Mechanistic insight into the activity of tyrosinase from variable-temperature studies in an aqueous/organic solvent

The activity of mushroom tyrosinase towards a representative series of phenolic and diphenolic substrates structurally related to tyrosine has been investigated in a mixed solvent of 34.4% methanol-glycerol (7:1, v/v) and 65.6% (v/v) aqueous 50 mM Hepes buffer at pH 6.8 at various temperatures. The kinetic activation parameters controlling the enzymatic reactions and the thermodynamic parameters associated with the process of substrate binding to the enzyme active species have been deduced from the temperature variation of the kcat and KM parameters. The activation free energy is dominated by the enthalpic term, the value of which lies in the relatively narrow range of 61+/-9 kJ mol(-1) irrespective of substrate or reaction type (monophenolase or diphenolase). The activation entropies are small and generally negative and contribute no more than 10% to the activation free energy. The substrate binding parameters are characterized by large and negative enthalpy and entropy contributions, which are typically dictated by polar protein-substrate interactions. The substrate 4-hydroxyphenylpropionic acid exhibits a strikingly anomalous temperature dependence of the enzymatic oxidation rate, with deltaH(double dagger) approximately = 150 kJ mol(-1) and deltaS(double dagger) approximately = 280 J K(-1) mol(-1), due to the fact that it can competitively bind to the enzyme through the phenol group, like the other substrates, or the carboxylate group, like carboxylic acid inhibitors. A kinetic model that takes into account the dual substrate/inhibitor nature of this compound enables rationalization of this anomalous behavior.     

Electroanalytical Determination of Some Phenolic Acids by High‐Performance Liquid Chromatography at Gold Electrodes

The electrochemical and amperometric behavior of a gold electrode was investigated towards the oxidation of several common phenolic acids in neutral phosphate solutions. Au electrodes show an appreciable stability and reproducibility of the amperometric signals by using a constant applied potential of 1.0 V vs. Ag/AgCl. Separations of selected phenolic acids using a reverse phase C₁₈ analytical column with a mobile phase containing 10 mM NaH₂PO₄ plus 10 mM Na₂HPO₄ (pH 7) and methanol as organic modifier, are achieved isocratically in less than 30 min. The detection limits at the level of nmol/L and linear ranges of four‐five orders of magnitude are generally achieved. The proposed chromatographic strategy coupled with the electrochemical detection at the Au electrode was successful tested for the quantitative determination of phenolic acids in beer, red wine and brandy with good sensitivity and recovery.     

Bacterial production of PHA from lactose and cheese whey permeate

Due to the large availability of agro-industry wastes containing potentially exploitable substrates, such as whey from dairy industry, a study on the bacterial conversion of lactose and whey permeate to poly(β-hydroxyalkanoate) (PHA) was undertaken. A first approach was carried out on culture collection strains. Among a number of strains tested, Hydrogenophaga pseudoflava DSM 1034 and Sinorhizobium meliloti 41 were found to grow on lactose and produce PHA. These findings suggested to investigate among a wider range of microorganisms by directly isolating new strains from soil. A number of soil bacteria were first isolated on a minimal medium containing lactose as unique carbon source and PHA-accumulating traits were then investigated. Three isolates, identified by 16S rDNA sequence analysis as Sinorhizobium sp., Bacillus megaterium and Bacillus sp., were selected for their efficient growth and PHA production using lactose as carbon source. The same strains were also tested for their ability to accumulate PHA by direct fermentation of whey and whey permeate. Our results suggest that production of the polymer from cheese whey or whey permeate may be possible, although further research is needed to determine whether these microorganisms have the potential for commercial production of such biodegradable polymers.     

Synthesis and crystal structure of [(PhenH)(PhenH2)][BiCl6]·2H2O with different o-phenanthroline protonations

The reaction between bismuthate oxide and phen (1,10-phenanthroline) in acid medium led to the isolation of the unusual [(PhenH)(PhenH₂)][BiCl₆]·2H₂O derivative, which has been characterized by X-ray analysis and IR spectroscopy. The compound crystallizes in the triclinic space group with a = 8.313(2), b = 9.349(2), c = 9.807(3) Å, = 86.39(3), = 110.27(3) and = 106.48(3)°. The crystal structure is made of [BiCl₆]^3– anions and [(PhenH)(PhenH₂)]³⁺ cations. A network of hydrogen bond interactions involving the two clathrated water molecules, the phenanthroline moiety and the chlorines characterizes the entire structure.     

Optimizing random scan Gibbs samplers

The Gibbs sampler is a popular Markov chain Monte Carlo routine for generating random variates from distributions otherwise difficult to sample. A number of implementations are available for running a Gibbs sampler varying in the order through which the full conditional distributions used by the Gibbs sampler are cycled or visited. A common, and in fact the original, implementation is the random scan strategy, whereby the full conditional distributions are updated in a randomly selected order each iteration. In this paper, we introduce a random scan Gibbs sampler which adaptively updates the selection probabilities or “learns” from all previous random variates generated during the Gibbs sampling. In the process, we outline a number of variations on the random scan Gibbs sampler which allows the practitioner many choices for setting the selection probabilities and prove convergence of the induced (Markov) chain to the stationary distribution of interest. Though we emphasize flexibility in user choice and specification of these random scan algorithms, we present a minimax random scan which determines the selection probabilities through decision theoretic considerations on the precision of estimators of interest. We illustrate and apply the results presented by using the adaptive random scan Gibbs sampler developed to sample from multivariate Gaussian target distributions, to automate samplers for posterior simulation under Dirichlet process mixture models, and to fit mixtures of distributions.     

Algorithms for improving efficiency of discrete Markov chains

Sampling from an intractable probability distribution is a common and important problem in scientific computing. A popular approach to solve this problem is to construct a Markov chain which converges to the desired probability distribution, and run this Markov chain to obtain an approximate sample. In this paper, we provide two methods to improve the performance of a given discrete reversible Markov chain. These methods require the knowledge of the stationary distribution only up to a normalizing constant. Each of these methods produces a reversible Markov chain which has the same stationary distribution as the original chain, and dominates the original chain in the ordering introduced by Peskun [11]. We illustrate these methods on two Markov chains, one connected to hidden Markov models and one connected to card shuffling. We also prove a result which shows that the Metropolis-Hastings algorithm preserves the Peskun ordering for Markov transition matrices.