MétaCan
Menu
Back to cohort
Record W2987201345 · doi:10.1016/j.celrep.2019.10.017

Anti-CRISPR AcrIIA5 Potently Inhibits All Cas9 Homologs Used for Genome Editing

2019· article· en· W2987201345 on OpenAlexafffund
Bianca Garcı́a, Jooyoung Lee, Alireza Edraki, Yurima Hidalgo-Reyes, Steven Erwood, Aamir Mir, Chantel N. Trost, Uri Seroussi, Sabrina Y. Stanley, Ronald D. Cohn, Julie M. Claycomb, Erik J. Sontheimer, Karen L. Maxwell, Alan R. Davidson

Bibliographic record

VenueCell Reports · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of General Medical SciencesCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsCRISPRGenome editingCas9GenomeComputational biologyBiologyGeneticsGene

Abstract

fetched live from OpenAlex

CRISPR-Cas9 systems provide powerful tools for genome editing. However, optimal employment of this technology will require control of Cas9 activity so that the timing, tissue specificity, and accuracy of editing may be precisely modulated. Anti-CRISPR proteins, which are small, naturally occurring inhibitors of CRISPR-Cas systems, are well suited for this purpose. A number of anti-CRISPR proteins have been shown to potently inhibit subgroups of CRISPR-Cas9 systems, but their maximal inhibitory activity is generally restricted to specific Cas9 homologs. Since Cas9 homologs vary in important properties, differing Cas9s may be optimal for particular genome-editing applications. To facilitate the practical exploitation of multiple Cas9 homologs, here we identify one anti-CRISPR, called AcrIIA5, that potently inhibits nine diverse type II-A and type II-C Cas9 homologs, including those currently used for genome editing. We show that the activity of AcrIIA5 results in partial in vivo cleavage of a single-guide RNA (sgRNA), suggesting that its mechanism involves RNA interaction.

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 score0.114
Threshold uncertainty score0.785

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 head0.007
GPT teacher head0.265
Teacher spread0.258 · 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

Citations54
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueCell ReportsSame topicCRISPR and Genetic EngineeringFrench-language works237,207