Structural Requirements of Angiotensin I‐Converting Enzyme Inhibitory Peptides: Quantitative Structure‐Activity Relationship Modeling of Peptides Containing 4‐10 Amino Acid Residues
Bibliographic record
Abstract
Abstract Models for angiotensin converting enzyme (ACE) inhibitory peptides with varied lengths are proposed based on results from partial least square regression analysis of the potency of known peptides. Modeling was performed using partial least square regression with inhibitory activity of peptides as the dependent variable (Y) and the physicochemical properties of amino acids as the predictor X matrix. Variable importance in the projection (VIP) analysis of individual amino acid residues at each position revealed that the C‐terminal tetrapeptide residues of long‐chain peptides were more important to their ACE‐inhibitory activity than the C‐terminal tripeptide residues. The most likely preferred amino acid residues starting from C‐terminus are tyrosine and cysteine for the first position, histidine, tryptophan and methionine for the second position with isoleucine, leucine, valine and methionine for the third position, and tryptophan for the fourth position. We concluded that the reported structural requirements of ACE‐inhibitory peptides provide useful information that can be used for the development of more efficacious ACE‐inhibitory peptides.
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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 | 0.000 | 0.001 |
| 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 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".