PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2012 | 62 | 3 |

Tytuł artykułu

Evaluation of in silico prediction possibility of epitope sequences using experimental data concerning allergenic food proteins summarised in BIOPEP database

Treść / Zawartość

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
The aim of the study was to evaluate the possibility of predicting potential epitope sequences and location in allergenic proteins from food using EVALLER program by comparison with experimental epitopes summarised in the BIOPEP database of allergenic proteins. Sequences of experimental epitopes from food allergens, present in the BIOPEP database of allergenic proteins were used in the study. Sequences of potential epitopes were found using EVALLER program. The Positive Predictive Value (PPV) has been used as a measure of prediction quality. The potential epitopes fully or partially overlapping with the experimental ones were considered as true positive results whereas these unrelated to the experimental ones as false positive results. The PPV for entire dataset containing 310 potential epitopes was 80.6%. The PPV varied signifi cantly among particular allergen families defi ned according to the AllFam database. Caseins revealed PPV=100% (with one exception), proteins from tropomyosin family and proteins from papain-like cystein protease family – exceeding 50%. The last two families possess also relatively low frequency of epitope occurrence. The predictive potential was poor (less than 50%) for plant allergens from cupin superfamily. Families such as lipocalins from milk and EF-hand family (parvalbumins) revealed high variability within family. The EVALLER program may be used as a tool for the prediction of epitope location although its potential varies considerably among allergen families. High PPV is associated with a high number of known experimental epitopes (such as in caseins) and/or a high degree of sequence conservation within family (caseins, tropomyosins).

Słowa kluczowe

Wydawca

-

Rocznik

Tom

62

Numer

3

Opis fizyczny

p.151-157,ref.

Twórcy

  • University of Warmia and Mazury in Olsztyn, Department of Food Biochemistry, Plac Cieszynski 1, 10-726 Olsztyn-Kortowo, Poland
autor
autor
autor
autor

