HIV-1 neutralizing antibodies induced by native-like envelope trimers
Bibliographic record
Abstract
Steps in the right direction HIV-1 mutates rapidly, making it difficult to design a vaccine that will protect people against all of the virus' iterations. A potential successful vaccine design might protect by eliciting broadly neutralizing antibodies (bNAbs), which target specific regions on HIV-1's trimeric envelope glycoprotein (Env) (see the Perspective by Mascola). Jardine et al. used mice engineered to express germline-reverted heavy chains of a particular bNAb and immunized them with an Env-based immunogen designed to bind to precursors of that bNAb. Sanders et al. compared rabbits and monkeys immunized with Env trimers that adopt a nativelike conformation. In both cases, immunized animals produced antibodies that shared similarities with bNAbs. Boosting these animals with other immunogens may drive these antibodies to further mutate into the longsought bNAbs. Chen et al. report that retaining the cytoplasmic domain of Env proteins may be important to attract bNAbs. Removing the cytoplasmic domain may distract the immune response and instead generate antibodies that target epitopes on Env that would not lead to protection. Science , this issue p. 139 , 10.1126/science.aac4223 , p. 156 ; see also p. 191
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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) | 0.003 | 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".