Systematic Coarse-Grained Lipid Force Fields with Semiexplicit Solvation via Virtual Sites

Area of Science:

• Biophysics
• Computational Biology
• Materials Science

Background:

• Lipids are crucial for biophysical functions, but their macroscopic behavior is poorly understood computationally.
• Implicit-solvent coarse-grained (CG) models aim to simulate large biological systems but struggle to replicate solvent-mediated forces driving lipid self-assembly.
• Accurately modeling hydrophilic and hydrophobic interactions is key for understanding lipid aggregation.

Purpose of the Study:

• To develop a novel methodological framework for incorporating semi-explicit solvent effects into CG models.
• To create virtual coarse-grained (VCG) models using a hybrid approach combining multiscale coarse-graining (MS-CG) and relative entropy minimization (REM).
• To validate the VCG models using two distinct lipid species: 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) and 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC).

Main Methods:

• Developed a hybrid VCG modeling framework by integrating MS-CG and REM.
• Represented solvent-lipid interface features using virtual CG particles.
• Applied the VCG models to simulate DOPC and DPPC lipids, comparing MS-CG and REM strengths/weaknesses.

Main Results:

• The VCG models successfully incorporated semi-explicit solvent effects, crucial for lipid behavior.
• Demonstrated the VCG framework's ability to recapitulate lipid self-assembly, morphological diversity, and multiple phases.
• Provided the first direct comparison of MS-CG and REM for complex biomolecules, detailing their respective advantages and limitations.

Conclusions:

• The VCG framework offers a powerful new approach for simulating lipid biophysics at biologically relevant scales.
• VCG models accurately capture essential solvent-mediated forces, enabling realistic simulation of lipid self-assembly and phase behavior.
• This methodology advances computational investigations into macromolecular biophysics and lipid-based biological systems.