MétaCan
Menu
Back to cohort
Record W2739549564 · doi:10.1002/biot.201600751

Exploring Bacterial Carboxylate Reductases for the Reduction of Bifunctional Carboxylic Acids

2017· article· en· W2739549564 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiotechnology Journal · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Catalysis and Immobilization
Canadian institutionsUniversity of Toronto
FundersNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsAdipic acidCarboxylateCofactorBifunctionalEnzymeBiocatalysisChemistryReductaseEscherichia coliCarboxylic acidPhosphofructokinase 2Amino acidBiochemistryStereochemistryOrganic chemistryCatalysisReaction mechanism

Abstract

fetched live from OpenAlex

Carboxylic acid reductases (CARs) selectively reduce carboxylic acids to aldehydes using ATP and NADPH as cofactors under mild conditions. Although CARs attracts significant interest, only a few enzymes have been characterized to date, whereas the vast majority of CARs have yet to be examined. Herein the authors report that 12 bacterial CARs reduces a broad range of bifunctional carboxylic acids containing oxo-, hydroxy-, amino-, or second carboxyl groups with several enzymes showing activity toward 4-hydroxybutanoic (4-HB) and adipic acids. These CARs exhibits significant reductase activity against substrates whose second functional group is separated from the carboxylate by at least three carbons with both carboxylate groups being reduced in dicarboxylic acids. Purified CARs supplemented with cofactor regenerating systems (for ATP and NADPH), an inorganic pyrophosphatase, and an aldo-keto reductase catalyzes a high conversion (50-76%) of 4-HB to 1,4-butanediol (1,4-BDO) and adipic acid to 1,6-hexanediol (1,6-HDO). Likewise, Escherichia coli strains expressing eight different CARs efficiently reduces 4-HB to 1,4-BDO with 50-95% conversion, whereas adipic acid is reduced to a mixture of 6-hydroxyhexanoic acid (6-HHA) and 1,6-HDO. Thus, our results illustrate the broad biochemical diversity of bacterial CARs and their compatibility with other enzymes for applications in biocatalysis.

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.

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 score0.018
Threshold uncertainty score0.466

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 studies0.0010.000
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 head0.058
GPT teacher head0.270
Teacher spread0.212 · 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