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Record W1882168920 · doi:10.1111/mmi.12283

Determinants in the <scp><i>rpsT</i> mRNAs</scp> recognized by the 5′‐sensor domain of <scp>RNase E</scp>

2013· article· en· W1882168920 on OpenAlexafffund
George A. Mackie

Bibliographic record

VenueMolecular Microbiology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsRNase PBiologyCleavage (geology)RNase PHRNase HRNase MRPMessenger RNARibonuclease IIIRNACleavage factorBiochemistryLimitingCell biologyGeneRNA interference

Abstract

fetched live from OpenAlex

RNase E plays a central role in processing virtually all classes of cellular RNA in many bacterial species. A characteristic feature of RNase E and its paralogue RNase G, as well as several other unrelated ribonucleases, is their preference for 5'-monophosphorylated substrates. The basis for this property has been explored in vitro. At limiting substrate, cleavage of the rpsT mRNA by RNase E (residues 1-529) is inefficient, requiring excess enzyme. The rpsT mRNA is cleaved sequentially in a 5' to 3' direction, with the initial cleavage(s) at positions 116/117 or 190/191 being largely driven by direct entry, independent of the 5'-terminus or the 5'-sensor domain of RNase E. Generation of the 147 nt 3'-limit product requires sequential cleavages that generate 5'-monophosphorylated termini on intermediates, and the 5'-sensor domain of RNase E. These requirements can be bypassed with limiting enzyme by deleting a stem-loop structure adjacent to the site of the major, most distal cleavage. Alternatively, this specific cleavage can be activated substantially by a 5'-phosphorylated oligonucleotide annealed 5' to the cleavage site. This finding suggests that monophosphorylated small RNAs may destabilize their mRNA targets by recruiting the 5-sensor domain of RNase E 'in trans'.

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 score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0010.000
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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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

Citations15
Published2013
Admission routes2
Has abstractyes

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