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BAHD Company: The Ever-Expanding Roles of the BAHD Acyltransferase Gene Family in Plants

2022· review· en· W4310470900 on OpenAlexaff
Gaurav D. Moghe, Lars Kruse, Maike Petersen, Federico Scossa, Alisdair R. Fernie, Emmanuel Gaquerel, John C. D’Auria

Bibliographic record

VenueAnnual Review of Plant Biology · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Gene Expression Analysis
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersDeutsche Forschungsgemeinschaft
KeywordsAcyltransferasesBiologyGene familyChemical ecologyGeneMoietyChemical biologyComputational biologyBiochemistrySmall moleculeChemistryBiosynthesisEcologyGene expressionStereochemistry

Abstract

fetched live from OpenAlex

Plants' ability to chemically modify core structures of specialized metabolites is the main reason why the plant kingdom contains such a wide and rich array of diverse compounds. One of the most important types of chemical modifications of small molecules is the addition of an acyl moiety to produce esters and amides. Large-scale phylogenomics analyses have shown that the enzymes that perform acyl transfer reactions on the myriad small molecules synthesized by plants belong to only a few gene families. This review is focused on describing the biochemistry, evolutionary origins, and chemical ecology implications of one of these families-the BAHD acyltransferases. The growth of advanced metabolomic studies coupled with next-generation sequencing of diverse plant species has confirmed that the BAHD family plays critical roles in modifying nearly all known classes of specialized metabolites. The current and future outlook for research on BAHDs includes expanding their roles in synthetic biology and metabolic engineering.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.006

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 head0.031
GPT teacher head0.325
Teacher spread0.294 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations79
Published2022
Admission routes1
Has abstractyes

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