Missense suppressor tRNA therapeutics correct disease-causing alleles by misreading the genetic code
Bibliographic record
Abstract
In all living cells, transfer RNAs (tRNAs) act as adaptor molecules, linking the nucleic acid sequences of gene products to the accurate production of proteins by the ribosome. Translation fidelity relies on accurate aminoacylation of tRNAs and faithful decoding of each codon through interactions with the anticodon of a cognate aminoacyl-tRNA. Although translation is a high-fidelity process, cells are tolerant to significantly elevated levels of mistakes in protein synthesis. 1 Ruan B. Palioura S. Sabina J. Marvin-Guy L. Kochhar S. Larossa R.A. Söll D. Quality control despite mistranslation caused by an ambiguous genetic code. Proc. Natl. Acad. Sci. USA. 2008; 105: 16502-16507 Crossref PubMed Scopus (96) Google Scholar Engineered mischarged transfer RNAs for correcting pathogenic missense mutationsHou et al.Molecular TherapyDecember 15, 2023In BriefPan and colleagues describe an experimental strategy to engineer mischarged tRNAs and apply such a tRNA to correct a pathogenic missense mutation in a disease-relevant protein and partially restore its function. This work opens the door to applying tRNA therapy in genetic diseases caused by missense mutations. Full-Text PDF
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".