Mutation-Guided Vaccine Design: A Strategy for Developing Boosting Immunogens for HIV Broadly Neutralizing Antibody Induction
Bibliographic record
Abstract
Abstract A major goal of HIV-1 vaccine development is induction of broadly neutralizing antibodies (bnAbs). While success has been achieved in initiating bnAb B cell lineages, design of boosting immunogens that select for bnAb B cell receptors with improbable mutations required for bnAb affinity maturation remains difficult. Here we demonstrate a process for designing boosting immunogens for a V3-glycan bnAb B cell lineage. The immunogens induced affinity-matured antibodies by selecting for functional improbable mutations in bnAb precursor knock-in mice. Moreover, we show similar success in prime and boosting with nucleoside-modified mRNA-encoded HIV-1 envelope trimer immunogens, with improved selection by mRNA immunogens of improbable mutations required for bnAb binding to key envelope glycans. These results demonstrate the ability of both protein and mRNA prime-boost immunogens for selection of rare B cell lineage intermediates with neutralizing breadth after bnAb precursor expansion, a key proof-of concept and milestone towards development of an HIV vaccine. One-Sentence Summary A vaccine strategy for selecting key rare antibody mutations is shown to induce HIV broadly neutralizing antibodies in mice.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".