Promotion of Peptide Antimicrobial Activity by Fatty Acid Conjugation
Bibliographic record
Abstract
Three peptides, YGAA[KKAAKAA](2) (AKK), KLFKRHLKWKII (SC4), and YG[AKAKAAKA](2) (KAK), were conjugated with lauric acid and tested for the effect on their structure, antibacterial activity, and eukaryotic cell toxicity. The conjugated AKK and SC4 peptides showed increased antimicrobial activity relative to unconjugated peptides, but the conjugated KAK peptide did not. The circular dichroism spectrum of AKK showed a significantly larger increase in its alpha-helical content in the conjugated form than peptide KAK in a solution containing phosphatidylethanolamine/phosphotidylglycerol vesicles, which mimics bacterial membranes. The KAK and AKK peptides and their corresponding fatty acid conjugates showed little change in their structure in the presence of phosphatidylcholine vesicles, which mimic the cell membrane of eukaryotic cells. The hemolytic activity of the KAK and AKK peptides and conjugates was low. However, the SC4 fatty acid conjugate showed a large increase in hemolytic activity and a corresponding increase in helical content in the presence of phosphatidylcholine vesicles. These results support the model of antimicrobial peptide hemolytic and antimicrobial activity being linked to changes in secondary structure as the peptides interact with lipid membranes. Fatty acid conjugation may improve the usefulness of peptides as antimicrobial agents by enhancing their ability to form secondary structures upon interacting with the bacterial membranes.
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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 | 0.000 |
| 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) | 0.001 | 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".