Roles of Hydrophobicity and Charge Distribution of Cationic Antimicrobial Peptides in Peptide-Membrane Interactions
Bibliographic record
Abstract
Cationic antimicrobial peptides (CAPs) occur as important innate immunity agents in many organisms, including humans, and offer a viable alternative to conventional antibiotics, as they physically disrupt the bacterial membranes, leading to membrane lysis and eventually cell death. In this work, we studied the biophysical and microbiological characteristics of designed CAPs varying in hydrophobicity levels and charge distributions by a variety of biophysical and biochemical approaches, including in-tandem atomic force microscopy, attenuated total reflection-FTIR, CD spectroscopy, and SDS-PAGE. Peptide structural properties were correlated with their membrane-disruptive abilities and antimicrobial activities. In bacterial lipid model membranes, a time-dependent increase in aggregated β-strand-type structure in CAPs with relatively high hydrophobicity (such as KKKKKKALFALWLAFLA-NH(2)) was essentially absent in CAPs with lower hydrophobicity (such as KKKKKKAAFAAWAAFAA-NH(2)). Redistribution of positive charges by placing three Lys residues at both termini while maintaining identical sequences minimized self-aggregation above the dimer level. Peptides containing four Leu residues were destructive to mammalian model membranes, whereas those with corresponding Ala residues were not. This finding was mirrored in hemolysis studies in human erythrocytes, where Ala-only peptides displayed virtually no hemolysis up to 320 μM, but the four-Leu peptides induced 40-80% hemolysis at the same concentration range. All peptides studied displayed strong antimicrobial activity against Pseudomonas aeruginosa (minimum inhibitory concentrations of 4-32 μM). The overall findings suggest optimum routes to balancing peptide hydrophobicity and charge distribution that allow efficient penetration and disruption of the bacterial membranes without damage to mammalian (host) 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".