Evidence for Assembly of Small Multidrug Resistance Proteins by a “Two-faced” Transmembrane Helix
Bibliographic record
Abstract
Clinically significant bacterial resistance to drugs and cytotoxic compounds can be conferred by the energy-dependent efflux of toxicants, catalyzed by proteins embedded in the bacterial cell membrane. One such group of proteins, the small multidrug resistance family, are drug/proton antiporters that must oligomerize to function, a process that requires the assembly of at least two inactive monomers by intermolecular association of their four transmembrane helices. Here, we have used peptides that correspond to each of the four wild type transmembrane helices of the Halobacterium salinarum protein Hsmr and a corresponding library of mutant peptides to determine the interactive surfaces that likely contribute to protein oligomerization. Hsmr peptides were examined for strong (sodium dodecyl sulfate-resistant) and weaker (perfluorooctanoate-resistant) helix-helix interactions, in conjunction with circular dichroism, fluorescence energy transfer measurements, and molecular modeling. The results are compatible with a scheme in which two faces of helix four permit self-assembly via a higher affinity asymmetric pairing and a lower affinity symmetric interaction, resulting in a discrete tetramer. Our finding that two surfaces of helix four can contribute to the stability of small multidrug resistance protein assembly provides a molecular basis for the design of therapeutics that target this antibiotic resistance mechanism.
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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".