tRNA Fusion to Streamline RNA Structure Determination: Case Studies in Probing Aminoacyl-tRNA Sensing Mechanisms by the T-Box Riboswitch
Bibliographic record
Abstract
RNAs are prone to misfolding and are often more challenging to crystallize and phase than proteins. Here, we demonstrate that tRNA fusion can streamline the crystallization and structure determination of target RNA molecules. This strategy was applied to the T-box riboswitch system to capture a dynamic interaction between the tRNA 3′-UCCA tail and the T-box antiterminator, which senses aminoacylation. We fused the T-box antiterminator domain to the tRNA anticodon arm to capture the intended interaction through crystal packing. This approach drastically improved the probability of crystallization and successful phasing. Multiple structure snapshots captured the antiterminator loop in an open conformation with some resemblance to that observed in the recent co-crystal structures of the full-length T box riboswitch–tRNA complex, which contrasts the resting, closed conformation antiterminator observed in an earlier NMR study. The anticipated tRNA acceptor–antiterminator interaction was captured in a low-resolution crystal structure. These structures combined with our previous success using prohead RNA–tRNA fusions demonstrates tRNA fusion is a powerful method in RNA structure determination.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".