Biomolecular <scp>NMR</scp> Spectroscopy of Ribonucleic Acids
Bibliographic record
Abstract
Abstract Biomolecular nuclear magnetic resonance (NMR) spectroscopy allows the characterisation of structural and dynamic properties of ribonucleic acids (RNAs) in solution. The NMR‐based determination of high‐resolution three‐dimensional (3D) structures by NMR spectroscopy in solution is especially useful for small‐to‐medium sized RNA molecules like aptamers and small ribozymes, but has also been achieved for RNAs up to about 100 nucleotides in total size. Biomolecular NMR also provides valuable information about the interaction between RNA and diverse binding partners such as drugs, peptides, proteins or other nucleic acids. In addition, novel methods can be utilised to characterise the role of metal ions, intramolecular dynamics across a range of motion time scales and shifted p K a values of exchangeable nucleobase protons in RNA structure and catalysis. Key Concepts: High‐resolution NMR spectroscopy in solution is a powerful method to determine the 3D structures of small and medium sized RNA molecule. The structure determination of larger RNAs generally requires labelling with stable isotopes The information about molecular dynamics that can be obtained by NMR experiments allows quantitative studies of molecular motion across a wide range of time scales. The possibility to directly observe changes in the pK a values of ionisable groups. The localisation of divalent metal ions can be defined using several NMR methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.080 | 0.042 |
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".