Synergistic Effects of Au and FeO_<i>x</i> Nanocomposites in Catalytic NO Reduction with CO

Promotion of Au/TiO₂ by FeO_<i>x</i> addition is shown to result in a very strong enhancement of activity and selectivity in the NO reduction with CO. The nanocomposite Au-FeO_<i>x</i>/TiO₂ catalysts were prepared by deposition–precipitation (Au/TiO₂) and subsequent impregnation (FeO_<i>x</i>). Structural characterization of the nanocomposite catalysts using XRD, TEM, TPR, XPS, and DRIFTS revealed that they consist of titania-supported Au particles of about 4.4–4.8 nm size, which were partially covered with a thin layer of FeO_x,, mainly made up of Fe²⁺ (FeO), in addition to a small fraction of Fe³⁺ (Fe₂O₃). In situ DRIFTS studies showed that, in comparison to Au/TiO₂, on the Au-FeO_<i>x</i>/TiO₂ nanocomposites new adsorption sites were created, which led to enrichment of CO and NO molecules on the Au surface and at its interfaces with FeO_<i>x</i> and TiO₂. At the reaction temperature (250 °C), a new surface NO–CO complex, where both Fe and Au are proposed to be involved in the adsorptive interaction, was discovered. The titania-supported Au-FeO_<i>x</i> nanocomposites considerably facilitated the formation of this complex and contributed to the striking enhancement of the catalytic performance (activity and selectivity) of the Au-based catalyst

Increased susceptibility to apoptosis in late passage HUVECs.

<p>A. Expression of <i>BAX</i> and <i>BCL2</i> mRNA in P4, P8 and P12 cells evaluated by qPCR; B. <i>BAX</i> and <i>BCL2</i> protein content evaluated by Western blots. Data from 3–5 independent experiments are expressed as fold change in relation to P4 values ± SEM. C. Apoptosis in HUVECs in response to 24-hr exposure to TNFα (50 µg/ml) measured using polycaspase staining. Upper panel- Representative images of apoptotic cells (green) in P4 and P12 cells treated with TNFα. Lower panel – Quantitation of apoptotic response as a percentage of total cell number. (*) – p<0.05 compared to P4; (†) – p<0.05 compared to the same passage control.</p

qPCR primers used in this study.

<p>qPCR primers used in this study.</p

Senescence-dependent decline in <i>VCAM-1</i> and <i>ICAM-1</i> expression.

<p>A. Expression of <i>VCAM-1</i> and <i>ICAM-1</i> in P4, P8 and P12 cells evaluated by qPCR; B. Protein content for <i>VCAM-1</i> and <i>ICAM-1</i> evaluated by Western blots. Data from 3 independent experiments are expressed as fold change in relation to P4 values ± SEM. (*) – p<0.05 compared to P4.</p

The expression of <i>LOX-1</i> in senescent endothelial cells.

<p><b>A. Changes in </b><b><i>LOX-1</i></b><b> expression (qPCR, left) and content (right) with senescence.</b> Data are expressed as fold change in relation to P4 values; data in mean ± SEM from 3 independent experiments. (*) – p<0.05 compared to P4. B. Representative images of Dil-ox-LDL uptake by P4 and P12 endothelial cells. C. LOX-1 immunostaining of aortas from 5- and 50-wk old mice (data representative of 3 separate aortas). Note that the LOX-1 signal in endothelial cells in 50-wk old mice aortas is almost undetectable.</p

Senescence-dependent changes in sub-cellular localization of NF-kB p65.

<p>A. Changes in NF-kB p65 and <i>IκBα</i> content in P4 and P12 cells evaluated by Western blots. Data are expressed as fold change in relation to P4 values ± SEM. B. NF-kB p65 immunostaining of P4 and P12 cells. Note that the significant fraction of p65 translocates to the nuclei in P12 cells.</p

Senescence affects morphology and angiogenic potential of endothelial cells.

