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Record W2951560455 · doi:10.1101/160036

Enhanced proofreading governs CRISPR-Cas9 targeting accuracy

2017· preprint· en· W2951560455 on OpenAlexfundno aff
Janice S. Chen, Yavuz S. Dagdas, Benjamin P. Kleinstiver, Moira M. Welch, Lucas B. Harrington, Samuel H. Sternberg, J. Keith Joung, Ahmet Yıldız, Jennifer A. Doudna

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2017
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCharles A. King TrustHoward Hughes Medical InstituteNational Institutes of HealthNational Science Foundation
KeywordsCas9NucleaseProofreadingCRISPRGenome editingComputational biologyFörster resonance energy transferGuide RNAFidelityBiologyPolymeraseGeneticsDNAComputer scienceGenePhysics

Abstract

fetched live from OpenAlex

The RNA-guided CRISPR-Cas9 nuclease from Streptococcus pyogenes (SpCas9) has been widely repurposed for genome editing 1-4 . High-fidelity (SpCas9-HF1) and enhanced specificity (eSpCas9(1.1)) variants exhibit substantially reduced off-target cleavage in human cells, but the mechanism of target discrimination and the potential to further improve fidelity were unknown 5-9 . Using single-molecule Förster resonance energy transfer (smFRET) experiments, we show that both SpCas9-HF1 and eSpCas9(1.1) are trapped in an inactive state 10 when bound to mismatched targets. We find that a non-catalytic domain within Cas9, REC3, recognizes target mismatches and governs the HNH nuclease to regulate overall catalytic competence. Exploiting this observation, we identified residues within REC3 involved in mismatch sensing and designed a new hyper-accurate Cas9 variant (HypaCas9) that retains robust on-target activity in human cells. These results offer a more comprehensive model to rationalize and modify the balance between target recognition and nuclease activation for precision genome editing.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.072
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.011
GPT teacher head0.274
Teacher spread0.263 · 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.

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
Published2017
Admission routes1
Has abstractyes

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