Structural characterization of the antimicrobial peptides myxinidin and WMR in bacterial membrane mimetic micelles and bicelles
Bibliographic record
Abstract
Abstract Antimicrobial peptides are a promising class of alternative antibiotics that interact selectively with negatively charged lipid bilayers. This paper presents the structural characterization of the antimicrobial peptides myxinidin and WMR associated with bacterial membrane mimetic micelles and bicelles by NMR, CD spectroscopy, and Molecular Dynamics simulations. Both peptides adopt a different conformation in the lipidic environment than in aqueous solution. The location of peptides in micelles and bicelles has been studied by paramagnetic relaxation enhancement experiments with paramagnetic tagged 5- and 16-doxyl stearic acid (5-/16-SASL). Multi-microsecond long molecular dynamics simulations of multiple copies of the peptides were used to gain an atomic level of detail on membrane-peptide and peptide-peptide interactions. Our results highlight an essential role of the negatively charged membrane mimetic in the structural stability of both myxinidin and WMR. The peptides localize predominantly in the membrane’s headgroup region and have a noticeable membrane thinning effect on the overall bilayer structure. Myxinidin and WMR show different tendency to selfaggregate, which is also influenced by the membrane composition (DOPE/DOPG versus DOPE/DOPG/CL) and can be related to the previously observed difference in the ability of the peptides to disrupt different types of model 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".