Comprehensive identification of native medium-sized and short bioactive peptides in sea bass muscle
peer-reviewed · Food Chemistry · 2020
| Date | 2020-10-22 |
| Type | peer-reviewed |
| Venue | Food Chemistry |
| Publisher | Elsevier BV |
| Contribution | downstream-application |
| DOI | 10.1016/j.foodchem.2020.128443 |
| Citations (OpenAlex) | 36 |
| Venue 2-year citedness | 10.07 |
Abstract
Native peptides from sea bass muscle were analyzed by two different approaches: medium-sized peptides by peptidomics analysis, whereas short peptides by suspect screening analysis employing an inclusion list of exact m/z values of all possible amino acid combinations (from 2 up to 4). The method was also extended to common post-translational modifications potentially interesting in food analysis, as well as non-proteolytic aminoacyl derivatives, which are well-known taste-active building blocks in pseudo-peptides. The medium-sized peptides were identified by de novo and combination of de novo and spectra matching to a protein sequence database, with up to 4077 peptides (2725 modified) identified by database search and 2665 peptides (223 modified) identified by de novo only; 102 short peptide sequences were identified (with 12 modified ones), and most of them had multiple reported bioactivities. The method can be extended to any peptide mixture, either endogenous or by protein hydrolysis, from other food matrices.
Methods and tools
- Sea bass muscle bioactive peptidome: Native-peptide discovery workflow combining suspect-screening for short peptides (2-4 aa, including modified aminoacyl derivatives) with database + de novo sequencing for medium-sized peptides (2,665 identified by de novo alone) from sea bass muscle, generalising to any food-matrix bioactive-peptide search.
Cites (2)
- pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework (2019) crossref
- De novo peptide sequencing by deep learning (2017) crossref
Cited by (1)
- Deep Learning Methods for De Novo Peptide Sequencing (2024) crossref