Virologica Sinica

, Volume 26, Issue 1, pp 1–7 | Cite as

Advances of bioinformatics tools applied in virus epitopes prediction

Article

Abstract

In recent years, the in silico epitopes prediction tools have facilitated the progress of vaccines development significantly and many have been applied to predict epitopes in viruses successfully. Herein, a general overview of different tools currently available, including T cell and B cell epitopes prediction tools, is presented. And the principles of different prediction algorithms are reviewed briefly. Finally, several examples are present to illustrate the application of the prediction tools.

Key words

Epitope Bioinformatics Epitope prediction algorithms 

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References

  1. 1.
    Blythe M J, Flower D R. 2005. Benchmarking B cell epitope prediction: Underperformance of existing methods. Protein Sci, 14(1): 246–248.CrossRefPubMedGoogle Scholar
  2. 2.
    Bui HH, Peters B, Assarsson E, et al. 2007. Ab and T cell epitopes of influenza A virus, knowledge and opportunities. Proc Natl Acad Sci USA, 104(1): 246–251.CrossRefPubMedGoogle Scholar
  3. 3.
    Buus S, Lauemøller S L, Worning P, et al. 2003. Sensitive quantitative predictions of peptide-MHC binding by a ‘Query by Committee’ artificial neural network approach. Tissue Antigens, 62(5): 378–384.CrossRefPubMedGoogle Scholar
  4. 4.
    Davies M N, Flower D R. 2007. Harnessing bioinformatics to discover new vaccines. Drug Discov Today, 12(9–10): 389–395.CrossRefPubMedGoogle Scholar
  5. 5.
    Díaz I, Pujols J, Ganges L, et al. 2009. In silico prediction and ex vivo evaluation of potential T-cell epitopes in glycoproteins 4 and 5 and nucleocapsid protein of genotype-I (European) of porcine reproductive and respiratory syndrome virus. Vaccine, 27(41): 5603–5611.CrossRefPubMedGoogle Scholar
  6. 6.
    Donnes P, Elofsson A. 2002. Prediction of MHC class Ibinding peptides, using SVMHC. BMC Bioinformatics, 3: 25.CrossRefPubMedGoogle Scholar
  7. 7.
    Donnes P, Kohlbacher O. 2006. SVMHC: a server for prediction of MHC-binding peptides. Nucl Acids Res, 34: W194–W197.CrossRefPubMedGoogle Scholar
  8. 8.
    Guan P, Doytchinova I A, Zygouri C, et al. 2003. MHCPred: bringing a quantitative dimension to the online prediction of MHC binding. Appl Bioinformatics, 2(1): 63–66.PubMedGoogle Scholar
  9. 9.
    Haste Andersen P, Nielsen M, Lund O. 2006. Prediction of residues in discontinuous B-cell epitopes using protein 3D structures. Protein Sci, 15(11): 2558–2567.CrossRefPubMedGoogle Scholar
  10. 10.
    Herd K A, Mahalingam S, Mackay I M, et al. 2006. Cytotoxic T-lymphocyte epitope vaccination protects against human metapneumovirus infection and disease in mice. J Virol, 80(4): 2034–2044.CrossRefPubMedGoogle Scholar
  11. 11.
    Jameson B A, Wolf H. 1988. The antigenic index: a novel algorithm for predicting antigenic determinants. Bioinformatics, 4(1): 181–186.CrossRefGoogle Scholar
  12. 12.
    Jin X, Newman M J, De-Rosa S, et al. 2009. A novel HIV T helper epitope-based vaccine elicits cytokine-secreting HIV-specific CD4+ T cells in a Phase I clinical trial in HIV-uninfected adults. Vaccine, 27(50): 7080–7086.CrossRefPubMedGoogle Scholar
  13. 13.
    Kulkarni-Kale U, Bhosles S, Kolaskar A S. 2005 CEP: a conformational epitope prediction server. Nucl Acids Res, 33: W168–W171.CrossRefPubMedGoogle Scholar
  14. 14.
    Larsen J E, Lund O, Nielsen M. 2006. Improved method for predicting linear B-cell epitopes. Immunome Res, 2: 2.CrossRefPubMedGoogle Scholar
  15. 15.
    Lv Y, Ruan Z, Wang L, et al. 2009. Identification of a novel conserved HLA-A*0201-restricted epitope from the spike protein of SARS-CoV. BMC Immunol, 10: 61.CrossRefPubMedGoogle Scholar
  16. 16.
    Noguchi H, Kato R, Hanai T, et al. 2002. Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules. J Biosci Bioeng, 94(3): 264–270.PubMedGoogle Scholar
  17. 17.
    Rammensee H, Bachmann J, Emmerich N P, et al. 1999. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics, 50(3–4): 213–219.CrossRefPubMedGoogle Scholar
  18. 18.
    Saha S, Raghava G P. 2006. Prediction of Continuous B-cell Epitopes in an Antigen Using Recurrent Neural Network. Proteins, 65(1): 40–48.CrossRefPubMedGoogle Scholar
  19. 19.
    Simon G G, Hu Y, Khan A M, et al. 2010. Dendritic cell mediated delivery of plasmid DNA encoding LAMP/HIV-1 Gag fusion immunogen enhances T cell epitope responses in HLA DR4 transgenic mice. PLoS One, 5(1): e8574.CrossRefPubMedGoogle Scholar
  20. 20.
    Singh H, Raghava G P. 2001. ProPred: Prediction of HLA-DR binding sites. Bioinformatics, 17(12): 1236–1237.CrossRefPubMedGoogle Scholar
  21. 21.
    Wang B, Yao K, Liu G, et al. 2009. Computational Prediction and Identification of Epstein-Barr Virus Latent Membrane Protein 2A Antigen-Specific CD8+ T-Cell. Cell Mol Immunol, 6(2): 97–103.CrossRefPubMedGoogle Scholar
  22. 22.
    Zhang Z W, Zhang Y G, Wang Y L, et al. 2010. Screening and identification of B cell epitopes of structural proteins of foot-and-mouth disease virus serotype Asia1. Vet Microbiol, 140(1–2): 25–33.CrossRefPubMedGoogle Scholar

Copyright information

© Wuhan Institute of Virology, CAS and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.State Key Laboratory of Virology, Wuhan Institute of VirologyChinese Academy of SciencesWuhanChina

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