Bibliografia

  • 1. Altschul S.F., Madden T.L., Schäffer A.A., Zhang J., Zhang Z., Miller W., Lipman D.J., Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res., 1997, 25, 3389–3402.
  • 2. Ayuso R., Lehrer S.B., Reese G., Identifi cation of continuous, allergenic regions of the major shrimp allergen Pen a 1 (tropomyosin). Int. Arch. Allergy Immunol., 2002, 127, 27–37.
  • 3. Björklund Å., Soeria-Atmadja D., Zorzet A., Hammerling U., Gustafsson M.G., Supervised identifi cation of allergen-representative peptides for in silico detection of potentially allergenic proteins. Bioinformatics, 2005, 21, 39–50.
  • 4. Bohle B., T-cell epitopes of food allergens. Clin. Rev. Allergy Immunol., 2006, 30, 97–108.
  • 5. Cianferoni A., Spergel J.M., Food allergy: review, classifi cation and diagnosis. Allergol. Int., 2009, 58, 457–466.
  • 6. Cummings A.J., Knibb R.C., King R.M., Lucas J.S., The psychosocial impact of food allergy and food hypersensitivity in children, adolescents and their families: a review. Allergy, 2010, 65, 933–945.
  • 7. Darewicz M., Dziuba B., Minkiewicz P., Dziuba J., The preventive potential of milk and colostrum proteins and protein fragments. Food Rev. Int., 2011, 27, 357–388.
  • 8. Dessailly B.H., Redfern O.C., Cuff A., Orengo C.A., Exploiting structural classifi cations for function prediction: towards a domain grammar for protein function. Curr. Opin. Struct. Biol., 2009, 19, 349–356.
  • 9. Dziuba J., Iwaniak A., Minkiewicz P., Computer-aided characteristics of proteins as potential precursors of bioactive peptides. Polimery, 2003, 48, 50–53.
  • 10. Gendel S.M., Allergen databases and allergen semantics. Regulatory Toxicol. Pharmacol., 2009, 54(3 Suppl.), S7-S10.
  • 11. Goodman R.E., Practical and predictive bioinformatic methods for the identifi cation of potentially cross-reactive protein matches. Mol. Nutr. Food Res., 2006, 50, 655–660.
  • 12. Gowthaman U., Agrewala J.N., In silico methods for predicting T-cell epitopes: Dr Jekyll or Mr Hyde? Expert Rev. Proteom., 2009, 6, 527–537.
  • 13. Ishikawa M., Nagashima Y., Shiomi K., Identifi cation of the oyster allergen Cra g 2 as tropomyosin. Fisheries Sci., 1998a, 64, 854–855.
  • 14. Ishikawa M., Ishida M., Shimakura K., Nagashima Y., Shiomi K., Purifi cation and IgE-binding epitopes of a major allergen in the gastropod Turbo cornutus. Biosci. Biotech. Biochem., 1998b, 62, 1337–1343.
  • 15. Ishikawa M., Suzuki F., Ishida M., Nagashima Y., Shiomi K., Identifi cation of tropomyosin as a major allergen in the octopus Octopus vulgaris and elucidation of its IgE-binding epitopes. Fisheries Sci., 2001, 67, 934–942.
  • 16. Iwaniak A., Dziuba J., Analysis of domains in selected plant and animal food proteins – precursors of biologically active peptides. Food Sci. Technol. Int., 2009, 15, 179–191.
  • 17. Iwaniak A., Dziuba J., BIOPEP-PBIL tool for analysis of the structure of biologically active motifs derived from food proteins. Food Technol. Biotechnol., 2011, 49, 118–127.
  • 18. Jain E., Bairoch A., Duvaud S., Phan I., Redaschi N., Suzek B.E., Martin M.J., McGarvey P., Gasteiger E., Infrastructure for the life sciences: design and implementation of the UniProt website. BMC Bioinform., 2009, 10, Article No 136.
  • 19. Jędrychowski L., Wróblewska B., Szymkiewicz A., State of the art on food allergens – a review. Pol. J. Food Nutr. Sci., 2008, 58, 165–175.
  • 20. Kim J.S., Sicherer S., Should avoidance of foods be strict in prevention and treatment of food allergy? Curr. Opin. Allergy Clin. Immunol., 2010, 10, 252–257.
  • 21. Mari A., Scala E., Palazzo P., Ridolfi S., Zennaro D., Carabella G., Bioinformatics applied to allergy: Allergen databases, from collecting sequence information to data integration. The Allergome platform as a model. Cell. Immunol., 2006, 244, 97–100.
  • 22. Mari A., Rasi C., Palazzo P., Scala E., Allergen databases: current status and perspectives. Curr. Allergy Asthma Rep., 2009, 9, 376–383.
  • 23. Martinez Barrio A., Soeria-Atmadja D., Nistér A., Gustafsson M.G., Hammerling U., Bongcam-Rudloff E., EVALLER: a web server for in silico assessment of potential protein allergenicity. Nucleic Acids Res., 2007, 35, W694-W700.
  • 24. Minkiewicz P., Dziuba J., Gładkowska-Balewicz I., Update of the list of allergenic proteins from milk, based on local amino acid sequence identity with known epitopes from bovine milk proteins – a short report. Pol. J. Food Nutr. Sci., 2011, 61, 153–158.
