Quantifying collective interactions in biomolecular phase separation
Bibliographic record
Abstract
Abstract Biomolecular phase separation plays a pivotal role in governing critical biological functions and arises from the collective interactions of large numbers of molecules. Characterising the underlying collective interactions of phase separation, however, has proven to be challenging with currently available tools. Here, we propose a general and easily accessible strategy to quantify collective interactions in biomolecular phase separation with respect to composition and energetics. By measuring the dilute phase concentration of one species only, we determine tie line gradients and free energy dominance as dedicated descriptors of collective interactions. We apply this strategy to dissect the role of salts and small molecules on phase separation of the protein fused in sarcoma (FUS). We discover that monovalent salts can display both exclusion from or preferential partitioning into condensates to either counteract charge screening or enhance non-ionic interactions. Moreover, we show that the common hydrophobic interaction disruptor 1,6-hexanediol inhibits FUS phase separation by acting as a solvation agent capable of expanding the protein polypeptide chain. Taken together, our work presents a widely applicable strategy that enables quantification of collective interactions and provides unique insights into the underlying mechanisms of condensate formation and modulation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".