Immunofocusing and enhancing autologous Tier-2 HIV-1 neutralization by displaying Env trimers on two-component protein nanoparticles
Bibliographic record
Abstract
Abstract The HIV-1 envelope glycoprotein trimer is poorly immunogenic because it is covered by a dense glycan shield. As a result, recombinant Env glycoproteins generally elicit inadequate antibody levels that neutralize clinically-relevant, neutralization-resistant (Tier-2) HIV-1 strains. Multivalent antigen presentation on nanoparticles is an established strategy to increase vaccine-driven immune responses. However, due to nanoparticle instability in vivo, the display of non-native Env structures, and the inaccessibility of many neutralizing antibody (NAb) epitopes, the effects of nanoparticle display have been modest for Env trimers. Here, we generated two-component self-assembling protein nanoparticles presenting twenty SOSIP trimers of the clade C Tier-2 genotype 16055. An immunization study in rabbits demonstrated that these nanoparticles induced 60-fold higher autologous Tier-2 NAb titers than the corresponding SOSIP trimers. Epitope mapping revealed that nanoparticle presentation focused antibody responses to an immunodominant apical epitope. Thus, these nanoparticles are a promising platform to improve the immunogenicity of Env trimers with apex-proximate NAb epitopes.
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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".