Nucleotide-specific RNA conformations and dynamics within ribonucleoprotein condensates
Bibliographic record
Abstract
Abstract Ribonucleoprotein (RNP) condensates have distinct physiological and pathological significance, but the structure of RNA within them is not well understood. Using contrast-variation solution X-ray scattering, which discerns only the RNA structures within protein-RNA complexes, alongside ensemble-based structural modeling we characterize the conformational changes of flexible poly-A, poly-U and poly-C single stranded RNA as it interacts with polybasic peptides, eventually forming condensed coacervate mixtures. At high salt concentrations, where macromolecular association is weak, we probe association events that precede the formation of liquid-like droplets. Structural changes occur in RNA that reflect charge screening by the peptides as well as π − π interactions of the bases with basic residues. At lower salt concentrations, where association is enhanced, poly-A RNA within phase separated RNP mixtures exhibit a broad scattering peak, suggesting subtle ordering. Coarse-grained molecular dynamics simulations are used to elucidate the nucleotide-specific dynamics within RNP condensates. While adenine-rich condensates behave like stable semidilute solutions, uracil-rich RNA condensates appear to be compositionally fluctuating. This approach helps understand how RNA sequence contributes to the molecular grammar of RNA-protein phase separation.
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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".