Effects of Ether-Linkage on Membrane Dipole Potential and

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Effects of Ether-Linkage on Membrane Dipole Potential and Cholesterol Flip-Flop Motion in Lipid Bilayer Membranes Hujun Shen, Kun Zhao, and Zhenhua Wu J. Phys. Chem. B, Just Accepted Manuscript • DOI: 10.1021/acs.jpcb.9b06570 • Publication Date (Web): 27 Aug 2019 Downloaded from pubs.acs.org on August 28, 2019

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The Journal of Physical Chemistry

Effects of Ether-linkage on Membrane Dipole Potential and Cholesterol Flip-Flop Motion in Lipid Bilayer Membranes

Hujun Shen1,2*, Kun Zhao2, Zhenhua Wu2 1

Guizhou Provincial Key Laboratory of Computational Nano-Material Science, Guizhou Synergetic Innovation Center of Scientific Big Data for Advanced Manufacturing Technology, Guizhou Education University No.115, Gaoxin Road, Guiyang, Guizhou, 550018, P. R. China

2.

Guizhou University of Finance and Economics, School of Information, University City of Huaxi District, Guiyang, Guizhou, 550025, P. R. China

ACS Paragon Plus Environment

The Journal of Physical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ABSTRACT In our previous work, we investigated the effect of ether-linkage on the physical properties of lipid bilayer using all-atom (AA) simulations with different water models. However, the influence of ether-linkage on the transportation of cholesterol in lipid bilayers is less well studied. In order to reduce computational costs in simulations at large time and length scales, we present coarse-grained (CG) simulations of diphytanyl phosphatidylcholine (ether-DPhPC) and diphytanoyl phosphatidylcholine (esterDPhPC) bilayer membranes in this work. First, the CG and AA simulations consistently show that the substitution of ether linkage for ester linkage would prevent the penetration of water into the lipid bilayer membranes. Second, it is encouraging that the CG simulations can nicely capture the ether effect on membrane dipole potential,


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showing that the ether substitution for ester would significantly decrease the dipole potential. In particular, the CG results agree with the AA simulation results, revealing that the change in the dipole potential is accompanied with the alteration in the orientation of linkage group. Finally, we carried out 60-μs coarse-grained (CG) simulations of ether-DPhPC and ester-DPhPC bilayers respectively at two cholesterol concentrations (10 % and 40% mole fractions), showing that the ether substitution for ester would facilitate the cholesterol flip-flop motion in lipid bilayer membranes.

INTRODUCITON



Shinoda et al.1 performed all-atom (AA) molecular dynamics (MD) simulations of diphytanyl phosphatidylcholine (ether-DPhPC) and diphytanoyl phosphatidylcholine (ester-DPhPC) bilayers, discovering that the replacement of ester-linkage by etherlinkage would significantly decrease the dipole potential of lipid bilayer membrane. This finding has been confirmed by Wang et al.,2 who employed the cryo-electron microscopy (cryo-EM) method to reveal the ether effect on membrane dipole potential. Recently, we investigated the effect of ether-linkage on the dipole potential of DPhPC bilayer membranes by using three different water models (such as TIP3P3, TIP4P3, and TIP5P4). It is encouraging that these AA MD simulations can correctly capture the decrease in the dipole potential caused by the ether substitution for ester.5 Cholesterol is a critical substance in mammalian cell membranes, and its distribution and flip-flop motion in cell membranes have been the subjects of considerable interest of research scientists for understanding how cells work. Different experimental techniques were employed to probe the cholesterol flip-flop motions in lipid membranes, such as fluorescence resonance energy transfer (FRET),6 nuclear magnetic resonance (NMR),7 sum frequency generation vibrational spectroscopy (SFGVS),8 time-resolved small-angle neutron scattering (TR-SANS),9 and so on. In addition, MD simulation approaches have provided abundant and useful information about the cholesterol movement in lipid membranes. For example, Choubey and coworkers10 carried out long AA MD simulations (the length of 15 μs), from which one can directly observe the cholesterol flip-flop events. Recently, Gu et al.11 calculated the cholesterol flip-flop rate in a ternary mixture using AA MD simulations, revealing that the flip-flop of cholesterol is faster in the disordered DOPC-enriched environment than in the ordered DPPC-enriched environment. Bennett et al.12 investigated the cholesterol flipflop movement between bilayer leaflets by using coarse-grained (CG) MD simulations, demonstrating that the cholesterol flip-flop would be slowed down by increasing cholesterol concentration in lipid membranes. Despite AA MD simulations are very useful for interpreting experimental observations, the computational costs would be extremely high when atomistic simulations are applied to the studies of some physical processes occurring on


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microsecond timescales or even longer. This promotes the use of coarse-grained (CG) approaches,13-20 in which the computational expenses can significantly be alleviated through the reduction of a complex structure and the use of a larger integration time step. Recently, we developed a CG model for water (see Figure S1 of Supporting Information), namely CAVS (charge attached to virtual site).21 In this approach, one CG bead represents a cluster of four real water molecules, and each CG bead consists of one van der Waals (vdW) interaction center (CGM) connected with two positively charged sites (namely CGPs) and one negatively charged virtual site (CGN). Then, we proposed the CAVS CG model for phospholipid, in which two electrostatic interaction sites were embedded into the ester groups of lipid (see examples in Figure S2 of Supporting Information).22 The CAVS model was extended to simulate phospholipid membranes, showing that the positive dipole potential inside PC lipid bilayers can be correctly predicted and the contribution of eater dipoles to the dipole potential can be captured. In addition, we presented the CAVS model for cholesterol, in which two electrostatic interaction sites were introduced in order to consider the contribution of cholesterol to the dipole potential (see Figure S3 of Supporting Information).23 Our CG simulations have showed that the cholesterol effect on the dipole potential can be successfully reproduced by the CAVS model. In this work, we compared the effect of cholesterol (CHOL) on the dipole potential of ether-DPhPC and ester-DPhPC membranes by using the CAVS CG model. Our CG results show that the ether-linkage substitution for ester-linkage would significantly decrease the dipole potential, in agreement with previous AA MD simulations and experiments. Meanwhile, 60-μs CAVS CG simulations of etherDPhPC/CHOL and ester-DPhPC/CHOL bilayers were carried out at two cholesterol concentrations (10 mol% and 40 mol%) respectively. From the CAVS CG simulations, we discovered that the ether substitution for ester would facilitate the cholesterol flipflop in lipid bilayer membranes.

