Membrane-Active Peptides Derived from Natural Transmembrane Domains Function as Antibiotic Potentiators
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Antimicrobial resistance continues to be a prevailing threat to human health worldwide, largely due to the arsenal of resistance mechanisms bacteria have evolved over years of exposure to traditional antibiotics. As an approach to devising therapeutics that can overcome this resistance, we hypothesized that peptides consisting of single transmembrane (TM) segments of membrane proteins may permeabilize bacterial membranes and thereby facilitate access of antimicrobials to their cytoplasmic targets. Using peptides derived from a natural TM helix from the AcrB component of the AcrAB-TolC efflux protein, we found that AcrB TM8 (wild type sequence: KKKK–FL(Abu)LAALYESWSI–NH 2 ) and a “scrambled” analog of identical composition, charge, and overall hydrophobicity (TM8-S: KKKK–FSLEALW(Abu)ISAYL–NH 2 ) (Abu = α-aminobutyric acid), resensitize Escherichia coli to sublethal doses of the antibiotics cloxacillin and nalidixic acid. These peptides induce 50–100% reduction of cell growth compared to bacteria treated with either peptide or antibiotic alone. The molecular basis for peptide-based permeabilization of the bacterial outer and inner membranes that contributes to the observed antibiotic potentiation was studied through several in vitro liposome-based assays and in vivo fluorescence-based assays. The overall results suggest that membrane proteins harbor a wealth of peptide sequences that may function as effective membrane-active peptides for the targeting of difficult-to-treat Gram-negative bacterial infections, and as such, provide valuable guidelines for the design of membrane-active peptides with synergistic antibacterial and antibiotic potentiation activity.
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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) | 0.002 | 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; 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".