B cell (or antibody) epitopes are 16 residues long on average but are not presented in the context of MHC molecules. Therefore, they are especially hard to predict as crystallographic studies have shown that BCRs are capable of binding discontinuous protein epitopes as well. Epitopes are called discontinuous if they are com posed of distant sequence segments which are brought into close proximity due to the protein’s tertiary structure. Contemporary tools for identifying B cell epitopes can be divided into those relying solely on primary structure information and those additionally incorporating structural data. The first group of tools calculate a prediction by considering a set of descriptors such as the propensity for a sequence segment to form a continuous, linear secondary structure, physicochemical attributes, surface accessibility, and amino acid composition [ 1 ]. In general, these tools yield reason able accuracy for continuous (linear) epitopes, but fall short when identifying discontinuous epitopes [ 2 ]. To surmount this short coming, prediction calculations by the second group of tools include secondary structure information, calculated surface accessibilities, and/ or protrusion indices, in addition to information about the protein’s three-dimensional structure and the structure of known antigen -BCR complexes. Popular sequence-based tools are BepiPred [ 3 ], ABCpred [ 4 ], and BEST [ 5 ]. Commonly used structure-based tools are ElliPro [ 6 ], Paratome [ 7 ], PEPOP [ 8 ], BEEPro [ 9 ], and DiscoTope [2 ]. The latter two tools even claim that benchmarking has shown that they are able to achieve high accuracy levels similar to MHC prediction tools [ 3 ].

Many of the tools for MHC I , II, and BCR epitope prediction offer web interfaces which allow thorough testing of their predictive powers before applying them in a larger scale. A very useful analysis resource is the immune epitope database ( IEDB ), funded by the National Institutes of Health [ 10 ]. In addition to providing a database of binding epitopes and their affinities, the IEDB furnishes a regularly updated compilation of self-developed and newly implemented popular prediction tools accessible via a single intuitive web interface.

References

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[1] Ponomarenko JV, van Regenmortel MHV (2009) B-cell epitope prediction. In: Gu J, Bourne PE (eds) Structural bioinformatics. Wiley-Blackwell, New York

[2] Kringelum JV, Lundegaard C, Lund O et al (2012) Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLoS Comput Biol 8, e1002829

[3] Larsen JEP, Lund O, Nielsen M (2006) Improved method for predicting linear B-cell epitopes. Immunome Res 2:2

[4] Saha S, Raghava GPS (2006) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 65:40–48

[5] Gao J, Faraggi E, Zhou Y et al (2012) BEST: improved prediction of B-cell epitopes from antigen sequences. PLoS One 7, e40104

[6] Ponomarenko J, Bui H-H, Li W et al (2008) ElliPro: a new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics 9:514

 [7] Kunik V, Ashkenazi S, Ofran Y (2012) Paratome: an online tool for systematic identification of antigen-binding regions in antibodies based on sequence or structure. Nucleic Acids Res 40:W521–W524

[8] Moreau V, Fleury C, Piquer D et al (2008) PEPOP: computational design of immunogenic peptides. BMC Bioinformatics 9:71

[9] Lin SY, Cheng C, Su EC (2013) Prediction of B-cell epitopes using evolutionary information and propensity scales. BMC Bioinformatics 14:S10

 [10] Kim Y, Ponomarenko J, Zhu Z et al (2012) Immune epitope database analysis resource. Nucleic Acid Res 40:W525–W530

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