METHODS ACS Paragon Plus Environment


All-Atom MD Simulations In our previous work, we carried out all-atom (AA) MD simulations of etherDPhPC and ester-DPhPC lipid bilayers by using different water models.24 The AA MD simulations were performed with the modified GAFF force field 25-27 in the simulation package GROMACS 4.6.7.28 In each case, 256 lipids were mixed with 10400 water molecules by using the PACKMOL program.29 The starting conformation of a bilayer system was minimized, and then the NPT and NVT equilibrations were performed for 200 ps and for 400 ps respectively. Finally, NPT production runs were carried out for 300 ns, and the last 200 ns simulation were used for final analysis. For all NPT production runs, the Parrinello-Rahman algorithm30 was used to maintain the semiisotropic pressure of 1 bar and the velocity rescaling method31 was employed to govern the constant temperature of 323 K. The LINC algorithm32 was applied to constrain all bonds involving hydrogen atoms such that the integration time step could be increased to 2 fs. Electrostatic interactions were calculated using the particle mesh Ewald (PME) algorithm33 and van der Waals (vdW) interactions were computed at a cutoff value of 1.5 nm respectively. CAVS Model for Cholesterol Recently, we proposed the CAVS CG model for cholesterol (see Figure S3 of Supporting Information).23 In this CG model, the cholesterol ring was represented by four CG beads (such as CI, CR1, CR2, and CR3), and two electrostatic interaction sites were introduced into the CI bead, in which a positively charged site is placed at the vdW interaction center (CIO) and the OI site carries a negative charge. It is known that the dipole moment of cholesterol was estimated to be 1.9 Debye,34 then we tentatively set the partial charges of the CIO and OI sites as 𝑞𝐶𝐼𝑂 = 0.25 and 𝑞𝑂𝐼 = ―0.25. In addition, three neutral CG beads (such as CT1, CT2 and CT3) were used to represent the hydrophobic tail of cholesterol. Finally, two off-plane methyl groups (such as CS1 and CS2) were explicitly attached to the CR1 and CR3 particles because of their important role in the cholesterol condensing effect. In our previous work,23 we have described the parameterization of the CAVS CG model for cholesterol in details:23


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1) The bond stretching and angle bending terms were determined by fitting to the bond and angle probability distributions calculated from the CHARMM36 AA simulations of lipid-cholesterol mixtures.36 2) The iterative inverse Boltzmann (IB) approach37 was used to derive the van der Waals (vdW) parameters. 3) The CAVS simulations of lipid-cholesterol mixtures were carried out, and the CAVS force field was optimized through the comparison between the CG results and the atomistic and experimental results. 4) Steps 1-3 were repeatedly done until the CAVS CG model could reproduce the atomistic and experimental results. CAVS Model for ester-DPhPC and ether-DPhPC Recently, we extended the CAVS model to different types of phospholipid,22,23 such as DMPC, DPPC, DOPC, POPC, and POPE. Similarly, the parameterization of the CAVS force field for ether-DPhPC (Figure 1A) and ester-DPhPC (Figure 1B) was carried out as follows: 1) Based on the AA MD simulations of ester-DPhPC and ether-DPhPC, the iterative inverse Boltzmann (IB) approach37 was used to obtain the vdW parameters for the DME bead (see Figures S4 of Supporting Information). The vdW parameters of DME is given in Table S1 of Supporting Information. Then, the vdW parameters for the other beads (such as C2P, C3P, CMF, PO4, and N4M) were directly adopted from previous works.22,23 2) As for ether-linkage (dimethyl ether) or ester-linkage (methyl formate), two electrostatic interaction sites were placed into each CG bead (for instance, DME and ODM were used for ether-linkage model, whereas CMF and OMF for the ester-linkage model). Please note that DME and CMF also represent the vdW interaction centers. Because the dipole moments of the dimethyl ether and methyl formate molecules were estimated to be 1.30 D and 1.77 D respectively, we set the partial charges of the electrostatic interacting sites as: 𝑞𝐷𝑀𝐸 = 0.27 and

𝑞𝑂𝑀𝐸 = ―0.27 for the ether-linkage, and


𝑞𝐶𝑀𝐹 = 0.37and 𝑞𝑂𝑀𝐹


= ―0.37 for the ester-linkage. Please note that the distance dCMF-OMF or dDMEOMF

was arbitrarily constrained to 1.0 Å.

Figure 1. CG mapping for (A) ether-DPhPC and (B) ester-DPhPC. The type names of interaction sites are indicated by black and blue colors respectively. The neutral, noninteraction, positively charged, and negatively charged sites are represented by the black filled, black open, red open or filled, and blue filled circles respectively. DME and CMF are placed at the vdW interaction centers while ODM and OMF are only the electrostatic interaction sites.

3) the bond-stretching and angle-bending terms were determined through fitting to the atomistic results based on the GAFF/TIP5P force field, see Figure S5 of Supporting Information. The dihedral angle potential was not applied in this work because we found that the CG simulations could produce acceptable results by ignoring the dihedral angle term.


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4) Finally, the bond-stretching and angle-bending terms were further optimized through the comparison to the experimental and AA MD results. Final bondstretching and angle-bending parameters are collected in Tables S2 and S3 of Supporting Information. Coarse-Grained (CG) MD Simulations Using the simulation package GROMACS 4.6.7,28 CAVS CG simulations were performed on the ether-DPhPC, ester-DPhPC, ether-DPhPC/CHOL, and esterDPhPC/CHOL bilayer structures. For each case, the starting configuration was generated by using the PACKMOL software.29 An energy minimization of a bilayer structure was done and followed by the NPT and NVT equilibrations, which were carried out for 10 ns respectively. Finally, a production run was performed under NPT condition. For all NPT production runs, the constant temperature of 323 K was maintained by using the velocity rescaling method 31 and the Parrinello-Rahman (with a time constant of 3.0 ps)30 was employed to control the semi-isotropic pressure at 1 bar. To calculate the vdW interactions, a shift scheme was adopted between 1.2 nm and 1.6 nm. The PME method33 was used to compute the electrostatic interactions at a cutoff value of 1.6 nm. The bonds inside a CG unit were constrained by employing the LINCS algorithm.32 The integration time step of 15 fs was adopted because it has been shown previously that a good balance can be made between system stability and computational efficiency.21 As for the CAVS simulations of ether-DPhPC and ester-DPhPC without cholesterol molecules, 512 lipid molecules were used and 6-μs NPT simulation was performed for each type of lipid. Finally, the last 5-μs NPT production runs were used for final analysis. As for the CAVS CG simulations of ether-DPhPC/CHOL and ester-DPhPC/CHOL, 512 lipid molecules (including lipid and cholesterol molecules) were used at two CHOL concentrations (10 mol% and 40 mol%). For each system, 10 independent CG simulations (each CG simulation was performed for 6 μs) were used for investigating the cholesterol transportation in lipid bilayers.



