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.

The blood-brain barrier (BBB) is formed by specialized tight junctions between endothelial cells that line brain capillaries to create a highly selective barrier between the brain and the rest of the body. A major problem to overcome in drug design is the ability of the compound in question to cross the BBB. Neuroactive drugs are required to cross the BBB to function. Conversely, drugs that target other parts of the body ideally should not cross the BBB to avoid possible psychotropic side effects. Thus, the task of predicting the BBB permeability of new compounds is of great importance. Two gold-standard experimental measures of BBB permeability are logBB (the concentration of drug in the brain divided by concentration in the blood) and logPS (permeability surface-area product). Both methods are time-consuming and expensive, and although logPS is considered the more informative measure, it is lower throughput and more resource intensive. With continual increases in computer power and improvements in molecular simulations, in silico methods may provide viable alternatives. Computational predictions of these two parameters for a sample of 12 small molecule compounds were performed. The potential of mean force for each compound through a 1,2-dioleoyl-sn-glycero-3-phosphocholine bilayer is determined by molecular dynamics simulations. This system setup is often used as a simple BBB mimetic. Additionally, one-dimensional position-dependent diffusion coefficients are calculated from the molecular dynamics trajectories. The diffusion coefficient is combined with the free energy landscape to calculate the effective permeability (Peff) for each sample compound. The relative values of these permeabilities are compared to experimentally determined logBB and logPS values. Our computational predictions correlate remarkably well with both logBB (R(2) = 0.94) and logPS (R(2) = 0.90). Thus, we have demonstrated that this approach may have the potential to provide reliable, quantitatively predictive BBB permeability, using a relatively quick, inexpensive method.

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.

Whether lipid rafts are present in the membranes of living cells remains hotly disputed despite their incontrovertible existence in liposomes at 298 K. In attempts to resolve this debate, molecular dynamics (MD) simulations have been extensively used to study lipid phase separation at high resolution. However, computation has been of limited utility in this respect because the experimental distributions of phases in lamellar lipid mixtures are poorly reproduced by simulations. In particular, all-atom (AA) approaches suffer from restrictions on accessible time scales and system sizes whereas the more efficient coarse-grained (CG) force fields remain insufficiently accurate to achieve correspondence with experiment. In this work, we refine the CG Martini parameters for the high- and low-melting temperature (T m) lipids 1,2-dipalmitoyl-sn-glycero-3-phosphatidylcholine (DPPC) and 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine (DOPC). Our approach involves the modification of bonded Martini parameters based on fitting to atomistic simulations conducted with the CHARMM36 lipid force field. The resulting CG parameters reproduce experimental structural and thermodynamic properties of homogeneous lipid membranes while concurrently improving simulation fidelity to experimental phase diagrams of DPPC, DOPC, and cholesterol lipid mixtures. Importantly, the refined parameters provide much better phase accuracy for regions near the critical point that mimic the lipid concentrations under physiological conditions.

Permeation of small molecules across cell membranes is a ubiquitous process in biology and is dependent on the principles of physical chemistry at the molecular level. Here we use atomistic molecular dynamics simulations to calculate the free energy of permeation of a range of small molecules through a model of the outer membrane of Escherichia coli, an archetypical Gram-negative bacterium. The model membrane contains lipopolysaccharide (LPS) molecules in the outer leaflet and phospholipids in the inner leaflet. Our results show that the energetic barriers to permeation through the two leaflets of the membrane are distinctly asymmetric; the LPS headgroups provide a less energetically favorable environment for organic compounds than do phospholipids. In summary, we provide the first reported estimates of the relative free energies associated with the different chemical environments experienced by solutes as they attempt to cross the outer membrane of a Gram-negative bacterium. These results provide key insights for the development of novel antibiotics that target these bacteria.