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Record W2792623004 · doi:10.1016/j.meteno.2018.02.001

Biocatalytic production of adipic acid from glucose using engineered Saccharomyces cerevisiae

2018· article· en· W2792623004 on OpenAlexafffund
Kaushik Raj, Siavash Partow, Kevin Correia, Anna N. Khusnutdinova, Alexander F. Yakunin, Radhakrishnan Mahadevan

Bibliographic record

VenueMetabolic Engineering Communications · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and Science
KeywordsAdipic acidChemistryMuconic acidSaccharomyces cerevisiaeFermentationLevulinic acidBiochemistryOrganic chemistryYeastCatalysisBenzene

Abstract

fetched live from OpenAlex

Adipic acid is an important industrial chemical used in the synthesis of nylon-6,6. The commercial synthesis of adipic acid uses petroleum-derived benzene and releases significant quantities of greenhouse gases. Biocatalytic production of adipic acid from renewable feedstocks could potentially reduce the environmental damage and eliminate the need for fossil fuel precursors. Recently, we have demonstrated the first enzymatic hydrogenation of muconic acid to adipic acid using microbial enoate reductases (ERs) - complex iron-sulfur and flavin containing enzymes. In this work, we successfully expressed the Bacillus coagulans ER in a Saccharomyces cerevisiae strain producing muconic acid and developed a three-stage fermentation process enabling the synthesis of adipic acid from glucose. The ability to express active ERs and significant acid tolerance of S. cerevisiae highlight the applicability of the developed yeast strain for the biocatalytic production of adipic acid from renewable feedstocks.

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 score0.002

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 head0.018
GPT teacher head0.253
Teacher spread0.236 · 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
Published2018
Admission routes2
Has abstractyes

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