Quantitative structure‐activity relationship study of bitter di‐ and tri‐peptides including relationship with angiotensin I‐converting enzyme inhibitory activity
Why this work is in the frame
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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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it