Solvation Free Energy Calculations To calculate the solvation free energy of a molecule in water (or hydration free energy), the thermodynamic integration (TI) method38 was employed. In this approach, a solute was inserted into a CAVS water box. The free energy difference (∆Gsol) between two states (completely with the solute and without the solute) is described by a function of a coupling parameter (λ). For instance, 21 NPT simulations (each run has a length of 100 ns) were carried out at different values of λ spacing from 0 (completely without the solute) and 1 (completely with the solute). The weighted histogram analysis method (WHAM)39 can be directly used to determine the hydration free energy. Potential of Mean Force (PMF) Calculations An umbrella sampling technique40 has been widely used to determine the free energy of cholesterol flip-flop between lipid bilayer leaflets.41-43 To calculate the free energy of cholesterol transportation from bilayer surface to bilayer center, the umbrella sampling method was employed to construct the PMF profiles as a function of the distance between the CI bead of cholesterol and the center of mass of lipid bilayer. Then, the free energy barrier of cholesterol flip-flop in lipid bilayers can be directly measured based on the PMF profiles. In the PMF calculations, the ether-DPhPC and ester-DPhPC bilayers were simulated at two CHOL concentrations (10 mol% and 40 mol%) respectively. For each bilayer system, 41 different conformations (or windows) were generated: 1) in the first window, one cholesterol molecule was placed at the membrane-water interface; 2) in subsequent windows, the cholesterol molecule was moved towards the bilayer center along z-axis (the bilayer normal) at an interval of 0.07 nm; 3) in the final window, the cholesterol molecule was placed at the bilayer center. For each window, 6 independent umbrella sampling simulations (3 simulations of 100 ns for each leaflet) were used for constructing the PMF profiles. During all umbrella sampling simulations, a force constant of 1000 kJ mol-1 nm-2 was applied to restrain the distance between the CI bead of cholesterol and the center of mass of lipid bilayer. The final PMF profiles were constructed using the weighted


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histogram analysis method (WHAM)39. For each system, the mean PMF values and their standard errors were determined based on the six independent PMF simulations.

RESULTS AND DISCUSSION Effect of Ether-linkage on the Structural Properties of Lipid Bilayer

Figure 2. Number density profiles for the phosphate group (PO4) of (A) ether-DPhPC and (B) ester-DPhPC, obtained from the GAFF/TIP5P all-atom and CAVS coarse-grained simulations.

Figure 2 illustrates the number density profiles for the phosphate (PO4) group of ester-DPhPC and ether-DPhPC as well as a comparison made between the GAFF/TIP5P all-atom (AA) and CAVS coarse-grained (CG) results. Based on the number density profiles, we calculated the bilayer thickness. Please note that the bilayer thickness is defined as the phosphate-to-phosphate distance (dPtP) in this work. Meanwhile, the grid-based method (GridMAT-MD)44,45 can also be used to calculate the bilayer thickness. Table 1 summarizes the CAVS results for the bilayer thickness as well as the comparison to experiment2,46 and AA simulations.5 Although the experimental bilayer thickness of ether-DPhPC is unknown, the wide-angle X-ray scattering (WAXS) experiment47 revealed that replacing an ester linkage of DPPC with an ether linkage would result in a slight increase in bilayer thickness. From Table 1, one can see that this trend is nicely reproduced by our CAVS model.

Table 1. Physical properties of ether-DPhPC and ester- DPhPC bilayers calculated from the CAVS and GAFF/TIP5P simulations, and a comparison is made between the simulations and



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experiment (Exp). The experimental values for membrane dipole potential, bilayer thickness (dPtP) and area per lipid (Ap) are respectively taken from Refs. 2 and 46. ether-DPhPC

dPtP (nm)

CAVS

GAFF/TIP5P

3.86±0.05

3.83±0.03

ester-DPhPC Exp

CAVS

GAFF/TIP5P

Exp

NA

3.78±0.04

3.79±0.03

3.80

0.74±0.01

0.76

0.515±0.042

0.510

3.88±0.06*

𝑨𝒑 (nm2)

3.80±0.05*

0.73±0.02

0.72±0.01

NA

0.72±0.03*

Φd (V)

0.71±0.03*

0.282±0.045

* using the grid-based method

0.72±0.02

0.190±0.030

0.260

0.498±0.068

44,45

Figure 3. Radial distribution functions g(COlipid-Owater), obtained from (A) the CAVS and (B) GAFF/TIP5P simulations of ether-DPhPC and ester-DPhPC bilayers. COlipid represents the center of ether- or ester-linkage group while Owater corresponds to the oxygen atom of water. As for the CAVS model, Owater represents the center of mass of four-water cluster.

