roe, D. R.; Cheatham, T. E. PTRAJ and CPPTRAJ: Software for processing and analysis of molecular dynamics trajectory data J. Chem. Theory Comput. 2013, 9 (7) 3084 3095
    54. 54
      Kučerka, N.; Liu, Y.; Chu, N.; Petrache, H. I.; Tristram-Nagle, S.; Nagle, J. F. Structure of fully hydrated fluid phase DMPC and DLPC lipid bilayers using X-ray scattering from oriented multilamellar arrays and from unilamellar vesicles Biophys. J. 2005, 88 (4) 2626 2637
    55. 55
      Kučerka, N.; Nieh, M.-P.; Katsaras, J. Fluid phase lipid areas and bilayer thicknesses of commonly used phosphatidylcholines as a function of temperature Biochim. Biophys. Acta 2011, 1808 (11) 2761 2771
    56. 56
      Nagle, J. F. Introductory lecture: Basic quantities in model biomembranes Faraday Discuss. 2013, 161 (0) 11 29
    57. 57
      Braun, A. R.; Sachs, J. N.; Nagle, J. F. Comparing simulations of lipid bilayers to scattering data: The GROMOS 43A1-S3 force field J. Phys. Chem. B 2013, 117 (17) 5065 5072
    58. 58
      Rawicz, W.; Olbrich, K. C.; McIntosh, T.; Needham, D.; Evans, E. Effect of chain length and unsaturation on elasticity of lipid bilayers Biophys. J. 2000, 79 (1) 328 339
    59. 59
      Petrache, H. I.; Tristram-Nagle, S.; Nagle, J. F. Fluid phase structure of EPC and DMPC bilayers Chem. Phys. Lipids 1998, 95 (1) 83 94
    60. 60
      Klauda, J. B.; Kučerka, N.; Brooks, B. R.; Pastor, R. W.; Nagle, J. F. Simulation-based methods for interpreting X-ray data from lipid bilayers Biophys. J. 2006, 90 (8) 2796 2807
    61. 61
      Kučerka, N.; Tristram-Nagle, S.; Nagle, J. F. Closer look at structure of fully hydrated fluid phase DPPC bilayers Biophys. J. 2006, 90 (11) L83 L85
    62. 62
      Kučerka, N.; Nagle, J. F.; Sachs, J. N.; Feller, S. E.; Pencer, J.; Jackson, A.; Katsaras, J. Lipid bilayer structure determined by the simultaneous analysis of neutron and X-ray scattering data Biophys. J. 2008, 95 (5) 2356 2367
    63. 63
      Evans, E.; Rawicz, W.; Smith, B. A. Concluding remarks back to the future: mechanics and thermodynamics of lipid biomembranes Faraday Discuss. 2013, 161 (0) 591 611
    64. 64
      Evans, E.Personal Communication - DOPC isothermal compressibility modulus from X-ray data at 293 K; 2014.
    65. 65
      Tristram-Nagle, S.; Petrache, H. I.; Nagle, J. F. Structure and interactions of fully hydrated dioleoylphosphatidylcholine bilayers Biophys. J. 1998, 75 (2) 917 925
    66. 66
      Pan, J.; Tristram-Nagle, S.; Kučerka, N.; Nagle, J. F. Temperature dependence of structure, bending rigidity, and bilayer interactions of dioleoylphosphatidylcholine bilayers Biophys. J. 2008, 94 (1) 117 124
    67. 67
      Liu, Y.; Nagle, J. F. Diffuse scattering provides material parameters and electron density profiles of biomembranes Phys. Rev. E 2004, 69 (4) 040901
    68. 68
      Kučerka, N.; Tristram-Nagle, S.; Nagle, J. F. Structure of fully hydrated fluid phase lipid bilayers with monounsaturated chains J. Membr. Biol. 2006, 208 (3) 193 202
    69. 69
      Binder, H.; Gawrisch, K. Effect of unsaturated lipid chains on dimensions, molecular order and hydration of membranes J. Phys. Chem. B 2001, 105 (49) 12378 12390
    70. 70
      Rand, R. P.; Parsegian, V. A. Hydration forces between phospholipid bilayers Biochim. Biophys. Acta 1989, 988 (3) 351 376
    71. 71
      Rappolt, M.; Hickel, A.; Bringezu, F.; Lohner, K. Mechanism of the lamellar/inverse hexagonal phase transition examined by high resolution X-ray diffraction Biophys. J. 2003, 84 (5) 3111 3122
    72. 72
      Nagle, J. F.; Tristram-Nagle, S. Lipid bilayer structure Curr. Opin. Struct. Biol. 2000, 10 (4) 474 480
    73. 73
      Anézo, C.; de Vries, A. H.; Höltje, H.-D.; Tieleman, D. P.; Marrink, S.-J. Methodological issues in lipid bilayer simulations J. Phys. Chem. B 2003, 107 (35) 9424 9433
    74. 74
