2-Keto-D-gluconate-yielding membrane-bound D-glucose dehydrogenase from Arthrobacter globiformis C224: purification and characterization.

Glucose dehydrogenase (GlcDH) is the rate-limiting catalyst for microbial conversion of glucose to the important organic acid 2-ketogluconic acid (2KGlcA). In this study, a D-glucose dehydrogenase was purified from the industrial 2KGlcA producer Arthrobacter globiformis C224. After four purification steps, the GlcDH was successfully purified over 180 folds and specific activity of 88.1 U/mg. A single protein band of 87 kDa was detected by SDS-PAGE. The purified GlcDH had the broad substrate specificity with the Km values for D-glucose, D-xylose, D-galactose and maltose of 0.21 mM, 0.34 mM, 0.46 mM and 0.59 mM, respectively. The kinetic studies proved that A. globiformis GlcDH followed the ping-pong kinetic mechanism. The GlcDH showed an optimum catalytic activity at pH 5.0 and 45 °C with the stable activity at temperature of 20-40 °C and pH of 6.0-7.0. Organic solvents, metal ions or EDTA could significantly influence the GlcDH activity to different degrees.

In silico panning for a non-competitive peptide inhibitor.

BACKGROUND: Peptide ligands have tremendous therapeutic potential as efficacious drugs. Currently, more than 40 peptides are available in the market for a drug. However, since costly and time-consuming synthesis procedures represent a problem for high-throughput screening, novel procedures to reduce the time and labor involved in screening peptide ligands are required. We propose the novel approach of 'in silico panning' which consists of a two-stage screening, involving affinity selection by docking simulation and evolution of the peptide ligand using genetic algorithms (GAs). In silico panning was successfully applied to the selection of peptide inhibitor for water-soluble quinoprotein glucose dehydrogenase (PQQGDH). RESULTS: The evolution of peptide ligands for a target enzyme was achieved by combining a docking simulation with evolution of the peptide ligand using genetic algorithms (GAs), which mimic Darwinian evolution. Designation of the target area as next to the substrate-binding site of the enzyme in the docking simulation enabled the selection of a non-competitive inhibitor. In all, four rounds of selection were carried out on the computer; the distribution of the docking energy decreased gradually for each generation and improvements in the docking energy were observed over the four rounds of selection. One of the top three selected peptides with the lowest docking energy, 'SERG' showed an inhibitory effect with Ki value of 20 microM. PQQGDH activity, in terms of the Vmax value, was 3-fold lower than that of the wild-type enzyme in the presence of this peptide. The mechanism of the SERG blockage of the enzyme was identified as non-competitive inhibition. We confirmed the specific binding of the peptide, and its equilibrium dissociation constant (KD) value was calculated as 60 microM by surface plasmon resonance (SPR) analysis. CONCLUSION: We demonstrate an effective methodology of in silico panning for the selection of a non-competitive peptide inhibitor from small virtual peptide library. This study is the first to demonstrate the usefulness of in silico evolution using experimental data. Our study highlights the usefulness of this strategy for structure-based screening of enzyme inhibitors.

Two coupled enzymes perform in parallel the 'AND' and 'InhibAND' logic gate operations.

The coupled activation of two enzymes: glucose dehydrogenase (GDH) and horseradish peroxidase (HRP), is used to construct the parallel-operating AND and InhibAND logic gates. The added substrates for the respective enzymes, glucose and H(2)O(2), act as the gate inputs, while the biocatalytically generated NADH and gluconic acid provide the output signals that follow the operations of the gates. The two gates are generated in the same vial, thus allowing the logic operations to take place in parallel, and the simultaneous readout of the functions of the gates.