Modelling of glucoamylase thermal inactivation in the presence of starch by artificial neural network

Neural Network Modeling of AChE Inhibition by New Carbazole-Bearing Oxazolones

Acetylcholine esterase (AChE) is one of the targeted enzymes in the therapy of important neurodegenerative diseases such as Alzheimer's disease. Many studies on carbazole- and oxazolone-based compounds have been conducted in the last decade due to the importance of these compounds. New carbazole-bearing oxazolones were synthesized from several carbazole aldehydes and p-nitrobenzoyl glycine as AChE inhibitors by the Erlenmeyer reaction in the present study. The inhibitory effects of three carbazole-bearing oxazolone derivatives on AChE were studied in vitro and the experimental results were modeled using artificial neural network (ANN). The developed ANN provided sufficient correlation between several dependent systems, including enzyme inhibition. The inhibition data for AChE were modeled by a two-layered ANN architecture. High correlation coefficients were observed between the experimental and predicted ANN results. Synthesized carbazole-bearing oxazolone derivatives inhibited AChE under in vitro conditions, and further research involving in vivo studies is recommended. An ANN may be a useful alternative modeling approach for enzyme inhibition.

Modeling in vitro inhibition of butyrylcholinesterase using molecular docking, multi-linear regression and artificial neural network approaches

Butyrylcholinesterase (BChE) has been an important protein used for development of anti-cocaine medication. Through computational design, BChE mutants with ∼2000-fold improved catalytic efficiency against cocaine have been discovered in our lab. To study drug-enzyme interaction it is important to build mathematical model to predict molecular inhibitory activity against BChE. This report presents a neural network (NN) QSAR study, compared with multi-linear regression (MLR) and molecular docking, on a set of 93 small molecules that act as inhibitors of BChE by use of the inhibitory activities (pIC₅₀ values) of the molecules as target values. The statistical results for the linear model built from docking generated energy descriptors were: r(2)=0.67, rmsd=0.87, q(2)=0.65 and loormsd=0.90; the statistical results for the ligand-based MLR model were: r(2)=0.89, rmsd=0.51, q(2)=0.85 and loormsd=0.58; the statistical results for the ligand-based NN model were the best: r(2)=0.95, rmsd=0.33, q(2)=0.90 and loormsd=0.48, demonstrating that the NN is powerful in analysis of a set of complicated data. As BChE is also an established drug target to develop new treatment for Alzheimer's disease (AD). The developed QSAR models provide tools for rationalizing identification of potential BChE inhibitors or selection of compounds for synthesis in the discovery of novel effective inhibitors of BChE in the future.

Artificial intelligence based optimization of exocellular glucansucrase production from Leuconostoc dextranicum NRRL B-1146

Two different artificial intelligence techniques namely artificial neural network (ANN) and genetic algorithm (GA) were integrated for optimizing fermentation medium for the production of glucansucrase. The experimental data reported in a previous study were used to build the neural network. The ANN was trained using the back propagation algorithm. The ANN predicted values showed good agreement with the experimentally reported ones from a response surface based experiment. The concentrations of three medium components: viz Tween 80, sucrose and K2HPO4 served as inputs to the neural network model and the enzyme activity as the output of the model. A model was generated with a coefficient of correlation (R2) of 1.0 for the training set and 0.90 for the test data. A genetic algorithm was used to optimize the input space of the neural network model to find the optimum settings for maximum enzyme activity. This artificial neural network supported genetic algorithm predicted a maximum glucansucrase activity of 6.92U/ml at medium composition of 0.54% (v/v) Tween 80, 5.98% (w/v) sucrose and 1.01% (w/v) K2HPO4. ANN-GA predicted model gave a 6.0% increase of enzyme activity over the regression based prediction for optimized enzyme activity. The maximum enzyme activity experimentally obtained using the ANN-GA designed medium was 6.75+/-0.09U/ml which was in good agreement with the predicted value.

Evaluation of man-tailored cellulose-based carriers in glucoamylase immobilization

Covalent immobilization of glucoamylase on the cellulose-based carrier Granocel was optimized by changing the anchor groups and the methods of activation/immobilization. Binding of the enzyme was via its primary amino groups. It was shown that using carbodiimide and divinyl sulfone for the activation of -COOH and -OH groups on the carrier resulted in the preparations with very low activity. A third method, using pentaethylenehexamine with glutaraldehyde, led to the attachment through a long spacer arm and to the preparations with the highest activity. Further optimization of the carrier's structure consisted of changing pore diameters and amount of functional groups on the carrier surface. The highest activity of bound glucoamylase was obtained by linking the protein via glutaraldehyde on NH(2)-Granocel having high pore size and high number of functional groups. The immobilized enzyme was stable throughout extended storage and possessed higher thermal stability.