Quantitative Structure−Activity Relationship Study of Bitter Peptides
Why this work is in the frame
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Bibliographic record
Abstract
A database consisting of 224 di- to tetradecapeptides and five amino acids was compiled to study quantitative structure-activity relationships of bitter peptides. Partial least-squares regression-1 analysis was conducted using the amino acid three z-scores and/or three parameters (total hydrophobicity, residue number, and log mass values) as X-variables and bitterness values (log 1/T where T is the bitterness threshold) as Y-variables. Using the three parameters only, significant models (p < 0.001) were obtained describing the entire data set as well as data subsets, except that comprised only of octa- to tetradecapeptides. For data sets comprising different peptide lengths, the models were improved by including the three z-scores at the N-terminal and C-terminal positions. Correlation coefficients for bitterness prediction of 48 dipeptides and 12 pentapeptides were 0.75 (RMSEP = 0.53) and 0.90 (RMSEP = 0.48), respectively. Bulky hydrophobic amino acids at the C terminus and bulky basic amino acids at the N terminus were highly correlated to bitterness.
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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 | 0.000 | 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 it