|JOHNSON, KATRINA - National Institute Of Environmental Health Sciences (NIEHS, NIH)|
|WILLIAMS, JASON - National Institute Of Environmental Health Sciences (NIEHS, NIH)|
|LONDON, ROBERT - National Institute Of Environmental Health Sciences (NIEHS, NIH)|
|MUELLER, GEOFF - National Institute Of Environmental Health Sciences (NIEHS, NIH)|
Submitted to: Journal of Agricultural and Food Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/26/2016
Publication Date: 1/25/2016
Citation: Johnson, K., Williams, J., Maleki, S.J., Hurlburt, B.K., London, R., Mueller, G. 2016. Enhanced approaches for identifying Amadori products: application to peanut allergens . Journal of Agricultural and Food Chemistry. 64:1406-1413.
Interpretive Summary: Food allergy in general and peanut allergy specifically are on the rise worldwide. Considerable efforts are underway to understand this disease. It has been known for years that roasting peanuts increases the allergenicity of the peanut proteins. This is the second study by this group to understand what happens during roasting. Specifically, sugar molecules become cross-linked to the protein. Using the major allergen Ara h 1 we determined new protein modifications. In addition and new computational tool was developed to enhance similar studies in the future.
Technical Abstract: The dry roasting of peanuts is suggested to influence allergenic sensitization due to formation of advanced glycation end products (AGE) on peanut proteins. Identifying AGEs is technically challenging. The AGE composition of peanut proteins was probed with nanoLC-ESI-MS and MS/MS analyses. Amadori product ions matched to expected peptides and yielded fragments that included a loss of 3 waters and HCHO. Due to the paucity of band y-ions in the MS/MS, standard search algorithms do not perform well. Isotopic labeling confirmed that the peptides contained Amadori products. An algorithm was developed based upon information content (Shannon entropy) and the loss of water and HCHO. Results with test data show that the algorithm finds the correct spectra with high precision and a high (66%) false positive rate reducing the time needed to manually inspect data. Computational and technical improvements allowed better identification of the chemical differences between modified and unmodified proteins.