Capturing Biologically Complex Tissue-Specific Membranes at Different Levels of Compositional Complexity.

Area of Science:

• Biophysics
• Computational Biology
• Membrane Biophysics

Background:

• Plasma membranes (PMs) possess complex lipid compositions influencing bilayer properties and protein function.
• Inhomogeneous lipid distribution and domain formation within PMs are crucial for protein sorting and localization.
• Current coarse-grained (CG) Martini simulations of complex PMs require extensive computational resources (∼80 μs).

Purpose of the Study:

• To determine the minimum lipid complexity required for accurate representation of "Average" and "Brain" plasma membrane properties.
• To develop computationally tractable CG Martini models that retain key bilayer characteristics.
• To validate reduced-complexity models as reliable mimetics for complex PMs.

Main Methods:

• Construction of CG Martini PM models with varying complexity (63 down to 8 lipid types).
• Maintained consistent lipid tail saturation and headgroup combinations across complexity levels for "Average" and "Brain" PMs.
• Analysis of membrane properties in reduced-complexity models to assess retained features.

Main Results:

• Identified specific eight-component bilayers that effectively mimic the properties of "Average" and "Brain" PMs.
• Demonstrated that reduced lipid complexity can preserve essential membrane characteristics.
• Validated the utility of simplified models for computational membrane studies.

Conclusions:

• Reduced complexity CG Martini models provide a more robust and adaptable approach for simulating PMs.
• These simplified models facilitate computationally feasible all-atom simulations and experimental validations.
• The study offers a pathway to more accessible and efficient computational membrane research.