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

Abstract

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.

Validating the CHARMM36m Protein Force Field with LJ-PME Reveals Altered Hydrogen Bonding Dynamics under Elevated Pressures

“…In comparison, the LJ potential makes more significant contribution to the lipid systems, which hastened the necessity of recent optimization of CHARMM36 lipid FF to explicitly include LJ-PME treatment. 34,35 Even though C36m protein FF was developed using cutoff method, it is still feasible and recommended to directly combine it with LJ-PME method in the simulation of protein systems.…”

Physical Characterization of Triolein and Implications for Its Role in Lipid Droplet Biogenesis

“…Recently, a LJ cutoff-free version of C36 (C36/LJ-PME) was released. [45][46] The second TG model was made based on the C36/LJ-PME parameters in the same fashion used in the first model. However, both first and second models suffer from the low surface tension at the water interface (Fig.…”

“…A systematic way to optimize parameters, not only partial charges but also LJ parameters, will be a focus of future work (see, e.g., ref 46 for an example). The partial charges of PL were unchanged.…”

“…Of note, DAG is also not included in the original C36 force field release; nonetheless, the CHARMM-GUI developers (The Im group) created a new topology for DAG based on the existing parameter set. All simulations were run by GROMACS 2018 and 2020 47 with the C36 43 , C36/LJ-PME [45][46] , or C36/LJ-PME-r force field. Simulations using C36 used the 1.2 nm LJ cutoff distance with a force-switching function between 1.0 nm to 1.2 nm, unless otherwise noted.…”

Re-charging your fats: Charmm36 parameters for neutral lipids triacylglycerol and diacylglycerol

“…In short, we initially got a conformationally-consistent set of CM5 atomic charges (Marenich et al 2012) for TOG that, as dictated by CHARMM parameterization philosophy, was determined from a population analysis of quantum wave functions determined by implicitly taking into account water-TOG interactions. Then, these initial set of charges was fine-tuned following a regularized gradient-based iterative procedure similar to that previously outlined (Yu et al 2021a) (see SI for further details on the parameterization protocol). With the new parameters obtained after running two cycles of the above-described iterative procedure (Figure 2A), the IT values for TOG and DOG converged to 31.2 ± 1.4 and 19.1 ± 1.4 mN/m, respectively (Figure 2B).…”