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Record W2809070750 · doi:10.1128/aem.01095-18

An Engineered Aro1 Protein Degradation Approach for Increased <i>cis,cis</i> -Muconic Acid Biosynthesis in Saccharomyces cerevisiae

2018· article· en· W2809070750 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

VenueApplied and Environmental Microbiology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsConcordia University
FundersFonds de recherche du Québec – Nature et technologiesConcordia UniversityNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsSaccharomyces cerevisiaeBiochemistryMuconic acidAuxotrophyAmino acidChemistryBiosynthesisFermentationDegronBiologyYeastMutantEnzymeGene

Abstract

fetched live from OpenAlex

have been hindered by a bottleneck at the PCA decarboxylation step and the creation of aromatic amino acid auxotrophy through deleterious manipulation of the pentafunctional Aro1 protein. In light of these studies, this work was undertaken with the central objective of preserving amino acid prototrophy, which we achieved by employing an Aro1 degradation strategy. Moreover, resolution of the key PCA decarboxylase bottleneck, as detailed herein, advances our understanding of yeast MA biosynthesis and will guide future strain engineering efforts. These strategies resulted in the highest titer reported to date for muconic acid produced in yeast. Overall, our study showcases the effectiveness of careful tuning of yeast Aro1 activity and the importance of host-pathway dynamics.

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 score0.007
Threshold uncertainty score0.809

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 studies0.0000.000
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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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