The area per lipid 𝐴𝑝 for the ether-DPhPC and ester-DPhPC bilayers can be calculated based on the following equation: 𝐴𝑝 =

2 ∙ 𝐿𝑥 ∙ 𝐿𝑦 𝑁

(1)

where 𝑁 corresponds to the number of lipids, 𝐿𝑥 and 𝐿𝑦 respectively represent the x- and y-dimension of the simulation box. Similarly, the GridMAT-MD method44,45 was employed to measure the area per lipid (APL) and 250×250 grid points were used for the calculation. The final results for APL are collected in Table 1, showing that the AA and experimental results can reasonably be predicted by the CAVS CG model. According to the wide-angle X-ray scattering (WAXS) experiment47, it is shown that


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the ether substitution in DPPC would result in a slight increase in APL, which can be captured by our CAVS CG model. The radial distribution functions (RDFs) are plotted for the ether-DPhPC and esterDPhPC bilayers based on the CAVS (Figure 3A) and GAFF/TIP5P (Figure 3B) simulations. In the RDF profiles, COlipid represents the mass center of ether- or esterlinkage group while Owater corresponds to the oxygen atom of water. Please note that a CG bead represents a cluster of four real water molecules (see Figure S1 of Supporting Information), such that direct comparison cannot be made between the GAFF/TIP5P and CAVS results. For instance, in the COlipid-Owater correlation function, the CAVS simulation shows that the peak of the first shell is located at about 0.5 nm and that of the second shell at about 1.0 nm (Figure 3A). However, the GAFF/TIP5P simulation demonstrates only one shark peak at about 0.4 nm (Figure 3B).

Figure 4. Number density profile for water, obtained from the (A) CAVS and (B) GAFF/TIP5P simulations of the ether-DPhPC and ester-DPhPC bilayers. Please note that one CAVS water represents four real water molecules.

From Figure 3, it is seen that both the CAVS and GAFF/TIP5P simulations consistently display that more water molecules interact with the linkage group of esterDPhPC than that of ether-DPhPC. Consequently, the ether-linkage substitution for the ester-linkage leads to the less water penetration into the DPhPC bilayer, shown in Figure 4. This agrees with previous atomistic MD simulation studies,1,24 showing that the difference in the water penetration arises from different hydrophilic nature of the linkage groups. In Table 2, we present the CAVS and experimental results for the



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hydration free energies of dimethyl ether (DME) and methyl formate (CMF), and it is shown that the ether linkage group (dimethyl ether) has a lower hydration free energy than the ester linkage (methyl formate). Recently, Leonard et al.48 optimized CHARMM36 force field for linear ethers by using ab initio results and found that the improved force field (namely CHARMM36e) would allow deeper water penetration into the DHPC bilayer in comparison with the CHARMM36 force field, suggesting that the force field dependence of water permeability. The increased water penetration would enlarge the surface area per lipid, nicely explaining the WAXS experimental observation47 that the ether-linkage substitution in DPPC would increase the surface area per lipid. Unfortunately, the CHARMM36e force field showed that the replacement of ester-linkage by ether-linkage would increase membrane dipole potential, which is contrary to the experimental and CHARMM27 simulation studies.

Table 2. Solvation free energies (or hydration free energies) of dimethyl ether (ether linkage group, DME) and methyl formate (ester linkage group, CMF) in water, and the comparison is made between the CAVS and experimental results.

CAVS (kJ/mol)

Experiment (kJ/mol)

dimethyl ether (DME)

-6.24±0.04

-7.98

methyl formate (CMF)

-11.63±0.06

-11.62

Effect of Ether-linkage on the Dipole Potential of Lipid Bilayers The electrostatic potential 𝜙(z) along z-axis (the bilayer normal) can be calculated according to the following relation: 𝜙(z) = ―

1 ε0

z′

z

∫ ∫ ρ(z )dz dz′

0

′′

′′

(2)

0

where ρ(z) represents the local charge density and ε0 corresponds to the vacuum permittivity. The electrostatic potential profiles were constructed for ether-DPhPC and ester-DPhPC bilayers based on the CAVS (Figure 5A) and GAFF/TIP5P (Figure 5B) simulations. From these electrostatic potential profiles, it is straightforward to


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determine the dipole potential of lipid bilayers, which represents the difference of electrostatic potential between the aqueous region (|z| ≈ 3.0 nm) and the bilayer center (|z| ≈ 0.0 nm).

Figure 5. Electrostatic potential profiles for ether-DPhPC (black) and ester-DPhPC (red) bilayer membranes obtained from the (A) CAVS and (B) GAFF/TIP5P simulations.

Figure 6. Positive contribution of water to the dipole potential of ether-DPhPC (black) and esterDPhPC (red) bilayer membranes, obtained from the (A) CAVS and (B) GAFF/TIP5P simulations.

Table 1 summarizes the calculated results for membrane dipole potential obtained from the CAVS CG model. When comparing to experiments2,49 and AA model, the CAVS simulations can nicely capture the ether effect on the dipole potential: the substitution of the ester-linkage for the ether-linkage would substantially decrease the dipole potential. However, one should be aware that the dipole potential determined by the cryo-EM method2 would produce a large difference from that obtained by the lipid bilayer method.49 For instance, the magnitude of dipole potential was measured to be



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0.228 V for DPhPC by using the lipid bilayer method,50 which is the half of that determined by the cryo-EM method. Similarly, by the cryo-EM method measurement, the ether-linkage substitution for ester-linkage would decrease the dipole potential about 0.25 V, which is the double of that measured by Klaus Gawrisch et al.49

Table 3. Individual contributions of water and lipid to total dipole potential of etherDPhPC and ester-DPhPC bilayers, and a comparison is made between the CAVS and GAFF/TIP5P models. CAVS

GAFF/TIP5P

ether-DPhPC

ester-DPhPC

ether-DPhPC

ester-DPhPC

Water

1.525±0.045

1.643±0.068

0.930±0.030

0.842±0.042

Lipid

-1.243±0.068

-1.145±0.068

-0.740±0.028

-0.327±0.042

It has been reported51 that the dipole potential of phospholipid bilayers is correlated with the orientation of the polar groups (including water molecules and lipid head groups) at the membrane-water interface. Basically, the contribution of water to membrane dipole potential is positive whereas the contribution of lipid is negative.51 Figures 6A shows the CAVS results for the individual contribution of water to total electrostatic potential of lipid bilayers in comparison with the GAFF/TIP5P results (Figure 6B). The CAVS and GAFF/TIP5P simulations consistently reveal that the contribution of water to membrane dipole potential is positive. In Table 3, the CAVS model reveals that the substitution of ether for ester slightly decreases the positive contributions of water, which is consistent with our previous work using the GAFF/TIP3P and GAFF/TIP4P models.5 Surprisingly, the GAFF/TIP5P results demonstrate that the ether substitution for ester slightly increases the positive contribution of water. However, the CG and AA simulations reach an agreement that the difference in the water contribution caused by the ether substitution is not compelling (shown in Figure 6 and Table 3).


