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Prediction of linear B-cell epitopes

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Abstract

The use of antigenicity scales based on physicochemical properties and the sliding window method in combination with an averaging algorithm and subsequent search for the maximum value is the classical method for B-cell epitope prediction. However, recent studies have demonstrated that the best classical methods provide a poor correlation with experimental data. We review both classical and novel algorithms and present our own implementation of the algorithms. The AAPPred software is available at http://www.bioinf.ru/aappred/.

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References

  1. Pellequer J., Westhof E., van Regenmortel M. 1991. Predicting location of continuous epitopes in proteins from their primary structures. Meth Enzymol. 203, 176–201.

    Article  PubMed  CAS  Google Scholar 

  2. Hopp T.P., Woods K.R. 1981. Prediction of protein antigenic determinants from amino acid sequences. Proc. Nat. Acad. Sci. USA. 78, 3824–3828.

    Article  PubMed  CAS  Google Scholar 

  3. Levitt M. 1976. A simplified representation of protein conformations for rapid simulation of protein folding. J. Mol. Biol. 1041, 59–107.

    Article  Google Scholar 

  4. Parker J.M., Guo D., Hodges R.S. 1986. New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: Correlation of predicted surface residues with antigenicity and X-rayderived accessible sites. Biochemistry. 25, 5425–5432.

    Article  PubMed  CAS  Google Scholar 

  5. Westhof E., Altschuh D., Moras D., Bloomer A.C., Mondragon A., Klug A., van Regenmortel M.H. 1984. Correlation between segmental mobility and the location of antigenic determinants in proteins. Nature. 311, 123–126.

    Article  PubMed  CAS  Google Scholar 

  6. Karplus P.A., Schulz G.E. 1985. Prediction of chain flexibility in proteins. Naturwissenschaften. 72, 212–213.

    Article  CAS  Google Scholar 

  7. Emini E.A., Hughes J.V., Perlow D.S., Boger J. 1985. Induction of hepatitis A virus-neutralizing antibody by a virus-specific synthetic peptide. J. Virol. 55, 836–839.

    PubMed  CAS  Google Scholar 

  8. Janin J., Wodak S. 1978. Conformation of amino acid side-chains in proteins. J. Mol. Biol. 125, 357–386.

    Article  PubMed  CAS  Google Scholar 

  9. Welling G.W., Weijer W.J., van der Zee R., Welling-Wester S. 1985. Prediction of sequential antigenic regions in proteins. FEBS Lett. 188, 215–218.

    Article  PubMed  CAS  Google Scholar 

  10. Kolaskar A.S., Tongaonkar P.C. 1990. A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett. 276, 172–174.

    Article  PubMed  CAS  Google Scholar 

  11. Pellequer J.L., Westhof E., van Regenmortel M.H. 1993. Correlation between the location of antigenic sites and the prediction of turns in proteins. Immunol. Lett. 36, 83–99.

    Article  PubMed  CAS  Google Scholar 

  12. Alix A.J. 1999. Predictive estimation of protein linear epitopes by using the program PEOPLE. Vaccine. 18, 311–314.

    Article  PubMed  CAS  Google Scholar 

  13. Saha S., Raghava G. 2004. BcePred: Prediction of continuous B-cell epitopes in antigenic sequences using physico-chemical properties. In: Artificial Immune Systems, vol. 3239. Eds. Nicosia G., Cutello V., Bentley P., Timmis J. Berlin: Springer, pp. 197–204.

    Google Scholar 

  14. Saha S., Bhasin M., Raghava G. 2005. Bcipep: A database of B-cell epitopes. BMC Genomics. 6, 79.

    Article  PubMed  Google Scholar 

  15. Ponnuswamy P.K., Prabhakaran M., Manavalan P. 1980. Hydrophobic packing and spatial arrangement of amino acid residues in globular proteins. Biochim. Biophys. Acta. 623, 301–316.

    PubMed  CAS  Google Scholar 

  16. Blythe M.J., Flower D.R. 2005. Benchmarking B cell epitope prediction: Underperformance of existing methods. Protein Sci. 14, 246–248.

