Aggregated Amphiphilic Antimicrobial Peptides Embedded in Bacterial Membranes
Bibliographic record
Abstract
Molecular dynamics (MD) simulations, stochastic optical reconstruction microscopy (STORM), and neutron reflection (NR) were combined to explore how antimicrobial peptides (AMPs) can be designed to promote the formation of nanoaggregates in bacterial membranes and impose effective bactericidal actions. Changes in the hydrophobicity of the designed AMPs were found to have a strong influence on their bactericidal potency and cytotoxicity. G(IIKK)3I-NH2 (G3) achieved low minimum inhibition concentrations (MICs) and effective dynamic kills against both antibiotic-resistant and -susceptible bacteria. However, a G3 derivative with weaker hydrophobicity, KI(KKII)2I-NH2 (KI), exhibited considerably lower membrane-lytic activity. In contrast, the more hydrophobic G(ILKK)3L-NH2 (GL) peptide achieved MICs similar to those observed for G3 but with worsened hemolysis. Both the model membranes studied by Brewster angle microscopy, zeta potential measurements, and NR and the real bacterial membranes examined with direct STORM contained membrane-inserted peptide aggregates upon AMP exposure. These structural features were well supported by MD simulations. By revealing how AMPs self-assemble in microbial membranes, this work provides important insights into AMP mechanistic actions and allows further fine-tuning of antimicrobial potency and cytotoxicity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".