Molecular Biology

, Volume 52, Issue 2, pp 285–293 | Cite as

BCIgEPRED—a Dual-Layer Approach for Predicting Linear IgE Epitopes

Bioinformatics
First Online:
Received:
Accepted:
  • 9 Downloads

Abstract

Allergy is a common health problem worldwide, especially food allergy. Since B cell epitopes that are recognized by the IgE antibodies act as antigenic determinants for allergy, they play a vital role in diagnostics. Hence, knowledge of an IgE binding epitope in a protein is of particular interest for identifying allergenic proteins. Though IgE epitopes may be conformational or linear, identification of the later is useful especially in food allergens that undergo processing or digestion. Very few computational tools are available for the prediction of linear IgE epitopes. Here we report a prediction system that predicts the exact linear IgE epitope. Since our earlier study on linear B-cell epitope prediction demonstrated the effectiveness of using an exact epitope dataset (in contrast to epitope containing region datasets), the dataset in this study uses only experimentally verified exact IgE, IgG, IgM and IgA epitopes. Models for Support Vector Machine (SVM) and Random Forest (RF) were constructed adopting Dipeptide Deviation from the Expected mean (DDE) feature vector. Extensive validation procedures including five-fold cross validation and two different independent dataset tests have been performed to validate the proposed method, which achieved a balanced accuracy ranging from 74 to 78% with area under receiver operator curve greater than 0.8. Performance of the proposed method was observed to be better (accuracy difference of 16–28%) in comparison to the existing available method. The proposed method is developed as a standalone tool that could be used for predicting IgE epitopes as well as to be incorporated into any allergen prediction toolhttps://github.com/brsaran/BCIgePred.

