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

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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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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: 2011-10-01

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  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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