Quantitative structure‐activity relationship study of bitter di‐ and tri‐peptides including relationship with angiotensin I‐converting enzyme inhibitory activity
Bibliographic record
Abstract
Bitterness represents a major challenge in industrial application of food protein hydrolysates or bioactive peptides and is a major factor that controls the flavor of formulated therapeutic products. The aim of this work was to apply quantitative structure-activity relationship modeling as a tool to determine the type and position of amino acids that contribute to bitterness of di- and tri-peptides. Datasets of bitter di- and tri-peptides were constructed using values from available literature, followed by modeling using partial least square (PLS) regression based on the three z-scores of 20 coded amino acids. Prediction models were validated using cross-validation and permutation tests. Results showed that a single-component model could explain 52 and 50% of the Y variance (bitterness threshold) of bitter di- and tri-peptides, respectively. Using PLS regression coefficients, it was determined that hydrophobic amino acids at the carboxyl-terminus and bulky amino acid residues adjacent to the carboxyl terminal are the major determinants of the intensity of bitterness of di- and tri-peptides. However, there was no significant (p > 0.05) correlation between bitterness of di- and tri-peptides and their angiotensin I-converting enzyme-inhibitory properties.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".