Sequence‐dependent folding and unfolding of ligand‐bound purine riboswitches
Bibliographic record
Abstract
Abstract Riboswitch regulation of gene expression requires ligand‐mediated RNA folding. From the fluorescence lifetime distribution of bound 2‐aminopurine ligand, we resolve three RNA conformers (Co, Ci, Cc) of the liganded G‐ and A‐sensing riboswitches from Bacillus subtilis. The ligand binding affinities, and sensitivity to Mg2+, together with results from mutagenesis, suggest that Co and Ci are partially unfolded species compromised in key loop‐loop interactions present in the fully folded Cc. These data verify that the ligand‐bound riboswitches may dynamically fold and unfold in solution, and reveal differences in the distribution of folded states between two structurally homologous purine riboswitches: Ligand‐mediated folding of the G‐sensing riboswitch is more effective, less dependent on Mg2+, and less debilitated by mutation, than the A‐sensing riboswitch, which remains more unfolded in its liganded state. We propose that these sequence‐dependent RNA dynamics, which adjust the balance of ligand‐mediated folding and unfolding, enable different degrees of kinetic discrimination in ligand binding, and fine‐tuning of gene regulatory mechanisms. © 2009 Wiley Periodicals, Inc. Biopolymers 91: 953–965, 2009. This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com
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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".