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Record W2793819196 · doi:10.1002/cbic.201800049

An RNA‐Cleaving Catalytic DNA Accelerated by Freezing

2018· article· en· W2793819196 on OpenAlexafffund
Tianmeng Yu, Wenhu Zhou, Juewen Liu

Bibliographic record

VenueChemBioChem · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaChina Sponsorship Council
KeywordsDeoxyribozymeChemistryRNACleavage (geology)DNADivalentBiophysicsCatalysisBiochemistryBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The EtNa DNAzyme was isolated during the isopropanol precipitation step of an in vitro selection effort. Although inactive with the intended cofactor, its RNA cleavage activity was observed under a few conditions. With Na + , EtNa was highly active in ∼50 % ethanol, whereas in water, it was highly active with Ca 2+ . In this work, we showed that the EtNa DNAzyme was accelerated by freezing in water in the presence of Na + . The apparent K d value reached 6.2 m m Na + under the frozen condition, over 20 times tighter than that in water at room temperature. With 10 m m Na + , EtNa had a cleavage rate of 0.12 h −1 after freezing at −20 °C. This effect was unique to EtNa, as all other tested DNAzymes were inhibited by freezing except for the Na + ‐specific NaA43. Freezing also inhibited EtNa if Ca 2+ was used. We attributed this to the concentrations of EtNa and Na + in the micropockets between ice crystals, but divalent metals might misfold DNA. Overall, we have systematically studied the effect of freezing on the RNA‐cleavage activity of DNAzymes. The DNAzyme sequence and the metal ion species are both crucial to determine the effect of freezing.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.005
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.285
Teacher spread0.272 · 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.

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

Citations15
Published2018
Admission routes2
Has abstractyes

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