Optimization and quantification of protein synthesis inside liposomes
Bibliographic record
Abstract
Synthetic biology aims at reprogramming existing, or creating new, biological systems, with the ultimate aim to obtain artificial cells whose functions can be tailored. For the latter, encapsulation of complex biochemical reactions into cell-sized compartments, such as liposomes, is required. Recently, several groups have demonstrated that proteins of interest can be produced de novo within liposomes by entrapping cell-free protein-synthesis systems and DNA templates inside liposomes. Although detectable, intraliposomal protein synthesis was generally poor. Here, we have optimized intraliposomal cell-free protein synthesis by changing several variables, including lipid composition as well as liposome, pyrophosphatase, and T7 RNA polymerase concentration. Further, by using an activity-based assay, we have quantified the amount of full-length protein that was produced from DNA templates inside liposomes before and after optimization of aforementioned variables. Based on the model protein beta-galactosidase, it is demonstrated that liposomal protein synthesis can yield microgram quantities of protein (30-40 microg/mL liposomes).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".