Revisiting tRNA chaperones: new players in an ancient game
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".