Characterization and Inhibition of the Dimer Interface in Bacterial Small Multidrug Resistance Proteins
Bibliographic record
Abstract
As one of the mechanisms of antibiotic resistance, bacteria use several families of membrane-embedded α-helical transporters to remove cytotoxic molecules from the cell. The small multidrug resistance protein family (SMR) is one such group of drug transporters that because of their relative small size [ca. 110 residues with four transmembrane (TM) helices] must form at the minimum dimers to efflux drugs. We have used the SMR homologue Hsmr from Halobacterium salinarum to investigate the oligomerization properties of the protein family at TM helix 4. We produced point mutations along the length of the TM4 helix in the full length Hsmr protein and assayed their dimerization and functional properties via SDS-PAGE and bacterial cell growth assays. We found that Hsmr forms functionally dependent dimers via an evolutionarily conserved 90GLxLIxxGV98 small residue heptad repeat. Upon investigation of the large hydrophobic residues in this motif by substituting each large residue to Ile, Leu, Met, Phe, and Val, we determined that Hsmr efflux function relies on an optimal level of dimerization. While some substitutions led to either decreased or increased dimer and substrate-binding strength, several Ile94 and Val98 mutants were equal to wild type dimerization levels but were nonfunctional, leading to the hypothesis of a mechanistic role at TM4 in addition to the locus of dimerization. The functionally sensitive TM4 dimer represents a potential target for SMR inhibition using a synthetic TM4 peptide mimetic. Using exponential decay measurements from a real-time cellular efflux assay, we observed the efflux decay constant was decreased by up to ~60% after treatment with the TM4 peptide inhibitor compared to control peptide treatments. Our 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 | 0.001 |
| 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".