Parameterization of the CHARMM All-Atom Force Field for Ether Lipids and Model Linear Ethers

Leonard, Alison N.; Pastor, Richard W.; Klauda, Jeffery B.

doi:10.1021/acs.jpcb.8b02743

Cited by 31 publications

(46 citation statements)

References 49 publications

(114 reference statements)

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“…77 The ether oxygen and bonded carbon of the tail received charges consistent with the recently published linear ether FF, C36e. 78 In this study, it was found that reducing the C3 glycerol charge relative to QM results for linear ethers allows more water to penetrate the bilayer, improving agreement with the overall experimental surface area per lipid (based on F(q) crossing points). Because the glycerol linkage is branched rather than linear, borrowing partial charge from C36 results tuned specifically to the glycerol region of lipids is chemically consistent.…”

Section: Force Field Development and Validationsupporting

confidence: 62%

How Do Ethanolamine Plasmalogens Contribute to Order and Structure of Neurological Membranes?

et al. 2020

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Ethanolamine plasmalogen (EtnPLA) is a conicalshaped ether lipid and an essential component of neurological membranes. Low stability against oxidation limits its study in experiments. The concentration of EtnPLA in the bilayer varies depending on cell type and disease progression. Here we report on mixed bilayers of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) and 1-(1Z-octadecenyl)-2-oleoyl-sn-glycero-3-phosphoethanolamine (C18(Plasm)-18:1PE, PLAPE), an EtnPLA lipid subtype, at mole ratios of 2:1, 1:1, and 1:2. We present X-ray diffuse scattering (XDS) form factors F(q z) from oriented stacks of bilayers, related electron-density profiles, and hydrocarbon chain NMR order parameters. To aid future research on EtnPLA lipids and associated proteins, we have also extended the CHARMM36 all-atom force field to include the PLAPE lipid. The ability of the new force-field parameters to reproduce both X-ray and NMR structural properties of the mixed bilayer is remarkable. Our results indicate a thickening of the bilayer upon incorporation of increasing amounts of PLAPE into mixed bilayers, a reduction of lateral area per molecule, and an increase in lipid tail-ordering. The lateral compressibility modulus (K A) calculated from simulations yielded values for PLAPE similar to POPC.

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Section: Force Field Development and Validationsupporting

confidence: 62%

How Do Ethanolamine Plasmalogens Contribute to Order and Structure of Neurological Membranes?

et al. 2020

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“…1). 3,10,11 However, as also shown in Figure 1, any deviation from the 12 Å cutoff in LJ leads to dramatic change in bilayer surface areas, and, as follows from the hexadecane results, surface tensions of alkane/air interfaces. This inconsistency was recognized in the original publication of C36, 1 but it was not possible to rectify the problem at that time because an efficient method for calculating long-range LJ interactions was not supported in CHARMM 12 and other major simulation programs.…”

Section: Introductionmentioning

confidence: 54%

“…1,2-dipropionyl-sn-glycero-3-phosphocholine C3-PC 3:0 3:0 1,2-dilauroyl-sn-glycero-3-phosphorylcholine DLPC 12:0 12:0 1,2-dimyristoyl-sn-glycero-3-phosphorylcholine DMPC 14:0 14:0 1,2-dipalmitoyl-sn-glycero-3-phosphocholine DPPC 16:0 16:0 1,2-dimyristoyl-sn-glycero-3-phospho-(1'-rac-glycerol) DMPG 14:0 14:0 DPPC is the linkage between the head group and tails, which can be parametrized separately as indicated by the parametrization of C36 lipid FF for ether lipids. 3 The second consideration is what properties should be covered in the training set. As commonly used benchmarks for lipid FF development, surface areas and membrane thicknesses were included.…”

Section: Lipids Covered and Nomenclaturesmentioning

confidence: 99%

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CHARMM36 Lipid Force Field with Explicit Treatment of Long-Range Dispersion: Parametrization and Validation for PE, PG, and Ether Lipids

