Ligand-Directed Dynamics of Adenine Riboswitch Conformers
Bibliographic record
Abstract
Riboswitches harness the structural and dynamic sophistication of RNA to coordinate specific ligand recognition with changes in gene expression. Design of molecules to manipulate riboswitch responses relies on our understanding of their RNA−ligand interactions. Here we demonstrate that for the adenine (A) riboswitch (ARNA) these interactions are highly dynamic. Given that 2-aminopurine (Ap) mimics A in its interactions with ARNA, we use the fluorescence lifetime of Ap to interrogate individual Ap-ARNA conformers (dynamic exchange times > ∼10 ns). Counter to predictions of two state and induced fit models, the ligand-bound A riboswitch is not a single, highly ordered structure: We detect at least three distinct Ap-ARNA conformers in ensemble solution. Their distribution indicates that they are not high-energy RNA folding intermediates but are instead energetically similar (Δ G < 1 kcal mol -1 ) conformers whose thermal stability, ligand, and Mg 2+ binding affinity differ substantially. Our experimental characterization suggests that these conformers are structurally distinct locally at the ligand binding site and globally in the arrangement of the P2−P3 stems. These results correlate well with recent single molecule characterization of P2−P3 end-to-end distances and exchange rates, but contrast with recent NMR results which suggest that the highly homologous G riboswitch exhibits a static global structure both with and without ligand. These distinct dynamics may well be the root of the divergent specificity and function of the A and G riboswitches. We predict that conformational dynamics within the bound A riboswitch underlie its regulatory responses and that these dynamics are directed by ligand structure.
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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".