Keywords

epitopes immunoglobulin E food allergy B-cell epitope dipeptide deviation from expected mean BCIgEPred 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Rueter K., Prescott S. 2014. Hot topics in paediatric immunology: IgE mediated food allergy and allergic rhinitis. Aust. Fam. Physician. 43, 680–685.PubMedGoogle Scholar
  2. 2.
    Lin J., Sampson H.A. 2009. The role of immunoglobulin E-binding epitopes in the characterization of food allergy. Curr. Opin. Allergy Clin. Immunol. 9, 357–363.CrossRefPubMedGoogle Scholar
  3. 3.
    Tanabe S. 2008. Analysis of food allergen structures and development of foods for allergic patients. Biosci. Biotechnol. Biochem. 72, 649–659.CrossRefPubMedGoogle Scholar
  4. 4.
    Chen X., Negi S.S., Liao S., et al. 2016. Conformational IgE epitopes of peanut allergens Ara h 2 and Ara h 6. Clin. Exp. Allergy. 46 (8), 1120–1128. doi 10.1111/cea.12764CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Matsuo H., Yokooji T., Taogoshi T. 2015. Common food allergens and their IgE-binding epitopes. Allergol. Int. 64, 332–343.CrossRefPubMedGoogle Scholar
  6. 6.
    Pomés A. 2009. Relevant B cell epitopes in allergic disease. Int. Arch. Allergy Immunol. 152, 1–11.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Zhong-Shan G., Hua-Hao S., Min Z., (Eds.). 2012. Multidisciplinary Approaches to Allergies. Berlin: Springer, vol. 7, pp. 113–126.Google Scholar
  8. 8.
    Costa J.G., Faccendini P.L., Sferco S.J., et al. 2013. Evaluation and comparison of the ability of online available prediction programs to predict true linear Bcell epitopes. Protein Pept. Lett. 20, 724–730.CrossRefPubMedGoogle Scholar
  9. 9.
    El-Manzalawy Y., Honavar V. 2010. Recent advances in B-cell epitope prediction methods. Immunome Res. 6 (Suppl 2), S2.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Soria-Guerra R.E., Nieto-Gomez R., Govea-Alonso D.O., Rosales-Mendoza S. 2015. An overview of bioinformatics tools for epitope prediction: Implications on vaccine development. J. Biomed. Inf. 53, 405–414.CrossRefGoogle Scholar
  11. 11.
    Dhanda S.K., Usmani S.S., Agrawal P., et al. 2017. Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics. BriefingsBioinf. 18 (3), 467–478. doi 10.1093/bib/bbw025Google Scholar
  12. 12.
    Davydov Y.I., Tonevitsky A.G. 2009. Prediction of linear B-cell epitopes. Mol. Biol. (Moscow). 43 (1), 150–158.CrossRefGoogle Scholar
  13. 13.
    Saha S., Raghava G. 2006. AlgPred: Prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res. 34, W202–W209.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Vita R., Overton J.A., Greenbaum J.A., Ponomarenko J., Clark J.D., Cantrell J.R., Wheeler D.K., Gabbard J.L., Hix D., Sette A., Peters B. 2015. The immune epitope database (IEDB) 3.0. Nucleic Acids Res. 43, D405–D412.CrossRefPubMedGoogle Scholar
  15. 15.
    Gupta S., Ansari H.R., Gautam A.; Open Source Drug Discovery Consortium, Raghava G.P. 2013. Identification of B-cell epitopes in an antigen for inducing specific class of antibodies. Biol. Direct. 8, 27. doi 10.1186/1745-6150-8-27CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Saravanan V., Gautham N. 2015. Harnessing computational biology for exact linear B-cell epitope prediction: A novel amino acid composition-based feature descriptor. OMICS. 19, 648–658.CrossRefPubMedGoogle Scholar
  17. 17.
    Huang Y., Niu B., Gao Y., Fu L., Li W. 2010. CD-HIT Suite: A web server for clustering and comparing biological sequences. Bioinformatics. 26, 680–682.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Nakamura R., Teshima R., Takagi K., Sawada J. 2004. Development of Allergen Database for Food Safety (ADFS): An integrated database to search allergens and predict allergenicity. Kokuritsu Iyakuhin Shokuhin Eisei Kenkyusho Hokoku. 123, 32–36.Google Scholar
  19. 19.
    Chen J., Liu H., Yang J., Chou K.C. 2007. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids. 33, 423–428.CrossRefPubMedGoogle Scholar
  20. 20.
    Singh H., Ansari H.R., Raghava G.P. 2013. Improved method for linear B-cell epitope prediction using antigen’s primary sequence. PloS One. 8, e62216.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    El-Manzalawy Y., Dobbs D., Honavar V. 2008. Predicting linear B-cell epitopes using string kernels. J. Mol. Recognit. 21, 243–255. doi 10.1002/jmr.893CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Yao B., Zhang L., Liang S., Zhang C. 2012. VMTriP: A method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity. PloS One. 7, e45152. doi 10.1371/journal. pone.0045152CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Chawla N.V. 2005. Data mining for imbalanced datasets: An overview. In Data Mining and Knowledge Discovery Handbook. New York: Springer-Verlag, pp. 853–867.CrossRefGoogle Scholar
  24. 24.
    Cortes C., VapnikV. 1995. Support-vector networks. Mach. Learn. 20, 273–297.Google Scholar
  25. 25.
    Breiman L. 2001. Random forests. Mach. Learn. 45, 5–32.CrossRefGoogle Scholar
  26. 26.
    Lin W.-Z., Fang J.A., Xiao X., Chou K.C. 2011. iDNAProt: Identification of DNA binding proteins using random forest with grey model. PloS One. 6, e24756. doi 10.1371/journal.pone.0024756CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Štambuk N., Konjevoda P. 2011. The role of independent test set in modeling of protein folding kinetics. In: Software Tools and Algorithms for Biological Systems. Eds. Arabnia, H.R.R., Tran, Q.N. Advances in Experimental Medicine and Biology, vol. 696. New York: Springer, pp. 279–284.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Inc. 2018

Authors and Affiliations

  1. 1.Center for Advanced Study in Crystallography and BiophysicsUniversity of Madras, Guindy CampusChennaiIndia

Personalised recommendations