Skip to main content
SpringerLink
Log in

Prediction of protein subcellular localization based on Hilbert-Huang transform

  • Published:
Wuhan University Journal of Natural Sciences

Abstract

Using Hilbert-Huang transform, subcellular localization for prokaryotic and eukaryotic proteins was predicted and tested with Reinhart and Hubbard’s dataset. The prediction accuracy of prokaryotic and eukaryotic protein sequences concentrated in the dataset all reached 100% by self-consistency, 91.8% for the former and 88% for the latter by the five fold cross-validation test. A significant improvement in prediction quality by incorporating the spectrum parameters with the conventional amino acid composition was observed. One of the crucial merits of this approach is that many existing tools in mathematics and engineering can be easily applied in the predicting process. It is anticipated that digital signal processing may serve as a useful vehicle for many other protein science areas.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Rajesh N, Burkhard R. LOC3D: Annotate sub-cellular localization for protein structures [J]. Nucleic Acids Res, 2003, 31(3): 3337–3340.

    Article  Google Scholar 

  2. Emanuelsson O, Nielsen H, Brunak S, et al. Predicting subcellular localization of proteins based on their N-terminal amino acid sequence [J]. Journal of Molecular Biology, 2000, 300(4): 1005–1016.

    Article  Google Scholar 

  3. Zhou G P, Doctor K. Subcellular location prediction of apoptosis proteins [J]. Proteins: Structure, Function, and Genetics, 2003, 50(1): 44–48.

    Google Scholar 

  4. Matsuda S, Vert J P, Saigo H, et al. A novel representation of protein sequences for prediction of subcellular location using support vector machines [J]. Protein Sci, 2005, 14(11): 2804–2813.

    Article  Google Scholar 

  5. Pierleoni A, Martelli P L, Fariselli P, et al. BaCelLo:A balanced subcellular localization predictor [J]. Bioinformatics, 2006, 22(14): e408–416.

    Article  Google Scholar 

  6. Wootton J C, Federhen S. Statistics of local complexity in amino acid sequences and sequence databases [J]. Comput Chem, 1993, 17(2): 149–163.

    Article  MATH  Google Scholar 

  7. Altschul S F. Evaluating the statistical significance of multiple distinct local alignments in Theoretical and Computational Methods in Genome Research [M]. New York: Plenum, 1997: 1–14.

    Google Scholar 

  8. Chou K C. Review: prediction of protein structural classes and subcellular locations [J]. Curr Protein Pept Sci, 2000, 1(2): 171–208.

    Article  Google Scholar 

  9. Chou K C. Prediction of G-protein-coupled receptor classes [J]. J Proteome Res, 2005, 4(4): 1413–1418.

    Article  Google Scholar 

  10. Feng K Y, Cai Y D, Chou K C. Boosting classifier for predicting protein domain structural class [J]. Biochem Biophys Res Comm, 2005, 334(1): 213–217.

    Article  Google Scholar 

  11. Du Q S, Jiang Z Q, He W Z, et al. Amino acid principal component analysis (AAPCA) and its applications in protein structural class prediction [J]. J Biomol Struct Dynam, 2006, 23(6): 635–640.

    Google Scholar 

  12. Jahandideh S, Abdolmaleki P, Jahandideh M, Asadabadi, EB. Novel twostage hybrid neural discriminant model for predicting proteins structural classes [J]. Biophys Chem, 2007, 128(1): 87–93.

    Article  MathSciNet  Google Scholar 

  13. Chou K C. Prediction of protein cellular attributes using pseudo amino acid composition [J]. Proteins: Struct Funct Genet, 2001, 43(3): 246–255.

    Article  Google Scholar 

  14. Shen H B, Chou K C. Pse AAC: A flexible web-server for generating various kinds of protein pseudo amino acid composition [J]. Anal Biochem, 2008, 373(2): 386–388.

    Article  Google Scholar 

  15. Chen Y L, Li Q Z. Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo amino acid composition [J]. J Theor Bio, 2007, 248(2): 377–381.

    Article  Google Scholar 

  16. Jiang X, Wei R, Zhang T L, et al. Using the concept of Chou’s pseudo amino acid composition to predict apoptosis proteins subcellular location: An approach by approximate entropy [J]. Protein Pept Lett, 2008, 15(4): 392–396.

    Article  Google Scholar 

  17. Lin H, Wang H, Ding H, et al. Prediction of subcellular localization of apoptosis protein using Chou’s pseudo amino acid composition [J]. Acta Biotheor, 2009, 57(3): 321–330.

    Article  Google Scholar 

  18. Kandaswamy K K, Pugalenthi G, Moller S, et al. Prediction of apoptosis protein locations with genetic algorithms and support vector machines through a new mode of pseudo amino acid composition [J]. Protein Pept Lett, 2010, 17(12): 1473–1479.

    Google Scholar 

  19. Liu T, Zheng X, Wang C, et al. Prediction of subcellular location of apoptosis proteins using pseudo amino acid composition: an approach from auto covariance transformation [J]. Protein Pept Lett, 2010, 17(10): 1263–1269.

