Peptide-Based Biomimetic Condensates via Liquid–Liquid Phase Separation as Biomedical Delivery Vehicles
Bibliographic record
Abstract
Biomolecular condensates are dynamic liquid droplets through intracellular liquid-liquid phase separation that function as membraneless organelles, which are highly involved in various complex cellular processes and functions. Artificial analogs formed via similar pathways that can be integrated with biological complexity and advanced functions have received tremendous research interest in the field of synthetic biology. The coacervate droplet-based compartments can partition and concentrate a wide range of solutes, which are regarded as attractive candidates for mimicking phase-separation behaviors and biophysical features of biomolecular condensates. The use of peptide-based materials as phase-separating components has advantages such as the diversity of amino acid residues and customized sequence design, which allows for programming their phase-separation behaviors and the physicochemical properties of the resulting compartments. In this Perspective, we highlight the recent advancements in the design and construction of biomimicry condensates from synthetic peptides relevant to intracellular phase-separating protein, with specific reference to their molecular design, self-assembly via phase separation, and biorelated applications, to envisage the use of peptide-based droplets as emerging biomedical delivery vehicles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".