Binary peptide coacervates as an active model for biomolecular condensates
Bibliographic record
Abstract
Biomolecular condensates formed by proteins and nucleic acids are critical for cellular processes. Macromolecule-based coacervate droplets formed by liquid-liquid phase separation serve as synthetic analogues, but are limited by complex compositions and high molecular weights. Recently, short peptides have emerged as an alternative component of coacervates, but tend to form metastable microdroplets that evolve into rigid nanostructures. Here we present programmable coacervates using binary mixtures of diphenylalanine-based short peptides. We show that the presence of different short peptides stabilizes the coacervate phase and prevents the formation of rigid structures, allowing peptide coacervates to be used as stable adaptive compartments. This approach allows fine control of droplet formation and dynamic morphological changes in response to physiological triggers. As compartments, short peptide coacervates sequester hydrophobic molecules and enhance bio-orthogonal catalysis. In addition, the incorporation of coacervates into model synthetic cells enables the design of Boolean logic gates. Our findings highlight the potential of short peptide coacervates for creating adaptive biomimetic systems and provide insight into the principles of phase separation in biomolecular condensates.
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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".