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 [Yu, Y.; et al. Semi-automated Optimization of the CHARMM36 Lipid Force Field to Include Explicit Treatment of Long-Range Dispersion. J. Chem. Theory Comput. 2021, 10.1021/acs.jctc.0c01326. (preceding article in this issue)]. The new FF is denoted C36/LJ-PME. While the first optimization was based on three phosphatidylcholines (PCs), this work 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 T 1 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.

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 work introduces a semi-automated approach to reparametrize the CHARMM lipid FF with consistent inclusion of long-range dispersion through the Lennard-Jones 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.

Accurate lipid force field (FF) parameters used in molecular dynamics (MD) simulations are crucial for understanding the properties of lipid-containing systems and biological processes related to lipids. The last update of the CHARMM36 united atom chain model (C36UA) was in [Lee, S. et al. J. Phys. Chem. B 2014; it utilized CHARMM36 (C36) lipid FF parameters for headgroups and OPLS-UA Lennard-Jones (LJ) parameters for tails. Simulations with the FF were able to reproduce many experimental observables of lipid bilayers accurately, but to be more applicable for a wide range of lipids, additional FF parameter optimization was needed. In this work, we present an update of the model, named C36UAr. The parameterization included the LJ parameters for hydrocarbons and related dihedrals. Bulk liquid properties (density, heat of vaporization, isothermal compressibility, and diffusion constant) of model compounds were used as targets for the LJ parameter fitting, and dihedrals were fit to either quantum mechanical (QM) or potential of mean force (PMF) calculations using C36. Thermodynamic reweighting was used to further improve the parameters. Bilayer simulations of various lipid headgroups (phosphatidylcholine, phosphatidylethanolamine, and phosphatidylglycerol) and tails (saturated, monounsaturated, and polyunsaturated) were performed to validate the model, and significant improvements were seen in bilayer properties, including surface area, membrane thicknesses, NMR deuterium order parameters, and density profiles. C36UAr was also compared to the hydrogen mass repartitioning (HMR) method. The high accuracy and competitive efficiency shown in this study make C36UAr one of the best choices for studies of membrane structure and membrane-associated proteins.