A complete workflow for discovering small bioactive peptides in foods by LC-MS/MS: A case study on almonds

As the availability of nutraceuticals and functional foods increases, along with the associated health claims, the need for high quality studies of bioactive food components is more important than ever. Mass spectrometry (MS) can be an excellent tool to study these compounds, and the addition of multiplex labelling methods can increase throughput for the analytical assessment of food products. However, since the majority of bioactive peptide are made up of a handful of amino acids (i.e. <5), traditional bottom-up MS and data analysis tools that are suited to longer peptides must be adapted to improve the identification and sequencing of these targets. In this case study, Huang and colleagues use stable isotope reductive dimethylation to label peptides extracted from almonds combined with de novo sequencing to identify over 2500 small- and medium-sized peptides . These peptides were then searched against a bioactive peptide database to identify over 200 relevant targets. This workflow describes sample preparation through to data analysis, and compares the results generated with unlabelled samples. Importantly, the authors show that the use of dimethyl labeling improved the quality of tandem MS spectra for small peptides, and led to improved sequence analysis with PEAKS de novo sequencing.

How was PEAKS used?

PEAKS Studio X+ was used to perform full length sequencing of small peptides. PEAKS de novo scores (average local confidence, ALC, i.e., the likelihood of each amino acid assignment in a resultant peptide) were used to identify high quality peptides that could then be manually inspected using the highly detailed peptide feature view available in the software. Dimethyl labeling was specified in the analysis using fixed and variable modifications, as defined by the user in the sequencing parameters. Medium size peptides were also examined using the PEAKS database search workflow.

Huang, Yu-Ping, et al. “A Complete Workflow for Discovering Small Bioactive Peptides in Foods by LC-MS/MS: A Case Study on Almonds.” Food Chemistry, Elsevier BV, Aug. 2021, p. 130834. Crossref, doi:10.1016/j.foodchem.2021.130834.

Abstract

Identification of bioactive peptides is an increasingly important target for food chemists, particularly in consideration of the widespread application of proteolytic enzymes in food processing. Because the characterization of small peptides by LC-MS/MS is challenging, we optimized a dimethyl labeling technique to facilitate small peptide identification, using almond proteins as a model. The method was validated by comparing the MS/MS spectra of standards and almond-derived peptides in their nonderivatized and derivatized forms. Signal enhancement of a1 ions was proved to effectively aid in the full-length sequencing of small peptides. We further validated this method using two industrially-relevant protein-rich extracts from almond flour: 1737 medium-sized peptides (5–39 amino acids) and 843 small peptides (2–4 amino acids) were identified. The use of an online bioactive peptide database, complemented by the existing literature, allowed the discovery of 208 small bioactive peptides, whereas for medium-sized peptides, only one was reported being bioactive.

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