Study of zinc protein ligands in a halophilic enzyme

Glucose dehydrogenase (EC 1.1.1.47) from the halophilic Archaeon Haloferax mediterranei belongs to the medium-chain alcohol dehydrogenase superfamily and requires a zinc ion for catalysis. The zinc ion is coordinated by a histidine, a water molecule and two other ligands from the protein or the substrate, which vary during the catalytic cycle of the enzyme. In many enzymes of this superfamily one of the zinc ligands is commonly cysteine, which is replaced by an aspartate residue at position 38 in the halophilic enzyme. This change has been only observed in glucose dehydrogenases from extremely halophilic microorganisms belonging to the Archaea Domain. This paper describes biochemical studies and structural comparisons to analyze the role of sequence differences between thermophilic and halophilic glucose dehydrogenases which contain a zinc ion within the protein surrounded by three ligands. Whilst the catalytic activity of the D38C GlcDH mutant is reduced, its thermal stability is enhanced, consistent with the greater structural similarity between this mutant and the homologous thermophilic enzyme from Thermoplasma acidophilum.

Morphological classification of microtidal sand and gravel beaches

A new classification of microtidal sand and gravel beaches with very different morphologies is presented below. In 557 studied transects, 14 variables were used. Among the variables to be emphasized is the depth of the Posidonia oceanica. The classification was performed for 9 types of beaches: Type 1: Sand and gravel beaches, Type 2: Sand and gravel separated beaches, Type 3: Gravel and sand beaches, Type 4: Gravel and sand separated beaches, Type 5: Pure gravel beaches, Type 6: Open sand beaches, Type 7: Supported sand beaches, Type 8: Bisupported sand beaches and Type 9: Enclosed beaches. For the classification, several tools were used: discriminant analysis, neural networks and Support Vector Machines (SVM), the results were then compared. As there is no theory for deciding which is the most convenient neural network architecture to deal with a particular data set, an experimental study was performed with different numbers of neuron in the hidden layer. Finally, an architecture with 30 neurons was chosen. Different kernels were employed for SVM (Linear, Polynomial, Radial basis function and Sigmoid). The results obtained for the discriminant analysis were not as good as those obtained for the other two methods (ANN and SVM) which showed similar success.