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

Plasma membranes (PMs) contain hundreds of different lipid species that contribute differently to overall bilayer properties. By modulation of these properties, membrane protein function can be affected. Furthermore, inhomogeneous lipid mixing and domains of lipid enrichment/depletion can sort proteins and provide optimal local environments. Recent coarse-grained (CG) Martini molecular dynamics efforts have provided glimpses into lipid organization of different PMs: an "Average" and a "Brain" PM. Their high complexity and large size require long simulations (∼80 μs) for proper sampling. Thus, these simulations are computationally taxing. This level of complexity is beyond the possibilities of all-atom simulations, raising the question-what complexity is needed for "realistic" bilayer properties? We constructed CG Martini PM models of varying complexity (63 down to 8 different lipids). Lipid tail saturations and headgroup combinations were kept as consistent as possible for the "tissues'" (Average/Brain) at three levels of compositional complexity. For each system, we analyzed membrane properties to evaluate which features can be retained at lower complexity and validate eight-component bilayers that can act as reliable mimetics for Average or Brain PMs. Systems of reduced complexity deliver a more robust and malleable tool for computational membrane studies and allow for equivalent all-atom simulations and experiments.