  • 25. Monaci L., Tregoat V., van Hengel A.J., Anklam E., Milk allergens; their characteristics and their detection in food: a review. Eur. Food Res. Technol., 2006, 223, 149–179.
  • 26. Pearson W.R., Searching protein sequence libraries: comparison of the sensitivity and selectivity of the Smith-Waterman and FASTA algorithm. Genomics, 1991, 11, 635–650.
  • 27. Pearson W.R., Flexible sequence similarity searching with the FASTA3 program package. Methods Mol. Biol., 2000, 132, 185–219.
  • 28. Petrey D., Honig B., Is protein classifi cation necessary? Toward alternative approaches to function annotation. Curr. Opin. Struct. Biol., 2009, 19, 363–368.
  • 29. Pomés A., Relevant B cell epitopes in allergic disease. Int. Arch. Allergy Immunol., 2010, 152, 1–11.
  • 30. Ponomarenko J., Papangelopoulos N., Zajonc D.M., Peters B., Sette A., Bourne P.E., IEDB-3D: structural data within the immune epitope database. Nucleic Acids Res., 2011, 39, D1164--D1170.
  • 31. Prescott S.L., Bouygue G.R., Videky D., Fiocchi A., Avoidance or exposure to foods in prevention and treatment of food allergy? Curr. Opin. Allergy Clin. Immunol., 2010, 10, 258–266.
  • 32. Pulido A., Ruisánchez I., Boqué R., Rius F.X., Uncertainty of results in routine qualitative analysis. Trends Anal. Chem., 2003, 22, 647–654.
  • 33. Radauer C., Bublin M., Wagner S., Mari A., Breiteneder H., Allergens are distributed into few protein families and possess a restricted number of biochemical functions. J. Allergy Clin. Immunol., 2008, 121, 847–852.
  • 34. Reese G., Ayuso R., Carle T., Lehrer S.B., IgE-binding epitopes of shrimp tropomyosin, the major allergen Pen a 1. Int. Arch. Allergy Immunol., 1999, 118, 300–301.
  • 35. Reese G., Ayuso R., Leong-Kee S.M., Plante M., Lehrer S.B., The IgE-binding regions of the major allergen Pen a 1: Multiple epitopes or intramolecular cross-reactivity? Int. Arch. Allergy Immunol., 2001, 124, 103–106.
  • 36. Reese G., Viebranz J., Leong-Kee S.M., Plante M., Lauer I., Randow S., Moncin M.S., Ayuso R., Lehrer S.B., Vieths S., Reduced allergenic potency of VR9–1, a mutant of the major shrimp allergen Pen a 1 (tropomyosin). J. Immunol., 2005, 175, 8354–8364.
  • 37. Salimi N., Fleri W., Peters B., Sette A., Design and utilization of epitope-based databases and predictive tools. Immunogenetics, 2010, 62, 185–196.
  • 38. Sammut S.J., Finn R.D., Bateman A., Pfam 10 years on: 10 000 families and still growing. Brief. Bioinform., 2008, 9, 210–219.
  • 39. Shanti K.N., Martin B.M., Nagpal S., Metcalfe D.D., Rao P.V., Identifi cation of tropomyosin as the major shrimp allergen and characterization of its IgE-binding epitopes, J. Immunol., 1993, 151, 5354–5363.
  • 40. Skripak J.M., Sampson H.A., Towards a cure for food allergy. Curr. Opin. Immunol., 2008, 20, 690–696.
  • 41. Smith T.F., Waterman M.S., Identifi cation of common molecular subsequences. J. Mol. Biol., 1981, 147, 195–197.
  • 42. Soeria-Atmadja D., Lundell T., Gustafsson M.G., Hammerling U., Computational detection of allergenic proteins attains a new level of accuracy with in silico variable-length peptide extraction and machine learning. Nucleic Acids Res., 2006, 34, 3779–3793.
  • 43. Steckelbroeck S., Ballmer-Weber B.K., Vieths S., Potential, pitfalls and prospects of food allergy diagnostics with recombinant allergens or synthetic sequential epitopes. J. Allergy Clin. Immunol., 2008, 121, 1323–1330.
  • 44. The UniProt Consortium, Ongoing and future developments at the Universal Protein Resource. Nucleic Acids Res., 2011, 39, D214-D219.
  • 45. Tomar N., De R. K., Immunoinformatics: An integrated scenario. Immunology, 2010, 131, 153–168.
  • 46. Tong J.C., Ren E.C., Immunoinformatics: current trends and future directions. Drug Discov. Today, 2009, 14, 684–689.
  • 47. Vaughan K., Greenbaum J., Kim Y., Vita R., Chung J., Peters B., Broide D., Goodman R., Grey H., Sette A., Towards defi ning molecular determinants recognized by adaptative immunity in allergic disease: an inventory of the available data. J. Allergy, 2010, Article No 628026.
  • 48. Vita R., Zarebski L., Greenbaum J.A., Emami H., Hoof I., Salimi N., Damle R., Sette A., Peters B., The Immune Epitope Database 2.0. Nucleic Acids Res., 2010, 38, D854-D862.
  • 49. Wren J.D., Bateman A., Databases, data tombs and dust in the wind. Bioinformatics, 2008, 24, 2127–2128.

Uwagi

PL
Rekord w opracowaniu

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

bwmeta1.element.agro-ddab6474-5227-475c-8c0c-d9ac6a97d743
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.