Peptide-Based Approach to Inhibition of the Multidrug Resistance Efflux Pump AcrB
Bibliographic record
Abstract
membrane protein AcrB is the archetypal protein utilized for bacterial efflux study because it can extrude a diverse range of antibiotic substrates and has close homologues in many Gram-negative pathogens. Three AcrB subunits, each of which contains 12 transmembrane (TM) helices, are known to trimerize to form the minimal functional unit, stabilized noncovalently by helix-helix interactions between TM1 and TM8. To inhibit the efflux activity of AcrB, we have rationally designed synthetic peptides aimed at destabilizing the AcrB trimerization interface by outcompeting the subunit interaction sites within the membrane. Here we report that peptides mimicking TM1 or TM8, with flanking N-terminal peptoid tags, and C-terminal lysine tags that aid in directing the peptides to their membrane-embedded target, decrease the AcrB-mediated efflux of the fluorescent substrate Nile red and potentiate the effect of the antimicrobials chloramphenicol and ethidium bromide. To further characterize the motif encompassing the interaction between TM1 and TM8, we used Förster resonance energy transfer to demonstrate dimerization. Using the TM1 and TM8 peptides, in conjunction with several selected mutant peptides, we highlight residues that may increase the potency and specificity of the peptide drug candidates. In targeting membrane-embedded protein-protein interactions, this work represents a novel approach to AcrB inhibition and, more broadly, a potential route to a new category of efflux pump inhibitors.
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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 | 0.001 |
| 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".