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Record W2990745985 · doi:10.1002/cbic.201900664

The Two Classic Pb<sup>2+</sup>‐Selective DNAzymes Are Related: Rational Evolution for Understanding Metal Selectivity

2019· article· en· W2990745985 on OpenAlexafffund
Wei Ren, Po‐Jung Jimmy Huang, Meilin He, Mingsheng Lyu, Shujun Wang, Changhai Wang, Juewen Liu

Bibliographic record

VenueChemBioChem · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDeoxyribozymeSelectivityChemistryStereochemistryNucleotideMetalSubstrate (aquarium)Cleavage (geology)DivalentDNACombinatorial chemistryCatalysisBiochemistryBiologyGeneOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In 1994, the first DNAzyme named GR5 was reported, which specifically requires Pb 2+ for its RNA cleavage activity. Three years later, the 8‐17 DNAzyme was isolated. The 8‐17 DNAzyme and the related 17 E DNAzyme are also most active with Pb 2+ , although other divalent metals can work as well. GR5 and 17 E have the same substrate sequence, and their catalytic loops in the enzyme strands also have a few similar and conserved nucleotides. Considering these, we hypothesized that 17 E might be a special form of GR5. To test this hypothesis, we performed systematic rational evolution experiments to gradually mutate GR5 toward 17 E . By using the activity ratio in the presence of Pb 2+ and Mg 2+ for defining these two DNAzymes, the critical nucleotide was identified to be T 12 in 17 E for metal specificity. In addition, G 9 in GR5 is a position not found in most 17 E or 8‐17 DNAzymes, and G 9 needs to be added to rescue GR5 activity if T 12 becomes a cytosine. This study highlights the links between these two classic and widely used DNAzymes, and offers new insight into the sequence–activity relationship related to metal selectivity.

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 score0.044
Threshold uncertainty score0.723

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 head0.014
GPT teacher head0.265
Teacher spread0.251 · 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

Citations21
Published2019
Admission routes2
Has abstractyes

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