Immunofocusing and enhancing autologous Tier-2 HIV-1 neutralization by displaying Env trimers on two-component protein nanoparticles
Bibliographic record
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 are generally modest for Env trimers. Here, we generate two-component self-assembling protein nanoparticles presenting twenty SOSIP trimers of the clade C Tier-2 genotype 16055. We show in a rabbit immunization study that these nanoparticles induce 60-fold higher autologous Tier-2 NAb titers than the corresponding SOSIP trimers. Epitope mapping studies reveal that the presentation of 16055 SOSIP trimers on these nanoparticle focuses 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".