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.
Similar content being viewed by others
References
Pawankar R., Canonica G.W., Holgate S.T., et al. 2011. WAO White Book on Allergy. Milwaukee, WI: World Allergy Organization.
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.
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.
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.
Irwin J.A. 2010. Extremophiles and their application to veterinary medicine. Environ Technol. 31 (8–9), 857–869.
Van Den Burg B. 2003. Extremophiles as a source for novel enzymes. Curr. Opin. Microbiol. 6 (3), 213–218
Pennisi E. 1997. Biotechnology: In industry, extremophiles begin to make their mark. Science. 276 (5313), 705–706.
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.
Lynch S.V. 2016. Gut microbiota and allergic disease: New insights. Ann. Am. Thoracic Soc. 13 (Suppl. 1), S51–S54
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.
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.
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.
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.
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.
Stadler M.B., Stadler B.M. 2003. Allergenicity prediction by protein sequence. Faseb J. 17 (9), 1141–1143.
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.
Li K.B., Issac P., Krishnan A. 2004. Predicting allergenic proteins using wavelet transform. Bioinformatics. 20 (16), 2572–2578.
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.
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.
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.
Dang H.X., Lawrence C.B. 2014. Allerdictor: Fast allergen prediction using text classification techniques. Bioinformatics. 30 (8), 1120–1128.
Dimitrov I., Naneva L., Doytchinova I., et al. 2014. AllergenFP: allergenicity prediction by descriptor fingerprints. Bioinformatics. 30 (6), 846–851.
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.
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.
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.
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.
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.
UniProt Consortium. 2014. UniProt: A hub for protein information. Nucleic Acids Res. gku989.
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.
Altschul S.F., Gish W., Miller W., et al. 1990. Basic local alignment search tool. J. Mol. Biol. 215 (3), 403–410.
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.
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.
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.
Karlin S., Mrázek J. 2000. Predicted highly expressed genes of diverse prokaryotic genomes. J. Bacteriol. 182 (18), 5238–5250.
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.
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.
Strachan D.P. 1989. Hay fever, hygiene, and household size. Br. Med. J. 299 (6710), 1259–1260.
Albers S.V. 2016. Extremophiles: Life at the deep end. Nature. 538 (7626), 457–457.
Author information
Authors and Affiliations
Corresponding author
Additional information
Original Russian Text © A.O. Bragin, V.S. Sokolov, P.S. Demenkov, T.V. Ivanisenko, E.Yu. Bragina, Yu.G. Matushkin, V.A. Ivanisenko, 2018, published in Molekulyarnaya Biologiya, 2018, Vol. 52, No. 2, pp. 326–332.
Rights and permissions
About this article
Cite this article
Bragin, A.O., Sokolov, V.S., Demenkov, P.S. et al. Prediction of Bacterial and Archaeal Allergenicity with AllPred Program. Mol Biol 52, 279–284 (2018). https://doi.org/10.1134/S0026893317050041
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0026893317050041