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Record W2955056885 · doi:10.1093/nar/gkz578

From general base to general acid catalysis in a sodium-specific DNAzyme by a guanine-to-adenine mutation

2019· article· en· W2955056885 on OpenAlexafffund
Lingzi Ma, Sanjana Kartik, Biwu Liu, Juewen Liu

Bibliographic record

VenueNucleic Acids Research · 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
KeywordsDeoxyribozymeGuanineStereochemistryNucleotideHypoxanthineBiologyNucleobaseCatalysisBiochemistryDNANucleic acidChemistryEnzyme

Abstract

fetched live from OpenAlex

Recently, a few Na+-specific RNA-cleaving DNAzymes were reported, where nucleobases are likely to play critical roles in catalysis. The NaA43 and NaH1 DNAzymes share the same 16-nt Na+-binding motif, but differ in one or two nucleotides in a small catalytic loop. Nevertheless, they display an opposite pH-dependency, implicating distinct catalytic mechanisms. In this work, rational mutation studies locate a catalytic adenine residue, A22, in NaH1, while previous studies found a guanine (G23) to be important for the catalysis of NaA43. Mutation with pKa-perturbed analogs, such as 2-aminopurine (∼3.8), 2,6-diaminopurine (∼5.6) and hypoxanthine (∼8.7) affected the overall reaction rate. Therefore, we propose that the N1 position of G23 (pKa ∼6.6) in NaA43 functions as a general base, while that of A22 (pKa ∼6.3) in NaH1 as a general acid. Further experiments with base analogs and a phosphorothioate-modified substrate suggest that the exocyclic amine in A22 and both of the non-bridging oxygens at the scissile phosphate are important for catalysis for NaH1. This is an interesting example where single point mutations can change the mechanism of cleavage from general base to general acid, and it can also explain this Na+-dependent DNAzyme scaffold being sensitive to a broad range of metal ions and molecules.

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)0.001
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 score0.021
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.328
Teacher spread0.308 · 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

Citations36
Published2019
Admission routes2
Has abstractyes

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