Optimization and quantification of protein synthesis inside liposomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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) | 0.000 | 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 it