Structural probing of D-fructose derived ligands for asymmetric addition of diethylzinc to aldehydes

A series of new chiral ligands derived from D-fructose have been synthesized and applied in the enantioselective addition of diethylzinc to aldehydes. Comparison of the enantioselectivities obtained with these ligands demonstrated that the catalytic properties are highly dependent upon the structure of ligands, a rational explanation of the structural effects on the catalytic properties is provided. (C) 2003 Elsevier Science Ltd. All rights reserved.

Stable Isotope Probing:Linking Functional Activity to Specific Members of Microbial Communities

Linking organisms or groups of organisms to specific functions within natural environments is a fundamental challenge in microbial ecology. Advances in technology for manipulating and analyzing nucleic acids have made it possible to characterize the members of microbial communities without the intervention of laboratory culturing. Results from such studies have shown that the vast majority of soil organisms have never been cultured, highlighting the risks of culture-based approaches in community analysis. The development of culture-independent techniques for following the flow of substrates through microbial communities therefore represents an important advance. These techniques, collectively known as stable isotope probing (SIP), involve introducing a stable isotope-labeled substrate into a microbial community and following the fate of the substrate by extracting diagnostic molecular species such as fatty acids and nucleic acids from the community and determining which specific molecules have incorporated the isotope. The molecules in which the isotope label appears provide identifying information about the organism that incorporated the substrate. Stable isotope probing allows direct observations of substrate assimilation in minimally disturbed communities, and thus represents an exciting new tool for linking microbial identity and function. The use of lipids or nucleic acids as the diagnostic molecule brings different strengths and weaknesses to the experimental approach, and necessitates the use of significantly different instrumentation and analytical techniques. This short review provides an overview of the lipid and nucleic acid approaches, discusses their strengths and weaknesses, gives examples of applications in various settings, and looks at prospects for the future of SIP technology.

Probing Charge Transport of Ruthenium-Complex-Based Molecular Wires at the Single-Molecule Level

A ruthenium(II) bis(sigma-arylacetylide)-complex-based molecular wire functionalized with thiolacetyl alligator clips at both ends (OPERu) was used to fabricate gold substrate-molecular wire-conductive tip junctions. To elucidate the ruthenium-complex-enhanced charge transport, we conducted a single-molecule level investigation using the technique-combination method, where electronic delay constant, single-molecular conductance, and barrier height were obtained by scanning tunneling microscopy (STM) apparent height measurements, STM break junction measurements, and conductive probe-atomic force microscopy (CP-AFM) measurements, respectively.

Probing structural changes of spin-coated polystyrene film after swelling and precipitation by synchrotron GIUSAXS and AFM

Polystyrenc film of about 50 nm in thickness on silicon wafer was obtained by spin-coating in tetrahydrofuran solution.The film exhibits a rough surface as shown by atomic force microscopy images and ellipsometry data.

Probing the kinetics of single molecule protein folding

We propose an approach to integrate the theory, simulations, and experiments in protein-folding kinetics. This is realized by measuring the mean and high-order moments of the first-passage time and its associated distribution. The full kinetics is revealed in the current theoretical framework through these measurements. In the experiments, information about the statistical properties of first-passage times can be obtained from the kinetic folding trajectories of single molecule experiments ( for example, fluorescence). Theoretical/simulation and experimental approaches can be directly related. We study in particular the temperature-varying kinetics to probe the underlying structure of the folding energy landscape. At high temperatures, exponential kinetics is observed; there are multiple parallel kinetic paths leading to the native state. At intermediate temperatures, nonexponential kinetics appears, revealing the nature of the distribution of local traps on the landscape and, as a result, discrete kinetic paths emerge. At very low temperatures, exponential kinetics is again observed; the dynamics on the underlying landscape is dominated by a single barrier.

