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Record W1513585608 · doi:10.1002/9780470514344.ch3

Evolution of Secondary Metabolite Production: Potential Roles for Antibiotics as Prebiotic Effectors of Catalytic RNA Reactions

2007· review· en· W1513585608 on OpenAlexaff
Julian Davies, Uwe von Ahsen, Herbert Wank, Renée Schroeder

Bibliographic record

VenueNovartis Foundation symposium · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRibozymeIntronRNARNA splicingLigase ribozymeEffectorRibosomal RNASecondary metabolitePrebioticBiologyAbiogenesisChemistryCatalysisBiochemistryGeneticsComputational biologyGene

Abstract

fetched live from OpenAlex

It has been proposed that organic molecules related to known secondary metabolites have existed since the beginning of biochemical evolution and were present in primordial soups. Under primitive earth conditions certain of these molecules may have played roles as effectors in prebiotic reactions, especially those involving catalytic RNA (ribozymes). We demonstrate that a number of antibiotic-related secondary metabolites bind to group I introns and either inhibit splicing reactions or promote the formation of intron oligomers. This is consistent with the functional co-evolution of catalytic RNA and secondary metabolites as antibiotic inhibitors of translation, and supports the notion of an evolutionary relationship between group I introns and ribosomal RNA.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.453
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.021
GPT teacher head0.310
Teacher spread0.288 · 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
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

Citations9
Published2007
Admission routes1
Has abstractyes

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