Skip to main content

Discrimination of Protein Thermostability Based on a New Integrated Neural Network

  • Conference paper
Neural Information Processing (ICONIP 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7062))

Included in the following conference series:

  • 3755 Accesses

Abstract

The research of protein thermostability has been vigorously studied in the field of biophysical and biological technology. What is more, protein thermostability in the level of amino acid sequence is still a challenge in the research of the protein pattern recognition. In this paper, we try to use new integrated feedforward artificial neural network which was optimized by particle swarm optimization (PSO-NN) to recognize the mesophilic and thermophilic proteins. Here, we adopted Genetic Algorithm based Selected Ensemble (GASEN) as our integration methods. A better accuracy was got by GASEN. So, the integrated methods were proved to be effectual.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Xiangyu, W., Shouliang, C., Mingde, G.: General Biology (Version 2). Higher Education Press, Beijing (2005)

    Google Scholar 

  2. Marc Robinson, R., Adam, G.: Structural genomics of Thermotoga maritime proteins shows that contact order is a major determinant of protein thermostability. Structure 6, 857–860 (2005)

    Article  Google Scholar 

  3. Changhui, Y., Vasant, H., Drena, D.: Predicting Protein-Protein Interaction Sites From Amino Acid Sequence. Technical report. Iowa State University (2002)

    Google Scholar 

  4. Zhang, G., Fang, B.: LogitBoost classifier for discriminating thermophilic and mesophilic proteins. Journal of Biotechnology 127(3), 417–424 (2007)

    Article  Google Scholar 

  5. Fredj, Y., Edouard, D., Bernard: Amino acid composition of genomes, lifestyles of organisms, and evolutionary trends: a global picture with correspondence analysis. Gene. 297, 51–60 (2002)

    Article  Google Scholar 

  6. David, J.L., Gregory, A.C., Donal, A.H.: Synonymous codon usage is subject to selection in thermophilic bacteria. Nucl. Acids Res. 30, 4272–4277 (2002)

    Article  Google Scholar 

  7. Zhang, G., Fang, B.: Discrimination of thermophilic and mesophilic proteins via pattern recognition methods. Process Biochemistry 41(3), 552–556 (2006)

    Article  Google Scholar 

  8. Chen, C., Chen, L., Zou, X., et al.: Predicting protein structural class based on multi-features fusion. Journal of Theoretical Biology 253, 388–392 (2008)

    Article  Google Scholar 

  9. Chou, K.C.: Prediction of protein cellular attributes using pseudo-amino-acid-composition. PROTEINS: Struct. Func. Genetics 43, 246–255 (2001)

    Article  Google Scholar 

  10. McCulloch, W.S., Pitts, W.A.: Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 5, 115–133 (1943)

    Article  MathSciNet  MATH  Google Scholar 

  11. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  12. Hansen, I.K., Salamon, P.: Neural network ensembles. IEEE Trans. Pattern Anal. 12(10), 993–1001 (1990)

    Article  Google Scholar 

  13. Zhihua, Z., Jianxin, W., Wei, T.: Ensembling neural networks: Many could be better than all. Artif. Intell. 137, 239–263 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  14. Xu, J., Chen, Y.: Discrimination of thermophilic and mesophilic proteins via artificial neural network. In: Advances in Neural Networks, ISNN 2011 (in press, 2011)

    Google Scholar 

  15. Zhang, G., Fang, B.: Application of amino acid distribution along the sequence for discriminating mesophilic and thermophilic proteins. Process Biochemistry 41(8), 1792–1798 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Xu, J., Chen, Y. (2011). Discrimination of Protein Thermostability Based on a New Integrated Neural Network. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-24955-6_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24954-9

  • Online ISBN: 978-3-642-24955-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics