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In Vitro Assay for the Characterization of RquA

2020· article· en· W3016292013 on OpenAlexaff
Melina Monlux, Evan Jacobs, Andrew J. Roger, David N. Langelaan, J.D. Cronk, Jennifer N. Shepherd

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicATP Synthase and ATPases Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsRhodospirillum rubrumBiochemistryCofactorBiologyIn vitroEnzymeChemistry

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.090

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.292
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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