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Record W2767757174 · doi:10.1021/jacs.7b08784

A Mechanistic Explanation for the Regioselectivity of Nonenzymatic RNA Primer Extension

2017· article· en· W2767757174 on OpenAlexfundno aff
Constantin Giurgiu, Li Li, Derek K. O’Flaherty, Chun Pong Tam, Jack W. Szostak

Bibliographic record

VenueJournal of the American Chemical Society · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrigins and Evolution of Life
Canadian institutionsnot available
FundersDivision of ChemistryFonds de recherche du Québec – Nature et technologiesSimons FoundationHoward Hughes Medical Institute
KeywordsRegioselectivityChemistryNucleobaseRNAYield (engineering)Primer extensionPrimer (cosmetics)Base pairStereochemistryDNAOrganic chemistryBiochemistryCatalysis

Abstract

fetched live from OpenAlex

A working model of nonenzymatic RNA primer extension could illuminate how prebiotic chemistry transitioned to biology. All currently known experimental reconstructions of nonenzymatic RNA primer extension yield a mixture of 2'-5' and 3'-5' internucleotide linkages. Although long seen as a major problem, the causes of the poor regioselectivity of the reaction are unknown. We used a combination of different leaving groups, nucleobases, and templating sequences to uncover the factors that yield selective formation of 3'-5' internucleotide linkages. We found that fast and high yielding reactions selectively form 3'-5' linkages. Additionally, in all cases with high 3'-5' regioselectivity, Watson-Crick base pairing between the RNA monomers and the template is observed at the extension site and at the adjacent downstream position. Mismatched base-pairs and other factors that would perturb the geometry of the imidazolium bridged intermediate lower both the rate and regioselectivity of the reaction.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.020
GPT teacher head0.288
Teacher spread0.268 · 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

Citations32
Published2017
Admission routes1
Has abstractyes

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