Interaction of designed cationic antimicrobial peptides with the outer membrane of gram-negative bacteria
Bibliographic record
Abstract
The outer membrane (OM) is a hallmark feature of gram-negative bacteria that provides the species with heightened resistance against antibiotic threats while cationic antimicrobial peptides (CAPs) are natural antibiotics broadly recognized for their ability to disrupt bacterial membranes. It has been well-established that lipopolysaccharides present on the OM are among major targets of CAP activity against gram-negative species. Here we investigate how the relative distribution of charged residues along the primary peptide sequence, in conjunction with its overall hydrophobicity, affects such peptide-OM interactions in the natural CAP Ponericin W1. Using a designed peptide library derived from Ponericin W1, we determined that the consecutive placement of Lys residues at the peptide N- or C-terminus (ex. "PonN": KKKKKKWLGSALIGALLPSVVGLFQ) enhances peptide binding affinity to OM lipopolysaccharides compared to constructs where Lys residues are interspersed throughout the primary sequence (ex. "PonAmp": WLKKALKIGAKLLPSVVKLFKGSGQ). Antimicrobial activity against multidrug resistant strains of Pseudomonas aeruginosa was similarly found to be highest among Lys-clustered sequences. Our findings suggest that while native Ponericin W1 exerts its initial activity at the OM, Lys-clustering may be a promising means to enhance potency towards this interface, thereby augmenting peptide entry and activity at the IM, with apparent advantage against multidrug-resistant species.
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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".