Quantum mechanics-guided parameterization enhances molecular dynamics accuracy for flavonoid-membrane biophysical...

Area of Science:

• Computational chemistry and biophysics
• Materials science and engineering

Background:

• Accurate molecular dynamics (MD) simulations are essential for understanding drug-membrane interactions.
• Small molecule parameterization significantly impacts the reliability of MD simulation outcomes.
• Current methods for parameterizing small molecules, like the analogy-based CHARMM General Force Field (CGenFF), may not fully capture their behavior in lipid bilayers.

Purpose of the Study:

• To investigate the influence of refined force-field parameterization on MD simulations of flavonoid-membrane interactions.
• To compare the accuracy of an analogy-based approach (CGenFF) with a quantum mechanics (QM)-based optimization protocol (ffTK) in reproducing experimental biophysical data.
• To assess the impact of parameterization on membrane electrostatics and elastic properties.

Main Methods:

• Employed MD simulations using both CGenFF (ver. 4.6) and QM-guided ffTK parameterization for three flavonoids (baicalein, chrysin, luteolin).
• Used model lipid membranes composed of dioleoylphosphocholine and dipalmitoylphosphocholine.
• Compared simulation results with in vitro experimental data on membrane dipole potential and elastic properties (differential scanning microcalorimetry, lipid bilayer order parameters).

Main Results:

• The ffTK-optimized model showed improved agreement with experimental changes in membrane dipole potential and elastic properties compared to the initial CGenFF parameters.
• QM-guided ffTK refinement provided a more accurate description of baicalein and chrysin behavior than the analogy-based CGenFF approach.
• CGenFF showed reasonable agreement for luteolin, but ffTK offered a more consistent and accurate representation across all tested flavonoids.

Conclusions:

• QM-guided force-field parameterization is crucial for accurately modeling small molecule interactions with lipid membranes, particularly for membrane electrostatics and elasticity.
• Refined parameterization enhances the consistency between MD simulations and in vitro experimental observations.
• This study provides a validated framework for future computational investigations of drug-membrane interactions using improved small molecule parameters.