Drug Efflux by a Small Multidrug Resistance Protein Is Inhibited by a Transmembrane Peptide
Bibliographic record
Abstract
Drug-resistant bacteria use several families of membrane-embedded transporters to remove antibiotics from the cell. One such family is the small multidrug resistance proteins (SMRs) that, because of their relatively small size (ca. 110 residues with four transmembrane [TM] helices), must form (at least) dimers to efflux drugs. Here, we use a Lys-tagged synthetic peptide with exactly the same sequence as TM4 of the full-length SMR Hsmr from Halobacterium salinarum [TM4 sequence: AcA(Sar)(3)-VAGVVGLALIVAGVVVLNVAS-KKK (Sar = N-methylglycine)] to compete with and disrupt the native TM4-TM4 interactions believed to constitute the locus of Hsmr dimerization. Using a cellular efflux assay of the fluorescent SMR substrate ethidium bromide, we determined that bacterial cells containing Hsmr are able to remove cellular ethidium via first-order exponential decay with a rate constant (k) of 10.1 × 10(-3) ± 0.7 × 10(-3) s(-1). Upon treatment of the cells with the TM4 peptide, we observed a saturable ~60% decrease in the efflux rate constant to 3.7 × 10(-3) ± 0.2 × 10(-3) s(-1). In corresponding experiments with control peptides, including scrambled sequences and a sequence with d-chirality, a decrease in ethidium efflux either was not observed or was marginal, likely from nonspecific effects. The designed peptides did not evoke bacterial lysis, indicating that they act via the α-helicity and membrane insertion propensities of the native TM4 helix. Our overall results suggest that this approach could conceivably be used to design hydrophobic peptides for disruption of key TM-TM interactions of membrane proteins and represent a valuable route to the discovery of new therapeutics.
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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".