Data for signal transduction mechanism of GraSR in Staphylococcus aureus
Bibliographic record
Abstract
The response to cationic antimicrobial peptides (CAMPs) in Staphylococcus aureus relies on a two-component system (TCS), GraSR, an auxiliary protein GraX and an ATP-binding cassette (ABC) transporter, VraF/G. To understand the signal transduction mechanism by GraSR, we investigated the kinase activity of the cytoplasmic domain of histidine kinase GraS and the interaction with its cognate response regulator GraR. We also investigated interactions among the auxiliary protein GraX, GraS/R and the ATPase protein of the ABC transporter, VraF. We found that GraS lacks autophosphorylation activity, unlike a similar histidine kinase, BceS, of Bacillus subtilis . In addition, the interaction between GraS and GraR is very weak in comparison to the stronger interaction observed between BceS and its conjugated response regulator, BceR, suggesting that CAMP signaling may not flow directly from GraS to GraR. We found that the auxiliary protein GraX interacts with VraF and GraR, and requires the histidine phosphotransfer and dimerization domain of GraS to interact with this protein. Further, VraF requires the GraS region that connects the membrane-bound domain with the cytoplasmic domain of this protein for interaction with GraS. The interactions of GraX with GraS/R and VraF indicate that GraX may serve as a scaffold to bring these proteins in close proximity to GraS, plausibly to facilitate activation of GraS to ultimately transduce the signal to GraR.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.027 |
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".