      Poger, D.; Mark, A. E. On the validation of molecular dynamics simulations of saturated and cis-monounsaturated phosphatidylcholine lipid bilayers: A comparison with experiment J. Chem. Theory Comput. 2009, 6 (1) 325 336
    75. 75
      Kučerka, N.; Katsaras, J.; Nagle, J. Comparing membrane simulations to scattering experiments: Introducing the SIMtoEXP software J. Membr. Biol. 2010, 235 (1) 43 50
    76. 76
      Shirts, M. R. Simple quantitative tests to validate sampling from thermodynamic ensembles J. Chem. Theory Comput. 2012, 9 (2) 909 926
    77. 77
      Seelig, J.; Waespe-Sarcevic, N. Molecular order in cis and trans unsaturated phospholipid bilayers Biochemistry 1978, 17 (16) 3310 3315
    78. 78
      Perly, B.; Smith, I. C. P.; Jarrell, H. C. Acyl chain dynamics of phosphatidylethanolamines containing oleic acid and dihydrosterculic acid: deuteron NMR relaxation studies Biochemistry 1985, 24 (17) 4659 4665
    79. 79
      Lafleur, M.; Bloom, M.; Eikenberry, E. F.; Gruner, S. M.; Han, Y.; Cullis, P. R. Correlation between lipid plane curvature and lipid chain order Biophys. J. 1996, 70 (6) 2747 2757
    80. 80
      Warschawski, D.; Devaux, P. Order parameters of unsaturated phospholipids in membranes and the effect of cholesterol: a 1H-13C solid-state NMR study at natural abundance Eur. Biophys. J. 2005, 34 (8) 987 96
    81. 81
      Petrache, H. I.; Dodd, S. W.; Brown, M. F. Area per lipid and acyl length distributions in fluid phosphatidylcholines determined by (2)H NMR spectroscopy Biophys. J. 2000, 79 (6) 3172 92
    82. 82
      Douliez, J. P.; Léonard, A.; Dufourc, E. J. Restatement of order parameters in biomembranes: calculation of C-C bond order parameters from C-D quadrupolar splittings Biophys. J. 1995, 68 (5) 1727 1739
    83. 83
      Aussenac, F.; Laguerre, M.; Schmitter, J.-M.; Dufourc, E. J. Detailed structure and dynamics of bicelle phospholipids using selectively deuterated and perdeuterated labels. 2H NMR and molecular mechanics study Langmuir 2003, 19 (25) 10468 10479
    84. 84
      Shaikh, S. R.; Brzustowicz, M. R.; Gustafson, N.; Stillwell, W.; Wassall, S. R. Monounsaturated PE does not phase-separate from the lipid raft molecules sphingomyelin and cholesterol: Role for polyunsaturation? Biochemistry 2002, 41 (34) 10593 10602
    85. 85
      Hitchcock, P. B.; Mason, R.; Thomas, K. M.; Shipley, G. G. Structural chemistry of 1,2 dilauroyl-DL-phosphatidylethanolamine: Molecular conformation and intermolecular packing of phospholipids Proc. Natl. Acad. Sci. U.S.A. 1974, 71 (8) 3036 3040
    86. 86
      Seelig, A.; Seelig, J. Bilayers of dipalmitoyl-3-sn-phosphatidylcholine: Conformational differences between the fatty acyl chains Biochim. Biophys. Acta 1975, 406 (1) 1 5
    87. 87
      Jämbeck, J. P. M.; Lyubartsev, A. P. An extension and further validation of an all-atomistic force field for biological membranes J. Chem. Theory Comput. 2012, 8 (8) 2938 2948
    88. 88
      Senak, L.; Davies, M. A.; Mendelsohn, R. A quantitative IR study of hydrocarbon chain conformation in alkanes and phospholipids: CH2 wagging modes in disordered bilayer and HII phases J. Phys. Chem. 1991, 95 (6) 2565 2571
    89. 89
      Moss, G. P. Basic terminology of stereochemistry Pure Appl. Chem. 1996, 68 (12) 2193 2222
    90. 90
      Cates, D. A.; Strauss, H. L.; Snyder, R. G. Vibrational modes of liquid n-alkanes: Simulated isotropic raman spectra and band progressions for C5H12-C20H42 and C16D34 J. Phys. Chem. 1994, 98 (16) 4482 4488
    91. 91
      Snyder, R. G.; Strauss, H. L.; Elliger, C. A. C-H stretching modes and the structure of n-alkyl chains. 1. Long, disordered chains J. Phys. Chem. 1982, 86, 5145 5150
    92. 92
      Mendelsohn, R.; Senak, L. Quantitative determination of conformational disorder in biological membranes by FTIR spectroscopy. In Biomolecular spectroscopy; Clark, R. J. H.; Hester, R. E., Eds.; Wiley: New York, 1993; pp 339 380.
