Epitope prediction

Pellequer, J. L., Westhof, E. andVanRegenmortel, M. H. V. (1994), Epitope prediction from primary structure of proteins , in G. B. Wisdom (Ed.), Peptide Antigens A Practical Approach, IRL Press, Oxford, UK, pp. 7-25. 

At the epitope level, the Immune Epitope Database and Analysis Resource (IEDB) is the largest immune epitope database so far (http //www.immuneepitope.org/). IEDB has stored more than 65,000 antibody and T-cell epitopes since the database was established in 2004 (9). These immune epitopes cover various species (e.g., humans, nonhuman primates, and rodents) that have been well studied in the areas of infectious diseases (9). Because all stored epitopes in IEDB are manually curated from experimental studies, the data in IEDB have often been used as the gold standard for evaluating in silico epitope prediction tools (10). 

Two major in silico tools in vaccine design are based on two related but distinct strategies immunoinformatics-based immune epitope prediction and reverse vaccinology-based protective protein antigen screening. 

Owing to the difficulty of evaluating discontinuous epitopes, most experimentally detected epitopes are linear epitopes. Tools for prediction of linear B-cell epitopes include 3DEX, CEP, and Pepito (26-29). IEDB has collected a list of Web tools for B-cell epitope prediction (http //tools.immuneepitope.org/main/... 

To counter the low success rate of B-cell epitope prediction, MHC class II epitopes for CD4+ T cells may be emphasized when antibody response is key in vaccine design. CD4+ T-helper cells are critical to induce the activation of B cells that produce antibodies. B-cell antigens that contain significant MHC class II epitopes may outperform B-cell antigens without cognate help. An identified T-cell epitope may sometimes contain a B-cell epitope. In addition, B-cell epitopes may colocalize near or overlap MHC class II epitopes (32, 33). 

Stranzl T, Larsen MV, Lundegaard C, Nielsen M (2010) NetCTLpan pan-specific MHC class I pathway epitope predictions. Immunogenetics 62 357-368... 

Kulkarni-Kale U, Bhosle S, Kolaskar AS (2005) CEP a conformational epitope prediction server. Nucleic Acids Res 33 W168-W171... 

Sweredoski MJ, Baldi P (2008) PEPITO improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure. Bioinformatics 24 1459-1460... 

Blythe MJ, Flower DR (2005) Benchmarking B cell epitope prediction underperformance of existing methods. Protein Sci 14 246-248... 

For decades researchers have been developing in silico models to minimize the number of experiments needed to identify or map the potential epitopes on the antigen surface. Because of the basic differences in the recognition of B- and T-cell epitopes, researchers have derived separate algorithms and tools for the two types of epitope. This chapter discusses only B-cell epitope prediction models (linear and conformational). Although they are not very different from basic B-cell epitope algorithms, T-cell epitope models have been reviewed in detail elsewhere (7, 8). 

Unlike linear B-cell epitopes, conformational epitope prediction models were limited by the need to understand antigen-antibody (Ag-Ab) complex structures before applying these algorithms. As with the linear epitopes, researchers started by seeking structural... 

Sun J, Wu D, Xu T et al (2009) SEPPA a computational server for spatial epitope prediction of protein antigens. Nucleic Acids Res 37 W612-W616... 

Chen WH, Sun PP, Lu Y et al (2011) MimoPro a more efficient Web-based tool for epitope prediction using phage display libraries. BMC... 

Huang YX, Bao YL, Guo SY et al (2008) Pep-3D-Search a method for B-cell epitope prediction based on mimotope analysis. BMC Bioinformatics 9 538... 

Moreau V, Granier C, Villard S et al (2006) Discontinuous epitope prediction based on mimotope analysis. Bioinformatics 22 1088-1095... 

Zhao, Y., PiniUa, G., Vaknori, D., Martin, R., Simon, R. Application of support vector machines for T-ceU epitopes prediction. Bioinformatics 2003,19,1978-84. 

F. DaU Antonia, T. Pavkov-Keller, K. Zangger and W. Keller, Structure of Allergens and Structure Based Epitope Predictions, Methods, 2014, 66, 3. 

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