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Record W4392203301 · doi:10.1016/j.chempr.2024.02.001

A prebiotic Krebs cycle analog generates amino acids with H2 and NH3 over nickel

2024· article· en· W4392203301 on OpenAlexaff
Harpreet Kaur, Sophia A. Rauscher, Emilie Werner, Youngdong Song, Jing Yi, Wahnyalo Kazöne, William Martin, Harun Tüysüz, Joseph Moran

Bibliographic record

VenueChem · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrigins and Evolution of Life
Canadian institutionsUniversity of Ottawa
FundersH2020 Marie Skłodowska-Curie ActionsChina Scholarship CouncilMax-Planck-GesellschaftH2020 European Research CouncilAgence Nationale de la RechercheVolkswagen FoundationEuropean Commission
KeywordsPrebioticChemistryNickelAmino acidBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Hydrogen (H 2 ) has powered microbial metabolism for roughly 4 billion years. The recent discovery that it also fuels geochemical analogs of the most ancient biological carbon fixation pathway sheds light on the origin of metabolism. However, it remains unclear whether H 2 can sustain more complex nonenzymatic reaction networks. Here, we show that H 2 drives the nonenzymatic reductive amination of six biological ketoacids and glyoxylate to give the corresponding amino acids in good yields using ammonium concentrations ranging from 6 to 150 mM. Catalytic amounts of nickel or ground meteorites enable these reactions at 22°C and pH 8. The same conditions promote an H 2 -dependent ketoacid-forming reductive aldol chemistry that co-occurs with reductive amination, producing a continuous reaction network resembling amino acid synthesis in the metabolic core of ancient microbes. The results support the hypothesis that the earliest biochemical networks could have emerged without enzymes or RNA.

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 score0.004

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.226
Teacher spread0.220 · 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

Citations38
Published2024
Admission routes1
Has abstractyes

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