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Record W2329859787 · doi:10.1021/acs.biochem.6b00132

<i>In Vitro</i> Selection of a DNAzyme Cooperatively Binding Two Lanthanide Ions for RNA Cleavage

2016· article· en· W2329859787 on OpenAlexafffund
Po‐Jung Jimmy Huang, Mahsa Vazin, Juewen Liu

Bibliographic record

VenueBiochemistry · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDeoxyribozymeLanthanideChemistryCleavage (geology)NucleotideStereochemistryDiastereomerMetal ions in aqueous solutionDNARNAIonCrystallographyBiochemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Trivalent lanthanide ions (Ln(3+)) were recently employed to select RNA-cleaving DNAzymes, and three new DNAzymes have been reported so far. In this work, dysprosium (Dy(3+)) was used with a library containing 50 random nucleotides. After six rounds of in vitro selection, a new DNAzyme named Dy10a was obtained and characterized. Dy10a has a bulged hairpin structure cleaving a RNA/DNA chimeric substrate. Dy10a is highly active in the presence of the five Ln(3+) ions in the middle of the lanthanide series (Sm(3+), Eu(3+), Gd(3+), Tb(3+), and Dy(3+)), while its activity descends on the two sides. The cleavage rate reaches 0.6 min(-1) at pH 6 with just 200 nM Sm(3+), which is the fastest among all known Ln(3+)-dependent enzymes. Dy10a binds two Ln(3+) ions cooperatively. When a phosphorothioate (PS) modification is introduced at the cleavage junction, the activity decreases by >2500-fold for both the Rp and Sp diastereomers, and thiophilic Cd(2+) cannot rescue the activity. The pH-rate profile has a slope of 0.37 between pH 4.2 and 5.2, and the slope was even lower at higher pH. On the basis of these data, a model of metal binding is proposed. Finally, a catalytic beacon sensor that can detect Ho(3+) down to 1.7 nM is constructed.

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 score0.001
Threshold uncertainty score0.440

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 head0.010
GPT teacher head0.283
Teacher spread0.273 · 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

Citations40
Published2016
Admission routes2
Has abstractyes

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