Proteomic and Bioinformatic Analyses for the Identification of Proteins with Low Allergenic Potential for Hazard Assessment

Nora L. Krutz, Jason Winget, Cindy A. Ryan, Rohan Wimalasena, Sebastian Maurer-Stroh, Rebecca J. Dearman, Ian Kimber, G. Frank Gerberick

Research output: Contribution to journalArticlepeer-review


Use of botanicals and natural substances in consumer products has increased in recent years. Such extracts can contain protein that may theoretically represent a potential risk of IgE-mediated allergy. No method has yet been generally accepted or validated for assessment of the allergenic potential of proteins. For development of suitable methods datasets of allergenic and nonallergenic (or low allergenic) proteins are required that can serve, respectively, as positive and negative controls. However, data are unavailable on proteins that lack or have low allergenic potential. Here, low allergenic potential proteins are identified based on the assumption that proteins with established human exposure, but with a lack of an association with allergy, possess low allergenic potential. Proteins were extracted from sources considered to have less allergenic potential (corn, potato, spinach, rice, and tomato) as well as higher allergenic potential (wheat) regarding common allergenic foods. Proteins were identified and semi-quantified by label-free proteomic analysis conducted using mass spectrometry. Predicted allergenicity was determined using AllerCatPro ( In summary, 9077 proteins were identified and semi-quantified from 6 protein sources. Within the top 10% of the most abundant proteins identified, 178 characterized proteins were found to have no evidence for allergenicity predicted by AllerCatPro and were considered to have low allergenic potential. This panel of low allergenic potential proteins provides a pragmatic approach to aid the development of alternative methods for robust testing strategies to distinguish between proteins of high and low allergenic potential to assess the risk of proteins from natural or botanical sources.

Original languageEnglish
Pages (from-to)210-222
Number of pages13
JournalToxicological Sciences
Issue number1
Early online date23 Mar 2019
Publication statusPublished - 1 Jul 2019


  • AllerCatPro
  • In silico prediction model
  • Label-free proteomic analysis
  • Risk assessment
  • Type I allergy


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