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Schomburg, I., Chang, A., Hofmann, O., Ebeling, C., Ehrentreich, F. and Schomburg, D. (2002) BRENDA: A Resource for Enzyme Data and Metabolic Information. Trends in Biochemical Sciences, 27, 54-56.
https://doi.org/10.1016/S0968-0004(01)02027-8

has been cited by the following article:

• TITLE: Predictors for Predicting Temperature Optimum in Beta-Glucosidases

  AUTHORS: Shaomin Yan, Guang Wu

  KEYWORDS: Beta-Glucosidase, Enzyme, Temperature Optimum, Prediction

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.12 No.8, August 27, 2019

  ABSTRACT: This is the continuation of our studies on beta-glucosidase, which plays an important role in biological processes and recently strong interests focus on their potential role in biofeul production. In order to develop simple methods to predict the optimal working condition for beta-glucosidase, we used a 20-1 feedforward backpropagation neural network to screen possible predictors to predict the temperature optimum of beta-glucosidase from 25 amino-acid properties related to the primary structure of beta-glucosidases. The results show that the normalized polarizability index and amino-acid distribution probability can predict the temperature optimum of beta-glucosidase, which highlights a cost-effective way to predict various enzymatic parameters of beta-glucosidase.