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Protein Hotspot Prediction Using S-Transform

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 283)

Abstract

Since experimental techniques of protein hotspot prediction are still financially extremely demanding and time consuming there is a strain to produce sufficiently reliable computational techniques for this particular task. We propose an algorithm based on Resonant Recognition Model relying heavily on signal processing techniques. Processed numerical signal is obtain solely form protein sequence using physical quantity EIIP. We therefore use no information of protein structure. The key element here is a time-frequency analysis tool – S-transform. This allows us to determine exact residues responsible for majority of performance on protein’s characteristic frequency. We achieve basic sensitivity of 85 % and PPV 49 %, while demanding very little computing resources, because simplicity is one of the biggest advantages of our approach.

Keywords

Protein hotspots prediction signal processing electron-ion interaction potential resonant recognition model protein sequence S-transform time-frequency analysis 

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References

  1. 1.
    Ito, T., Tashiro, K., Muta, S., Ozawa, R., Chiba, T., Nishizawa, M., Yamamoto, K., Kuhara, S., Sakaki, Y.: Toward a protein-protein interaction map of the budding yeast: A comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins. Proc. Natl. Acad. Sci. USA 97, 1143–1147 (2000)CrossRefGoogle Scholar
  2. 2.
    Schächter, V., Wojcik, J.: Protein-protein interaction map inference using domain profile pairs. Bioinformatics 17, S296–S305 (2001)Google Scholar
  3. 3.
    Davy, A., Bello, P., Thierry-Mieg, N., Vaglio, P., Hitti, J., Doucette-Stamm, L., Thierry-Mieg, D., Reboul, J., Boulton, S., Walhout, A.J., Coux, O., Vidal, M.: A protein-protein interaction map of the Caenorhabditis elegans 26S proteasome. EMBO Rep. 2, 821–828 (2001)CrossRefGoogle Scholar
  4. 4.
    Hobohm, U., Sander, C.: Enlarged representative set of protein structures. Protein Sci. 3, 522–524 (1994)CrossRefGoogle Scholar
  5. 5.
    Ma, B., Elkayam, T., Wolfson, H., Nussinov, R.: Protein-protein interactions: structurally conserved residues distinguish between binding sites and exposed protein surfaces. Proc. Natl. Acad. Sci. USA 100, 5772–5777 (2003)CrossRefGoogle Scholar
  6. 6.
    Clackson, T., Wells, J.A.: A Hot Spot of Binding Energy in Hormone-Receptor Interface. Science 267, 383–386 (1995)CrossRefGoogle Scholar
  7. 7.
    Cosic, I., Hearn, M.T.W.: Protein Active Sites are Defined as Resonant Spots in Protein 3D Structure. Engineering in Medicine and Biology Society, 206–207 (1992)Google Scholar
  8. 8.
    Rajamani, D., Thiel, S., Vajda, S., Camacho, C.J.: Anchor residues in protein-protein interactions. Proc. Natl. Acad. Sci. USA 101, 11287–11292 (2004)CrossRefGoogle Scholar
  9. 9.
    Bogan, A.A., Thorn, K.S.: Anatomy of hot spots in protein interfaces. J. Mol. Biol. 280, 1–9 (1998)CrossRefGoogle Scholar
  10. 10.
    Fernández-Recio, J.: Prediction of protein binding sites and hot spots. Wiley Interdiscip. Rev. Comput. Mol. Sci. 1, 680–698 (2011)CrossRefGoogle Scholar
  11. 11.
    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, 284–285 (2001)CrossRefGoogle Scholar
  12. 12.
