Stress Granule Protein Features Allow for Accurate Prediction of Biological Condensate Localization with GraPES
Bibliographic record
Abstract
Biological condensates have been thrust to the forefront of molecular biology over the past decade for their implications in enzymatic activity, neurodegenerative diseases, and cellular organization. It is thought that many of these membraneless organelles are formed through a process known as liquid‐liquid phase separation, in which key protein or nucleic acid drivers seed the formation of protein‐rich foci within the cellular milieu. Stress granules are stress‐induced assemblies that are belong to this group of biological condensates and are of keen interest to the biomolecular research community due to being linked to both long‐term cell viability and a variety of protein aggregation‐based diseases. Recently, a large amount of proteomic data has been generated that provides unprecedented insight into stress granule composition and stands as fruitful ground for further analysis. Interrogation of this data revealed that stress granule proteins are enriched in features that favor protein liquid‐liquid phase separation. Proteins within stress granules were found to be more disordered, soluble, and abundant than their proteome and cytosolic controls while also having an increased potential for post‐translational modifications. Furthermore, these “stress granuleomes” were found to be enriched for multivalent character by possessing multiple ordered domains and being likely to interact with RNA maintaining a high level of protein‐protein interactions under basal conditions. Our 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. We culminate results from our analysis into novel predictors for granule incorporation in yeast and mammalian cells that out performs similar computational tools. Using this predictor, we were able to correctly identify new stress granule components, two of which we validated with colocalization microscopy in mammalian tissue culture cells [1]. We then developed a second series of sequence‐base predictors and integrate all of these computational tools into a user‐friendly web‐based interface, called GraPES (Granule Protein Enrichment Server), where users can either look up a variety of pre‐calculated likelihood z‐scores for human and yeast proteins or obtain novel predictions from their own input FASTA formatted protein sequences.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".