    93. 93
      Casal, H. L.; McElhaney, R. N. Quantitative determination of hydrocarbon chain conformational order in bilayers of saturated phosphatidylcholines of various chain lengths by Fourier transform infrared spectroscopy Biochemistry 1990, 29 (23) 5423 5427
    94. 94
      Tuchtenhagen, J.; Ziegler, W.; Blume, A. Acyl chain conformational ordering in liquid-crystalline bilayers: comparative FT-IR and 2H-NMR studies of phospholipids differing in headgroup structure and chain length Eur. Biophys. J. 1994, 23 (5) 323 335
    95. 95
      Mendelsohn, R.; Davies, M. A.; Brauner, J. W.; Schuster, H. F.; Dluhy, R. A. Quantitative determination of conformational disorder in the acyl chains of phospholipid bilayers by infrared spectroscopy Biochemistry 1989, 28 (22) 8934 8939
    96. 96
      Kučerka, N.; Gallová, J.; Uhríková, D.; Balgavý, P.; Bulacu, M.; Marrink, S.-J.; Katsaras, J. Areas of Monounsaturated Diacylphosphatidylcholines Biophys. J. 2009, 97 (7) 1926 1932
    97. 97
      Ratto, T. V.; Longo, M. L. Obstructed diffusion in phase-separated supported lipid bilayers: A combined atomic force microscopy and fluorescence recovery after photobleaching approach Biophys. J. 2002, 83 (6) 3380 3392
    98. 98
      Almeida, P. F. F.; Vaz, W. L. C.; Thompson, T. E. Lateral diffusion in the liquid phases of dimyristoylphosphatidylcholine/cholesterol lipid bilayers: a free volume analysis Biochemistry 1992, 31 (29) 6739 6747
    99. 99
      Orädd, G.; Lindblom, G.; Westerman, P. W. Lateral diffusion of cholesterol and dimyristoylphosphatidylcholine in a lipid bilayer measured by pulsed field gradient NMR spectroscopy Biophys. J. 2002, 83 (5) 2702 2704
    100. 100
      Filippov, A.; Orädd, G.; Lindblom, G. Influence of cholesterol and water content on phospholipid lateral diffusion in bilayers Langmuir 2003, 19 (16) 6397 6400
    101. 101
      Vaz, W. L. C.; Clegg, R. M.; Hallmann, D. Translational diffusion of lipids in liquid crystalline phase phosphatidylcholine multibilayers. A comparison of experiment with theory Biochemistry 1985, 24 (3) 781 786
    102. 102
      Scheidt, H. A.; Huster, D.; Gawrisch, K. Diffusion of cholesterol and its precursors in lipid membranes studied by 1H pulsed field gradient magic angle spinning NMR Biophys. J. 2005, 89 (4) 2504 2512
    103. 103
      Kuśba, J.; Li, L.; Gryczynski, I.; Piszczek, G.; Johnson, M.; Lakowicz, J. R. Lateral diffusion coefficients in membranes measured by resonance energy transfer and a new algorithm for diffusion in two dimensions Biophys. J. 2002, 82 (3) 1358 1372
    104. 104
      Jin, A. J.; Edidin, M.; Nossal, R.; Gershfeld, N. L. A singular state of membrane lipids at cell growth temperatures Biochemistry 1999, 38 (40) 13275 13278
    105. 105
      Poger, D.; Mark, A. E. Lipid bilayers: The effect of force field on ordering and dynamics J. Chem. Theory Comput. 2012, 8 (11) 4807 4817
    106. 106
      Wohlert, J.; Edholm, O. Dynamics in atomistic simulations of phospholipid membranes: Nuclear magnetic resonance relaxation rates and lateral diffusion J. Chem. Phys. 2006, 125 (20) 204703
    107. 107
      Wang, Y.; Markwick, P. R. L.; de Oliveira, C. A. F.; McCammon, J. A. Enhanced lipid diffusion and mixing in accelerated molecular dynamics J. Chem. Theory Comput. 2011, 7 (10) 3199 3207
    108. 108
      Basconi, J. E.; Shirts, M. R. Effects of temperature control algorithms on transport properties and kinetics in molecular dynamics simulations J. Chem. Theory Comput. 2013, 9 (7) 2887 2899
    109. 109
      König, S.; Bayerl, T. M.; Coddens, G.; Richter, D.; Sackmann, E. Hydration dependence of chain dynamics and local diffusion in L-alpha-dipalmitoylphosphtidylcholine multilayers studied by incoherent quasi-elastic neutron scattering Biophys. J. 1995, 68 (5) 1871 1880
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    Details of the Lipid14 atom types, partial charges, and force field parameters. Also included are the bilayer results for additional GPU and CPU runs. This material is available free of charge via the Internet at http://pubs.acs.org.


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