Hydrophobic moment drives penetration of bacterial membranes by transmembrane peptides
Bibliographic record
Abstract
With antimicrobial resistance (AMR) remaining a persistent and growing threat to human health worldwide, membrane-active peptides are gaining traction as an alternative strategy to overcome the issue. Membrane-embedded multi-drug resistant (MDR) efflux pumps are a prime target for membrane-active peptides, as they are a well-established contributor to clinically relevant AMR infections. Here, we describe a series of transmembrane peptides (TMs) to target the oligomerization motif of the AcrB component of the AcrAB-TolC MDR efflux pump from Escherichia coli . These peptides contain an N-terminal acetyl-A-(Sar) 3 (sarcosine; N-methylglycine) tag and a C-terminal lysine tag—a design strategy our lab has utilized to improve the solubility and specificity of targeting for TMs previously. While these peptides have proven useful in preventing AcrB-mediated substrate efflux, the mechanisms by which these peptides associate with and penetrate the bacterial membrane remained unknown. In this study, we have shown peptide hydrophobic moment ( μ H)—the measure of concentrated hydrophobicity on one face of a lipopathic α -helix—drives bacterial membrane permeabilization and depolarization, likely through lateral-phase separation of negatively-charged POPG lipids and the disruption of lipid packing. Our results show peptide μ H is an important consideration when designing membrane-active peptides and may be the determining factor in whether a TM will function in a permeabilizing or non-permeabilizing manner when embedded in the bacterial membrane.
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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".