A modular method for rapidly prototyping targeted gas vesicle protein nanoparticles
Bibliographic record
Abstract
Abstract Gas vesicles (GVs) are air-filled protein nanoparticles which are proving to be useful in a number of biomedical applications. We hypothesized that it could be possible to develop a modular method for creating rapidly prototyped GVs by modifying their surface chemistry to include targeting peptides in an orientation-specific manner. Here, we describe a modular method to create targeted GVs using His-tagged antibody fragments, ensuring that the antibody fragments are connected to the GV in an orientation-specific manner. This is achieved via the functionalization of the GVs with nickel-nitrilotriacetic acid (Ni-NTA) group. First, we validated that these functionalized GVs can bind His-tagged green fluorescent protein and characterized the particle size and surface charge of functionalized GVs. Then, GVs targeted to prostate-specific membrane antigen (PSMA) using a minibody were validated using a knockout validation in vitro .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".