The coarse-grained Martini force field is widely used in biomolecular simulations. Here, we present the refined model, Martini 3 (http://cgmartini.nl), with an improved interaction balance, new bead types, and expanded ability to include specific interactions representing, e.g. hydrogen bonding and electronic polarizability. The new model allows more accurate predictions of molecular packing and interactions in general, which is exemplified with a vast and diverse set of applications, ranging from oil/water partitioning and miscibility data to complex molecular systems, involving protein-protein and protein-lipid interactions and material science applications as ionic liquids and aedamers.

Cell membranes contain a large variety of lipid types and are crowded with proteins, endowing them with the plasticity needed to fulfill their key roles in cell functioning. The compositional complexity of cellular membranes gives rise to a heterogeneous lateral organization, which is still poorly understood. Computational models, in particular molecular dynamics simulations and related techniques, have provided important insight into the organizational principles of cell membranes over the past decades. Now, we are witnessing a transition from simulations of simpler membrane models to multicomponent systems, culminating in realistic models of an increasing variety of cell types and organelles. Here, we review the state of the art in the field of realistic membrane simulations and discuss the current limitations and challenges ahead.

Membrane lipids interact with proteins in a variety of ways, ranging from providing a stable membrane environment for proteins to being embedded in to detailed roles in complicated and well-regulated protein functions. Experimental and computational advances are converging in a rapidly expanding research area of lipid–protein interactions. Experimentally, the database of high-resolution membrane protein structures is growing, as are capabilities to identify the complex lipid composition of different membranes, to probe the challenging time and length scales of lipid–protein interactions, and to link lipid–protein interactions to protein function in a variety of proteins. Computationally, more accurate membrane models and more powerful computers now enable a detailed look at lipid–protein interactions and increasing overlap with experimental observations for validation and joint interpretation of simulation and experiment. Here we review papers that use computational approaches to study detailed lipid–protein interactions, together with brief experimental and physiological contexts, aiming at comprehensive coverage of simulation papers in the last five years. Overall, a complex picture of lipid–protein interactions emerges, through a range of mechanisms including modulation of the physical properties of the lipid environment, detailed chemical interactions between lipids and proteins, and key functional roles of very specific lipids binding to well-defined binding sites on proteins. Computationally, despite important limitations, molecular dynamics simulations with current computer power and theoretical models are now in an excellent position to answer detailed questions about lipid–protein interactions.

Cell membranes contain hundreds of different proteins and lipids in an asymmetric arrangement. Our current understanding of the detailed organization of cell membranes remains rather elusive, because of the challenge to study fluctuating nanoscale assemblies of lipids and proteins with the required spatiotemporal resolution. Here, we use molecular dynamics simulations to characterize the lipid environment of 10 different membrane proteins. To provide a realistic lipid environment, the proteins are embedded in a model plasma membrane, where more than 60 lipid species are represented, asymmetrically distributed between the leaflets. The simulations detail how each protein modulates its local lipid environment in a unique way, through enrichment or depletion of specific lipid components, resulting in thickness and curvature gradients. Our results provide a molecular glimpse of the complexity of lipid–protein interactions, with potentially far-reaching implications for our understanding of the overall organization of real cell membranes.

PurposeNeutrophil gelatinase-associated lipocalin (NGAL) is a useful marker for acute kidney injury (AKI), particularly when the timing of renal insult is known. However, its performance in an adult critical care setting has not been well described. We performed this study to estimate the diagnostic accuracy of plasma NGAL for early detection of AKI and need for renal replacement therapy (RRT) in an adult intensive care unit (ICU).MethodsWe enrolled 307 consecutive adult patients admitted to a general medical-surgical ICU; 301 were included in the final analysis. Serial blood samples were analyzed for plasma NGAL using a standardized clinical platform. The primary outcome was AKI, defined as an increase in creatinine of at least 50% from baseline or a reduction in urine output to <0.5 ml/kg/h for >6 h.ResultsOf 301 patients, 133 (44%) had AKI during their ICU stay. Plasma NGAL was a good diagnostic marker for AKI development within the next 48 h (area under ROC 0.78, 95% CI 0.65–0.90), and for RRT use (area under ROC 0.82, 95% CI 0.70–0.95). Peak plasma NGAL concentrations increased with worsening AKI severity (R = 0.554, P < 0.001).ConclusionsPlasma NGAL is a useful early marker for AKI in a heterogeneous adult ICU population, in which the timing of renal insult is largely unknown. It allows the diagnosis of AKI up to 48 h prior to a clinical diagnosis based on AKI consensus definitions. Additionally, it predicts need for RRT and correlates with AKI severity. Early identification of high risk patients may allow potentially beneficial therapies to be initiated early in the disease process before irreversible injury occurs.Electronic supplementary materialThe online version of this article (doi:10.1007/s00134-009-1711-1) contains supplementary material, which is available to authorized users.