Author response: Tailored design of protein nanoparticle scaffolds for multivalent presentation of viral glycoprotein antigens
Bibliographic record
Abstract
Vaccines train the immune system to recognize a specific virus or bacterium so that the body can be better prepared against these harmful agents. To do so, many vaccines contain viral molecules called glycoproteins, which are specific to each type of virus. Glycoproteins that sit at the surface of the virus can act as ‘keys’ that recognize and unlock the cells of certain organisms, leading to viral infection. To ensure a stronger immune response, glycoproteins in vaccines are often arranged on a protein scaffold which can mimic the shape of the virus of interest and trigger a strong immune response. Many scaffolds, however, are currently made from natural proteins which cannot always display viral glycoproteins. Here, Ueda, Antanasijevic et al. developed a method that allows for the design of artificial proteins which can serve as scaffold for viral glycoproteins. This approach was tested using three viruses: influenza, HIV, and RSV – a virus responsible for bronchiolitis. The experiments showed that in each case, the relevant viral glycoproteins could attach themselves to the scaffold. These structures could then assemble themselves into vaccine particles with predicted geometrical shapes, which mimicked the virus and maximized the response from the immune system. Designing artificial scaffold for viral glycoproteins gives greater control over vaccine design, allowing scientists to manipulate the shape of vaccine particles and test the impact on the immune response. Ultimately, the approach developed by Ueda, Antanasijevic et al. could lead to vaccines that are more efficient and protective, including against viruses for which there is currently no suitable scaffold.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.047 | 0.016 |
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".