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Record W4393162104 · doi:10.1002/anie.202404069

A Sensitive Technique Unravels the Kinetics of Activation and <i>Trans</i> ‐Cleavage of CRISPR‐Cas Systems

2024· article· en· W4393162104 on OpenAlexafffund
Wei Feng, Hanyong Peng, Hongquan Zhang, Michael Weinfeld, X. Chris Le

Bibliographic record

VenueAngewandte Chemie International Edition · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsTrans-activating crRNACRISPRKineticsRibonucleoproteinChemistryCleavage (geology)Activator (genetics)Ternary complexRNABiophysicsStereochemistryEnzymeBiochemistryBiologyGenome editingGenePhysics

Abstract

fetched live from OpenAlex

Abstract Activation of the CRISPR‐Cas13a system requires the formation of a crRNA‐Cas13a ribonucleoprotein (RNP) complex and the binding of an RNA activator to the RNP. These two binding processes play a crucial role in the performance of the CRISPR‐Cas13a system. However, the binding kinetics remain poorly understood, and a main challenge is the lack of a sensitive method for real‐time measurements of the dynamically formed active CRISPR‐Cas13a enzyme. We describe here a new method to study the binding kinetics and report the rate constants ( k on and k off ) and dissociation constant ( K d ) for the binding between Cas13a and its activator. The method is able to unravel and quantify the kinetics of binding and cleavage separately, on the basis of measuring the real‐time trans ‐cleavage rates of the CRISPR‐Cas system and obtaining the real‐time concentrations of the active CRISPR‐Cas ternary complex. We further discovered that once activated, the Cas13a system operates at a wide range of temperatures (7–37 °C) with fast trans ‐cleavage kinetics. The new method and findings are important for diverse applications of the Cas13a system, such as the demonstrated quantification of microRNA at ambient temperatures (e.g., 25 °C).

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
gptno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
models agreeAgreement compares identical category sets and study designs across arms.

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: none
Teacher disagreement score0.991
Threshold uncertainty score0.256

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 head0.008
GPT teacher head0.281
Teacher spread0.274 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical · Methods

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

Citations11
Published2024
Admission routes2
Has abstractyes

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