Evaluation of the RNA Determinants for Bacterial and Yeast RNase III Binding and Cleavage
Bibliographic record
Abstract
Bacterial double-stranded RNA-specific RNase III recognizes the A-form of an RNA helix with little sequence specificity. In contrast, baker yeast RNase III (Rnt1p) selectively recognizes NGNN tetraloops even when they are attached to a B-form DNA helix. To comprehend the general mechanism of RNase III substrate recognition, we mapped the Rnt1p binding signal and directly compared its substrate specificity to that of both Escherichia coli RNase III and fission yeast RNase III (PacI). Rnt1p bound but did not cleave long RNA duplexes without NGNN tetraloops, whereas RNase III indiscriminately cleaved all RNA duplexes. PacI cleaved RNA duplexes with some preferences for NGNN-capped RNA stems under physiological conditions. Hydroxyl radical footprints indicate that Rnt1p specifically interacts with the NGNN tetraloop and its surrounding nucleotides. In contrast, Rnt1p interaction with GAAA-capped hairpins was weak and largely unspecific. Certain duality of substrate recognition was exhibited by PacI but not by bacterial RNase III. E. coli RNase III recognized RNA duplexes longer than 11 bp with little specificity, and no specific features were required for cleavage. On the other hand, PacI cleaved long, but not short, RNA duplexes with little sequence specificity. PacI cleavage of RNA stems shorter than 27 bp was dependent on the presence of an UU-UC internal loop two nucleotides upstream of the cleavage site. These observations suggest that yeast RNase IIIs have two recognition mechanisms, one that uses specific structural features and another that recognizes general features of the A-form RNA helix.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".