Prediction of michaelis-menten constant of beta-glucosidases using nitrophenyl-beta-D-glucopyranoside as substrate

In this study,we attempted to use the neural network to model a quantitative structure-k m (michaelis-menten constant) relationship for beta-glucosidase,which is an important enzyme to cut the beta-bond linkage in glucose while k m 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 k m in beta-glucosidases based on their amino-acid features. © 2011 bentham science publishers.