Methodology for Analysis of Peptide Consumption by Yeast during Fermentation of Enzymatic Protein Hydrolysate Supplemented Synthetic Medium Using UPLC-IMS-HRMS
Bibliographic record
Abstract
Several studies have shown the ability of yeast to consume peptides as a nitrogen source in single-peptide containing media. However, a suitable and cost-effective methodology to study the utilization of peptides by yeast and other microorganisms in a complex peptide mixture has yet to be put forward. This article addresses this issue by presenting a screening methodology for tracking the consumption of peptides by yeast during alcoholic fermentation. As a peptide source, the methodology makes use of an in-house prepared peptide-mapped bovine serum albumin (BSA) proteolytic digest, which was applied to a synthetic grape must. The peptide uptake was analyzed using high-throughput ultra-high-pressure liquid chromatography coupled to data-independent acquisition-based ion mobility separation-enabled high-resolution mass spectrometry (UPLC-DIA-IMS-HRMS) analysis. The relative changes of abundance of 123 di- to hexapeptides were monitored and reported during fermentations with three commercial wine strains, demonstrating different uptake kinetics for individual peptides. Using the same peptide-mapped BSA hydrolysate, the applicability of an untargeted workflow was additionally assessed for peptide profiling in unelucidated matrixes. The comparison of the results from peptide mapping and untargeted analysis experiments highlighted the ability of untargeted analysis to consistently identify small molecular weight peptides on the length and amino acid composition. The proposed method, in combination with other analytical techniques, such as gene or protein expression analysis, can be a useful tool for different metabolic studies related to the consumption of complex nitrogen sources by yeast or other microorganisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".