Characterization of an Isozyme of β-Glucosidase from Sweet Almond

Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening

Beta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and the data miner tool available in the STATISTICA package (STATISTICA Automated Neural Networks, SANN). In order to evaluate the models' accuracy and select the best classifiers among automatically generated SANNs, the Matthews correlation coefficient (MCC) was used. The application of the combination of maxHBint3 and SpMax8_Bhs descriptors leads to the highest predicting abilities of SANNs, as evidenced by the averaged test set prediction results (MCC = 0.748) calculated for ten different dataset splits. Additionally, the models were analyzed employing receiver operating characteristics (ROC) and cumulative gain charts. The thirteen final classifiers obtained as a result of the model development procedure were applied for a natural compounds collection available in the BIOFACQUIM database. As a result of this beta-glucosidase inhibitors screening, eight compounds were univocally classified as active by all SANNs.

Galacto configured N-aminoaziridines: a new type of irreversible inhibitor of β-galactosidases

N-Aminoaziridines are potent irreversible inhibitors of galactosidases.

Salt effects on beta-glucosidase: pH-profile narrowing

Salts inhibit the activity of sweet almond beta-glucosidase. For cations (Cl(-) salts) the effectiveness follows the series: Cu(+2), Fe(+2)>Zn(+2)>Li(+)>Ca(+2)>Mg(+2)>Cs(+)>NH(4)(+)>Rb(+)>K(+)>Na(+) and for anions (Na(+) salts) the series is: I(-)>ClO(4)(-)>(-)SCN>Br(-) approximately NO(3)(-)>Cl(-) approximately (-)OAc>F(-) approximately SO(4)(-2). The activity of the enzyme, like that of most glycohydrolases, depends on a deprotonated carboxylate (nucleophile) and a protonated carboxylic acid for optimal activity. The resulting pH-profile of k(cat)/K(m) for the beta-glucosidase-catalyzed hydrolysis of p-nitrophenyl glucoside is characterized by a width at half height that is strongly sensitive to the nature and concentration of the salt. Most of the inhibition is due to a shift in the enzymic pK(a)s and not to an effect on the pH-independent second-order rate constant, (k(cat)/K(m))(lim). For example, as the NaCl concentration is increased from 0.01 M to 1.0 M the apparent pK(a1)increases (from 3.7 to 4.9) and the apparent pK(a2)decreases (from 7.2 to 5.9). With p-nitrophenyl glucoside, the value of the pH-independent (k(cat)/K(m))(lim) (=9 x 10(4) M(-1) s(-1)) is reduced by less than 4% as the NaCl concentration is increased. There is a similar shift in the pK(a)s when the LiCl concentration is increased to 1.0 M. The results of these salt-induced pK(a) shifts rule out a significant contribution of reverse protonation to the catalytic efficiency of the enzyme. At low salt concentration, the fraction of the catalytically active monoprotonated enzyme in the reverse protonated form (i.e., proton on the group with a pK(a) of 3.7 and dissociated from the group with a pK(a) of 7.2) is very small ( approximately 0.03%). At higher salt concentrations, where the two pK(a)s become closer, the fraction of the monoprotonated enzyme in the reverse protonated form increases over 300-fold. However, there is no increase in the intrinsic reactivity, (k(cat)/K(m))(lim), of the monoprotonated species. For other enzymes which may show such salt-induced pK(a) shifts, this provides a convenient test for the role of reverse protonation.