Supervised identification of allergen-representative peptides for in silico detection of potentially allergenic proteins.
BACKGROUND
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.


BACKGROUND
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@slv.se
Division of Toxicology, National Food Administration, P.O. Box 622, SE-751 26 Uppsala, Sweden.
Asa K
AK
Björklund
Daniel
D
Soeria-Atmadja
Anna
A
Zorzet
Ulf
U
Hammerling
Mats G
MG
Gustafsson
D000465:Algorithms; D000485:Allergens / Q000032:analysis / Q000737:chemistry* / Q000145:classification*; D020816:Amino Acid Motifs; D001185:Artificial Intelligence; D020407:Internet; D010363:Pattern Recognition, Automated / Q000379:methods; D010455:Peptides / Q000032:analysis / Q000737:chemistry / Q000145:classification; D011506:Proteins / Q000032:analysis / Q000737:chemistry* / Q000145:classification*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D012984:Software; D013329:Structure-Activity Relationship
D000485:Allergens; D010455:Peptides; D011506:Proteins