Journal of Biomedical Science and Engineering

Volume 12, Issue 8 (August 2019)

ISSN Print: 1937-6871   ISSN Online: 1937-688X

Google-based Impact Factor: 0.66  Citations  h5-index & Ranking

Predictors for Predicting Temperature Optimum in Beta-Glucosidases

HTML  XML Download Download as PDF (Size: 1691KB)  PP. 414-426  
DOI: 10.4236/jbise.2019.128033    494 Downloads   1,190 Views  Citations
Author(s)

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.

Share and Cite:

Yan, S. and Wu, G. (2019) Predictors for Predicting Temperature Optimum in Beta-Glucosidases. Journal of Biomedical Science and Engineering, 12, 414-426. doi: 10.4236/jbise.2019.128033.

Cited by

[1] α-淀粉酶 Amy7C 及其突变体催化常数的定量预测
广西科学院学报, 2014

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.