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
Protein & Peptide Letters
Title: Prediction of Michaelis-Menten Constant of Beta-Glucosidases Using Nitrophenyl-beta-D-Glucopyranoside as Substrate
Volume: 18 Issue: 10
Author(s): Shaomin Yan and Guang Wu
Affiliation:
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
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.
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Cite this article as:
Yan Shaomin and Wu Guang, Prediction of Michaelis-Menten Constant of Beta-Glucosidases Using Nitrophenyl-beta-D-Glucopyranoside as Substrate, Protein & Peptide Letters 2011; 18 (10) . https://dx.doi.org/10.2174/092986611796378747
DOI https://dx.doi.org/10.2174/092986611796378747 |
Print ISSN 0929-8665 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5305 |
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