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Record W2745982283 · doi:10.1002/cctc.201701068

Engineered Aminotransferase for the Production of <scp>d</scp>‐Phenylalanine Derivatives Using Biocatalytic Cascades

2017· article· en· W2745982283 on OpenAlexafffund
Curtis J. W. Walton, Fabio Parmeggiani, Janet Elizabeth Beattie Barber, Jenna L. McCann, Nicholas J. Turner, Roberto A. Chica

Bibliographic record

VenueChemCatChem · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmino Acid Enzymes and Metabolism
Canadian institutionsUniversity of Ottawa
FundersBiotechnology and Biological Sciences Research CouncilRoyal SocietyCanada Foundation for InnovationOntario Ministry of Economic Development and InnovationNatural Sciences and Engineering Research Council of CanadaGlaxoSmithKline
KeywordsPhenylalanineChemistryAmino acidEnantiomeric excessBiocatalysisEnantiomerCofactorStereochemistryCombinatorial chemistryEnzymeOrganic chemistryBiochemistryCatalysisEnantioselective synthesisReaction mechanism

Abstract

fetched live from OpenAlex

Abstract d‐Phenylalanine derivatives are valuable chiral building blocks for a wide range of pharmaceuticals. Here, we developed stereoinversion and deracemization biocatalytic cascades to synthesize d‐phenylalanine derivatives that contain electron‐donating or ‐withdrawing substituents of various sizes and at different positions on the phenyl ring with a high enantiomeric excess (90 to >99 % ee) from commercially available racemic mixtures or l‐amino acids. These whole‐cell systems couple Proteus mirabilis l‐amino acid deaminase with an engineered aminotransferase that displays native‐like activity towards d‐phenylalanine, which we generated from Bacillus sp. YM‐1 d‐amino acid aminotransferase. Our cascades are applicable to preparative‐scale synthesis and do not require cofactor‐regeneration systems or chemical reducing agents.

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)0.0010.000

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 head0.273
Teacher spread0.247 · 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

Citations31
Published2017
Admission routes2
Has abstractyes

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