A method to identify and quantify the complete peptide composition in protein hydrolysates

Gijs J.C. Vreeke, Wouter Lubbers, Jean Paul Vincken, Peter A. Wierenga*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Automated approaches from proteomics are used to characterise peptides for food applications and in protein digests. Peptide annotations and confidence in these annotations are then based on the fragment spectra. Low reproducibility in repeat analyses has been reported even for annotations with high confidence. When analysing protein hydrolysates (in food) it is important to determine criteria that yield highly reproducible annotations. This study provides a structured approach to determine these criteria. Tryptic hydrolysates of α-lactalbumin, β-lactoglobulin and β-casein were analysed manually and automatically, using an UPLC-PDA-MS method for untargeted identification and absolute label-free quantification of peptides. A lock mass with two components was introduced resulting in an average mass error of 1 ppm. Processing filters were set to ensure reliable annotations based on MS/MS fragmentation, while maintaining maximum amount of information. Peptides in the individual hydrolysates with an MS intensity above the limit of annotation represented 99% of total MS intensity and were 100% consistently annotated between four replicates. Amino acid and peptide sequence coverages for the individual protein hydrolysates were 99–100% and 89–95%, respectively. Mixing the hydrolysates resulted in a loss of 11% of the peptide annotations above the LOA and lower reproducibility (97%) for the remaining annotations, as well as more co-eluting peptides. Calculated concentrations of co-eluting peptides in mixed hydrolysates varied 37 ± 21% from the value for single hydrolysates. The proposed approach allows complete description of peptide composition with highly repeatable annotations and quantification of peptides even in mixed hydrolysates.

Original languageEnglish
Article number339616
JournalAnalytica Chimica Acta
Volume1201
DOIs
Publication statusPublished - 8 Apr 2022

Keywords

  • Digestion
  • Food peptides
  • Peptide release kinetics
  • Peptidomics
  • Quantitative proteomics
  • UNIFI

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