Erythro-VLPs: Anchoring SARS-CoV-2 spike proteins in erythrocyte liposomes
Bibliographic record
Abstract
Novel therapeutic strategies are needed to control the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic. Here, we present a protocol to anchor the SARS-CoV-2 spike (S-)protein in the cytoplasmic membranes of erythrocyte liposomes. A surfactant was used to stabilize the S-protein's structure in the aqueous environment before insertion and to facilitate reconstitution of the S-proteins in the erythrocyte membranes. The insertion process was studied using coarse grained Molecular Dynamics (MD) simulations. Liposome formation and S-protein anchoring was studied by dynamic light scattering (DLS), ELV-protein co-sedimentation assays, fluorescent microcopy and cryo-TEM. The Erythro-VLPs (erythrocyte based virus like particles) have a well defined size of ∼200 nm and an average protein density on the outer membrane of up to ∼300 proteins/μm2. The correct insertion and functional conformation of the S-proteins was verified by dose-dependent binding to ACE-2 (angiotensin converting enzyme 2) in biolayer interferometry (BLI) assays. Seroconversion was observed in a pilot mouse trial after 14 days when administered intravenously, based on enzyme-linked immunosorbent assays (ELISA). This red blood cell based platform can open novel possibilities for therapeutics for the coronavirus disease (COVID-19) including variants, and other viruses in the future.
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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 | 0.000 |
| 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 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".