Predictors for Predicting Temperature Optimum in Beta-Glucosidases

Authors: Shaomin Yan, Guang Wu

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.

Source:

Journal: Journal of Biomedical Science and Engineering
DOI: 10.4236/jbise.2019.128033(PDF)
Paper Id: 94623 (metadata)

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