Helix-length compensation studies reveal the adaptability of the VS ribozyme architecture
Bibliographic record
Abstract
Compensatory mutations in RNA are generally regarded as those that maintain base pairing, and their identification forms the basis of phylogenetic predictions of RNA secondary structure. However, other types of compensatory mutations can provide higher-order structural and evolutionary information. Here, we present a helix-length compensation study for investigating structure-function relationships in RNA. The approach is demonstrated for stem-loop I and stem-loop V of the Neurospora VS ribozyme, which form a kissing-loop interaction important for substrate recognition. To rapidly characterize the substrate specificity (k(cat)/K(M)) of several substrate/ribozyme pairs, a procedure was established for simultaneous kinetic characterization of multiple substrates. Several active substrate/ribozyme pairs were identified, indicating the presence of limited substrate promiscuity for stem Ib variants and helix-length compensation between stems Ib and V. 3D models of the I/V interaction were generated that are compatible with the kinetic data. These models further illustrate the adaptability of the VS ribozyme architecture for substrate cleavage and provide global structural information on the I/V kissing-loop interaction. By exploring higher-order compensatory mutations in RNA our approach brings a deeper understanding of the adaptability of RNA structure, while opening new avenues for RNA research.
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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 | 0.000 |
| 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) | 0.001 | 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 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".