Membrane lipid composition varies greatly within submembrane compartments, different organelle membranes, and also between cells of different cell stage, cell and tissue types, and organisms. Environmental factors (such as diet) also influence membrane composition. The membrane lipid composition is tightly regulated by the cell, maintaining a homeostasis that, if disrupted, can impair cell function and lead to disease. This is especially pronounced in the brain, where defects in lipid regulation are linked to various neurological diseases. The tightly regulated diversity raises questions on how complex changes in composition affect overall bilayer properties, dynamics, and lipid organization of cellular membranes. Here, we utilize recent advances in computational power and molecular dynamics force fields to develop and test a realistically complex human brain plasma membrane (PM) lipid model and extend previous work on an idealized, "average" mammalian PM. The PMs showed both striking similarities, despite significantly different lipid composition, and interesting differences. The main differences in composition (higher cholesterol concentration and increased tail unsaturation in brain PM) appear to have opposite, yet complementary, influences on many bilayer properties. Both mixtures exhibit a range of dynamic lipid lateral inhomogeneities ("domains"). The domains can be small and transient or larger and more persistent and can correlate between the leaflets depending on lipid mixture, Brain or Average, as well as on the extent of bilayer undulations.

Membrane permeability is a key property to consider during the drug design process, and particularly vital when dealing with small molecules that have intracellular targets as their efficacy highly depends on their ability to cross the membrane. In this work, we describe the use of umbrella sampling molecular dynamics (MD) computational modeling to comprehensively assess the passive permeability profile of a range of compounds through a lipid bilayer. The model was initially calibrated through in vitro validation studies employing a parallel artificial membrane permeability assay (PAMPA). The model was subsequently evaluated for its quantitative prediction of permeability profiles for a series of custom synthesized and closely related compounds. The results exhibited substantially improved agreement with the PAMPA data, relative to alternative existing methods. Our work introduces a computational model that underwent progressive molding and fine-tuning as a result of its synergistic collaboration with numerous in vitro PAMPA permeability assays. The presented computational model introduces itself as a useful, predictive tool for permeability prediction.

Significance: Here we present an unprecedented multiscale simulation platform that enables modeling, hypothesis generation, and discovery across biologically relevant length and time scales to predict mechanisms that can be tested experimentally. We demonstrate that our predictive simulation-experimental validation loop generates accurate insights into RAS-membrane biology. Evaluating over 100,000 correlated simulations, we show that RAS–lipid interactions are dynamic and evolving, resulting in: 1) a reordering and selection of lipid domains in realistic eight-lipid bilayers, 2) clustering of RAS into multimers correlating with specific lipid fingerprints, 3) changes in the orientation of the RAS G-domain impacting its ability to interact with effectors, and 4) demonstration that RAS–RAS G-domain interfaces are nonspecific in these putative signaling domains.