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Computational Prediction of B Cell Epitopes from Antigen Sequences

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Immunoinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1184))

Abstract

Computational identification of B-cell epitopes from antigen chains is a difficult and actively pursued research topic. Efforts towards the development of method for the prediction of linear epitopes span over the last three decades, while only recently several predictors of conformational epitopes were released. We review a comprehensive set of 13 recent approaches that predict linear and 4 methods that predict conformational B-cell epitopes from the antigen sequences. We introduce several databases of B-cell epitopes, since the availability of the corresponding data is at the heart of the development and validation of computational predictors. We also offer practical insights concerning the use and availability of these B-cell epitope predictors, and motivate and discuss feature research in this area.

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References

  1. Kringelum JV, Lundegaard C, Lund O, Nielsen M (2012) Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLoS Comput Biol 8(12):e1002829

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  2. Pellequer JL, Westhof E, van Regenmortel MH (1991) Predicting location of continuous epitopes in proteins from their primary structures. Methods Enzymol 203:176–201

    Article  CAS  PubMed  Google Scholar 

  3. Reineke U, Schutkowski M (2009) Epitope mapping protocols. Methods Mol Biol, vol 524

    Google Scholar 

  4. El-Manzalawy Y, Honavar V (2010) Recent advances in B-cell epitope prediction methods. Immunome Res 6(Suppl 2):S2

    Article  PubMed Central  PubMed  Google Scholar 

  5. Yao B, Zheng D, Liang S, Zhang C (2013) Conformational B-cell epitope prediction on antigen protein structures: a review of current algorithms and comparison with common binding site prediction methods. PLoS One 8(4):e62249

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Ansari HR, Raghava GP (2013) In silico models for B-cell epitope recognition and signaling. Methods Mol Biol 993:129–138

    Article  CAS  PubMed  Google Scholar 

  7. Yang X, Yu X (2009) An introduction to epitope prediction methods and software. Rev Med Virol 19(2):77–96

    Article  CAS  PubMed  Google Scholar 

  8. Vita R, Zarebski L, Greenbaum JA, Emami H, Hoof I, Salimi N, Damle R, Sette A, Peters B (2010) The immune epitope database 2.0. Nucleic Acids Res 38:D854–D862

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Sharma OP, Das AA, Krishna R, Kumar SM, Mathur PP (2012) Structural Epitope Database (SEDB): a Web-based database for the epitope, and its intermolecular interaction along with the tertiary structure information. J Proteomics Bioinform 5:84–89

    CAS  Google Scholar 

  10. Toseland CP, Clayton DJ, McSparron H, Hemsley SL, Blythe MJ, Paine K, Doytchinova AI, Guan P, Hattotuwagama CK, Flower DR (2005) AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data. Immunome Res 1:4

    Article  PubMed Central  PubMed  Google Scholar 

  11. Kim Y, Ponomarenko J, Zhu Z, Tamang D, Wang P, Greenbaum J, Lundegaard C, Sette A, Lund O, Bourne PE, Nielsen M, Peters B (2012) Immune epitope database analysis resource. Nucleic Acids Res 40:W525–W530

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Zhang Q, Wang P, Kim Y, Haste-Andersen P, Beaver J, Bourne PE, Bui HH, Buus S, Frankild S, Greenbaum J, Lund O, Lundegaard C, Nielsen M, Ponomarenko J, Sette A, Zhu Z, Peters B (2008) Immune epitope database analysis resource (IEDB-AR). Nucleic Acids Res 36:W513–W518

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  13. Rubinstein ND, Mayrose I, Martz E, Pupko T (2009) Epitopia: a web-server for predicting B-cell epitopes. BMC Bioinformatics 10:287

    Article  PubMed Central  PubMed  Google Scholar 

  14. Rubinstein ND, Mayrose I, Pupko T (2009) A machine-learning approach for predicting B-cell epitopes. Mol Immunol 46:840–847

    Article  CAS  PubMed  Google Scholar 

  15. Gao J, Faraggi E, Zhou Y, Ruan J, Kurgan L (2012) BEST: improved prediction of B-cell epitopes from antigen sequences. PLoS One 7:e40104

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  16. Blythe MJ, Doytchinova IA, Flower DR (2002) JenPep: a database of quantitative functional peptide data for immunology. Bioinformatics 18:434–439

    Article  CAS  PubMed  Google Scholar 

  17. McSparron H, Blythe MJ, Zygouri C, Doytchinova IA, Flower DR (2003) JenPep: a novel computational information resource for immunobiology and vaccinology. J Chem Inf Comput Sci 43:1276–1287

