Mistranslating tRNA identifies a deleterious S213P mutation in the <i>Saccharomyces cerevisiae eco1-1</i> allele
Bibliographic record
Abstract
ABSTRACT Mistranslation occurs when an amino acid not specified by the standard genetic code is incorporated during translation. Since the ribosome does not read the amino acid, tRNA variants aminoacylated with a non-cognate amino acid or containing a non-cognate anticodon dramatically increase the frequency of mistranslation. In a systematic genetic analysis, we identified a suppression interaction between tRNA Ser UGG, G26A, which mistranslates proline codons by inserting serine, and eco1-1, a temperature sensitive allele of the gene encoding an acetyltransferase required for sister chromatid cohesion. The suppression was partial with a tRNA that inserts alanine at proline codons and not apparent for a tRNA that inserts serine at arginine codons. Sequencing of the eco1-1 allele revealed a mutation that would convert the highly conserved serine 213 within β7 of the GCN5-related N-acetyltransferase core to proline. Mutation of P213 in eco1-1 back to the wild-type serine restored function of the enzyme at elevated temperature. Our results indicate the utility of mistranslating tRNA variants to identify functionally relevant mutations and identify eco1 as a reporter for mistranslation. We propose that mistranslation could be used as a tool to treat genetic disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".