Interplay between intrinsically disordered proteins inside membraneless protein liquid droplets
Bibliographic record
Abstract
Intrinsically disordered proteins (IDPs) in cells phase separate to form diverse membraneless organelles, which have condensed liquid droplet-like properties and often contain multiple IDPs. However, how potential interactions between different IDPs affect the dynamic behavior of these protein droplets is largely unknown. Here, we develop a rapid IDP clustering system to generate protein droplets with varied residue compositions and examine diverse interacting IDPs inside droplets. Three different IDP droplets actively recruited other diverse IDPs inside droplets with extremely varied enrichment (inside/outside) degrees (over 100-fold variation) under highly crowded conditions. The recruited IDPs were mostly mobile even inside highly immobile droplets. Among the five tested IDPs, the disordered region of Ddx4 helicase with its unique multiple charged residue blocks was noticeably influenced by droplet mobility. We also discovered that droplets of different IDPs could rapidly fuse to each other. Interestingly, some droplets were heterogeneously fused with segregated subcompartments, and this segregation was enhanced by droplet maturation and was more apparent for specific IDP pairs, in which the polar and charged residue compositions are highly different. The present study not only reports multiple peculiar behaviors of interacting IDP pairs inside droplets but also provides valuable information on generating membraneless organelle models with controllable droplet properties.
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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 | 0.000 |
| 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 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".