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Figure 7. Negative contribution of lipid to the dipole potential of ether-DPhPC (black) and esterDPhPC (red) bilayer membranes, obtained from the (A) CAVS and (B) GAFF/TIP5P simulations.

From Figure 7 and Table 3, one can see that the contribution of lipid to total electrostatic potential is negative. The CAVS and GAFF/TIP5P simulations consistently show that the ether substitution for ester would increase the negative contribution of lipid to the dipole potential. It is known that the negative contribution of lipid to the dipole potential is mainly influenced by the orientation of lipid head groups,51 which is defined by the tilt angle of the P-N vector. Please note that the P-N vector is the vector connecting the phosphate atom (P) with the nitrogen atom (N). We calculated the average tilt angle 〈𝜃〉 of P-N vector by using the following equation: 〈𝜃〉 =

∫

180

𝜃 ∙ 𝜌(𝜃)𝑑𝜃

(3)

0

where 𝜃 and 𝜌(𝜃) represent the tilt angle and the tilt distribution respectively. Figure 8 shows the distributions of the tilt angle 𝜃 of P-N vector. The CAVS results (Figure 8A) show that the ether substitution for ester has a limited influence on the average tilt angle 𝜃 (about 850) of the P-N vector, in consistence with the GAFF/TIP5P results. However, the GAFF/TIP5P simulations demonstrate that the ether substitution results in a wide distribution (shown in Figure 8B), and it is unclear whether the difference in the tilt distribution would influence the negative contribution of lipid to the dipole potential (in Table 3). Unfortunately, this feature is not captured by the CAVS model, suggesting that the CAVS model needs further optimization to improve this



performance.

Figure 8. Distributions of the tilt angle θ of P-N vector, obtained from the (A) CAVS and GAFF/TIP5P simulations.

Figure 9. Distributions of the tilt angle θ of C-O vector (represents the dipole moment vector of the linkage group), obtained from the (A) CAVS and GAFF/TIP5P simulations. θether defines the orientation of the ether linkage with respect to the bilayer normal (z-axis) while θester represents the orientation of the ester linkage with respect to the bilayer normal.

In this work, we calculated the orientations of the ether-linkage and ester-linkage groups from the CAVS CG simulations, given in Figure 9A. The CAVS results demonstrate that the ether-linkage group is oriented more parallel to the x-y plane (membrane surface) than the ester-linkage group, in agreement with the GAFF/TIP5P results (shown in Figure 9A). Thus, it is clear that the decrease in the dipole potential caused by the ether substitution should arise from the increase in the tilt angle of linkage group (with respect to the bilayer normal). In fact, the water penetration into the lipid bilayers (or water organization at the membrane-water interface) should influence the


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change in the orientation of the linkage group (ether or ester). Effect of Ether-linkage on Cholesterol Flip-Flop Motion

Figure 10. Normalized number density profiles for the CI particle (head group) of cholesterol (CHOL), calculated from the CAVS simulations of ether-DPhPC (black) and ester-DPhPC (red) bilayers at two cholesterol (CHOL) concentrations of (A) 10 mol% CHOL and (B) 40 mol% CHOL.

An important aspect of cholesterol transportation in lipid bilayers is the flip-flop motion between lipid bilayer leaflets. A variety of experimental and computational approaches have been used to investigate the cholesterol flip-flop in different kinds of lipid bilayer membranes. These methods reported different cholesterol flip-flop rates ranging from milliseconds to hours, depending on the type of lipid and the cholesterol concentration. However, it is quite challenging for an experiment to monitor the motions of cholesterol molecules in lipid bilayer membranes owing to the structural or dynamic complexity of these systems. Thus, molecular dynamics (MD) simulation can be used as a valuable complementary tool to experiments. In particular, CG MD simulation can be useful for investigating the cholesterol flip-flop motion on large length and time scales. In this study, 60-μs CAVS simulations of ether-DPhPC and ester-DPhPC bilayers were carried out at two cholesterol concentrations (10 mol% and 40 mol%) respectively, from which we constructed the normalized number density profiles for the CI particle of cholesterol, given in Figure 10. At low cholesterol concentration (Figure 10A), it is seen that one wide peak is identified at the bilayer center and two sharp peaks at the



equilibrium positions (about 1.8 nm from the bilayer center). At high cholesterol concentration (Figure 10B), the two peaks at the equilibrium positions become sharper. Meanwhile, Figures 10A and 10B show that the population of surface cholesterols (located at equilibrium positions) is less in the ether-DPhPC bilayers than in the esterDPhPC bilayers. This is because the ether-linkage substitution for ester-linkage would weaken the cholesterol-lipid interaction, shown in Figure 11.

Figure 11. Radial distribution functions g(CICHOL-Clipid), obtained from the CAVS simulations of ether-DPhPC (black) and ester-DPhPC (red) at two different cholesterol concentrations: (A) 10 mol% and (B) 40 mol%. CI represents the headgroup (CI particle) of cholesterol (CHOL) and Clipid corresponds to the mass center of ether or ester.

In our previous work,23 we employed the CAVS CG model to calculate the free energies of cholesterol transportation in DPPC/CHOL bilayers at two CHOL concentrations (such as 0 mol% CHOL and 40 mol% CHOL). The PMF profiles constructed from the CAVS CG simulations displayed a similar pattern as those from the atomistic simulations by Bennett et al.12 Similarly, we used the CAVS CG model to determine the free energies of cholesterol transportation in the ether-DPhPC and ester-DPhPC bilayers at two different CHOL concentrations (such as 10 mol% and 40 mol%) respectively, presented in Figure 12. In term of the free energy barrier for the cholesterol flip-flop motion (∆Gff), the CAVS CG results show that the flip-flop motions of cholesterol are dependent on the lipid type: 1) at the low concentration (10 mol% CHOL, Figure 12A), the free energy barrier (∆Gff ≈ 16 ± 3 kJ/mol) for the cholesterol flip-flop in the ether-DPhPC bilayer is lower than that (∆Gff ≈21±3 kJ/mol)


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in the ester-DPhPC bilayer; 2) similarly, at the high concentration (40 mol% CHOL, Figure 12B), the free energy barrier (∆Gff ≈ 34±5 kJ/mol) for the cholesterol flip-flop in the ether-DPhPC bilayer is lower than that (∆Gff ≈ 41±5 kJ/mol) in the ester-DPhPC bilayer. These results suggest that the cholesterol flip-flop motion in the lipid bilayers can be facilitated by the ether-linkage substitution for ester-linkage.

