Molecular Biology

, Volume 52, Issue 2, pp 279–284 | Cite as

Prediction of Bacterial and Archaeal Allergenicity with AllPred Program

  • A. O. Bragin
  • V. S. Sokolov
  • P. S. Demenkov
  • T. V. Ivanisenko
  • E. Yu. Bragina
  • Yu. G. Matushkin
  • V. A. Ivanisenko
Bioinformatics
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Abstract

Nowadays, allergic disorders have become one of the most important social problems in the world. This can be related to the advent of new allergenic agents in the environment, as well as an increasing density of human contact with known allergens, including various proteins. Thus, the development of computer programs designed for the prediction of allergenic properties of proteins becomes one of the urgent tasks of modern bioinformatics. Previously we developed a web accessible Allpred Program (http://www-bionet.sscc.ru/ psd/cgi-bin/programs/Allpred/allpred.cgi) that allows users to assess the allergenicity of proteins by taking into account the characteristics of their spatial structure. In this paper, using AllPred, we predicted the allergenicity of proteins from 462 archaea and bacteria species for which a complete genome was available. The segregation of considered proteins on archaea and bacteria has shown that allergens are predicted more often among archaea than among bacteria. The division of these proteins into groups according to their intracellular localization has revealed that the majority of allergenic proteins were among the secreted proteins. The application of methods for predicting the level of gene expression of microorganisms based on DNA sequence analysis showed a statistically significant relationship between the expression level of the proteins and their allergenicity. This analysis has revealed that potentially allergenic proteins were more common among highly expressed proteins. Sorting microorganisms into the pathogenic and nonpathogenic groups has shown that pathogens can potentially be more allergenic because of a statistically significant greater number of allergens predicted among their proteins.

Keywords

pathogenic microorganisms allergenicity prediction protein expression 
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References

