Direct comparison of membrane interactions of model peptides composed of only Leu and Lys residues
Bibliographic record
Abstract
We compared the properties of two peptides of identical size and amino acid composition, Ac-(LKKL)(5)-NHEt and Ac-(KL)(10)-NHEt. Both are amphipathic, but only Ac-(LKKL)(5)-NHEt is a potent promoter of negative curvature. CD studies performed in the presence of lipids confirmed that under these conditions Ac-(LKKL)(5)-NHEt forms an alpha-helix, and Ac-(KL)(10)-NHEt adopts a beta structure. We studied their binding affinity by centrifugation and isothermal titration calorimetry techniques. The Ac-(LKKL)(5)-NHEt bound to zwitterionic and anionic liposomes, while Ac-(KL)(10)-NHEt interacted mainly with anionic liposomes. Ac-(LKKL)(5)-NHEt was more lytic than Ac-(KL)(10)-NHEt for zwitterionic palmitoyloleoylphosphatidylcholine (POPC) liposomes, and for liposomes composed of lipids extracted from either sheep or human erythrocytes (RBC). Both peptides had similar lytic and lipid mixing activities for liposomes containing anionic lipids. Both peptides were highly hemolytic, with Ac-(LKKL)(5)-NHEt active against sheep RBC and Ac-(KL)(10)-NHEt more active against human RBC. From their respective minimal effective concentrations (MECs) as antimicrobial agents, we judged Ac-(KL)(10)-NHEt to be 2 to 5-fold more potent than Ac-(LKKL)(5)-NHEt in media that contained physiological concentrations of NaCl. Notwithstanding, both peptides had MECs <1 microg/mL for Escherichia coli and Pseudomonas aeruginosa and <4 microg/mL for Staphylococcus aureus and methicillin-resistant Staphylococcus aureus. Although selectivity of antimicrobial peptides for bacterial membranes may result, in part, from the preferential display of anionic residues in these membranes, inability to interact with or bind to zwitterionic phospholipids offers no guarantee that the peptide will lack appreciable cytotoxicity for host cells.
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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".