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Record W3129874419 · doi:10.1261/rna.078428.120

Revisiting tRNA chaperones: new players in an ancient game

2021· article· en· W3129874419 on OpenAlexafffund
Jennifer Porat, Ute Kothe, Mark A. Bayfield

Bibliographic record

VenueRNA · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsUniversity of LethbridgeYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransfer RNABiologyRNAChaperone (clinical)RNA polymerase IIIProtein foldingBiochemistryComputational biologyCell biologyRNA polymeraseGene

Abstract

fetched live from OpenAlex

tRNAs undergo an extensive maturation process including posttranscriptional modifications that influence secondary and tertiary interactions. Precursor and mature tRNAs lacking key modifications are often recognized as aberrant and subsequently targeted for decay, illustrating the importance of modifications in promoting structural integrity. tRNAs also rely on tRNA chaperones to promote the folding of misfolded substrates into functional conformations. The best characterized tRNA chaperone is the La protein, which interacts with nascent RNA polymerase III transcripts to promote folding and offers protection from exonucleases. More recently, certain tRNA modification enzymes have also been demonstrated to possess tRNA folding activity distinct from their catalytic activity, suggesting that they may act as tRNA chaperones. In this review, we will discuss pioneering studies relating posttranscriptional modification to tRNA stability and decay pathways, present recent advances into the mechanism by which the RNA chaperone La assists pre-tRNA maturation, and summarize emerging research directions aimed at characterizing modification enzymes as tRNA chaperones. Together, these findings shed light on the importance of tRNA folding and how tRNA chaperones, in particular, increase the fraction of nascent pre-tRNAs that adopt a folded, functional conformation.

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 score0.304
Threshold uncertainty score0.248

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 head0.022
GPT teacher head0.283
Teacher spread0.261 · 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

Citations32
Published2021
Admission routes2
Has abstractyes

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