Predictors for Predicting Temperature Optimum in Beta-Glucosidases — Oak Academic Publishing
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Predictors for Predicting Temperature Optimum in Beta-Glucosidases
State Key Laboratory of Non-Food Biomass and Enzyme Technology, Guangxi Key Laboratory of Bio-Refinery, Guangxi Biomass Engineering Technology Research Center, National Engineering Research Center for Non-Food Biorefinery, Guangxi Academy of Sciences, Nanning, China
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State Key Laboratory of Non-Food Biomass and Enzyme Technology, Guangxi Key Laboratory of Bio-Refinery, Guangxi Biomass Engineering Technology Research Center, National Engineering Research Center for Non-Food Biorefinery, Guangxi Academy of Sciences, Nanning, China
1 State Key Laboratory of Non-Food Biomass and Enzyme Technology, Guangxi Key Laboratory of Bio-Refinery, Guangxi Biomass Engineering Technology Research Center, National Engineering Research Center for Non-Food Biorefinery, Guangxi Academy of Sciences, Nanning, China
2 State Key Laboratory of Non-Food Biomass and Enzyme Technology, Guangxi Key Laboratory of Bio-Refinery, Guangxi Biomass Engineering Technology Research Center, National Engineering Research Center for Non-Food Biorefinery, Guangxi Academy of Sciences, Nanning, China
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.
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