Toward a broad‐spectrum peptide‐based inhibitor of small multidrug resistance efflux pumps
Bibliographic record
Abstract
Abstract As an alternative approach to conventional antibiotics, here we explore a novel, rational drug design to target a resistance mechanism in antibiotic‐resistant bacteria—the small multidrug resistance (SMR) efflux pump. SMRs are a membrane protein that consist of four transmembrane (TM) helices, which homodimerize via TM4 in an anti‐parallel fashion through a GG7 motif. To target this interaction, we synthesized a series of peptides, typified by Ac‐A‐(Sar)3‐IIGMMLISAGVLI‐KKK‐NH2 (Sar = N‐methyl‐glycine, GG7 underlined), with a sequence derived from Escherichia coli TM4, designed to bind to—and competitively disrupt—the TM4–TM4 interaction site. The peptides also contain an uncharged N‐terminal sarcosine (N‐methyl‐glycine) tag to promote membrane insertion, and a C‐terminal tri‐lysine tag to direct the peptides toward the negatively‐charged bacterial membrane. As we found that the GG7 dimerization motif was highly conserved across bacterial species, including a range of priority pathogens, the peptides were tested for interspecies inhibitory activity where we varied the SMR overexpressed in E. coli bacterium and the peptide. The peptides included the TM4 sequence of E. coli (EmrE), Pseudomonas aeruginosa (PAsmr), and Mycobacterium tuberculosis (Mmr). We performed a series of assays measuring the efflux rates of the fluorescent toxin ethidium bromide, finding that the PAsmr peptide was broadly active against all SMRs expressed in E. coli. Interestingly, a peptide derived from E. coli TM4, but where the GG7 motif was scrambled, showed high activity against several SMRs. Overall, the efflux inhibitors designed herein show promise not only for improved treatment of bacterial infections, but more generally, may provide a successful approach to targeting and disrupting membrane‐embedded protein–protein interactions.
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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) | 0.002 | 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".