A comparative analysis of the triloops in all high-resolution RNA structures reveals sequence–structure relationships
Bibliographic record
Abstract
Despite an increasing number of experimentally determined RNA structures, the gap between the number of structures and that of RNA families is still growing. To overcome this limitation, efficient and reliable RNA modeling methodologies must be developed. In order to reach this goal, here, we show how triloop sequence-structure relationships have been inferred through a systematic analysis of all triloops found in available high-resolution structures. The structural annotation of all triloops allowed us to define discrete states of the triloop's conformational space, and therefore an explicit sequence-to-structure relation. The sequence-structure relationships inferred from this explicit relation are presented in a convenient modeling table that provides a limited set of possible three-dimensional structures given any triloop sequence. The table is indexed by the two nucleotides that form the triloop's flanking base pair, since they are shown to provide the most information about the triloop three-dimensional structures. We also report the observations in the X-ray crystallographic structures of important conformational variations, which we believe might be the result of RNA dynamic.
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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 | 0.001 | 0.001 |
| 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".