    Article  PubMed  CAS  Google Scholar 

  17. Kawashima S., Ogata H., Kanehisa M. 1999. AAindex: Amino Acid Index Database. Nucleic Acids Res. 27, 368–369.

    Article  PubMed  CAS  Google Scholar 

  18. Blythe M.J., Doytchinova I.A., Flower D.R. 2002. JenPep: A database of quantitative functional peptide data for immunology. Bioinformatics. 18, 434–439.

    Article  PubMed  CAS  Google Scholar 

  19. McSparron H., Blythe M.J., Zygouri C., Doytchinova I.A., Flower D.R. 2003. JenPep: A novel computational information resource for immunobiology and vaccinology. J. Chem. Inf. Comput. Sci. 43, 1276–1287.

    PubMed  CAS  Google Scholar 

  20. Fawcett T. 2006. An introduction to ROC analysis. Pattern Recognition Lett. 27, 861–874.

    Article  Google Scholar 

  21. Chen J., Liu H., Yang J., Chou K. 2007. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids. 33, 423–428.

    Article  PubMed  CAS  Google Scholar 

  22. Sollner J., Mayer B. 2006. Machine learning approaches for prediction of linear B-cell epitopes on proteins. J. Mol. Recognit. 19, 200–208.

    Article  PubMed  Google Scholar 

  23. Gasteiger E., Hoogland C., Gattiker A., Duvaud S., Wilkins M., Appel R., Bairoch A. 2005. Protein identification and analysis tools on the ExPASy server. In: The Proteomics Protocols Handbook. Ed. Walker J.M. N.Y.: Humana Press, pp. 571–607.

    Google Scholar 

  24. Henikoff S., Henikoff J.G. 1992. Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. USA. 89, 10915–10919.

    Article  PubMed  CAS  Google Scholar 

  25. Saha S., Raghava G.P.S. 2006. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins. 65, 40–48.

    Article  PubMed  CAS  Google Scholar 

  26. Vapnik V. 1998. Statistical Learning Theory. N.Y.: Wiley-Interscience.

    Google Scholar 

  27. Scholkopf B., Sung K.K., Burges C.J.C., Girosi F., Niyogi P., Poggio T., Vapnik V. 1997. Comparing support vector machines with Gaussian kernels to radialbasis function classifiers. IEEE Trans. Signal Processing. 45, 2758–2765.

    Article  Google Scholar 

  28. Greenbaum J., Andersen P., Blythe M., Bui H., Cachau R., Crowe J., Davies M., Kolaskar A., Lund O., Morrison S., Mumey B., Ofran Y., Pellequer J., Pinilla C., Ponomarenko J., Raghava G., van Regenmortel M., Roggen E., Sette A., Schlessinger A., Sollner J., Zand M., Peters B. 2007. Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools. J. Mol. Recognit. 20, 75–82.

    Article  PubMed  CAS  Google Scholar 

  29. Schonbach C., Koh J., Flower D., Wong L., Brusic V. 2002. FIMM, a database of functional molecular immunology: Update 2002. Nucleic Acids Res. 30, 226–229.

    Article  PubMed  Google Scholar 

  30. Peters B., Sidney J., Bourne P., Bui H., Buus S., Doh G., Fleri W., Kronenberg M., Kubo R., Lund O., Nemazee D., Ponomarenko J., Sathiamurthy M., Schoenberger S., Stewart S., Surko P., Way S., Wilson S., Sette A. 2005. The immune epitope database and analysis resource: From vision to blueprint. PLoS Biol. 3, e91.

    Article  PubMed  Google Scholar 

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Correspondence to Ya. I. Davydov.

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Original Russian Text © Ya.I. Davydov, A.G. Tonevitsky, 2009, published in Molekulyarnaya Biologiya, 2009, Vol. 43, No. 1, pp. 166–174.

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Davydov, Y.I., Tonevitsky, A.G. Prediction of linear B-cell epitopes. Mol Biol 43, 150–158 (2009). https://doi.org/10.1134/S0026893309010208

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