Investigating the interplay between RNA structural dynamics and RNA chemical probing experiments
Bibliographic record
Abstract
Small molecule chemical probes that covalently bond atoms of flexible nucleotides are widely employed in RNA structure determination. Atomistic molecular dynamic (MD) simulations recently suggested that RNA-probe binding can be cooperative, leading to measured reactivities that differ from expected trends as probe concentrations are varied. Here, we use selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE), dimethyl sulfate (DMS) chemical probing, and nuclear magnetic resonance (NMR) spectroscopy to explore the relationship between RNA structural dynamics and chemical probe reactivity. Our NMR chemical exchange experiments revealed that SHAPE-reactive base-paired nucleotides exhibit high imino proton exchange rates. Additionally, we find that as the concentration of a probe increases, some nucleotides' modification rates shift unexpectedly. For instance, some base-paired nucleotides that are unreactive at one probe concentration become reactive at another, often corresponding with a shift in the modification rate of the complementary nucleotide. We believe this effect can be harnessed to infer pairing interactions. Lastly, our results suggest that the overmodification of an RNA can impact its conformational dynamics, leading to modulations in the structural ensembles representing the RNA's fold. Our findings suggest an intricate interplay between RNA conformational dynamics and chemical probing reactivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".