Effect of Fluorescent Proteins on Fusion Partners Using Polyglutamine Toxicity Assays in Yeast
Bibliographic record
Abstract
For the investigation of protein localization and trafficking using live cell imaging, researchers often rely on fusing their protein of interest to a fluorescent reporter. The constantly evolving list of genetically encoded fluorescent proteins (FPs) presents users with several alternatives when it comes to fluorescent fusion design. Each FP has specific optical and biophysical properties that can affect the biochemical, cellular, and functional properties of the resulting fluorescent fusions. For instance, several FPs tend to form nonspecific oligomers that are susceptible to impede on the function of the fusion partner. Unfortunately, only a few methods exist to test the impact of FPs on the behavior of the fluorescent reporter. Here, we describe a simple method that enables the rapid assessment of the impact of FPs using polyglutamine (polyQ) toxicity assays in the budding yeast Saccharomyces cerevisiae. PolyQ-expanded huntingtin proteins are associated with the onset of Huntington's disease (HD), where the expanded huntingtin aggregates into toxic oligomers and inclusion bodies. The aggregation and toxicity of polyQ expansions in yeast are highly dependent on the sequences flanking the polyQ region, including the presence of fluorescent tags, thus providing an ideal experimental platform to study the impact of FPs on the behavior of their fusion partner.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".