Contribution of γ-Glutamyl-Cysteine Ligases of <i>Limosilactobacillus reuteri</i> to the Formation of Kokumi-Active γ-Glutamyl Dipeptides in Sourdough Bread
Bibliographic record
Abstract
Kokumi-active γ-glutamyl dipeptides accumulate during sourdough fermentation. γ-Glutamylcysteine ligases (Gcls) of Limosilactobacillus reuteri synthesize γ-glutamyl dipeptides during growth in sourdough. This study aimed to evaluate the contribution of Gcls from strains of L. reuteri in the formation of kokumi-active γ-glutamyl dipeptides in sourdough bread. Among 12 acceptor amino acids, the three Gcls of L. reuteri were the most active to Cys. With the acceptor amino acids Ile, Leu, and Phe, Gcl1 was more active than Gcl2 and Gcl3. Accordingly, Gcl1 contributed to the γ-Glu-Ile synthesis in sourdough fermentation. Proofing and baking strongly influenced the concentration of γ-glutamyl dipeptides in bread. The addition of 10% sourdough increased the content of γ-Glu-Leu and γ-Glu-Phe but not of other γ-glutamyl dipeptides in bread. In conclusion, the accumulation of kokumi γ-glutamyl dipeptides in sourdoughs was attributed to the combined activity of cereal enzymes, γ-glutamyl-cysteine ligases, and other microbial enzymes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".