Identification of compounds that decrease the fidelity of start codon recognition by the eukaryotic translational machinery
Bibliographic record
Abstract
Translation initiation in eukaryotes involves more than a dozen protein factors. Alterations in six factors have been found to reduce the fidelity of start codon recognition by the ribosomal preinitiation complex in yeast, a phenotype referred to as Sui(-). No small molecules are known that affect the fidelity of start codon recognition. Such compounds would be useful tools for probing the molecular mechanics of translation initiation and its regulation. To find compounds with this effect, we set up a high-throughput screen using a dual luciferase assay in S. cerevisiae. Screening of over 55,000 compounds revealed two structurally related molecules that decrease the fidelity of start codon selection by approximately twofold in the dual luciferase assay. This effect was confirmed using additional in vivo assays that monitor translation from non-AUG start codons. Both compounds increase translation of a natural upstream open reading frame previously shown to initiate translation at a UUG. The compounds were also found to exacerbate increased use of UUG as a start codon (Sui(-) phenotype) conferred by haploinsufficiency of wild-type eukaryotic initiation factor (eIF) 1, or by mutation in eIF1. Furthermore, the effects of the compounds are suppressed by overexpressing eIF1, which is known to restore the fidelity of start codon selection in strains harboring Sui(-) mutations in various other initiation factors. Together, these data strongly suggest that the compounds affect the translational machinery itself to reduce the accuracy of selecting AUG as the start codon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".