    Article  Google Scholar 

  20. Cai Y D, Chou K C. Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudo amino acid composition [J]. Biochem Biophys Res Commun, 2003, 305(2): 407–411.

    Article  Google Scholar 

  21. Li F M, Li Q Z. Predicting protein subcellular location using Chou’s pseudo amino acid composition and improved hybrid approach [J]. Protein Pept Lett, 2008, 15(6): 612–616.

    Article  Google Scholar 

  22. Shi J Y, Zhang S W, Pan Q, et al. Using pseudo amino acid composition to predict protein subcellular location: approached with amino acid composition distribution [J]. Amino Acids, 2008, 35(2): 321–327.

    Article  Google Scholar 

  23. Zhang S W, Zhang Y L, Yang H F, et al. Using the concept of Chou’s pseudo amino acid composition to predict protein subcellular localization: an approach by incorporating evolutionary information and von Neumann entropies [J]. Amino Acids, 2008, 34(4): 565–572.

    Article  Google Scholar 

  24. Yu L, Guo Y, Li Y, et al. SecretP: identifying bacterial secreted proteins by fusing new features into Chou’s pseudo-amino acid composition [J]. J Theor Biol, 2010, 267(1): 1–6.

    Article  Google Scholar 

  25. Pan Y X, Zhang Z Z, Guo Z M, et al. Application of pseudo amino acid composition for predicting protein subcellular location: stochastic signal processing approach [J]. J Protein Chem, 2003, 22(4): 395–402.

    Article  Google Scholar 

  26. Liu H, Yang J, Wang M, et al. Using Fourier spectrum analysis and pseudo amino acid composition for prediction of membrane protein types [J]. The Protein J, 2005, 24(6): 385–389.

    Article  Google Scholar 

  27. Jiang X, Wei R, Zhao Y, Zhang T. Using Chou’s pseudo amino acid composition based on approximate entropy and an ensemble of AdaBoost classifiers to predict protein subnuclear location [J]. Amino Acid, 2008, 34(4): 669–675.

    Article  Google Scholar 

  28. Li Z C, Zhou X B, Dai Z, et al. Prediction of protein structural classes by Chou’s pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis [J]. Amino Acids, 2009, 37(2): 415–425.

    Article  Google Scholar 

  29. Qiu J D, Huang J H, Shi S P, et al. Using the concept of Chou’s pseudo amino acid composition to predict enzyme family classes: an approach with support vector machine based on discrete wavelet transform [J]. Protein Pept Lett, 2010, 17(6): 715–722.

    Article  Google Scholar 

  30. Reinhardt A, Hubbard T. Using neural networks for prediction of the subcellular location of proteins [J]. Nucleic Acids Res, 1998, 26(9): 2230–2236.

    Article  Google Scholar 

  31. Huang N E, Zhang S R, Long M L, et al. The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis [J]. Proc Roy Soc London A, 1998, 454: 903–995.

    Article  MATH  Google Scholar 

  32. Vapnik V N. Tatistical Learning Theory [M]. New York: Wiley Interscience, 1998.

    Google Scholar 

  33. Song C H, Shi F. Wavelet transfrom for predicting apoptosis proteins subcellular location[J]. Wuhan University Journal of Natural Sciences, 2010, 15(2): 103–108.

    Article  MATH  Google Scholar 

  34. Chou K C, Elrod D W. Using discriminant function for prediction of subcellular location of prokaryotic proteins [J]. Biochem Biophys Res Commun, 1998, 252(1): 63–68.

    Article  Google Scholar 

  35. Yuan Z. Prediction of protein subcellular localizations using Markov chain models [J]. FEBS Lett, 1999, 451(1): 23–26.

    Article  Google Scholar 

  36. Hua S J, Sun Z R. Support vector machine approach for protein subcellular localization prediction [J]. Bioinformatics, 2001, 17(8): 721–728.

    Article  Google Scholar 

  37. Matsuda S, Vert J P, Saigo H, et al. A novel representation of protein sequences for prediction of subcellular lacotion using support machines [J]. Protein Sci, 2005, 14(1): 2804–2813.

    Article  Google Scholar 

  38. Guo J, Lin Y, Sun Z. A novel method for protein subcellular localization: Combining residue-couple model and SVM [C] //Proceedings of the 3rd Asia-Pacific Bioinformatics Conference, Singapore: Imperial College Press, 2005: 117–129.

    Google Scholar 

  39. Ding C H, Dubchak I. Multi-class protein fold recognition using support vector machines and neural networks [J]. Bioinformatics, 2001, 17(4): 349–358.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Science, Huazhong Agricultural University, Wuhan, 430070, Hubei, China

    Chaohong Song & Feng Shi

Authors
  1. Chaohong Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feng Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feng Shi.

Additional information

Foundation item: Supported by the Fundamental Research Founds for the Central Universities(2010JC003)

Biography: SONG Chaohong, female, Associate professor, Ph. D., research direction: data mining and bioinformatics.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Song, C., Shi, F. Prediction of protein subcellular localization based on Hilbert-Huang transform. Wuhan Univ. J. Nat. Sci. 17, 48–54 (2012). https://doi.org/10.1007/s11859-012-0803-x

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11859-012-0803-x

Key words

CLC number

Search

Navigation