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Record W4404219668 · doi:10.26434/chemrxiv-2024-pjx2h

Nonenzymatic Carboxylate Phosphorylation in Water

2024· preprint· en· W4404219668 on OpenAlexaff
Weiqiang Chen, Joris Zimmermann, Jonas Dechent, Joseph Moran

Bibliographic record

VenueChemRxiv · 2024
Typepreprint
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of Ottawa
FundersEuropean Research Council
KeywordsCarboxylatePhosphorylationChemistryBusinessBiochemistry

Abstract

fetched live from OpenAlex

The core pathways of autotrophic microbial metabolism have been proposed to be fossils of self-organized prebiotic chemistry. In recent years, numerous reactions within these pathways have been shown to occur nonenzymatically, supporting this hypothesis. However, the phosphorylation of carboxylic acids to the corresponding acyl phosphates, a recurring metabolic reaction, has yet to be demonstrated without enzymes. Here we show that carboxylate phosphorylation is promoted by diamidophosphate (DAP) and sodium nitrite in the presence of water, driven by the release of dinitrogen. The reaction occurs in minutes at 0-50 °C to convert carboxylic acids to acyl amidophosphate intermediates, which then undergo nitrite-promoted hydrolysis to give the corresponding acyl phosphates. Though we do not claim the conditions used here were directly relevant to the origin of life, the observation of aqueous nonenzymatic carboxylate phosphorylation to simple acyl phosphates in the absence of adenosine triphosphate (ATP) raises the enticing prospect that it might also be achieved using driving forces more congruent with the principles of cellular bioenergetics.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.257
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes1
Has abstractyes

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