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Record W4232210604 · doi:10.1002/ange.202004934

Template‐Directed Copying of RNA by Non‐enzymatic Ligation

2020· article· en· W4232210604 on OpenAlexfundno aff
Lijun Zhou, Derek K. O’Flaherty, Jack W. Szostak

Bibliographic record

VenueAngewandte Chemie · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrigins and Evolution of Life
Canadian institutionsnot available
FundersDivision of ChemistryFonds de recherche du Québec – Nature et technologiesCanadian Institutes of Health ResearchSimons FoundationHoward Hughes Medical InstituteNational Science Foundation
KeywordsOligonucleotideTemplateLigationRNAChemical ligationChemistryCombinatorial chemistryComputational biologyBiologyBiochemistryDNAMolecular biologyNanotechnologyGeneMaterials science

Abstract

fetched live from OpenAlex

Abstract The non‐enzymatic replication of the primordial genetic material is thought to have enabled the evolution of early forms of RNA‐based life. However, the replication of oligonucleotides long enough to encode catalytic functions is problematic due to the low efficiency of template copying with mononucleotides. We show that template‐directed ligation can assemble long RNAs from shorter oligonucleotides, which would be easier to replicate. The rate of ligation can be greatly enhanced by employing a 3′‐amino group at the 3′‐end of each oligonucleotide, in combination with an N‐alkyl imidazole organocatalyst. These modifications enable the copying of RNA templates by the multistep ligation of tetranucleotide building blocks, as well as the assembly of long oligonucleotides using short splint oligonucleotides. We also demonstrate the formation of long oligonucleotides inside model prebiotic vesicles, which suggests a potential route to the assembly of artificial cells capable of evolution.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.237
Teacher spread0.222 · 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 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

Citations19
Published2020
Admission routes1
Has abstractyes

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