A new in vitro assay measuring direct interaction of nonsense suppressors with the eukaryotic protein synthesis machinery [preprint]
Bibliographic record
Abstract
Nonsense suppressors (NonSups) treat premature termination codon (PTC) disorders by inducing the selection of near cognate tRNAs at the PTC position, allowing readthrough of the PTC and production of full-length protein. Studies of NonSup-induced readthrough of eukaryotic PTCs have been carried out using animals, cells or crude cell extracts. In these studies, NonSups can promote readthrough directly, by binding to components of the protein synthesis machinery, or indirectly, by inhibiting nonsense-mediated mRNA decay or by other mechanisms. Here we utilize a highly-purified in vitro system (Zhang et al., 2016. eLife 5: e13429) to measure exclusively direct NonSup-induced readthrough. Of 17 previously identified NonSups, 13 display direct effects, apparently via at least two different mechanisms. We can monitor such direct effects by single molecule FRET (smFRET). Future smFRET experiments will permit elucidation of the mechanisms by which NonSups stimulate direct readthrough, aiding ongoing efforts to improve the clinical usefulness of NonSups.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".