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Record W1980641226 · doi:10.1093/nar/gku1285

CRISPR RNA binding and DNA target recognition by purified Cascade complexes from Escherichia coli

2014· article· en· W1980641226 on OpenAlexaff
Natalia Beloglazova, Konstantin Kuznedelov, Robert Flick, Kirill A. Datsenko, Greg Brown, Ana Popovic, Sofia Lemak, Ekaterina Semenova, Konstantin Severinov, Alexander F. Yakunin

Bibliographic record

VenueNucleic Acids Research · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of Toronto
FundersNational Institute of General Medical Sciences
KeywordsTrans-activating crRNACRISPRBiologyCas9RNAEscherichia coliDNAPlasmidGeneticsComputational biologyGene

Abstract

fetched live from OpenAlex

Clustered regularly interspaced short palindromic repeats (CRISPRs) and their associated Cas proteins comprise a prokaryotic RNA-guided adaptive immune system that interferes with mobile genetic elements, such as plasmids and phages. The type I-E CRISPR interference complex Cascade from Escherichia coli is composed of five different Cas proteins and a 61-nt-long guide RNA (crRNA). crRNAs contain a unique 32-nt spacer flanked by a repeat-derived 5' handle (8 nt) and a 3' handle (21 nt). The spacer part of crRNA directs Cascade to DNA targets. Here, we show that the E. coli Cascade can be expressed and purified from cells lacking crRNAs and loaded in vitro with synthetic crRNAs, which direct it to targets complementary to crRNA spacer. The deletion of even one nucleotide from the crRNA 5' handle disrupted its binding to Cascade and target DNA recognition. In contrast, crRNA variants with just a single nucleotide downstream of the spacer part bound Cascade and the resulting ribonucleotide complex containing a 41-nt-long crRNA specifically recognized DNA targets. Thus, the E. coli Cascade-crRNA system exhibits significant flexibility suggesting that this complex can be engineered for applications in genome editing and opening the way for incorporation of site-specific labels in crRNA.

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 score0.059
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.027
GPT teacher head0.331
Teacher spread0.304 · 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

Citations25
Published2014
Admission routes1
Has abstractyes

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