RNA canonical and non-canonical base pairing types: a recognition method and complete repertoire
Bibliographic record
Abstract
The problem of systematic and objective identification of canonical and non-canonical base pairs in RNA three-dimensional (3D) structures was studied. A probabilistic approach was applied, and an algorithm and its implementation in a computer program that detects and analyzes all the base pairs contained in RNA 3D structures were developed. The algorithm objectively distinguishes among canonical and non-canonical base pairing types formed by three, two and one hydrogen bonds (H-bonds), as well as those containing bifurcated and C-H.X...H-bonds. The nodes of a bipartite graph are used to encode the donor and acceptor atoms of a 3D structure. The capacities of the edges correspond to probabilities computed from the geometry of the donor and acceptor groups to form H-bonds. The maximum flow from donors to acceptors directly identifies base pairs and their types. A complete repertoire of base pairing types was built from the detected H-bonds of all X-ray crystal structures of a resolution of 3.0 A or better, including the large and small ribosomal subunits. The base pairing types are labeled using an extension of the nomenclature recently introduced by Leontis and Westhof. The probabilistic method was implemented in MC-Annotate, an RNA structure analysis computer program used to determine the base pairing parameters of the 3D modeling system MC-Sym.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".