Dipeptide coacervates as artificial membraneless organelles for bioorthogonal catalysis
Bibliographic record
Abstract
Artificial organelles can manipulate cellular functions and introduce non-biological processes into cells. Coacervate droplets have emerged as a close analog of membraneless cellular organelles. Their biomimetic properties, such as molecular crowding and selective partitioning, make them promising components for designing cell-like materials. However, their use as artificial organelles has been limited by their complex molecular structure, limited control over internal microenvironment properties, and inherent colloidal instability. Here we report the design of dipeptide coacervates that exhibit enhanced stability, biocompatibility, and a hydrophobic microenvironment. The hydrophobic character facilitates the encapsulation of hydrophobic species, including transition metal-based catalysts, enhancing their efficiency in aqueous environments. Dipeptide coacervates carrying a metal-based catalyst are incorporated as active artificial organelles in cells and trigger an internal non-biological chemical reaction. The development of coacervates with a hydrophobic microenvironment opens an alternative avenue in the field of biomimetic materials with applications in catalysis and synthetic biology.
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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) | 0.001 | 0.001 |
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".