Figure 12. PMFs for the cholesterol transfer between the surface and the bilayer center of etherDPhPC (black) and ester-DPhPC (red) at two cholesterol concentrations: (A) 10 mol% CHOL and (B) 40 mol% CHOL. Error bars are the standard errors estimated from 6 independent PMF calculations (3 for each leaflet).

The 60-μs CAVS CG simulations can help us to have a direct observation on the cholesterol flip-flop in lipid bilayer membranes. Figure 13 shows the snapshots obtained from a CAVS CG simulation of the ether-DPhPC bilayer at a low CHOL concentration (10 mol% CHOL). From these snapshots, it is seen that one fast CHOL flip-flop event can occur within 100 ns. Based on the 60-μs CAVS simulations, we computed the observed flip-flop rate of cholesterol for each type of lipid at two concentrations (10 mol% CHOL and 40 mol% CHOL). To measure the cholesterol flipflop rate, we have monitored the transportation of all cholesterol molecules between lipid bilayer leaflets. In this study, the cholesterol flip-flop motion is regarded to be complete: 1) the CI bead of cholesterol at the membrane-water interface moves toward the bilayer center, and 2) then the CI bead moves from the bilayer center to opposite membrane-water interface.



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Figure 13. Snapshots for one cholesterol (CHOL) flip-flop event, obtained from a CAVS simulation of ether-DPhPC lipid bilayer at the concentration of 10 mol% CHOL. Water particles are represented by green spheres, the head groups (choline and phosphate) of ether-DPhPC by blue and red spheres respectively. The head group of cholesterol (CI particle) is indicated by pink sphere and the tail particles by yellow spheres.

When we counted total flip-flop events F𝑡𝑜𝑡𝑎𝑙 from the CAVS CG simulations, we found that only a small portion of cholesterol molecules did involve the flip-flop events. Thus, the flip-flop frequency 𝑓𝑠 per molecule is determined as follows: 𝑓𝑠 =

F𝑡𝑜𝑡𝑎𝑙 𝑡∗𝑁

(4)

where N represents the total number of sterol molecules and 𝑡 is the total simulation time (such as 𝑡 = 60 μs). The final results are collected in Table 4. From this table, one can see that the cholesterol flip-flop motion is more favorable in the ether-DPhPC bilayer than in the ester-DPhPC bilayer, in consistence with the PMF results given in Figure 12. In addition, it is seen in Table 4 that the cholesterol flip-flop motion is also affected by the cholesterol concentration. Meanwhile, we calculated the number of cholesterol molecules on each leaflet as the function of simulation time, given in Figure S6 of Supporting Information. From these figures, we can see the concentration dependence of cholesterol transportation between leaflets. From the CAVS CG simulations, we


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calculated the area per molecule and bilayer thickness at two cholesterol concentrations, given in Table 5. It is shown in Table 5 that increasing the cholesterol concentration would significantly increase the lipid bilayer thickness and decrease the area per lipid, implying the change of phase state of lipid bilayers from a liquid disordered state (Ld) to a liquid ordered state (Ld). Thus, the concentration dependence of cholesterol flipflop motion is actually ascribed to the change in the phase state of lipid bilayers. Table 4. Observed flip-flop frequency (s-1) per molecule calculated from the 60-μs CAVS simulations of ether-DPhPC/CHOL and ester-DPhPC/CHOL bilayers at two different sterol concentrations of 10 mol% and 40 mol%.

Cholesterol Concentration

10 mol% 40 mol%

Observed flip-flop frequency (s-1) ether-DPhPC/CHOL ester-DPhPC/CHOL 5 5 3.34×10 ±0.64×10 1.04×105±0.26×105 1.08×103±0.45×103 3.69×102±0.33×102

Table 5. Physical properties of ether-DPhPC and ester-DPhPC bilayers calculated from the CAVS CG simulations at two cholesterol concentrations (10 mol% CHOL and 40 mol% CHOL).

Bilayer Thickness (nm)

Area per molecule (nm2)

Cholesterol Concentration

ether-DPhPC

ester-DPhPC

ether-DPhPC

ester-DPhPC

10 mol% 40 mol%

3.95±0.05 4.26±0.06

3.92±0.05 4.24±0.06

0.69±0.02 0.52±0.01

0.67±0.02 0.50±0.01

CONCLUSIONS In this study, we used the CAVS CG model to investigate the ether effect on the physical properties of lipid bilayer membranes. First, it is shown that our CAVS model can nicely capture the experimental observation that the ether-linkage substitution for the ester-linkage would result in a slight increase in bilayer thickness and area per lipid. Meanwhile, the CAVS CG simulations revealed that the ether substitution would influence the water penetration into lipid bilayer because of different hydrophilic nature of the linkage group. This is in support of recent all-atom (AA) simulation studies. Second, the CAVS simulations showed that the ether substitution for ester would substantially decrease the dipole potential, in agree with atomistic simulation and



experimental studies. The CAVS and GAFF/TIP5P simulations consistently demonstrated that the ether substitution for ester brings about the change in the orientation of linkage group at the membrane-water interface, leading to the decrease in the dipole potential. Finally, 60-μs CAVS CG simulations of ether-DPhPC and esterDPhPC bilayers were carried out respectively at two cholesterol concentrations. The CAVS CG simulations and PMF calculations consistently demonstrate the concentration dependence of the cholesterol flip-flop, which is ascribed to the change of phase state of lipid bilayers. In addition, the ether substitution for ester would speed up the cholesterol flip-flop motion in lipid bilayer membranes because replacing the ester-linkage with the ether-linkage undermines the interaction between cholesterol and lipid.

AUTHOR INFORMATION Corresponding Author *E-mail: [email protected] Notes The authors declare no competing financial interest.

