A Yeast System for Discovering Optogenetic Inhibitors of Eukaryotic Translation Initiation
Bibliographic record
Abstract
The precise spatiotemporal regulation of protein synthesis is essential for many complex biological processes such as memory formation, embryonic development, and tumor formation. Current methods used to study protein synthesis offer only a limited degree of spatiotemporal control. Optogenetic methods, in contrast, offer the prospect of controlling protein synthesis noninvasively within minutes and with a spatial scale as small as a single synapse. Here, we present a hybrid yeast system where growth depends on the activity of human eukaryotic initiation factor 4E (eIF4E) that is suitable for screening optogenetic designs for the down-regulation of protein synthesis. We used this system to screen a diverse initial panel of 15 constructs designed to couple a light switchable domain (PYP, RsLOV, AsLOV, Dronpa) to 4EBP2 (eukaryotic initiation factor 4E binding protein 2), a native inhibitor of translation initiation. We identified cLIPS1 (circularly permuted LOV inhibitor of protein synthesis 1), a fusion of a segment of 4EBP2 and a circularly permuted version of the LOV2 domain from Avena sativa, as a photoactivated inhibitor of translation. Adapting the screen for higher throughput, we tested small libraries of cLIPS1 variants and found cLIPS2, a construct with an improved degree of optical control. We show that these constructs can both inhibit translation in yeast harboring a human eIF4E in vivo, and bind human eIF4E in vitro in a light-dependent manner. This hybrid yeast system thus provides a convenient way for discovering optogenetic constructs that can regulate human eIF4E-dependent translation initiation in a mechanistically defined manner.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, 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".