    Article  CAS  PubMed  Google Scholar 

  18. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410

    Article  CAS  PubMed  Google Scholar 

  19. Peters B, Sidney J, Bourne P, Bui HH, Buus S, Doh G, Fleri W, Kronenberg M, Kubo R, Lund O, Nemazee D, Ponomarenko JV, Sathiamurthy M, Schoenberger S, Stewart S, Surko P, Way S, Wilson S, Sette A (2005) The immune epitope database and analysis resource: from vision to blueprint. PLoS Biol 3(3):e91

    Article  PubMed Central  PubMed  Google Scholar 

  20. Peters B, Sette A (2007) Integrating epitope data into the emerging web of biomedical knowledge resources. Nat Rev Immunol 7(6):485–490

    Article  CAS  PubMed  Google Scholar 

  21. Saha S, Bhasin M, Raghava GP (2005) Bcipep: a database of B-cell epitopes. BMC Genomics 6(1):79

    Article  PubMed Central  PubMed  Google Scholar 

  22. Huang J, Honda W (2006) CED: a conformational epitope database. BMC Immunol 7:7

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Schlessinger A, Ofran Y, Yachdav G, Rost B (2006) Epitome: database of structure-inferred antigenic epitopes. Nucleic Acids Res 34:D777–D780

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28(1):235–242

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  25. Laskowski RA (2001) PDBsum: summaries and analyses of PDB structures. Nucleic Acids Res 29:221–222

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  26. Bhasin M, Singh H, Raghava GP (2003) MHCBN: a comprehensive database of MHC binding and non-binding peptides. Bioinformatics 19:665–666

    Article  CAS  PubMed  Google Scholar 

  27. Kaas Q, Ruiz M, Lefranc MP (2004) IMGT/3Dstructure-DB and IMGT/StructuralQuery, a database and a tool for immunoglobulin, T cell receptor and MHC structural data. Nucleic Acids Res 32:D208–D210

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  28. Magrane M, UniProt Consortium (2011) UniProt Knowledgebase: a hub of integrated protein data. Database:bar009

    Google Scholar 

  29. Pruitt KD, Tatusova T, Brown GR, Maglott DR (2012) NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy. Nucleic Acids Res 40:D130–D135

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  30. Hopp TP, Woods KR (1981) Prediction of protein antigenic determinants from amino acid sequences. Proc Natl Acad Sci U S A 78:3824–3828

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Welling GW, Weijer WJ, van der Zee R, Welling-Wester S (1985) Prediction of sequential antigenic regions in proteins. FEBS Lett 188:215–218

    Article  CAS  PubMed  Google Scholar 

  32. Karplus PA, Schulz GE (1985) Prediction of chain flexibility in proteins: a tool for the selection of peptide antigen. Naturwissenschaften 72:212–213

    Article  CAS  Google Scholar 

  33. 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 25:5425–5432

    Article  CAS  PubMed  Google Scholar 

  34. Kolaskar AS, Tongaonkar PC (1990) A semi empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett 276:172–174

    Article  CAS  PubMed  Google Scholar 

  35. Pellequer JL, Westhof E, van Regenmortel MH (1993) Correlation between the location of antigenic sites and the prediction of turns in proteins. Immunol Lett 36(1):83–99

    Article  CAS  PubMed  Google Scholar 

  36. Pellequer JL, Westhof E (1993) PREDITOP: a program for antigenicity prediction. J Mol Graph 11:191–202

    Google Scholar 

  37. Alix AJ (1999) Predictive estimation of protein linear epitopes by using the program PEOPLE. Vaccine 18:311–314

    Article  CAS  PubMed  Google Scholar 

  38. Odorico M, Pellequer JL (2003) BEPITOPE: predicting the location of continuous epitopes and patterns in proteins. J Mol Recognit 16(1):20–22

    Article  CAS  PubMed  Google Scholar 

  39. Saha S, Raghava GP (2004) BcePred: prediction of continuous b-cell epitopes in antigenic sequences using physico-chemical properties. Third Intern Conf on Artificial Immune Systems. pp 197–204

    Google Scholar 

  40. Chang HT, Liu CH, Pai TW (2008) Estimation and extraction of B-cell linear epitopes predicted by mathematical morphology approaches. J Mol Recognit 21(6):431–441

    Article  CAS  PubMed  Google Scholar 

  41. Blythe MJ, Flower D (2005) Benchmarking B cell epitope prediction: underperformance of existing methods. Protein Sci 14:246–248

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  42. Saha S, Raghava GP (2006) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 65(1):40–48

    Article  CAS  PubMed  Google Scholar 

  43. Larsen JE, Lund O, Nielsen M (2006) Improved method for predicting linear B-cell epitopes. Immunome Res 2:2

    Article  PubMed Central  PubMed  Google Scholar 

  44. Chen J, Liu H, Yang J, Chou KC (2007) Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids 33(3):423–428

    Article  CAS  PubMed  Google Scholar 

  45. El-Manzalawy Y, Dobbs D, Honavar V (2008) Predicting linear B-cell epitopes using string kernels. J Mol Recognit 21(4):243–255

