It takes two (Las1 HEPN endoribonuclease domains) to cut RNA correctly
Bibliographic record
Abstract
assays, we show that both HEPN nuclease motifs are required for Las1 nuclease activity and fidelity. Through in-depth sequence analysis and systematic mutagenesis, we determined the consensus HEPN motif in the Las1 subfamily and uncovered its canonical and specialized elements. Using reconstituted Las1 HEPN-HEPN' chimeras, we defined the molecular requirements for RNA cleavage. Intriguingly, both copies of the Las1 HEPN motif were important for nuclease function, revealing that both HEPN motifs participate in coordinating the RNA within the Las1 active site. We also established that conformational flexibility of the two HEPN domains is important for proper nuclease function. The results of our work reveal critical information about how dual HEPN domains come together to drive Las1-mediated RNA cleavage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.000 |
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.
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 teacher head, 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".