Prediction of optimal pH in hydrolytic reaction of beta-glucosidase

Applied Biochemistry and Biotechnology

Volumn 169, Issue 6, 2013, Pages 1884-1894

  Yan, Shaomin ^a   Wu, Guang ^a,b  

^a National Engineering Research Center for Non food Biorefinery   (China)

^b DreamSciTechcom ^*  (China)

Author keywords

Amino acid feature; Beta glucosidase; Distribution probability; Optimal pH; Prediction

Indexed keywords

ACID DISTRIBUTION; BETA-GLUCOSIDASE; DISTRIBUTION PROBABILITY; ENZYMATIC REACTION; ENZYME SEQUENCE; FEEDFORWARD BACKPROPAGATION NEURAL NETWORKS; HYDROLYTIC REACTIONS; REACTION PARAMETERS;

AMINO ACIDS; ENZYMES; NEURAL NETWORKS; OPTIMIZATION; PROBABILITY DISTRIBUTIONS;

FORECASTING;

AMINO ACID; BETA GLUCOSIDASE; BIOFUEL;

ARTICLE; ENZYME ANALYSIS; HYDROLYSIS; NUCLEOTIDE SEQUENCE; PREDICTION; PROCESS OPTIMIZATION; SEQUENCE ANALYSIS; AMINO ACID SEQUENCE; CHEMISTRY; METABOLISM; PH; STATISTICAL MODEL;

AMINO ACID SEQUENCE; BETA-GLUCOSIDASE; HYDROGEN-ION CONCENTRATION; HYDROLYSIS; LINEAR MODELS; MODELS, STATISTICAL;

EID: 84883479056     PISSN: 02732289     EISSN: 15590291     Source Type: Journal    
DOI: 10.1007/s12010-013-0103-8     Document Type: Article

References (31)

1. Yan, S., & Wu, G. (2011). Applied Biochemistry and Biotechnology, 165, 856-69.
2. Yan, S., & Wu, G. (2012). Applied Biochemistry and Biotechnology, 166, 997-1007.
3. Jeng, W. Y., Wang, N. C., Lin, M. H., Lin, C. T., Liaw, Y. C., Chang, W. J., et al. (2011). Journal of Structural Biology, 173, 46-56.
4. Sticklen, M. (2006). Current Opinion in Biotechnology, 17, 315-319.
5. Alexov, E. (2004). European Journal of Biochemistry, 271, 173-185.
6. Garcia-Moreno, B. (2009). Journal of Biology, 8, 98.
7. Mitra, R. C., Zhang, Z., & Alexov, E. (2011). Proteins, 79, 925-936.
8. Talley, K., & Alexov, E. (2010). Proteins, 78, 2699-2706.
9. Scheer, M., Grote, A., Chang, A., Schomburg, I., Munaretto, C., Rother, M., Söhngen, C., Stelzer, M., Thiele, J. and Schomburg, D. (2011) Nucleic Acids Research, 39 (Database issue), D670-D676
10. Berrin, J. G., Czjzek, M., Kroon, P. A., McLauchlan, W. R., Puigserver, A., Williamson, G., et al. (2003). Biochemical Journal, 373, 41-48.
11. Tsukada, T., Igarashi, K., Fushinobu, S., & Samejima, M. (2008). Biotechnology and Bioengineering, 15, 1295-1302.
12. The UniProt Consortium (2010) Nucleic Acids Research, 38, D142-D148.
13. Burlingame, A. L., & Carr, S. A. (1996). Mass spectrometry in the biological sciences. Totowa: Humana.
14. Darby, N. J., & Creighton, T. E. (1993). Journal of Molecular Biology, 232, 873-896.
15. Zamyatin, A. A. (1972). Progress in Biophysics and Molecular Biology, 24, 107-123.
16. Cooper, G. M. (2004). The cell: a molecular approach (p. 51). Washington: ASM.
17. Kyte, J., & Doolittle, R. F. (1982). Journal of Molecular Biology, 157, 105-132.
18. Trinquier, G., Sanejouand, Y. H., & Hausman, R. E. (1998). Protein Engineering, 11, 153-169.
19. Dwyer, D. S. (2005). BMC Chemical Biology, 5, 2.
20. Chou, P. Y., & Fasman, G. D. (1978). Advance in Enzymology and Related Subjects in Biochemistry, 47, 45-148.
21. Wu, G., & Yan, S. (2006). Protein Peptides Letters, 13, 377-384.
22. Wu, G., & Yan, S. (2006). Acta Pharmacologia Sinica, 27, 513-526.
23. Wu, G., & Yan, S. (2008). Lecture notes on computational mutation. New York: Nova Science.
24. Wu, G., & Yan, S. M. (2002). Molecular Biology Today, 3, 55-69.
25. Yan, S., & Wu, G. (2010). Journal of Guangxi Academy of Sciences, 17, 145-150.
26. Demuth, H. and Beale, M. (2001) Neural network toolbox for use with MatLab. User's guide. version 4.
27. MathWorks (1984-2001) MatLab-the language of technical computing. (version 6.1.0.450, release 12.1).
28. Chou, K. C. (2011). Journal of Theoritical Biology, 273, 236-247.
29. Sokal, R. R., & Rohlf, F. J. (1995). Biometry: the principles and practices of statistics in biological research (3rd ed., pp. 203-218). New York: W. H. Freeman.
30. Chan, P., & Warwicker, J. (2009). BMC Biology, 7, 69.
31. Feller, W. (1968). An introduction to probability theory and its applications (3rd ed., Vol. I). New York: Wiley.