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Record W2927149912 · doi:10.1039/c8np00076j

Piperazic acid-containing natural products: structures and biosynthesis

2019· review· en· W2927149912 on OpenAlexafffund
Kalindi D. Morgan, Raymond J. Andersen, Katherine S. Ryan

Bibliographic record

VenueNatural Product Reports · 2019
Typereview
Languageen
FieldMedicine
TopicMicrobial Natural Products and Biosynthesis
Canadian institutionsVancouver Biotech (Canada)University of British Columbia
FundersCanadian Institutes of Health Research
KeywordsPolyketideBiosynthesisChemistryAmino acidPeptideStereochemistryBiochemistryRibosomal RNAHydrazine (antidepressant)Combinatorial chemistryGene

Abstract

fetched live from OpenAlex

Covering: up to the end of 2018 Piperazic acid is a cyclic hydrazine and a non-proteinogenic amino acid found in diverse non-ribosomal peptide (NRP) and hybrid NRP-polyketide (PK) structures. Piperazic acid was first identified as a residue in the monamycins in 1959. Since then, the piperazic acid residue has been found in >30 families of natural products, representing >140 compounds. Many of these compounds have potent biological activity, ranging from anti-malarial to anti-apoptotic to anti-bacterial activity, although high toxicity often accompanies this potent biological activity. Recently, we identified a piperazate synthase, responsible for N-N bond formation to give piperazic acid. Here, we review piperazic acid-containing natural products discovered from 1959 to 2018, with an emphasis on the biosynthetic routes to these natural products.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.032
GPT teacher head0.307
Teacher spread0.275 · 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

Citations92
Published2019
Admission routes2
Has abstractyes

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