Glycolipids are important components of biological membranes. High concentrations of glycolipids are particularly found in lipid rafts, which take part in many physiological phenomena. This different partitioning and interaction pattern of glycolipids in the membrane as compared to those of phospholipids are likely due to their different chemical structures: the polar regions of glycosphingolipids can be even larger than for their hydrophobic moieties, giving rise to a rich conformational landscape. Here we study the influence of glycosphingolipids galactosylceramide (GCER) and monosialotetrahexosylganglioside (GM1) on the structural and thermodynamic properties of a phospholipid (DPPC) bilayer. Using the method of coarse-grained molecular dynamics simulation we show that both glycolipids increase the phase-transition temperature of phospholipid membranes and that the extent of this increase depends on the headgroup size and structure. GM1 shows a strong tendency to form mixed clusters with phospholipids, thereby stabilizing the membrane. In contrast, GCER is dispersed in the membrane. By occupying the interstitial space between phospholipids it causes a tighter packing of the lipids in the membrane.

The recognition of carbohydrate receptors on host cell membranes by pathogenic lectins is a crucial step in the microbial invasion. Two bacterial lectins, the B-subunit of Shiga toxin from Shigella dysenteria (StxB) and lectin I from Pseudomonas aeruginosa (LecA), are specific to the same galactolipid–globotriaosylceramide (Gb3). In this study we present a coarse-grained (cg) model of Gb3, which we further apply to unravel the molecular details of glycolipid binding by two lectins on the surface of a DOPC/cholesterol/Gb3 bilayer. In cg molecular dynamics simulations with time scales of dozens of microseconds, Gb3 was randomly distributed. The binding of both StxB or LecA is accompanied by Gb3 clustering in a cholesterol environment and with exclusion of DOPC in protein vicinity. StxB being bound by all 15 binding sites induced membrane bending, while LecA interacted with two out of four binding sites for most of the time causing a smaller inward curvature of the model membrane. Stable interactions occurred preferably when LecA was normal to the membrane surface. Furthermore, all-atom simulations revealed that LecA bound Gb3’s headgroup at only one out of two possible conformations of the carbohydrate moiety observed at protein-free conditions. The results shed light on the mechanism of interactions between two lectins and Gb3 on the membrane surface and offer a coarse-grained model to study more complex systems at large spatiotemporal scales.

The binding of the pentameric capsid protein VP1 of simian virus 40 to its glycosphingolipid receptor GM1 is a key step for the entry of the virus into the host cell. Recent experimental studies have shown that the interaction of variants of soluble VP1 pentamers with giant unilamellar vesicles composed of GM1, DOPC, and cholesterol leads to the formation of tubular membrane invaginations to the inside of the vesicles, mimicking the initial steps of endocytosis. We have used coarse-grained and atomistic molecular dynamics (MD) simulations to study the interaction of VP1 with GM1/DOPC/cholesterol bilayers. In the presence of one VP1 protein, we monitor the formation of small local negative curvature and membrane thinning at the protein binding site as well as reduction of area per lipid. These membrane deformations are also observed under cholesterol-free conditions. However, here, the number of GM1 molecules attached to the VP1 binding pockets increases. The membrane curvature is slightly increased for asymmetric GM1 distribution that mimics conditions in vivo, compared to symmetric GM1 distributions which are often applied in experiments. Slightly smaller inward curvature was observed in atomistic control simulations. Binding of four VP1 proteins leads to an increase of the average intrinsic area per lipid in the protein binding leaflet. Membrane fluctuations appear to be the driving force of VP1 aggregation, as was previously shown for membrane-adhering particles because no VP1 aggregation is observed in the absence of a lipid membrane.

For hospitals, realistic forecasting of bed demand during impending epidemics of infectious diseases is essential to avoid being overwhelmed by a potential sudden increase in the number of admitted patients. Short-term forecasting can aid hospitals in adjusting their planning and freeing up beds in time.We created an easy-to-use online on-request tool based on local data to forecast COVID-19 bed demand for individual hospitals. The tool is flexible and adaptable to different settings, and it is based on a stochastic compartmental model for estimating the epidemic dynamics and coupled with an exponential smoothing model for forecasting.The models are written in R and Julia and implemented as an R-shiny dashboard. The model is parameterized using COVID-19 incidence, vaccination, and bed occupancy data at customizable geographical resolutions, loaded from official online sources or uploaded manually. Users can select their hospital's catchment area and adjust the number of COVID-19 occupied beds at the start of the simulation. The tool provides short-term forecasts of disease incidence and past and forecasted estimation of the epidemic reproductive number at the chosen geographical level. These quantities are then used to estimate the bed occupancy in both general wards and intensive care unit beds. The platform has proven efficient, providing results within seconds while coping with many concurrent users.By providing ad-hoc, local data informed forecasts, this platform allows decisionmakers to evaluate realistic scenarios for allocating scarce resources, such as ICU beds, at various geographic levels.