Probing the micellization of diblock and triblock copolymers of poly(L-lactide) and poly(ethylene glycol) in aqueous and NaCl salt solutions

The micelle formation of a series of amphiphilic block copolymers in aqueous and NaCl solutions was studied by a fluorescent probe technique using pyrene as a 'model drug'. These copolymers were synthesized from poly (ethylene glycol) (PEG) and L-lactide by a new calcium ammoniate catalyst. They had fixed PEG block lengths (44, 104 or 113 ethylene oxide units) and various poly(L-lactide) (PLLA) block lengths (15-280 lactide units). The critical micelle concentration (cmc) was found to decrease with increasing PLLA content. The distinct dissimilarity of the cmc values of diblock and triblock copolymers based on the same block length of PEG provided evidence for the different configurations of their micelles. It was also observed that the introduction of NaCl salt significantly contributed to a decrease in the cmcs of the copolymers with short PEG and PLLA blocks, while it had less influence on the cmcs of copolymers with long PEG or PLLA blocks. The dependence of partition coefficients ranging from 0.2x10(5) to 1.9x10(5) on the PLLA content in the copolymer and on the micelle configuration was also discussed.

Probing UPD-induced surface atomic rearrangement of polycrystalline gold nanofilms with surface plasmon resonance spectroscopy and cyclic voltammetry

Silver underpotential deposition (UPD)-induced surface atomic rearrangement of polycrystalline gold nanofilms was probed with use of surface plasmon resonance spectroscopy (SPRs) as a novel probe tool in combination with cyclic voltammetry. Interestingly, upon repetitive electrochemical UPD and stripping of Ag, the surface structure of the resulting bare Au film is rearranged due to strong adatom-substrate interactions, which causes a large angle shift of SPR R-theta curves, in a good linear relationship with the number of UPDs, to a lower SPR angle. The n, K values of the surfacial Au monolayers before and after the repetitive Ag UPD and stripping for 27 times are found to be 0.133, 3.60 and 0.565, 9.39, respectively, corresponding to the huge shift of 1.61degrees to the left of the SPR minima. Cyclic voltammetry experiments in 0.10 M H2SO4 are carried out before and after the UPD treatment to examine the quality of the whole electrode surface and confirmed this change. To correlate the angle change in SPRs with the profile change in the cyclic voltammogram, the UPD treatment was also performed on a Au(111) textured thin film. It was therefore confirmed that the resonance position of the SPR spectrum is very sensitive to the surface crystallographic orientation of the bare Au substrates. Some surface atomic rearrangement can cause a pronounced SPR angle shift.

Probing lanthanide ions binding on tRNA(Phe) by H-1 NMR

The structure of phenylalanine transfer ribonucleic acid (tRNA(Phe)) in solution was explored by H-1 NMR spectroscopy to evaluate the effect of lanthanide ion on the structural and conformational change. It was found that La3+ ions possess specific effects on the imino proton region of the H-1 NMR spectra for yeast tRNA(Phe). The dependence of the imino proton spectra of yeast tRNA(Phe) as a function of La3+ concentration was examined, and the results suggest that the tertiary base pair G(15). C-48, which is located in the terminal in the augmented dihydrouridine helix (D-helix), was markedly affected by La3+ (shifted to downfield by as much as 0.35). Base pair U-8. A(14) in yeast tRNA(Phe), which are stacked on G(15). C-48, was also affected by added La3+ when 1 similar to 2 Mg2+ were also present. Another imino proton that may be affected by La3+ in yeast tRNA(Phe) is that of the tertiary base pair G(19). C-56. The assignment of this resonance in yeast tRNA(Phe) is tentative since it is located in the region of highly overlapping resonances beween 12.6 and 12.2. This base pair helps to anchor the D-loop to the T Psi C loop. The binding of La3+ caused conformational change of tRNA, which is responsible for shifts to upfield or downfield in H-1 NMR spectra.

On the Dirichlet Prior and Bayesian Regularization

A common objective in learning a model from data is to recover its network structure, while the model parameters are of minor interest. For example, we may wish to recover regulatory networks from high-throughput data sources. In this paper we examine how Bayesian regularization using a Dirichlet prior over the model parameters affects the learned model structure in a domain with discrete variables. Surprisingly, a weak prior in the sense of smaller equivalent sample size leads to a strong regularization of the model structure (sparse graph) given a sufficiently large data set. In particular, the empty graph is obtained in the limit of a vanishing strength of prior belief. This is diametrically opposite to what one may expect in this limit, namely the complete graph from an (unregularized) maximum likelihood estimate. Since the prior affects the parameters as expected, the prior strength balances a "trade-off" between regularizing the parameters or the structure of the model. We demonstrate the benefits of optimizing this trade-off in the sense of predictive accuracy.

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.