Computational detection of allergenic proteins attains a new level of accuracy with in silico variable-length peptide extraction and machine learning
2006 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 34, no 13, 3779-3793 p.Article in journal (Refereed) Published
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 email@example.com.
Place, publisher, year, edition, pages
2006. Vol. 34, no 13, 3779-3793 p.
Medical and Health Sciences Engineering and Technology
IdentifiersURN: urn:nbn:se:uu:diva-97617DOI: 10.1093/nar/gkl467ISI: 000240583100028PubMedID: 16977698OAI: oai:DiVA.org:uu-97617DiVA: diva2:172632