The catalytic efficiency of yeast ribonuclease III depends on substrate specific product release rate
Bibliographic record
Abstract
Members of the ribonuclease III (RNase III) family regulate gene expression by triggering the degradation of double stranded RNA (dsRNA). Hundreds of RNase III cleavage targets have been identified and their impact on RNA maturation and stability is now established. However, the mechanism defining substrates' reactivity remains unclear. In this study, we developed a real-time FRET assay for the detection of dsRNA degradation by yeast RNase III (Rnt1p) and characterized the kinetic bottlenecks controlling the reactivity of different substrates. Surprisingly, the results indicate that Rnt1p cleavage reaction is not only limited by the rate of catalysis but can also depend on base-pairing of product termini. Cleavage products terminating with paired nucleotides, like the degradation signals found in coding mRNA sequence, were less reactive and more prone to inhibition than products having unpaired nucleotides found in non-coding RNA substrates. Mutational analysis of U5 snRNA and Mig2 mRNA confirms the pairing of the cleavage site as a major determinant for the difference between cleavage rates of coding and non-coding RNA. Together the data indicate that the base-pairing of Rnt1p substrates encodes reactivity determinants that permit both constitutive processing of non-coding RNA while limiting the rate of mRNA degradation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".