In Vitro Assay for the Characterization of RquA
Bibliographic record
Abstract
To survive in low‐oxygenic environments, microbial organisms employ anaerobic respiration. In certain organisms, the terpenoid quinone known as rhodoquinone (RQ) plays a key role in this process, though few details are known about how RQ itself is produced or what impact it may have on the cellular respiration process from a biomedical standpoint. Investigations into the rquA gene product (RquA), an enzyme that is required for the production of RQ in Rhodospirillum rubrum and other microbial species, and its role in anaerobic energy metabolism may provide a new target for antibacterial drug treatments through inhibition. Previous findings have shown that the in vivo expression of RquA results in the production of RQ in two species that do not naturally produce it, E. coli and yeast. RquA is responsible for the conversion of ubiquinone (Q) to RQ but it is not clear what additional cofactors are required for the conversion. Using in vitro assays with synthetic Q, purified RquA or whole cell lysate, potential amino donors, and a variety of co‐factors such as S ‐adenosyl methionine, we investigated the essential chemical components to perform this conversion. With these results, potential inhibition pathways may be identified and further investigated through assays and visualization methods such as X‐ray crystallography. Support or Funding Information New Frontiers in Research Fund, Gonzaga Science Research Program, CURCI
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".