Sequence-based prediction of condensate composition reveals that specificity can emerge from multivalent interactions among disordered regions
Bibliographic record
Abstract
While specificity of biomolecular interactions is typically understood to require interactions involving ordered structures, several biomolecular condensates exhibit specificity in the absence of apparent structural order. We have previously shown that condensates composed of the disordered region of MED1 partition specific proteins, mediated by sequence patterns of charged amino acids on the disordered regions of both MED1 and the interacting partner. Whether this specificity is due to an unknown ordered-structure-mediated interaction or from the dynamic multivalent interactions between the patterned charged amino acids in the disordered regions was unresolved. Here we show that a polymer physics-based model that only accounts for multivalent interactions among polymers in a statistical manner can largely explain published data on selective partitioning and make predictions that are subsequently experimentally validated. These results suggest that the specificity of condensate composition is underpinned to a substantial extent by multivalent interactions in the context of conformational disorder. Condensates composed of the disordered region of the mediator of RNA polymerase II transcription subunit 1 (MED1) are known to partition specific proteins, but whether this specificity arises from ordered-structure-mediated or dynamic multivalent amino acid interactions remains unclear. Here, the authors show that a physics-based model that only accounts for multivalent polymer interactions is able to explain and predict selective partitioning, suggesting that the specificity of condensate composition is underpinned by multivalent interactions in the context of conformational disorder.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".