A Novel “Prebinding” Strategy Dramatically Enhances Sortase-Mediated Coupling of Proteins to Liposomes
Bibliographic record
Abstract
We have examined quantitatively the efficiency and the kinetics of sortase A-mediated coupling of model substrate proteins (derived from green fluorescent protein and the SNAP variant of O-alkylguanine-DNA alkyltransferase) to large unilamellar liposomes incorporating low levels of oligopeptide-modified acceptor lipids. Under normal reaction conditions, even using high concentrations of S. aureus or S. pyogenes sortase A and optimal protein coupling substrates and acceptor lipids, protein-liposome coupling is slow, gives at best modest coupling yields, and is markedly limited by the hydrolytic activity of sortase. We demonstrate, however, that these limitations can be overcome under "prebinding" conditions that promote initial reversible association of sortase and the substrate protein with the liposome surface. Using oligohistidine-tagged sortase and substrate proteins and liposomes incorporating an acceptor lipid together with a Ni(II)-chelating lipid derivative, high coupling rates and yields can be obtained at low sortase concentrations, while virtually eliminating adverse effects of sortase hydrolytic activity on protein coupling. The prebinding approach described here can readily be adapted, and if necessary rendered virtually "traceless", to accommodate diverse protein coupling substrates and end uses of the protein-modified 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 | 0.000 | 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".