Computational detection of allergenic proteins attains a new level of accuracy with in silico variable-length peptide extraction and machine learning.
The placing of novel or new-in-the-context proteins on the market, appearing in genetically modified foods, certain bio-pharmaceuticals and some household products leads to human exposure to proteins that may elicit allergic responses. Accurate methods to detect allergens are therefore necessary to ensure consumer/patient safety. We demonstrate that it is possible to reach a new level of accuracy in computational detection of allergenic proteins by presenting a novel detector, Detection based on Filtered Length-adjusted Allergen Peptides (DFLAP). The DFLAP algorithm extracts variable length allergen sequence fragments and employs modern machine learning techniques in the form of a support vector machine. In particular, this new detector shows hitherto unmatched specificity when challenged to the Swiss-Prot repository without appreciable loss of sensitivity. DFLAP is also the first reported detector that successfully discriminates between allergens and non-allergens occurring in protein families known to hold both categories. Allergenicity assessment for specific protein sequences of interest using DFLAP is possible via ulfh@slv.se.
Soeria-Atmadja
D
D
Division of Toxicology, National Food Administration, Uppsala, Sweden
Lundell
T
T
Gustafsson
M G
MG
Hammerling
U
U
D000465:Algorithms; D000485:Allergens / Q000032:analysis* / Q000737:chemistry; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D010455:Peptides / Q000737:chemistry / Q000276:immunology / Q000302:isolation & purification; D011506:Proteins / Q000737:chemistry / Q000276:immunology*; D015203:Reproducibility of Results; D020539:Sequence Analysis, Protein / Q000379:methods*; D014335:Tropomyosin / Q000737:chemistry / Q000276:immunology
D000485:Allergens; D010455:Peptides; D011506:Proteins; D014335:Tropomyosin