Spectral Analysis of Protein Sequences

  • Tuan D. Pham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)


Analysis of protein sequences can avoid many problems inherently existing in the study of nucleotide sequences given the knowledge that DNA sequences contain all the information for regulating protein expression. This paper presents a spectral approach for calculating the similarity of protein sequences, which can be useful for the inferences of protein functions. The proposed method is based on the mathematical concepts of linear predictive coding and cepstral distortion measure. We show that this spectral approach can reveal non-trivial results from an experimental study of a set of functionally related and functionally non-related protein sequences, and has advantages over some existing approaches.


Distortion Measure Linear Predictive Code Resonant Recognition Model Globin Family Globin Protein 
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  1. 1.
    Nagl, S.B.: Function prediction from protein sequence. In: Orengo, C.A., Jones, D.T., Thornton, J.M. (eds.) Bioinformatics: Genes, Proteins & Computers. BIOS Scientific Publishers, Oxford (2003)Google Scholar
  2. 2.
    Bishop, M., Rawlings, C.: Nucleic Acid and Protein Sequence Analysis – A Practical Approach. IRL Press, Oxford (1987)Google Scholar
  3. 3.
    Veljkovic, V., Cosic, I., Dimitrijevic, B., Lalovic, D.: Is it possible to analyze DNA and protein sequences by the methods of digital signal processing? IEEE Trans. Biomed. Eng. 32, 337–341 (1985)CrossRefGoogle Scholar
  4. 4.
    Anatassiou, D.: Frequency-domain analysis of biomolecular sequences. Bioinformatics 16, 1073–1082 (2000)CrossRefGoogle Scholar
  5. 5.
    Lio, P.: Wavelets in bioinformatics and computational biology: state of art and perspectives. Bioinformatics 19, 2–9 (2003)CrossRefGoogle Scholar
  6. 6.
    Li, L., Jin, R., Kok, P.L., Wan, W.: Pseudo-periodic partitions of biological sequences. Bioinformatics 20, 295–306 (2004)MATHCrossRefGoogle Scholar
  7. 7.
    del Carpio-Munoz, C.A., Carbajal, J.C.: Folding pattern recognition in proteins using spectral analysis methods. Genome Informatics 13, 163–172 (2001)Google Scholar
  8. 8.
    Anatassiou, D.: Genomic signal processing. IEEE Signal Processing Magazine 18, 8–20 (2001)CrossRefGoogle Scholar
  9. 9.
    Cosic, I.: Macromolecular bioactivity: Is it resonant interaction between macromolecules? – theory and applications. IEEE trans. Biomedical Engineering 41, 1101–1114 (1994)CrossRefGoogle Scholar
  10. 10.
    Veljkovic, V., Slavic, I.: General model of pseudopotentials. Physical Review Lett. 29, 105–108 (1972)CrossRefGoogle Scholar
  11. 11.
    Lazovic, J.: Selection of amino acid parameters for Fourier transform-based analysis of proteins. CABIOS 12, 553–562 (1996)Google Scholar
  12. 12.
    de Trad, C.H., Fang, Q., Cosic, I.: Protein sequence comparison based on the wavelet transform approach. Protein Engineering 15, 193–203 (2002)CrossRefGoogle Scholar
  13. 13.
    Pirogova, E., Simon, G.P., Cosic, I.: Investigation of the applicability of dielectic relaxation properties of amino acid solutions within the resonant recognition model. IEEE Trans. Nanobioscience 2, 63–69 (2003)CrossRefGoogle Scholar
  14. 14.
    Rabiner, L., Juang, B.H.: Fundamentals of Speech Recognition. Prentice-Hall, New Jersey (1993)Google Scholar
  15. 15.
    Nocerino, N., Soong, F.K., Rabiner, L.R., Klatt, D.H.: Comparative study of several distortion measures for speech recognition. In: IEEE Proc. Int. Conf. Acoustics, Speech, and Signal Processing, vol. 11.4.1, pp. 387–390 (1985)Google Scholar
  16. 16.
    O’Shaughnessy, D.: Speech Communication – Human and Machine. Addison-Wesley, Reading (1987)Google Scholar
  17. 17.
    Ingle, V.K., Proakis, J.G.: Digital Signal Processing Using Matlab V.4. PWS Publishing, Boston (1997)Google Scholar
  18. 18.
    Lehninger, A.L., Nelson, D.L., Cox, M.M.: Principles of Biochemistry. Worth Publishing, New York (1993)Google Scholar
  19. 19.
    Epstein, R.J.: Human Molecular Biology: An Introduction to the Molecular Basis of Health and Disease. Cambridge University Press, Cambridge (2003)Google Scholar
  20. 20.
    Mount, D.W.: Bioinformatics - Sequence and Genome Analysis, 2nd edn. Cold Spring Harbor Laboratory Press, New York (2004)Google Scholar
  21. 21.
    Doolittle, R.: Similar amino acid sequences: Chance or common ancestry? Science 214, 149–159 (1981)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tuan D. Pham
    • 1
  1. 1.Bioinformatics Applications Research Centre, School of Information TechnologyJames Cook UniversityTownsvilleAustralia

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