Programmable Peptide-Based Complex Coacervate Microenvironments for Cellular Engineering
Bibliographic record
Abstract
Peptide-based coacervates demonstrate remarkable potential across interdisciplinary fields of biomedicine and materials science due to their sequence programmability, dynamic self-assembly capability, and exceptional biocompatibility. Despite progress in understanding their phase behavior, the high charge density and complex intermolecular interactions present significant challenges in precisely tailoring their microenvironments and biological functions. In this study, we utilized decapeptide sequences (decaarginine R 10, decalysine K 10, and decaaspartic acid D 10 ) to explore the impact of substituting aspartic acid (D) with phenylalanine (F) in D 10 or lysine (K) with arginine (R) in K 10 on the microenvironment of coacervates. The replacement of D with F in the R 10 /D 10 system led to a thermodynamic shift from enthalpy-driven (low F%) to entropy-driven (high F%) phase separation and enhanced phase separation propensity and salt resistance, while reducing internal polarity and molecular mobility. Varying R% in K 10 /D 10 systems demonstrated limited impact on microdroplet viscosity and polarity compared to F% modulation, despite stabilizing droplets at R% ≥ 20%. Neither the D-to-F nor K-to-R substitutions altered the enrichment of biological macromolecules; however, the D-to-F substitution disrupted the secondary structure of double-stranded DNA. Cell coculture experiments confirmed that both R 10 /(FD) 5 and R 10 /D 10 complex coacervate microdroplets adhered to cell membranes rapidly, but R 10 /D 10 exhibited stronger proliferation inhibition. This molecular-level analysis establishes a foundation for connecting the peptide sequence, condensate microenvironments, and biological functions.
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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 | 0.001 |
| 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".