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Record W3194954656 · doi:10.1021/acs.oprd.1c00250

Enzyme Optimization and Process Development for a Scalable Synthesis of (<i>R</i>)-2-Methoxymandelic Acid

2021· article· en· W3194954656 on OpenAlexaff
Mark E. Scott, Xiaotian Wang, Luke Humphreys, Michael J. Geier, Balamurali Kannan, Johann Chan, Gareth Brown, Daniel F. A. R. Dourado, Darren Gray, Stefan Mix, Aliaksei V. Pukin

Bibliographic record

VenueOrganic Process Research & Development · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Catalysis and Immobilization
Canadian institutionsGilead Sciences (Canada)
Fundersnot available
KeywordsChemistryNitrilaseKinetic resolutionEnantioselective synthesisCombinatorial chemistryProcess developmentYield (engineering)EnzymeMutantCyanohydrinProtein engineeringOrganic chemistryBiochemistryCatalysisMaterials science

Abstract

fetched live from OpenAlex

The rational protein engineering of wild type BCJ2315 nitrilase and its use in the development of a one-pot, enantioselective, dynamic kinetic resolution for ( R )-2-methoxymandelic acid is reported. Through a combination of molecular docking and B -factor analyses, a focused library of mutants was identified and screened to improve nitrilase selectivity, activity, and stability. Initial optimization revealed that the addition of sodium bisulfite prevented enzyme deactivation by the aldehyde starting material, removing the need to isolate the cyanohydrin and allowing for development of a one-pot process for mutant screening. Further optimization of the process with the preferred mutants revealed subtle interactions between temperature, pH, substrate loading, and enzyme source which ultimately led to development of a suitable lyophilized whole cell process for scale-up, affording ( R )-2-methoxymandelic acid in 97% ee and 70% isolated yield on multigram scale.

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)0.001
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
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.324
Teacher spread0.298 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

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