Applied Biochemistry and Biotechnology

, Volume 166, Issue 4, pp 997–1007 | Cite as

Exhausted Jackknife Validation Exemplified by Prediction of Temperature Optimum in Enzymatic Reaction of Cellulases

  • Shaomin Yan
  • Guang WuEmail author


This was the continuation of our previous study along the same line with more focus on technical details because the data are usually divided into two datasets, one for model development and the other for model validation during the development of predictive model. The widely used validation method is the delete-1 jackknife validation. However, no systematical studies were conducted to determine whether the jackknife validation with different deletions works better because the number of validations with different deletions increases in a factorial fashion. Therefore it is only small dataset that can be used for such an exhausted study. Cellulase is an enzyme playing an important role in modern industry, and many parameters related to cellulase in enzymatic reactions were poorly documented. With increased interests in cellulases in bio-fuel industry, the prediction of parameters in enzymatic reactions is listed on agenda. In this study, two aims were defined (a) which amino acid property works better to predict the temperature optimum and (b) with which deletion the jackknife validation works. The results showed that the amino acid distribution probability works better in predicting the optimum temperature of catalytic reaction by cellulase, and the delete-4, more precisely one-fifth deletion, jackknife validation works better.


Cellulase Enzyme Jackknife validation Prediction Temperature optimum 



This study was partly supported by Guangxi Science Foundation (07-109-001-3, 0907016, 10-046-06, 11-031-11, 2010GXNSFF013003, and 2010GXNSFA013046). The authors wish to thank the Library of Guangxi Zhuang Autonomous Region for purchasing the book, Biometry.

Supplementary material

12010_2011_9487_MOESM1_ESM.xls (21 kb)
Supplementary data (XLS 21 kb)


