Proteomic analysis reveals the recruitment of intrinsically disordered regions to stress granules
Bibliographic record
Abstract
Abstract Heat-stress triggers the formation of condensates known as stress granules (SGs), which store non-translating mRNA and stalled translation initiation complexes. To gain a better understanding of SGs, we identified yeast proteins that sediment after heat-shock by mass spectrometry. Heat-regulated proteins are biased toward a subset of abundant proteins that are significantly enriched in intrinsically disordered regions (IDRs). SG localization of over 80 heat-regulated proteins was confirmed using microscopy, including 32 proteins that were not known previously to localize to SGs. We find that several IDRs are sufficient to mediate SG recruitment. Moreover, the diffusive exchange of IDRs within SGs, observed via FRAP, can be highly dynamic while other components remain immobile. Lastly, we showed that the IDR of the Ubp3 deubiquitinase is critical for SG formation. This work confirms that IDRs play an important role in cellular compartmentalization upon stress, can be sufficient for SG incorporation, can remain dynamic in vitrified SGs, and play a vital role during heat-stress. Summary The authors provide an in-depth proteomic study of yeast heat stress granule (SG) proteins. They identified intrinsic disordered regions (IDRs) as one of the main features shared by these proteins and demonstrated IDRs can be sufficient for SG recruitment.
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) | 0.000 | 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) | 0.001 | 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".