Membrane composition influences the topology bias of bacterial integral membrane proteins
Bibliographic record
Abstract
Small multidrug resistance (SMR) protein family members confer bacterial resistance to toxic antiseptics and are believed to function as dual topology oligomers. If dual topology is essential for SMR activity, then the topology bias should change as bacterial membrane lipid compositions alter to maintain a "neutral" topology bias. To test this hypothesis, a bioinformatic analysis of bacterial SMR protein sequences was performed to determine a membrane protein topology based on charged amino acid residues within loops, and termini regions according to the positive inside rule. Three bacterial lipid membrane parameters were examined, providing the proportion of polar lipid head group charges at the membrane surface (PLH), the relative hydrophobic fatty acid length (FAL), and the proportion of fatty acid unsaturation (FAU). Our analysis indicates that individual SMR pairs, and to a lesser extent SMR singleton topology biases, are significantly correlated to increasing PLH, FAL and FAU differences validating the hypothesis. Correlations between the topology biases of SMR proteins identified in Gram+ compared to Gram- species and each lipid parameter demonstrated a linear inverse relationship.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".