Uncoupling Amphipathicity and Hydrophobicity: Role of Charge Clustering in Membrane Interactions of Cationic Antimicrobial Peptides
Bibliographic record
Abstract
Peptides with a combination of high positive charge and high hydrophobicity have high antimicrobial activity, as epitomized by peptide venoms, which are designed by nature as disruptors of host membranes yet also display significant efficacy against pathogens. To investigate this phenomenon systematically, here we focus on ponericin W1, a peptide venom isolated from Pachycondyla goeldii ants (WLGSALKIGAKLLPSVVGLFKKKKQ) to examine whether Lys positioning can be broadly applied to optimize the functional range of existing natural sequences. We prepared sets of ponericin W1 analogues, where Lys residues were either distributed in an amphipathic manner throughout the sequence (PonAmp), clustered at the N-terminus (PonN), or clustered at the C-terminus (PonC), along with their counterparts of reduced hydrophobicity through 2–4 Leu-to-Ala replacements. We found that wild-type ponericin W1 and all three variants displayed toxicity against human erythrocytes, but hemolysis was eliminated by the replacement of two or more Leu residues by Ala residues. As well, peptides containing up to 3 Leu-to-Ala replacements retained antimicrobial activity against E. coli bacteria. Biophysical analyses of peptide–membrane interaction patterns by circular dichroism spectroscopy revealed a novel mode of cluster-dependent peptide positioning vis-à-vis the water–membrane interface, where PonAmp and PonC peptides displayed full or partial helical structures, while PonN peptides were unstructured, likely due, in part, to dynamic interchange between aqueous and membrane surface environments. The overall findings suggest that the lower membrane penetration of N-terminal charge-clustered constructs coupled with moderate sequence hydrophobicity may be advantageous for conferring enhanced target selectivity for bacterial versus mammalian 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) | 0.000 | 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".