Peptidomics of Peptic Digest of Selected Potato Tuber Proteins: Post-Translational Modifications and Limited Cleavage Specificity
Bibliographic record
Abstract
Bioinformatic tools are useful in predicting bioactive peptides from food proteins. This study was focused on using bioinformatics and peptidomics to evaluate the specificity of peptide release and post-translational modifications (PTMs) in a peptic digest of potato protein isolate. Peptides in the protein hydrolysate were identified by LC-MS/MS and subsequently aligned to their parent potato tuber proteins. Five major proteins were selected for further analysis, namely, lipoxygenase, α-1,4-glucan phosphorylase, annexin, patatin, and polyubiquitin, based on protein coverage, abundance, confidence levels, and function. Comparison of the in silico peptide profile generated with ExPASy PeptideCutter and experimental peptidomics data revealed several differences. The experimental peptic cleavage sites were found to vary in number and specificity from PeptideCutter predictions. Average peptide chain length was also found to be higher than predicted with hexapeptides as the smallest detected peptides. Moreover, PTMs, particularly Met oxidation and Glu/Asp deamidation, were observed in some peptides, and these were unaccounted for during in silico analysis. PTMs can be formed during aging of potato tubers, or as a result of processing conditions during protein isolation and hydrolysis. The findings provide insights on the limitations of current bioinformatics tools for predicting bioactive peptide release from proteins, and on the existence of structural modifications that can alter the peptide bioactivity and functionality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".