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Record W4321606888 · doi:10.1002/cbic.202300001

Rapid Biocatalytic Synthesis of Aromatic Acid CoA Thioesters by Using Microbial Aromatic Acid CoA Ligases

2023· article· en· W4321606888 on OpenAlexaff
Debayan Chaudhury, Ella R. Torkelson, Kaya A. Meyers, Justin Acheson, Leta Landucci, Yucen Pu, Zimou Sun, Marco Tonelli, C.A. Bingman, Rebecca A. Smith, Steven D. Karlen, Shawn D. Mansfield, John Ralph, Brian G. Fox

Bibliographic record

VenueChemBioChem · 2023
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of British Columbia
FundersNational Institute of General Medical SciencesBasic Energy SciencesGreat Lakes Bioenergy Research CenterOffice of ScienceNational Institutes of HealthU.S. Department of Energy
KeywordsThioesterChemistryMonolignolCoenzyme AOrganic chemistryLigninBiochemistryEnzymeBiosynthesis

Abstract

fetched live from OpenAlex

Chemically labile ester linkages can be introduced into lignin by incorporation of monolignol conjugates, which are synthesized in planta by acyltransferases that use a coenzyme A (CoA) thioester donor and a nucleophilic monolignol alcohol acceptor. The presence of these esters facilitates processing and aids in the valorization of renewable biomass feedstocks. However, the effectiveness of this strategy is potentially limited by the low steady-state levels of aromatic acid thioester donors in plants. As part of an effort to overcome this, aromatic acid CoA ligases involved in microbial aromatic degradation were identified and screened against a broad panel of substituted cinnamic and benzoic acids involved in plant lignification. Functional fingerprinting of this ligase library identified four robust, highly active enzymes capable of facile, rapid, and high-yield synthesis of aromatic acid CoA thioesters under mild aqueous reaction conditions mimicking in planta activity.

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 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 categoriesMeta-epidemiology (narrow)
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 score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.209
Teacher spread0.192 · 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 teacher head, not a consensus.

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

Citations4
Published2023
Admission routes1
Has abstractyes

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