ACKNOWLEDGEMENTS This work is supported by the National Natural Science Foundation of China (No. 21863002), the Natural Science Foundation of Guizhou Province (No. QKHJC[2016]1109), the construction project for Guizhou Provincial Key Disciplines (No. ZDXK[2015]10), the start-up fund from the Guizhou Education University, The Shanghai Supercomputer Center (SSC) is gratefully acknowledged for providing the computational resources.

Supporting Information Supporting Information contains the CAVS model for water, phospholipids and


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cholesterol, radial distribution functions (RDFs) for the pair of DME particles, fitting to the bond and angle probability distributions, the number of cholesterol (CHOL) molecules in the upper (black) and lower (red) leaflets obtained from CG simulations, and the parameters of the CAVS force field for ether-DPhPC. This information is available free of charge via the Internet at http://pubs.acs.org.

REFERENCES 1. Shinoda, K.; Shinoda, W.; Baba, T.; Mikami, M. Comparative Molecular Dynamics Study of Ether- and Ester-link Phospholid Bilayers. J. Chem. Phys. 2004, 121, 9648-9654. 2. Wang, L.; Bose, P. S.; Sigworth, F. J. Using Cryo-EM to Measure the Dipole Potential of a Lipid Membrane. Proc. Natl. Acad. Sci. USA. 2006, 103, 18528– 18533. 3. 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, 926-935. 4. Mahoney, M. W.; Jorgensen, W. L. A Five-Site Model for Liquid Water and the Reproduction of the Density Anomaly by Rigid, Nonpolarizable Potential Functions. J. Chem. Phys. 2000, 112, 8910-8922. 5. Shen, H.; Wu, Z.; Deng, M.; Wen, S.; Gao, C.; Li, S.; Wu, X. Molecular Dynamics Simulations of Ether- and Ester-linked Phospholipid Bilayers: A Comparative Study of Water Models. J. Phys. Chem. B 2018, 122, 9399-9408. 6. John, K.; Kubelt, J.; Müller, P.; Wüstner, D.; Herrmann, A. Rapid Transbilayer Movement of the Fluorescent Sterol Dehydroergosterol in Lipid Membranes. Biophys. J. 2002, 83, 1525-1534. 7. Bruckner, R. J.; Mansy, S. S.; Ricardo, A.; Mahadevan, L.; Szostak, J. W. Flip-FlopInduced Relaxation of Bending Energy: Implications for Membrane Remodeling. Biophys. J. 2009, 97, 3113-3122. 8. Ma, S.; Li, H.; Tian, K.; Ye, S.; Luo, Y. In Situ and Real-Time SFG Measurements Revealing Organization and Transport of Cholesterol Analogue 6-Ketocholestanol in a Cell Membrane. J. Phys. Chem. Lett. 2014, 5, 419-424. 9. Garg, S.; Porcar, L.; Woodka, A. C.; Butler, P. D.; Perez-Salas, U. Noninvasive Neutron Scattering Measurements Reveal Slower Cholesterol Transport in Model Lipid Membranes. Biophys. J. 2011, 101, 370-377. 10. Choubey, A.; Kalia, R. K.; Malmstadt, N.; Nakano, A.; Vashishta, P. Cholesterol Translocation in a Phospholipid Membrane. Biophys. J. 2013, 104, 2429-2436. 11. Gu, R.; Baoukina, S.; Tieleman, D. P. Cholesterol Flip-Flop in Heterogeneous Membranes. J. Chem. Thoery Comput., 2019, 15, 2064-2070.



12. Bennett, W. F. D.; MacCallum, J. L.; Hinner, M. J.; Marrink, S. J.; Tieleman, D. P. Molecular View of Cholesterol Flip-Flop and Chemical Potential in Different Membrane Environments. J. Am. Chem. Soc. 2009, 131, 12714–12720. 13. Shen, H.; Li, Y.; Ren, P.; Zhang, D.; Li, G. Anisotropic Coarse-Grained Model for Proteins Based On Gay-Berne and Electric Multipole Potentials. J. Chem. Theory Comput. 2014, 10, 731–750. 14. Shen, H.; Li, Y.; Xu, P.; Li, X.; Chu, H.; Zhang, D.; Li, G. An Anisotropic CoarseGrained Model Based on Gay–Berne and Electric Multipole Potentials and its Application to Simulate a DMPC Bilayer in an Implicit Solvent Model. J. Comput. Chem. 2015, 36, 1103-1113. 15. Li, G.; Shen, H.; Zhang, D.; Li, Y.; Wang, H. Coarse-Grained Modeling of Nucleic Acids Using Anisotropic Gay-Berne and Electric Multipole Potentials. J. Chem. Theory Comput. 2016, 12, 676–693. 16. Shen, H.; Xia, Z.; Li, G.; Ren, P. A Review of Physics-Based Coarse-grained Potentials for the Simulations of Protein Structure and Dynamics. Annu. Rep. Comput. Chem. 2012, 8, 129-148. 17. Marrink, S. J.; de Vries, A. H.; Mark, A. E. Coarse Grained Model for Semiquantitative Lipid Simulations. J. Phys. Chem. B 2004, 108, 750-760. 18. 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, 7812-7824. 19. Melo, M. N.; Ingólfsson, H. I.; Marrink, S. J. Parameters for MARTINI Sterols and Hopanoids based on a Virtual-Site Description. J. Chem. Phys. 2015, 143, 243152. 20. Orsi, M.; Essex, J. W. The ELBA Force Field for Coarse-Grained Modeling of Lipid Membranes. PLoS ONE 2011, 6, e28637. 21. Deng, M.; Shen, H. Coarse-Grained Model for Water Involving a Virtual Site. J. Phys. Chem. B 2016, 120, 733-739. 22. Shen, H.; Deng, M.; Zhang, Y. Extension of CAVS Coarse-Grained Model to Phospholipid Membranes: The Importance of Electrostatics. J. Comput. Chem. 2017, 38, 971-980. 23. Shen, H.; Deng, M.; Wu, Z.; Zhang, J.; Zhang, Y.; Gao, C.; Cen, C. Effect of Cholesterol on Membrane Dipole Potential: Atomistic and Coarse-Grained Molecular Dynamics Simulations. J. Chem. Theory Comput. 2018, 14, 3780-3795. 24. Shen, H.; Wu, Z.; Deng, M.; Wen, S.; Gao, C.; Li, S.; Wu, X. Molecular Dynamics Simulations of Ether- and Ester-linked Phospholipid Bilayers: A Comparative Study of Water Models. J. Phys. Chem. B 2018, 122, 9399-9408. 25. 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. 26. Rosso, L.; Gould, I. R. Structure and Dynamics of Phospholipid Bilayers Using Recently Developed General All-Atom Force Fields. J. Comput. Chem. 2008, 29, 24–37. 27. 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


