High Resolution LC-MS based Discovery of Tree Nut Protein Marker Peptides, and Verification/Validation of Perilla Seeds Protein Marker Peptides

Abstract

Food allergy is an adverse immune response to food proteins, causing a wide range of allergic reactions from mild to fatal symptoms such as anaphylaxis. The best way to prevent damage from a food allergy is to avoid eating the offending foods. Many countries designate and manage some foods with a high risk of allergy, and various studies on detection methods of food allergens are ongoing. In this study, using high-resolution LC-MS, potential marker peptides for tree nuts, one of the major food allergens, were selected. In addition, marker peptides for perilla seeds, which lacked allergy-related studies, were discovered, verified, and validated. In PART I, proteins were extracted from raw materials of Brazil nut, hazelnut, chestnut, and acorn, proteins and peptides present in tree nuts were identified through LC-MS/MS DDA analysis, and peptides whose specificity was confirmed for each tree nut species by NCBI BLASTP search were selected as marker peptide candidates. Four different extraction buffer combinations were compared to establish an effective protein extraction method for four types of tree nuts, and n-hexane + SDS was adopted as the optimal extraction buffer. The MS/MS data were processed using the genus database of tree nuts, and the identified peptides were examined based on species specificity and PSMs. As a result, 8 peptides for Brazil nut, 10 peptides for hazelnut, 12 peptides for chestnut, and 4 peptides for acorn were selected as candidates. Also, overlapping peptides were investigated among hazelnut, chestnut, and acorn according to the phylogenetic association, and among the peptides detected from both chestnut and acorn, 38 peptides were found to be species-specific. In PART II, proteins extracted from raw material of perilla seeds were analyzed in DDA mode of LC-MS/MS to identify proteins and peptides present in perilla seeds, through PRM mode analysis of four processed foods with various perilla seed contents and processing types, selected marker peptide candidates were verified, and validation was performed by analyzing in MRM mode using synthesized isotope-labeled peptides. To obtain the best identification results, the protein digestion method and database were optimized by comparing the DDA analysis results. 10 peptides with species specificity were selected as candidates, and some of them proved to be more suitable as markers than the reference sequences. The preliminary marker peptides selected in consideration of the verification results were validated for linearity, matrix effect, sensitivity, accuracy, precision, and recovery, and two peptides (GSQTFLLSPTR and NQVAGYTSALR) were selected as the final marker peptides of perilla seeds.