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SVMTriP: A Method to Predict B-Cell Linear Antigenic Epitopes

  • Bo Yao
  • Dandan Zheng
  • Shide Liang
  • Chi ZhangEmail author
Protocol
  • 104 Downloads
Part of the Methods in Molecular Biology book series (MIMB, volume 2131)

Abstract

Identifying protein antigenic epitopes recognizable by antibodies is the key step for new immuno-diagnostic reagent discovery and vaccine design. To facilitate this process and improve its efficiency, computational methods were developed to predict antigenic epitopes. For the linear B-cell epitope prediction, many methods were developed, including BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, BEST, and SVMTriP. Among these methods, SVMTriP, a frontrunner, utilized Support Vector Machine by combining the tri-peptide similarity and Propensity scores. Applied on non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieved a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value was 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. A webserver based on this method was constructed for public use. The server and all datasets used in the corresponding study are available at http://sysbio.unl.edu/SVMTriP. This chapter describes the webserver of SVMTriP.

Key words

Linear B-cell epitope prediction Support vector machine 

Notes

Acknowledgments

The work is supported by funding under C.Z.’s startup funds from the University of Nebraska, Lincoln, NE. This work was completed utilizing the Holland Computing Center of the University of Nebraska.

References

  1. 1.
    Getzoff ED, Tainer JA, Lerner RA et al (1988) The chemistry and mechanism of antibody binding to protein antigens. Adv Immunol 43:1–98CrossRefGoogle Scholar
  2. 2.
    Milich DR (1989) Synthetic T and B cell recognition sites: implications for vaccine development. Adv Immunol 45:195–282CrossRefGoogle Scholar
  3. 3.
    Parker JM, Guo D, Hodges RS (1986) New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. Biochemistry 25(19):5425–5432CrossRefGoogle Scholar
  4. 4.
    Hopp TP, Woods KR (1981) Prediction of protein antigenic determinants from amino acid sequences. Proc Natl Acad Sci U S A 78(6):3824–3828CrossRefGoogle Scholar
  5. 5.
    Emini EA, Hughes JV, Perlow DS et al (1985) Induction of hepatitis a virus-neutralizing antibody by a virus-specific synthetic peptide. J Virol 55(3):836–839CrossRefGoogle Scholar
  6. 6.
    Pellequer JL, Westhof E, MHV V (1993) Correlation between the location of antigenic sites and the prediction of turns in proteins. Immunol Lett 36(1):83–100CrossRefGoogle Scholar
  7. 7.
    Karplus PA, Schulz GE (1985) Prediction of chain flexibility in proteins - a tool for the selection of peptide antigens. Naturwissenschaften 72(4):212–213CrossRefGoogle Scholar
  8. 8.
    Kolaskar AS, Tongaonkar PC (1990) A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett 276(1–2):172–174CrossRefGoogle Scholar
  9. 9.
    Vita R, Zarebski L, Greenbaum JA et al (2010) The immune epitope database 2.0. Nucleic Acids Res 38(Database issue):D854–D862.  https://doi.org/10.1093/nar/gkp1004CrossRefPubMedGoogle Scholar
  10. 10.
    Saha S, Bhasin M, Raghava GP (2005) Bcipep: a database of B-cell epitopes. BMC Genomics 6:79.  https://doi.org/10.1186/1471-2164-6-79CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Schonbach C, JLY K, Sheng X et al (2000) FIMM, a database of functional molecular immunology. Nucleic Acids Res 28(1):222–224CrossRefGoogle Scholar
  12. 12.
    Larsen JE, Lund O, Nielsen M (2006) Improved method for predicting linear B-cell epitopes. Immunome Res 2:2.  https://doi.org/10.1186/1745-7580-2-2CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Saha S, GPS R (2006) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 65(1):40–48.  https://doi.org/10.1002/Prot.21078CrossRefGoogle Scholar
  14. 14.
    Chen J, Liu H, Yang J et al (2007) Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids 33(3):423–428.  https://doi.org/10.1007/S00726-006-0485-9CrossRefGoogle Scholar
  15. 15.
    El-Manzalawy Y, Dobbs D, Honavar V (2008) Predicting linear B-cell epitopes using string kernels. J Mol Recognit 21(4):243–255.  https://doi.org/10.1002/Jmr.893CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Yao B, Zhang L, Liang S et al (2012) SVMTriP: a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity. PLoS One 7(9):e45152.  https://doi.org/10.1371/journal.pone.0045152CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Alix AJ (1999) Predictive estimation of protein linear epitopes by using the program PEOPLE. Vaccine 18(3–4):311–314CrossRefGoogle Scholar
  18. 18.
    Odorico M, Pellequer JL (2003) BEPITOPE: predicting the location of continuous epitopes and patterns in proteins. J Mol Recognit 16(1):20–22.  https://doi.org/10.1002/jmr.602CrossRefGoogle Scholar
  19. 19.
    Wee LJ, Simarmata D, Kam YW et al (2010) SVM-based prediction of linear B-cell epitopes using Bayes feature extraction. BMC Genomics 11(Suppl 4):S21.  https://doi.org/10.1186/1471-2164-11-S4-S21CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Wang Y, Wu W, Negre NN et al (2011) Determinants of antigenicity and specificity in immune response for protein sequences. BMC Bioinformatics 12:251.  https://doi.org/10.1186/1471-2105-12-251CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Gao J, Faraggi E, Zhou Y et al (2012) BEST: improved prediction of B-cell epitopes from antigen sequences. PLoS One 7(6):e40104.  https://doi.org/10.1371/journal.pone.0040104CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Altschul SF, Madden TL, Schaffer AA et al (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25(17):3389–3402CrossRefGoogle Scholar
  23. 23.
    Joachims T (1999) Making large-Scale SVM Learning Practical. In: Schölkopf B, Burges C (eds) Advances in Kernel Methods-Support Vector Learning. MIT Press, Cambridge, MA, pp 169–184Google Scholar
  24. 24.
    Pruitt KD, Tatusova T, Klimke W et al (2009) NCBI reference sequences: current status, policy and new initiatives. Nucleic Acids Res 37(Database issue):D32–D36.  https://doi.org/10.1093/nar/gkn721CrossRefPubMedGoogle Scholar
  25. 25.
    Biswas AK, Noman N, Sikder AR (2010) Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information. BMC Bioinformatics 11:273.  https://doi.org/10.1186/1471-2105-11-273CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Lei Z, Dai Y (2005) An SVM-based system for predicting protein subnuclear localizations. BMC Bioinformatics 6:291.  https://doi.org/10.1186/1471-2105-6-291CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Quantitative Biomedical Research CenterUniversity of Texas Southwestern Medical CenterDallasUSA
  2. 2.Department of Radiation OncologyUniversity of Nebraska Medical CenterOmahaUSA
  3. 3.Department of R&DBio-Thera SolutionsGuangzhouChina
  4. 4.School of Biological Sciences, University of Nebraska – LincolnLincolnUSA

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