Page 26 of 29

Page 27 of 29 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Phospholipid. Soft Matt. 2012, 8, 9617-9627. 28. Hess, B.; Kutzner, C.; Van Der Spoel, D.; Lindahl, E. GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J. Chem. Theory Comput. 2008, 4, 435–447. 29. Martínez, L.; Andrade, R.; Birgin, E. G.; Martínez, J. M. PACKMOL: A Package for Building Initial Configurations for Molecular Dynamics Simulations. J. Comput. Chem. 2009, 30, 2157-2164. 30. Parrinello, M.; Rahman, A. Polymorphic Transitions in Single Crystals: A New Molecular Dynamics Method, J. Appl. Phys. 1981, 52, 7182–7190. 31. Bussi, G.; Donadio, D.; Parrinello, M. Canonical Sampling through Velocity Rescaling. J. Chem. Phys. 2007, 126, 014101. 32. Hess, B. P-LINCS: A Parallel Linear Constraint Solver for Molecular Simulation, J. Chem. Theory Comput. 2008, 4, 116–122. 33. Darden, T.; York, D.; Pedersen, L. Particle Mesh Ewald: An N⋅ log (N) Methodfor Ewald Sums in Large Systems, J. Chem.Phys. 1993, 98, 10089-10092. 34. Starke-Peterkovic, T.; Turner, N.; Vitha, M. F.; Waller, M. P.; Hibbs, D. E.; Clarke, R. J. Cholesterol Effect on the Dipole Potential of Lipid Membranes. Biophys. J. 2006, 90, 4060-4070. 35. Poyry, S.; Rog, T.; Karttunen, M.; Vattulainen, I. Significance of Cholesterol Methyl Groups. J. Phys. Chem. B 2008, 112, 2922. 36. Klauda, J. B.; Venable, R. M.; Freites, J. A.; O’Connor, J. W.; Tobias, D. J.; Mondragon-Ramirez, C.; Vorobyov, I.; Mackerell Jr, 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, 7830-7843. 37. Reith, D.; Pütz, M.; Müller-Plathe, F. Deriving Effective Mesoscale Potentials from Atomistic Simulations. J. Comput. Chem. 2003, 24, 1624-1636. 38. Villa, A.; Mark, A. E. Calculation of the Free Energy of Solvation for Neutral Analogs of Amino Acid Side Chains. J. Comput. Chem. 2002, 23, 548-553. 39. Kumar, S.; Bouzida, D.; Swendsen, R. H.; Kollman, P. A.; Rosenberg, J. M. The Weighted Histogram Analysis Method for Free-Energy Calculations on Biomolecules. I. The method. J. Comput. Chem. 1992, 13, 1011–1021. 40. Torrie, G. M.; Valleau, J. P. Non-physical Sampling Distributions in Monte-Carlo Free-Energy Estimation-Umbrella Sampling. J. Comp. Phys. 1977, 23, 187-199. 41. Zhang, Z.; Lu, L.; Berkowitz, M. Energetics of Cholesterol Transfer between Lipid Bilayers. J. Phys. Chem. B 2008, 112, 3807-3811. 42. Jo, S.; Rui, H.; Joseph, B. L.; Klauda, J. B.; Im, W. Cholesterol Flip-Flop: Insights from Free Energy Simulation Studies J. Phys. Chem. B 2010, 114, 13342–13348. 43. Parisio, G.; Sperotto, M. M.; Ferrarini, A. Flip-Flop of Steroids in Phospholipid Bilayers: Effects of the Chemical Structure on Transbilayer Diffusion. J. Am. Chem. Soc. 2012, 134, 12198–12208. 44. Allen, W. J.; Lemkul, J. A.; Bevan, D. R. GridMAT-MD: A Grid-based Membrane Analysis Tool for Use With Molecular Dynamics. J. Comput. Chem. 2009, 30, 19521958. 45. Kapla, J.; Stevensson, B.; Dahlberg, M.; Maliniak, A. Molecular Dynamics Simulations of Membranes Composed of Glycolipids and Phospholipids. J. Phys. Chem. B 2008, 116, 244-252.



46. We, Y.; He, K.; Ludtke, S. J.; Huang, H. W. X-Ray Diffraction Study of Lipid Bilayer Membranes Interacting with Amphiphilic Helical Peptides: Diphytanoyl Phosphatidylcholine with Alamethicin at Low Concentrations. Biophys. J. 1995, 68, 2361-2369. 47. Guler, S. D.; Ghosh, D. D.; Pan, J.; Mathai, J. C.; Zeidel, M. L.; Nagle, J. F. Tristram-Nagle, S. Effects of Ether vs. Ester Linkage on Lipid Bilayer Structure and Water Permeability. Chem. Phys. Lipids, 2009, 160, 33-44. 48. Leonard, A. N.; Pastor, R. W.; Klauda, J. B. Parameterization of the CHARMM All-Atom Force Field for Ether Lipids and Model Linear Ethers. J. Phys. Chem. B 2018, 122, 6744-6754. 49. Gawrisch, K.; Ruston, D.; Zimmerberg, J.; Parsegian, V. A.; Rand, R. P.; Fuller, N. Membrane Dipole Potentials, Hydration Forces, and the Ordering of Water at Membrane Surfaces. Biophys. J. 1992, 61, 1213-1223. 50. Peterson, U.; Mannock, D. A.; Lewis, R.; Pohl, P.; McElhaney, R. N.; Pohl, E. E. Origin of Membrane Dipole Potential: Contribution of the Phospholipid Fatty Acid Chains. Chem. Phys. Lipids 2002, 117, 19–27. 51. Clarke, R.J. Effect of Lipid Structure on the Dipole Potential of Phosphatidylcholine Bilayers. Biochim. Biophys. Acta, Biomembr. 1997, 1327, 269-278.


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TOC Graphic


Effects of Ether-Linkage on Membrane Dipole Potential and

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