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.
References
Blythe M J, Flower D R. 2005. Benchmarking B cell epitope prediction: Underperformance of existing methods. Protein Sci, 14(1): 246–248.
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.
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.
Davies M N, Flower D R. 2007. Harnessing bioinformatics to discover new vaccines. Drug Discov Today, 12(9–10): 389–395.
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.
Donnes P, Elofsson A. 2002. Prediction of MHC class Ibinding peptides, using SVMHC. BMC Bioinformatics, 3: 25.
Donnes P, Kohlbacher O. 2006. SVMHC: a server for prediction of MHC-binding peptides. Nucl Acids Res, 34: W194–W197.
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.
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.
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.
Jameson B A, Wolf H. 1988. The antigenic index: a novel algorithm for predicting antigenic determinants. Bioinformatics, 4(1): 181–186.
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.
Kulkarni-Kale U, Bhosles S, Kolaskar A S. 2005 CEP: a conformational epitope prediction server. Nucl Acids Res, 33: W168–W171.
Larsen J E, Lund O, Nielsen M. 2006. Improved method for predicting linear B-cell epitopes. Immunome Res, 2: 2.
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.
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.
Rammensee H, Bachmann J, Emmerich N P, et al. 1999. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics, 50(3–4): 213–219.
Saha S, Raghava G P. 2006. Prediction of Continuous B-cell Epitopes in an Antigen Using Recurrent Neural Network. Proteins, 65(1): 40–48.
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.
Singh H, Raghava G P. 2001. ProPred: Prediction of HLA-DR binding sites. Bioinformatics, 17(12): 1236–1237.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Fundation items: The National Natural Science Foundations of China (30870131) and the National Key Projects in the Infectious Fields (2008ZX10002-011, 2008ZX10004-004).
Rights and permissions
About this article
Cite this article
Chen, P., Rayner, S. & Hu, Kh. Advances of bioinformatics tools applied in virus epitopes prediction. Virol. Sin. 26, 1–7 (2011). https://doi.org/10.1007/s12250-011-3159-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12250-011-3159-4
Key words
- Epitope
- Bioinformatics
- Epitope prediction algorithms