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Record W2759401751 · doi:10.1002/anie.201707918

Adenylation Activity of Carboxylic Acid Reductases Enables the Synthesis of Amides

2017· article· en· W2759401751 on OpenAlexaff
Alexander James Wood, Nicholas J. Weise, Joseph D. Frampton, Mark S. Dunstan, Michael A. R. Hollas, Sasha R. Derrington, Richard C. Lloyd, Daniela Quaglia, Fabio Parmeggiani, David Leys, Nicholas J. Turner, Sabine L. Flitsch

Bibliographic record

VenueAngewandte Chemie International Edition · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical Synthesis and Analysis
Canadian institutionsUniversité de MontréalCegep Edouard Montpetit
FundersBiotechnology and Biological Sciences Research CouncilEngineering and Physical Sciences Research Council
KeywordsChemistryCarboxylic acidAmine gas treatingAmideAdenylylationCofactorElectrophileCombinatorial chemistryOrganic chemistryNucleophilic acyl substitutionNucleophilePeptide bondEnzymeCatalysisAcylationBiosynthesis

Abstract

fetched live from OpenAlex

Carboxylic acid reductases (CARs) catalyze the reduction of a broad range of carboxylic acids to aldehydes using the cofactors adenosine triphosphate and nicotinamide adenine dinucleotide phosphate, and have become attractive biocatalysts for organic synthesis. Mechanistic understanding of CARs was used to expand reaction scope, generating biocatalysts for amide bond formation from carboxylic acid and amine. CARs demonstrated amidation activity for various acids and amines. Optimization of reaction conditions, with respect to pH and temperature, allowed for the synthesis of the anticonvulsant ilepcimide with up to 96 % conversion. Mechanistic studies using site-directed mutagenesis suggest that, following initial enzymatic adenylation of substrates, amidation of the carboxylic acid proceeds by direct reaction of the acyl adenylate with amine nucleophiles.

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 integrity0.0000.001
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 head0.015
GPT teacher head0.264
Teacher spread0.250 · 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

Citations94
Published2017
Admission routes1
Has abstractyes

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