Prediction of Michaelis-Menten Constant of Beta-Glucosidases Using Nitrophenyl-beta-D-Glucopyranoside as Substrate

Shaomin      Yan; Guang      Wu

doi:10.2174/092986611796378747

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: Beta-glucosidase, Km value, nitrophenyl-beta-D-glucopyranoside, prediction, Michaelis-Menten constant, pH optimum, BRENDA, UniProt, jackknife test, predictor, backpropagation neural network, hydrophobic properties, polarizability index, epochsBeta-glucosidase, Km value, nitrophenyl-beta-D-glucopyranoside, prediction, Michaelis-Menten constant, pH optimum, BRENDA, UniProt, jackknife test, predictor, backpropagation neural network, hydrophobic properties, polarizability index, epochs