Chemotaxis kinase CheA is activated by three neighbouring chemoreceptor dimers as effectively as by receptor clusters
Bibliographic record
Abstract
Chemoreceptors are central to bacterial chemotaxis. These transmembrane homodimers form trimers of dimers. Trimers form clusters of a few to thousands of receptors. A crucial receptor function is 100-fold activation, in signalling complexes, of sensory histidine kinase CheA. Significant activation has been shown to require more than one receptor dimer but the number required for full activation was unknown. We investigated this issue using Nanodiscs, soluble, nanoscale (∼10 nm diameter) plugs of lipid bilayer, to limit the number of neighbouring receptors contributing to activation. Utilizing size-exclusion chromatography, we separated primary preparations of receptor-containing Nanodiscs, otherwise heterogeneous for number and orientation of inserted receptors, into fractions enriched for specific numbers of dimers per disc. Fractionated, clarified Nanodiscs carrying approximately five dimers per disc were as effective in activating kinase as native membrane vesicles containing many neighbouring dimers. At five independently inserted dimers per disc, every disc would have at least three dimers oriented in parallel and thus able act together as they would in native membrane. We conclude full kinase activation involves interaction of CheA with groups of three receptor dimers, presumably as a trimer of dimers, and that more extensive interactions among receptors are not necessary for full kinase activation.
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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".