  1. 1.
    Pawankar R., Canonica G.W., Holgate S.T., et al. 2011. WAO White Book on Allergy. Milwaukee, WI: World Allergy Organization.Google Scholar
  2. 2.
    Breiteneder H., Chapman M. D. 2014. Allergen nomenclature. In: Allergens and Allergen Immunotherapy, 5th ed. Eds. Lockey R.F., Ledford D.K. Boca Raton, FL: CRC Press, pp. 37–49.CrossRefGoogle Scholar
  3. 3.
    Sweeney T.E., Morton J.M. 2013. The human gut microbiome: A review of the effect of obesity and surgically induced weight loss. JAMA Surg. 148 (6), 563–569.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Antranikian G., Vorgias C.E., Bertoldo C. 2005. Extreme environments as a resource for microorganisms and novel biocatalysts. In: Marine Biotechnology I, vol. 96. Berlin: Springer, pp. 219–262.CrossRefGoogle Scholar
  5. 5.
    Irwin J.A. 2010. Extremophiles and their application to veterinary medicine. Environ Technol. 31 (8–9), 857–869.CrossRefPubMedGoogle Scholar
  6. 6.
    Van Den Burg B. 2003. Extremophiles as a source for novel enzymes. Curr. Opin. Microbiol. 6 (3), 213–218CrossRefPubMedGoogle Scholar
  7. 7.
    Pennisi E. 1997. Biotechnology: In industry, extremophiles begin to make their mark. Science. 276 (5313), 705–706.CrossRefPubMedGoogle Scholar
  8. 8.
    Compare D., Nardone G. 2013. The role of gut microbiota in the pathogenesis and management of allergic diseases. Eur. Rev. Med. Pharmacol. 17 (Suppl. 2), 11–17.Google Scholar
  9. 9.
    Lynch S.V. 2016. Gut microbiota and allergic disease: New insights. Ann. Am. Thoracic Soc. 13 (Suppl. 1), S51–S54Google Scholar
  10. 10.
    Hollams E.M., Hales B.J., Bachert C., et al. 2010. Th2-associated immunity to bacteria in teenagers and susceptibility to asthma. Eur. Respir. J. 36 (3), 509–516.CrossRefPubMedGoogle Scholar
  11. 11.
    Reginald K., Westritschnig K., Werfel T., et al. 2011. Immunoglobulin E antibody reactivity to bacterial antigens in atopic dermatitis patients. Clin. Exp. Allergy. 41 (3), 357–369.CrossRefPubMedGoogle Scholar
  12. 12.
    Nahori M.A., Lagranderie M., Lefort J., et al. 2001. Effects of Mycobacterium bovis BCG on the development of allergic inflammation and bronchial hyperresponsiveness in hyper-IgE BP2 mice vaccinated as newborns. Vaccine. 19 (11), 1484–1495.CrossRefPubMedGoogle Scholar
  13. 13.
    Platts-Mills T. A. 2012. Allergy in evolution. In: New Trends in Allergy and Atopic Eczema, vol. 96. Eds. Ring J., Darsow U., Behrendt H. Munich: Karger, pp. 1–6.Google Scholar
  14. 14.
    Jenkins J.A., Breiteneder H., Mills E.N.C. 2007. Evolutionary distance from human homologs reflects allergenicity of animal food proteins. J. Allergy Clin. Immun. 120 (6), 1399–1405.CrossRefPubMedGoogle Scholar
  15. 15.
    Stadler M.B., Stadler B.M. 2003. Allergenicity prediction by protein sequence. Faseb J. 17 (9), 1141–1143.CrossRefPubMedGoogle Scholar
  16. 16.
    Kong W., Tan T.S., Tham L., et al. 2007. Improved prediction of allergenicity by combination of multiple sequence motifs. In Silico Biol. 7 (1), 77–86.PubMedGoogle Scholar
  17. 17.
    Li K.B., Issac P., Krishnan A. 2004. Predicting allergenic proteins using wavelet transform. Bioinformatics. 20 (16), 2572–2578.CrossRefPubMedGoogle Scholar
  18. 18.
    Zorzet A., Gustafsson M., Hammerling U. 2002. Prediction of food protein allergenicity: A bio-informatic learning systems approach. In Silico Biol. 2 (4), 525–534.PubMedGoogle Scholar
  19. 19.
    Saha S., Raghava G.P.S. 2006. AlgPred: prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res. 34 (Suppl. 2), W202–W209.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Muh H.C., Tong J.C., Tammi M.T. 2009. AllerHunter: A SVM-pairwise system for assessment of allergenicity and allergic cross-reactivity in proteins. PLoS ONE. 4 (6), e5861.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Dang H.X., Lawrence C.B. 2014. Allerdictor: Fast allergen prediction using text classification techniques. Bioinformatics. 30 (8), 1120–1128.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Dimitrov I., Naneva L., Doytchinova I., et al. 2014. AllergenFP: allergenicity prediction by descriptor fingerprints. Bioinformatics. 30 (6), 846–851.CrossRefPubMedGoogle Scholar
  23. 23.
    Saravanan V., Lakshmi P.T.V. 2014. Fuzzy logic for personalized healthcare and diagnostics: FuzzyApp-A fuzzy logic based allergen-protein predictor. Omics: J. Integr. Biol. 18 (9), 570–581.CrossRefGoogle Scholar
  24. 24.
    Dimitrov I., Bangov I., Flower D.R., et al. 2014. Aller-TOP v. 2: A server for in silico prediction of allergens. J. Mol. Modeling. 20 (6), 1–6.Google Scholar
  25. 25.
    He Y., Tao A. 2015. Bioinformatics methods to predict allergen epitopes. In: Allergy Bioinformatics, vol. 8. Eds. Ailin T., Eyal R. Dordrecht: Springer, pp. 223–238.CrossRefGoogle Scholar
  26. 26.
    Bragin A.O., Demenkov P.S., Kolchanov N.A., et al. 2013. Accuracy of protein allergenicity prediction can be improved by taking into account data on allergenic protein discontinuous peptides. J. Biomol. Struct. Dyn. 31 (1), 59–64.CrossRefPubMedGoogle Scholar
  27. 27.
    Barrett T., Clark K., Gevorgyan R., et al. 2012. Bio-Project and BioSample databases at NCBI: Facilitating capture and organization of metadata. Nucleic Acids Res. 40 (D1), D57–D63.CrossRefPubMedGoogle Scholar
  28. 28.
    UniProt Consortium. 2014. UniProt: A hub for protein information. Nucleic Acids Res. gku989.Google Scholar
  29. 29.
    Bragin A.O., Demenkov P.S., Tiys E.S., Hofestädt R., Ivanisenko V.A., et al. 2013. Computerized analysis of the relationship between allergenicity of microorganisms and their habitats. Russ. J. Gen.: Appl. Res. 3 (3), 171–175.CrossRefGoogle Scholar
  30. 30.
    Altschul S.F., Gish W., Miller W., et al. 1990. Basic local alignment search tool. J. Mol. Biol. 215 (3), 403–410.CrossRefPubMedGoogle Scholar
  31. 31.
    Sokolov V.S., Zuraev B.S., Lashin S.A., et al. 2015. EloE: Web application for estimation of gene translation elongation efficiency. Russ. J. Gen. Appl. Res. 5 (4), 335–339.CrossRefGoogle Scholar
  32. 32.
    Sokolov V., Zuraev B., Lashin S., et al. 2015. Web application for automatic prediction of gene translation elongation efficiency. J. Integr. Bioinform. 12 (1), 16–23.PubMedGoogle Scholar
  33. 33.
    Vladimirov N.V., Likhoshvai V.A., Matushkin Y.G. 2007. Correlation of codon biases and potential secondary structures with mRNA translation efficiency in unicellular organisms. Mol. Biol. (Moscow). 41 (5), 843–850.CrossRefGoogle Scholar
  34. 34.
    Karlin S., Mrázek J. 2000. Predicted highly expressed genes of diverse prokaryotic genomes. J. Bacteriol. 182 (18), 5238–5250.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Falsey A.R., Treanor J.J., Tornieporth N., et al. 2009. Randomized, double-blind controlled phase 3 trial comparing the immunogenicity of high-dose and standard-dose influenza vaccine in adults 65 years of age and older. J. Infect. Dis. 200 (2), 172–180.CrossRefPubMedGoogle Scholar
  36. 36.
    Bertino J.S., Tirrell P., Greenberg R.N., et al. 1997. A comparative trial of standard or high-dose S subunit recombinant hepatitis B vaccine versus a vaccine containing S subunit, pre-S1, and pre-S2 particles for revaccination of healthy adult nonresponders. J. Infect. Dis. 175 (3), 678–681.CrossRefPubMedGoogle Scholar
  37. 37.
    Strachan D.P. 1989. Hay fever, hygiene, and household size. Br. Med. J. 299 (6710), 1259–1260.CrossRefGoogle Scholar
  38. 38.
    Albers S.V. 2016. Extremophiles: Life at the deep end. Nature. 538 (7626), 457–457.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Inc. 2018

Authors and Affiliations

  • A. O. Bragin
    • 1
  • V. S. Sokolov
    • 1
  • P. S. Demenkov
    • 1
  • T. V. Ivanisenko
    • 1
  • E. Yu. Bragina
    • 2
  • Yu. G. Matushkin
    • 1
  • V. A. Ivanisenko
    • 1
  1. 1.Institute of Cytology and Genetics, Siberian BranchRussian Academy of SciencesNovosibirskRussia
  2. 2.Research Institute of Medical Genetics, Tomsk National Research Medical CenterRussian Academy of SciencesTomskRussia

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