Yu¹,

Krämer²,

Venable³

et al. 2020

Preprint

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Long-range Lennard-Jones (LJ) interactions have been incorporated into the CHARMM36 (C36) lipid force field (FF) using the LJ particle-mesh Ewald (LJ-PME) method in order to remove the inconsistency of bilayer and monolayer properties arising from the exclusion of long-range dispersion [citation to paper I]. The new FF is denoted C36/LJ-PME. While the first optimization was based on three phosphatidylcholines (PCs), this paper extends the validation and parametrization to more lipids including PC, phosphatidylethanolamine (PE), phosphatidylglycerol (PG) and ether lipids. The agreement with experimental structure data is excellent for PC, PE and ether lipids. C36/LJ-PME also compares favorably with scattering data of PG bilayers but less so with NMR deuterium order parameters of 1,2-dimyristoyl-sn-glycero-3-phospho-(1'-rac-glycerol) (DMPG) at 303.15 K, indicating a need for future optimization regarding PG-specific parameters. Frequency dependence of NMR T1 spin-lattice relaxation times is well described by C36/LJ-PME and the overall agreement with experiment is comparable to C36. Lipid diffusion is slower than C36 due to the added long-range dispersion causing a higher viscosity, although it is still too fast compared to experiment after correction for periodic boundary conditions. When using a 10 Å real-space cutoff, the simulation speed of C36/LJ-PME is roughly equal to C36. While more lipids will be incorporated into the FF in the future, C36/LJ-PME can be readily used for common lipids and extends the capability of the CHARMM FF by supporting monolayers and eliminating the cutoff dependence.<br>

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“…[13][14][15][16][17] The CHARMM36 (C36) set has been especially successful. It covers many important lipid types 14,[18][19][20][21][22][23][24] and is well-validated for various properties such as bilayer areas, compressibilities, spontaneous curvature, and bending constants. 25 However, monolayer surface tensions from C36 substantially underestimate experiment, 14 as expected from the lack of long-range dispersion.…”

Section: Introductionmentioning

confidence: 99%

Semi-Automated Optimization of the CHARMM36 Lipid Force Field to Include Explicit Treatment of Long-Range Dispersion

Yu¹,

Krämer²,

Simmonett³

et al. 2020

Preprint

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The development of the CHARMM lipid force field (FF) can be traced back to the early 1990s with its current version denoted CHARMM36 (C36). The parametrization of C36 utilized high-level quantum mechanical data and free energy calculations of model compounds before parameters were manually adjusted to yield agreement with experimental properties of lipid bilayers. While such manual fine-tuning of FF parameters is based on intuition and trial-and-error, automated methods can identify beneficial modifications of the parameters via their sensitivities and thereby guide the optimization process. This paper introduces a semi-automated approach to reparametrize the CHARMM lipid FF with consistent inclusion of long-range dispersion through the LennardJones particle-mesh Ewald (LJ-PME) approach. The optimization method is based on thermodynamic reweighting with regularization with respect to the C36 set. Two independent optimizations with different topology restrictions are presented. Targets of the optimizations are primarily liquid crystalline phase properties of lipid bilayers and the compression isotherm of monolayers. Pair correlation functions between water and lipid functional groups in aqueous solution are also included to address headgroup hydration. While the physics of the reweighting strategy itself is well understood, applying it to heterogeneous, complex anisotropic systems poses additional challenges. These were overcome through careful selection of target properties and reweighting settings allowing for the successful incorporation of the explicit treatment of long-range dispersion, and we denote the newly optimized lipid force field as C36/LJ-PME. The current implementation of the optimization protocol will facilitate the future development of the CHARMM and related lipid force fields.<br>

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Parameterization of the CHARMM All-Atom Force Field for Ether Lipids and Model Linear Ethers

Cited by 31 publications

References 49 publications

How Do Ethanolamine Plasmalogens Contribute to Order and Structure of Neurological Membranes?

How Do Ethanolamine Plasmalogens Contribute to Order and Structure of Neurological Membranes?

CHARMM36 Lipid Force Field with Explicit Treatment of Long-Range Dispersion: Parametrization and Validation for PE, PG, and Ether Lipids

Semi-Automated Optimization of the CHARMM36 Lipid Force Field to Include Explicit Treatment of Long-Range Dispersion

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