Bioinformatics Advance Access originally published online on August 19, 2004
Bioinformatics 2005 21(1):39-50; doi:10.1093/bioinformatics/bth477
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Bioinformatics vol. 21 issue 1 © Oxford University Press 2005; all rights reserved.
Supervised identification of allergen-representative peptides for in silico detection of potentially allergenic proteins

1 Division of Toxicology, National Food Administration P.O. Box 622, SE-751 26 Uppsala, Sweden
2 Department of Engineering Sciences, Uppsala University P.O. Box 528, SE-751 20 Uppsala, Sweden
*To whom correspondence should be addressed.
Motivation: Identification of potentially allergenic proteins is needed for the safety assessment of genetically modified foods, certain pharmaceuticals and various other products on the consumer market. Current methods in bioinformatic allergology exploit common features among allergens for the detection of amino acid sequences of potentially allergenic proteins. Features for identification still unexplored include the motifs occurring commonly in allergens, but rarely in ordinary proteins. In this paper, we present an algorithm for the identification of such motifs with the purpose of biocomputational detection of amino acid sequences of potential allergens.
Results: Identification of allergen-representative peptides (ARPs) with low or no occurrence in proteins lacking allergenic properties is the essential component of our new method, designated DASARP (Detection based on Automated Selection of Allergen-Representative Peptide). This approach consistently outperforms the criterion based on identical peptide match for predicting allergenicity recommended by ILSI/IFBC and FAO/WHO and shows results comparable to the alignment-based criterion as outlined by FAO/WHO.
Availability: The detection software and the ARP set needed for the analysis of a query protein reported here are properties of the Swedish National Food Agency and are available upon request. The protein sequence sets used in this work are publicly available on http://www.slv.se/templatesSLV/SLV_Page____9343.asp. Allergenicity assessment for specific protein sequences of interest is also possible via ulfh{at}slv.se
Contact: ulfh{at}slv.se
Received on December 4, 2003; revised on July 2, 2004; accepted on August 5, 2004
This article has been cited by other articles:
![]() |
A. M. Barrio, D. Soeria-Atmadja, A. Nister, M. G. Gustafsson, U. Hammerling, and E. Bongcam-Rudloff EVALLER: a web server for in silico assessment of potential protein allergenicity Nucleic Acids Res., July 13, 2007; 35(suppl_2): W694 - W700. [Abstract] [Full Text] [PDF] |
||||
![]() |
Z. H. Zhang, J. L. Y. Koh, G. L. Zhang, K. H. Choo, M. T. Tammi, and J. C. Tong AllerTool: a web server for predicting allergenicity and allergic cross-reactivity in proteins Bioinformatics, February 15, 2007; 23(4): 504 - 506. [Abstract] [Full Text] [PDF] |
||||
![]() |
D. Soeria-Atmadja, T. Lundell, M. G. Gustafsson, and U. Hammerling Computational detection of allergenic proteins attains a new level of accuracy with in silico variable-length peptide extraction and machine learning Nucleic Acids Res., August 29, 2006; 34(13): 3779 - 3793. [Abstract] [Full Text] [PDF] |
||||
![]() |
S. Saha and G. P. S. Raghava AlgPred: prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res., July 1, 2006; 34(Web Server issue): W202 - W209. [Abstract] [Full Text] [PDF] |
||||
![]() |
T. Riaz, H. L. Hor, A. Krishnan, F. Tang, and K.-B. Li WebAllergen: a web server for predicting allergenic proteins Bioinformatics, May 15, 2005; 21(10): 2570 - 2571. [Abstract] [Full Text] [PDF] |
||||

