Prediction of Michaelis-Menten Constant of Beta-Glucosidases Using Nitrophenyl-beta-D-Glucopyranoside as Substrate
Abstract:In this study, we attempted to use the neural network to model a quantitative structure-Km (Michaelis-Menten constant) relationship for beta-glucosidase, which is an important enzyme to cut the beta-bond linkage in glucose while Km is a very important parameter in enzymatic reactions. Eight feedforward backpropagation neural networks with different layers and neurons were applied for the development of predictive model, and twenty-five different features of amino acids were chosen as predictors one by one. The results show that the 20-1 feedforward backpropagation neural network can serve as a predictive model while the normalized polarizability index as well as the amino-acid distribution probability can serve as the predictors. This study threw lights on the possibility of predicting the Km in beta-glucosidases based on their amino-acid features.
Keywords: BRENDA; Beta-glucosidase; Km value; Michaelis-Menten constant; UniProt; backpropagation neural network; epochs; hydrophobic properties; jackknife test; nitrophenyl-beta-D-glucopyranoside; pH optimum; polarizability index; prediction; predictor
Document Type: Research Article
Publication date: October 1, 2011
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