Lumazine Synthase Nanoparticles as a Versatile Platform for Multivalent Antigen Presentation and Cross-Protective Coronavirus Vaccines
Bibliographic record
Abstract
Lumazine synthase (LS), a bacterial protein that self-assembles into 60-mer icosahedral virus-like nanoparticles, has emerged as a promising platform for nanoparticle-based drug delivery and vaccine design. However, detailed biophysical characterization of the LS nanoparticle vaccine has not been well-studied. In this study, we generated LS nanoparticles fused with domain B of protein A (pA-LS), enabling their binding to the hFc-tagged S1 domain of the SARS-CoV-2 spike protein harboring two critical mutations (E484K and D614G) associated with increased infectivity and antibody escape. Biophysical analysis, such as transmission electron microscopy (TEM), revealed an extended size (∼45 nm) compared with the empty particle (∼15 nm). Similarly, atomic force microscopy (AFM) and dynamic light scattering (DLS) analyses confirmed increases in height and diameter. The spike-decorated nanoparticles demonstrated multivalent surface presentation by binding to the ACE2 receptor with a speckle-like appearance. Immunization of mice with pA-LS-S1-hFc elicited neutralizing antibodies against SARS-CoV-2 and its variants. Further, immunization followed by a live SARS-CoV-2 challenge (Wuhan-Hu-1, B.1.617.2 (Delta), or B.1.1.529 (Omicron)) in K18-hACE2 transgenic mice significantly reduced the lung viral load and pathology. Additionally, we generated mosaic nanoparticles displaying spike proteins from two epidemic coronaviruses, SARS-CoV-1 and MERS-CoV, which exhibited binding to their respective cellular receptors, ACE2 and DPP4, with similar binding patterns. Immunization with these mosaic nanoparticles elicited cross-reactive neutralizing antibodies against SARS-CoV-1 and MERS-CoV pseudoviruses. Our proof-of-concept data demonstrate the versatility of the LS nanoparticle platform for antigen presentation, supporting the development of multivalent vaccine designs targeting diverse antigens and contributing to immunogen design strategies.
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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 | 0.000 | 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 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".