Tie-lines reveal interactions driving heteromolecular condensate formation
Bibliographic record
Abstract
Phase separation of biomolecules give rise to membraneless organelles that contribute to the spatiotemporal organisation of the cell. In most cases, such biomolecular condensates contain multiple components, but the manner in which interactions between components control the stability of condensates remained challenging to elucidate. Here, we develop an approach to determine tie-line gradients in ternary liquid-liquid phase separation (LLPS) systems, based on measurements of the dilute phase concentration of only one component. We show that the sign of the tie-line gradient is related to the cross-interaction energy between the polymers in the system and discriminates between competitive and cooperative phase separation. Using this approach, we studied the interaction between protein Fused in Sarcoma (FUS) and polyethylene glycol (PEG) polymer chains, and measured positive tie-line gradients. Our results show that PEG drives LLPS through an associative interaction with FUS and is not an inert crowder. We further studied the interaction between PolyA RNA (3.0±0.5kDa) and the protein G3BP1, and using the tie-line gradient as a reporter for the stoichiometry of polymers in the condensate we determined a G3BP1-to-PolyA RNA molar ratio of 1:4 in the dense phase. Our framework for measuring tie-line gradients opens up a route for the characterisation of interaction types and compositions in ternary LLPS systems.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".