    Shulman-Peleg, A., Shatsky, M., Nussinov, R., Wolfson, H.J.: MAPPIS: Multiple 3D alignment of protein-protein interfaces. In: Berthold, M., Glen, R.C., Diederichs, K., Kohlbacher, O., Fischer, I. (eds.) CompLife 2005. LNCS (LNBI), vol. 3695, pp. 91–103. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Tuncbag, N., Keskin, O., Gursoy, A.: HotPoint: hot spot prediction server for protein interfaces. Nucleic Acids Res. 38, W402–W406 (2010)Google Scholar
  14. 14.
    Guney, E., Tuncbag, N., Keskin, O., Gursoy, A.: HotSprint: database of computational hot spots in protein interfaces. Nucleic Acids Res. 36, D662–D666 (2008)Google Scholar
  15. 15.
    Grosdidier, S., Fernández-Recio, J.: Identification of hot-spot residues in protein-protein interactions by computational docking. BMC Bioinformatics 9, 447 (2008)CrossRefGoogle Scholar
  16. 16.
    Ofran, Y., Rost, B.: Protein-protein interaction hotspots carved into sequences. PLoS Comput. Biol. 3, e119 (2007)Google Scholar
  17. 17.
    Lise, S., Buchan, D., Pontil, M., Jones, D.T.: Predictions of hot spot residues at protein-protein interfaces using support vector machines. PLoS One 6, e16774 (2011)Google Scholar
  18. 18.
    Ramachandran, P., Antoniou, A.: Identification of Hot-Spot Locations in Proteins Using Digital Filters. J. Sel. Top. Signal Process. 2, 378–389 (2008)CrossRefGoogle Scholar
  19. 19.
    Ramachandran, P., Antoniou, A., Vaidyanathan, P.P.: Identification and location of hot spots in proteins using the short-time discrete fourier transform. Conf. Rec. Thirty-Eighth Asilomar Conf. Signals, Syst. Comput. 2, 1656–1660 (2004)Google Scholar
  20. 20.
    Veljković, V., Ćosić, I., Dimitrijević, B., Lalović, D.: Is It Possible to Analyze DNA and Protein Sequences by the Methods of Digital Signal Processing? Trans. Biomed. Eng. BME-32, 337–341 (1985)CrossRefGoogle Scholar
  21. 21.
    Cosic, I.: Macromolecular bioactivity: is it resonant interaction between macromolecules? – Theory and applications. IEEE Trans. Biomed. Eng. 41, 1101–1114 (1994)CrossRefGoogle Scholar
  22. 22.
    Stockwell, R.G., Mansinha, L., Lowe, R.P.: Localization of the Complex Spectrum: The S Transform. Trans. Signal Process. 44, 998–1001 (1996)CrossRefGoogle Scholar
  23. 23.
    Nguyen, Q., Fablet, R., Pastor, D.: Protein Interaction Hotspot Identification Using Sequence-Based Frequency-Derived Features. Trans. Biomed. Eng. 60, 2993–3002 (2013)CrossRefGoogle Scholar
  24. 24.
    Tuncbag, N., Gursoy, A., Keskin, O.: Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy. Bioinformatics 25, 1513–1520 (2009)CrossRefGoogle Scholar
  25. 25.
    Cho, K., Kim, D., Lee, D.: A feature-based approach to modeling protein-protein interaction hot spots. Nucleic Acids Res. 37, 2672–2687 (2009)CrossRefGoogle Scholar
  26. 26.
    Kortemme, T., Baker, D.: A simple physical model for binding energy hot spots in protein-protein complexes. Proc. Natl. Acad. Sci. USA 99, 14116–14121 (2002)CrossRefGoogle Scholar
  27. 27.
    Sahu, S.S., Panda, G.: Efficient Localization of Hot Spots in Proteins Using a Novel S-Transform Based Filtering Approach. Trans. Comput. Biol. Bioinforma. 8, 1235–1246 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jan Kasparek
    • 1
  • Denisa Maderankova
    • 1
  • Ewaryst Tkacz
    • 1
    • 2
  1. 1.Faculty of Electrical Engineering, Department of Biomedical EngineeringBrno University of TechnologyBrnoCzech Republic
  2. 2.Institute of Theoretical and Applied InformaticsPolish Academy of SciencesGliwicePoland

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