<p>A. Cell size in late (P12) passage cells is increased by 36% compared to early (P4) passage cells as judged by a number of cells within the field of view in confluent cultures, and the number of nuclei stained with DAPI in 100% confluent cultures is smaller in P12 cells. B. Graph depicts the length of tubes formed by P4, P8 and P12 cells on matrigel in the absence or the presence of 20 ng/ml VEGF. Values are expressed as fold change in relation to unexposed P4 Control. C. Representative Western blots for 4-HNE modified proteins in lysates from P4 and P12 HUVECs. D. Representative Western blots for VE-cadherin and von Willebrand factor, and E. Relative protein content in relation to P4 values; data in mean ± SEM from 3 independent experiments. (*) – p<0.05 compared to P4 cells; (†) – p<0.05 compared to the same passage Control.</p

Predicting Mutation-Induced Stark Shifts in the Active Site of a Protein with a Polarized Force Field

The electric field inside a protein has a significant effect on the protein structure, function, and dynamics. Recent experimental developments have offered a direct approach to measure the electric field by utilizing a nitrile-containing inhibitor as a probe that can deliver a unique vibration to the specific site of interest in the protein. The observed frequency shift of the nitrile stretching vibration exhibits a linear dependence on the electric field at the nitrile site, thus providing a direct measurement of the relative electric field. In the present work, molecular dynamics simulations were carried out to compute the electric field shift in human aldose reductase (hALR2) using a polarized protein-specific charge (PPC) model derived from fragment-based quantum-chemistry calculations in implicit solvent. Calculated changes of electric field in the active site of hALR2 between the wild type and mutants were directly compared with measured vibrational frequency shifts (Stark shifts). Our study demonstrates that the Stark shifts calculated using the PPC model are in much better agreement with the experimental data than widely used nonpolarizable force fields, indicating that the electronic polarization effect is important for the accurate prediction of changes in the electric field inside proteins

How Well Can the M06 Suite of Functionals Describe the Electron Densities of Ne, Ne⁶⁺, and Ne⁸⁺?

The development of better approximations to the exact exchange-correlation functional is essential to the accuracy of density functionals. A recent study suggested that functionals with few parameters provide more accurate electron densities than recently developed many-parameter functionals for light closed-shell atomic systems. In this study, we calculated electron densities, their gradients, and Laplacians of Ne, Ne⁶⁺, and Ne⁸⁺ using 19 electronic structure methods, and we compared them to the CCSD reference results. Two basis sets, namely, aug-cc-pωCV5Z and aug-cc-pV5Z, are utilized in the calculations. We found that the choice of basis set has a significant impact on the errors and rankings of some of the selected methods. The errors of electron densities, their gradients, and Laplacians calculated with the aug-cc-pV5Z basis set are substantially reduced, especially for Minnesota density functionals, as compared to the results using the aug-cc-pωCV5Z basis set (a larger basis set utilized in earlier work (Medvedev et al. <i>Science</i> <b>2017</b>, <i>355</i>, 49–52)). The rankings of the M06 suite of functionals among the 19 methods are greatly improved with the aug-cc-pV5Z basis set. In addition, the performances of the HSE06, BMK, MN12-L, and MN12-SX functionals are also improved with the aug-cc-pV5Z basis set. The M06 suite of functionals is capable of providing accurate electron densities, gradients, and Laplacians using the aug-cc-pV5Z basis set, and thus it is suitable for a wide range of applications in chemistry and physics

Electrostatically Embedded Generalized Molecular Fractionation with Conjugate Caps Method for Full Quantum Mechanical Calculation of Protein Energy

An electrostatically embedded generalized molecular fractionation with conjugate caps (EE-GMFCC) method is developed for efficient linear-scaling quantum mechanical (QM) calculation of protein energy. This approach is based on our previously proposed GMFCC/MM method (He; et al. J. Chem. Phys. 2006, 124, 184703), In this EE-GMFCC scheme, the total energy of protein is calculated by taking a linear combination of the QM energy of the neighboring residues and the two-body QM interaction energy between non-neighboring residues that are spatially in close contact. All the fragment calculations are embedded in a field of point charges representing the remaining protein environment, which is the major improvement over our previous GMFCC/MM approach. Numerical studies are carried out to calculate the total energies of 18 real three-dimensional proteins of up to 1142 atoms using the EE-GMFCC approach at the HF/6-31G* level. The overall mean unsigned error of EE-GMFCC for the 18 proteins is 2.39 kcal/mol with reference to the full system HF/6-31G* energies. The EE-GMFCC approach is also applied for proteins at the levels of the density functional theory (DFT) and second-order many-body perturbation theory (MP2), also showing only a few kcal/mol deviation from the corresponding full system result. The EE-GMFCC method is linear-scaling with a low prefactor, trivially parallel, and can be readily applied to routinely perform structural optimization of proteins and molecular dynamics simulation with high level ab initio electronic structure theories