Distinct Features of Stress Granule Proteins Predict Localization in Membraneless Organelles
Bibliographic record
Abstract
Recently generated proteomic data provides unprecedented insight into stress granule composition and stands as fruitful ground for further analysis. Stress granules are stress-induced biological assemblies that are of keen interest due to being linked to both long-term cell viability and a variety of protein aggregation-based diseases. Herein, we compile recently published stress granule composition data, formed specifically through heat and oxidative stress, for both mammalian (Homo sapiens) and yeast (Saccharomyces cerevisiae) cells. Interrogation of the data reveals that stress granule proteins are enriched in features that favor protein liquid-liquid phase separation, being highly disordered, soluble, and abundant while maintaining a high level of protein-protein interactions under basal conditions. Furthermore, these "stress granuleomes" are shown to be enriched for multidomained, RNA-binding proteins with increased potential for post-translational modifications. Findings are consistent with the notion that stress granule formation is driven by protein liquid-liquid phase separation. Furthermore, stress granule proteins appear poised near solubility limits while possessing the ability to dynamically alter their phase behavior in response to external threat. Interestingly, several features, such as protein disorder, are more prominent among stress granule proteins that share homologs between yeast and mammalian systems also found within stress-induced foci. We culminate results from our stress granule analysis into novel predictors for granule incorporation and validate the mammalian predictor's performance against multiple types of membraneless condensates and by colocalization microscopy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".