  1. 1.
    Yan, S. and Wu, G. (2011) Applied Biochemistry and Biotechnology, 165, 856–869.CrossRefGoogle Scholar
  2. 2.
    Chou, K. C. (2011). Journal of Theoretical Biology, 273, 236–247.CrossRefGoogle Scholar
  3. 3.
    Levitin, A. (2003). Introduction to the design and analysis of algorithms (1st ed.). NJ: Pearson Education.Google Scholar
  4. 4.
    Porter, C. T., Bartlett, G. J., & Thornton, J. M. (2004). Nucleic Acids Research, 32, D129–D133.CrossRefGoogle Scholar
  5. 5.
    Enzyme Structures Database. (2011).
  6. 6.
  7. 7.
    Comprehensive Enzyme Information System BRENDA. (2011).
  8. 8.
    Duan, C. J., & Feng, J. X. (2010). Biotechnology Letters, 32, 1765–1775.CrossRefGoogle Scholar
  9. 9.
    Gonçalves, A. R., Benar, P., Costa, S. M., Ruzene, D. S., Moriya, R. Y., Luz, S. M., et al. (2005). Applied Biochemistry and Biotechnology, 121–124, 821–826.CrossRefGoogle Scholar
  10. 10.
    Hahn-Hägerdal, B., Galbe, M., Gorwa-Grauslund, M. F., Lidén, G., & Zacchi, G. (2006). Trends in Biotechnology, 24, 549–556.CrossRefGoogle Scholar
  11. 11.
    Sticklen, M. (2006). Current Opinion in Biotechnology, 17, 315–319.CrossRefGoogle Scholar
  12. 12.
    Dashtban, M., Schraft, H., & Qin, W. (2009). International Journal of Biological Sciences, 5, 578–595.CrossRefGoogle Scholar
  13. 13.
    Dhepe, P. L., & Fukuoka, A. (2008). ChemSusChem, 1, 969–975.CrossRefGoogle Scholar
  14. 14.
    Carroll, A., & Somerville, C. (2009). Annual Review of Plant Biology, 60, 165–182.CrossRefGoogle Scholar
  15. 15.
    Sánchez, C. (2009). Biotechnology Advances, 27, 185–194.CrossRefGoogle Scholar
  16. 16.
    Kang, H. J., & Ishikawa, K. (2007). Journal of Microbiology and Biotechnology, 17, 1249–1253.Google Scholar
  17. 17.
    Kim, H. W., Takagi, Y., Hagihara, Y., & Ishikawa, K. (2007). Bioscience, Biotechnology, and Biochemistry, 71, 2585–2587.CrossRefGoogle Scholar
  18. 18.
    The UniProt Consortium. (2010). Nucleic Acids Research, 38, D142–D148.CrossRefGoogle Scholar
  19. 19.
    Kawashima, S., Pokarowski, P., Pokarowska, M., Kolinski, A., Katayama, T., & Kanehisa, M. (2008). Nucleic Acids Research, 36, D202–D205.CrossRefGoogle Scholar
  20. 20.
    Yang, X. Y., Shi, X. H., Meng, X., Li, X. L., Lin, K., Qian, Z. L., et al. (2010). Protein and Peptide Letters, 17, 899–908.CrossRefGoogle Scholar
  21. 21.
    Burlingame, A. L., & Carr, S. A. (1996). Mass spectrometry in the biological sciences. Totowa: Humana Press.CrossRefGoogle Scholar
  22. 22.
    Zamyatin, A. A. (1972). Progress in Biophysics and Molecular Biology, 24, 107–123.CrossRefGoogle Scholar
  23. 23.
    Darby, N. J., & Creighton, T. E. (1993). Journal of Molecular Biology, 232, 873–896.CrossRefGoogle Scholar
  24. 24.
    Kyte, J., & Doolittle, R. F. (1982). Journal of Molecular Biology, 157, 105–132.CrossRefGoogle Scholar
  25. 25.
    Trinquier, G., Sanejouand, Y. H., & Hausman, R. E. (1998). Protein Engineering, 11, 153–169.CrossRefGoogle Scholar
  26. 26.
    Cooper, G. M. (2004). The cell: A molecular approach (p. 51). Washington: ASM Press.Google Scholar
  27. 27.
    Dwyer, D. S. (2005). BMC Chemical Biology, 5, 2.CrossRefGoogle Scholar
  28. 28.
    Chou, P. Y., & Fasman, G. D. (1978). Advances in Enzymology and Related Subjects of Biochemistry, 47, 45–148.Google Scholar
  29. 29.
    Wu, G., & Yan, S. (2002). Molecular Biology Today, 3, 55–69.Google Scholar
  30. 30.
    Wu, G., & Yan, S. (2006). Acta Pharmacologica Sinica, 27, 513–526.CrossRefGoogle Scholar
  31. 31.
    Wu, G., & Yan, S. (2006). Protein and Peptide Letters, 13, 377–384.CrossRefGoogle Scholar
  32. 32.
    Yan, S., & Wu, G. (2010). Journal of Guangxi Academy of Sciences, 17, 145–150.Google Scholar
  33. 33.
    Wu, G., & Yan, S. (2008). Lecture notes on computational mutation. New York: Nova.Google Scholar
  34. 34.
    Feller, W. (1968). An introduction to probability theory and its applications, Vol. I (3rd ed.). New York: Wiley.Google Scholar
  35. 35.
    Hagan, M. T., Demuth, H. B., & Beale, M. H. (1996). Neural network design. Boston: PWS Publishing Company.Google Scholar
  36. 36.
    Demuth, H., & Beale, M. (2001). Neural network toolbox for use with MatLab. User’s guide. Version 4.Google Scholar
  37. 37.
    MathWorks Inc. (2001). MatLab—The Language of Technical Computing (version, release 12.1). 1984–2001.Google Scholar
  38. 38.
    Chou, K. C., & Shen, H. B. (2007). Analytical Biochemistry, 370, 1–16.CrossRefGoogle Scholar
  39. 39.
    Chou, K. C., & Shen, H. B. (2010). Natural Science, 2, 1090–1103.CrossRefGoogle Scholar
  40. 40.
    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.Google Scholar
  41. 41.
    Wu, G., Cossettini, P., & Furlanut, M. (1996). Pharmacological Research, 34, 47–57.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.State Key Laboratory of Non-food Biomass Enzyme Technology, National Engineering Research Center for Non-food Biorefinery, Guangxi Key Laboratory of BiorefineryGuangxi Academy of SciencesNanningChina
  2. 2.DreamSciTech ConsultingShenzhenChina

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