Cell-Membrane-Mimicking Lipid-Coated Nanoparticles Confer Raman Enhancement to Membrane Proteins and Reveal Membrane-Attached Amyloid-beta Conformation

Identifying the structures of membrane bound proteins is critical to understanding their function in healthy and diseased states. We introduce a surface enhanced Raman spectroscopy technique which can determine the conformation of membrane-bound proteins, at low micromolar concentrations, and also in the presence of a substantial membrane-free fraction. Unlike conventional surface enhanced Raman spectroscopy, our approach does not require immobilization of molecules, as it uses spontaneous binding of proteins to lipid bilayer-encapsulated Ag nanoparticles. We apply this technique to probe membrane-attached oligomers of Amyloid-beta(40) (A beta(40)), whose conformation is keenly sought in the context of Alzheimer's disease. Isotope-shifts in the Raman spectra help us obtain secondary structure information at the level of individual residues. Our results show the presence of a beta-turn, flanked by two beta-sheet regions. We use solid-state NMR data to confirm the presence of the beta-sheets in these regions. In the membrane-attached oligomer, we find a strongly contrasting and near-orthogonal orientation of the backbone H-bonds compared to what is found in the mature, less-toxic A beta fibrils. Significantly, this allows a ``porin'' like beta-barrel structure, providing a structural basis for proposed mechanisms of A beta oligomer toxicity.

Experimental Analysis of Water Vapor Diffusion Through Porous Membranes in a Proton Exchange Membrane Fuel Cell

We report the diffusion characteristics of water vapor through two different porous media, viz., membrane electrode assembly (MEA) and gas diffusion layer (GDL) in a nonoperational fuel cell. Tunable diode laser absorption spectroscopy (TDLAS) was employed for measuring water vapor concentration in the test channel. Effects of the membrane pore size and the inlet humidity on the water vapor transport are quantified through mass flux and diffusion coefficient. Water vapor transport rate is found to be higher for GDL than for MEA. The flexibility and wide range of application of TDLAS in a fuel cell setup is demonstrated through experiments with a stagnant flow field on the dry side.

Differential dynamics of the serotonin(1A) receptor in membrane bilayers of varying cholesterol content revealed by all atom molecular dynamics simulation

The serotonin(1A) receptor belongs to the superfamily of G protein-coupled receptors (GPCRs) and is a potential drug target in neuropsychiatric disorders. The receptor has been shown to require membrane cholesterol for its organization, dynamics and function. Although recent work suggests a close interaction of cholesterol with the receptor, the structural integrity of the serotonin(1A) receptor in the presence of cholesterol has not been explored. In this work, we have carried out all atom molecular dynamics simulations, totaling to 3s, to analyze the effect of cholesterol on the structure and dynamics of the serotonin(1A) receptor. Our results show that the presence of physiologically relevant concentration of membrane cholesterol alters conformational dynamics of the serotonin(1A) receptor and, on an average lowers conformational fluctuations. Our results show that, in general, transmembrane helix VII is most affected by the absence of membrane cholesterol. These results are in overall agreement with experimental data showing enhancement of GPCR stability in the presence of membrane cholesterol. Our results constitute a molecular level understanding of GPCR-cholesterol interaction, and represent an important step in our overall understanding of GPCR function in health and disease.

Numerical prediction of nozzle flow separation: Issue of turbulence modeling

Numerical simulation of separated flows in rocket nozzles is challenging because existing turbulence models are unable to predict it correctly. This paper addresses this issue with the Spalart-Allmaras and Shear Stress Transport (SST) eddy-viscosity models, which predict flow separation with moderate success. Their performances have been compared against experimental data for a conical and two contoured subscale nozzles. It is found that they fail to predict the separation location correctly, exhibiting sensitivity to the nozzle pressure ratio (NPR) and nozzle type. A careful assessment indicated how the model had to be tuned for better, consistent prediction. It is learnt that SST model's failure is caused by limiting of the shear stress inside boundary layer according to Bradshaw's assumption, and by over prediction of jet spreading rate. Accordingly, SST's coefficients were empirically modified to match the experimental wall pressure data. Results confirm that accurate RANS prediction of separation depends on the correct capture of the jet spreading rate, and that it is feasible over a wide range of NPRs by modified values of the diffusion coefficients in the turbulence model. (C) 2015 Elsevier Masson SAS. All rights reserved.