Hot Spot Analysis by Means of Continuous Wavelet Transform and Time-Frequency Filtering

  • Anna TamulewiczEmail author
  • Ewaryst Tkacz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 925)


In the paper, the analysis of hot spots in proteins with the aid of digital signal processing methods was conducted. The algorithm employs time-frequency filtering and continuous wavelet transform (CWT); its aim is to find amino acid regions where the characteristic frequency is dominant by detecting peaks in energy plot. The research showed that the choice of wavelet function has big impact on the results. The best results were achieved by using CWT with the Morlet wavelet and the sixth order derivative of a Gaussian wavelet.


Hot spot Protein interactions Continuous wavelet transform 


  1. 1.
    Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The protein data bank. Nucleic Acids Res. 28(1), 235–242 (2000)CrossRefGoogle Scholar
  2. 2.
    Clackson, T., Wells, J.A.: A hot spot of binding energy in a hormone-receptor interface. Science 267(5196), 383–386 (1995)CrossRefGoogle Scholar
  3. 3.
    Cosic, I.: Macromolecular bioactivity: is it resonant interaction between macromolecules?–Theory and applications. IEEE Trans. Biomed. Eng. 41(12), 1101–1114 (1994)CrossRefGoogle Scholar
  4. 4.
    Cosic, I.: Analysis of HIV proteins using DSP techniques. In: 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2886–2889. IEEE (2001)Google Scholar
  5. 5.
    European Bioinformatics Institute (EMBL-EBI), Swiss Institute of Bioinformatics (SIB), Protein Information Resource (PIR). The Universal Protein Resource (UniProt).
  6. 6.
    Komorowski, D., Pietraszek, S.: The use of continuous wavelet transform based on the fast fourier transform in the analysis of multi-channel electrogastrography recordings. J. Med. Syst. 40(1), 10 (2015)CrossRefGoogle Scholar
  7. 7.
    Lazovic, J.: Selection of amino acid parameters for Fourier transform-based analysis of proteins. Comput. Appl. Biosci. 12(6), 553–562 (1996)Google Scholar
  8. 8.
    Pirogova, E., Fang, Q., Akay, M., Cosic, I.: Investigation of the structural and functional relationships of oncogene proteins. Proc. IEEE 90(12), 1859–1867 (2002)CrossRefGoogle Scholar
  9. 9.
    Ramachandran, P., Antoniou, A.: Identification of hot-spot locations in proteins using digital filters. IEEE J. Sel. Top. Signal Process. 2(3), 378–389 (2008)CrossRefGoogle Scholar
  10. 10.
    Ramachandran, P., Antoniou, A., Vaidyanathan, P.: Identification and location of hot spots in proteins using the short-time discrete Fourier transform. In: Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, vol. 2, pp. 1656–1660. IEEE (2004)Google Scholar
  11. 11.
    Rao, K.D., Swamy, M.N.S.: Analysis of genomics and proteomics using DSP techniques. IEEE Trans. Circuits Syst. I Regul. Pap. 55–I(1), 370–378 (2008)MathSciNetGoogle Scholar
  12. 12.
    Sahu, S.S., Panda, G.: Efficient localization of hot spots in proteins using a novel S-transform based filtering approach. IEEE/ACM Trans. Comput. Biol. Bioinform. 8(5), 1235–1246 (2011)CrossRefGoogle Scholar
  13. 13.
    Shakya, D.K., Saxena, R., Sharma, S.: Identification of hot spots in proteins using modified Gabor wavelet transform. Pertanika J. Sci. Technol. 22(2), 457–470 (2014)Google Scholar
  14. 14.
    Tamulewicz, A., Tkacz, E.: Human fibroblast growth factor 2 hot spot analysis by means of time-frequency transforms. In: Information Technologies in Medicine. Advances in Intelligent Systems and Computing, vol. 472, pp. 147–159. Springer (2016)Google Scholar
  15. 15.
    The MathWorks, Inc. Continuous wavelet transform and scale-based analysis.
  16. 16.
  17. 17.
    The UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 43(Database issue), D204–D212 (2015)Google Scholar
  18. 18.
    Thorn, K.S., Bogan, A.A.: ASEdb: a database of alanine mutations and their effects on the free energy of binding in protein interactions. Bioinformatics 17(3), 284–285 (2001)CrossRefGoogle Scholar
  19. 19.
    Torrence, C., Compo, G.P.: A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 79(1), 61–78 (1998)CrossRefGoogle Scholar
  20. 20.
    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(5), 337–341 (1985)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Biosensors and Biomedical Signal Processing, Faculty of Biomedical EngineeringSilesian University of TechnologyZabrzePoland

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