Small molecule chaperones facilitate the folding of RNA G-quadruplexes
Bibliographic record
Abstract
RNA G-quadruplexes (rG4) have recently emerged as major regulatory elements in both mRNA and non-coding RNA. In order to investigate the biological roles of rG4 structures, chemists have developed a variety of highly specific and potent ligands. All of these ligands bind to the rG4s by stacking on top of them. The binding specificity is demonstrated by comparison to other structures such as duplex or three-way junctions. It remains unclear whether rG4-ligands merely stabilize fully formed rG4 structures, or if they actively participate in the folding of the rG4 structure through their association with an unfolded RNA sequence. In order to elucidate the innate steps of ligand-rG4 associations and mechanisms robust in vitro techniques, including FRET, electrophoretic mobility shift assays and reverse transcriptase stalling assays, were used to examine the capacity of five well-known G4 ligands to induce rG4 structures derived from either long non-coding RNAs or from synthetic RNAs. It was found that both PhenDC3 and PDS induce rG4 formation in single RNA strands. This discovery has important implications for the interpretation of RNA-seq experiments. Overall, in vitro data that can assist biochemists in selecting the optimal G4-ligands for their RNA cellular experiments are presented, and the effects induced by these ligands on the rG4s are also considered.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".