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  46. El-Manzalawy Y, Dobbs D, Honavar V (2008) Predicting flexible length linear B-cell epitopes. Comput Syst Bioinformatics Conf 7:121–132

    PubMed Central  PubMed  Google Scholar 

  47. Sweredoski MJ, Baldi P (2009) COBEpro: a novel system for predicting continuous B-cell epitopes. Protein Eng Des Sel 22(3):113–120

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  48. Wee LJ, Simarmata D, Kam YW, Ng LF, Tong JC (2010) SVM-based prediction of linear B-cell epitopes using Bayes feature extraction. BMC Genomics 11(Suppl 4):S21

    Article  PubMed Central  PubMed  Google Scholar 

  49. Wang Y, Wu W, Negre NN, White KP, Li C, Shah PK (2011) Determinants of antigenicity and specificity in immune response for protein sequences. BMC Bioinformatics 12:251

    Article  PubMed Central  PubMed  Google Scholar 

  50. Wang HW, Lin YC, Pai TW, Chang HT (2011) Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification. J Biomed Biotechnol 2011:432830

    PubMed Central  PubMed  Google Scholar 

  51. Yao B, Zhang L, Liang S, Zhang C (2012) SVMTriP: a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity. PLoS One 7(9):e45152

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  52. Singh H, Ansari HR, Raghava GP (2013) Improved method for linear B-cell epitope prediction using antigen’s primary sequence. PLoS One 8(5):e62216

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  53. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25(17):3389–3402

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  54. Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292(2):195–202

    Article  CAS  PubMed  Google Scholar 

  55. Cheng J, Randall AZ, Sweredoski MJ, Baldi P (2005) SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res 33:W72–W76

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  56. Vucetic S, Brown CJ, Dunker AK, Obradovic Z (2003) Flavors of protein disorder. Proteins 52:573–584

    Article  CAS  PubMed  Google Scholar 

  57. Wootton JC, Federhen S (1996) Analysis of compositionally biased regions in sequence databases. Methods Enzymol 266:554–571

    Article  CAS  PubMed  Google Scholar 

  58. Ansari HR, Raghava GP (2010) Identification of conformational B-cell epitopes in an antigen from its primary sequence. Immunome Res 6:6

    Article  PubMed Central  PubMed  Google Scholar 

  59. Zhang W, Niu Y, Xiong Y, Zhao M, Yu R, Liu J (2012) Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning. PLoS One 7(8):e43575

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  60. Pollastri G, Baldi P, Fariselli P, Casadio R (2002) Prediction of coordination number and relative solvent accessibility in proteins. Proteins 47(2):142–153

    Article  CAS  PubMed  Google Scholar 

  61. Pollastri G, Przybylski D, Rost B, Baldi P (2002) Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins 47(2):228–235

    Article  CAS  PubMed  Google Scholar 

  62. Faraggi E, Xue B, Zhou Y (2009) Improving the prediction accuracy of residue solvent accessibility and real-value backbone torsion angles of proteins by guided-learning through a two-layer neural network. Proteins 74:847–856

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  63. Dor O, Zhou Y (2007) Achieving 80 % ten-fold cross-validated accuracy for secondary structure prediction by large-scale training. Proteins 66:838–845

    Article  CAS  PubMed  Google Scholar 

  64. Adamczak R, Porollo A, Meller J (2005) Combining prediction of secondary structure and solvent accessibility in proteins. Proteins 59:467–475

    Article  PubMed  Google Scholar 

  65. Wang P, Sidney J, Dow C, Mothé B, Sette A, Peters B (2008) A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLoS Comput Biol 4(4):e1000048

    Article  PubMed Central  PubMed  Google Scholar 

  66. Moutaftsi M, Peters B, Pasquetto V, Tscharke DC, Sidney J, Bui HH, Grey H, Sette A (2006) A consensus epitope prediction approach identifies the breadth of murine T(CD8+)-cell responses to vaccinia virus. Nat Biotechnol 24(7):817–819

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported by National Science Foundation of China (NSFC) grants 31050110432 and 31150110577 to L.K. J.G. was supported by the Fundamental Research Funds for the Central Universities grant 65011491.

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Correspondence to Lukasz Kurgan .

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Gao, J., Kurgan, L. (2014). Computational Prediction of B Cell Epitopes from Antigen Sequences. In: De, R., Tomar, N. (eds) Immunoinformatics. Methods in Molecular Biology, vol 1184. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1115-8_11

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  • DOI: https://doi.org/10.1007/978-1-4939-1115-8_11

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