Computationally designed mRNA-launched protein nanoparticle immunogens elicit protective antibody and T cell responses in mice
Bibliographic record
Abstract
Messenger RNA (mRNA) vaccines and computationally designed protein nanoparticle vaccines were both clinically derisked and licensed for the first time during the coronavirus disease 2019 (COVID-19) pandemic. These vaccine modalities have complementary immunological benefits that provide strong motivation for their combination. Here, we demonstrate proof of concept for genetic delivery of computationally designed protein nanoparticle immunogens. Using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a model system, we genetically fused a stabilized variant of the Wuhan-Hu-1 spike protein receptor binding domain (RBD) to a protein nanoparticle we previously designed for optimal secretion from human cells. Upon secretion, the nanoparticle formed monodisperse and antigenically intact assemblies displaying 60 copies of the RBD in an immunogenic array. Compared with mRNA vaccines encoding membrane-anchored spike protein and a secreted RBD trimer, an mRNA vaccine encoding the RBD nanoparticle elicited 5- to 28-fold higher titers of neutralizing antibodies in mice. In addition, the "mRNA-launched" RBD nanoparticle vaccine induced higher frequencies of antigen-specific CD8 T cells than the same immunogen delivered as adjuvanted protein and protected mice from either Wuhan-Hu-1 or Omicron BA.5 challenge. These results establish that delivering computationally designed protein nanoparticle immunogens through mRNA can combine the benefits of both vaccine modalities. More broadly, our data highlight the utility of computational protein design in genetic vaccination strategies.
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.
How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".