Vaccination with a structure-based stabilized version of malarial antigen Pfs48/45 elicits ultra-potent transmission-blocking antibody responses
Bibliographic record
Abstract
Malaria transmission-blocking vaccines (TBVs) aim to elicit human antibodies that inhibit sporogonic development of Plasmodium falciparum in mosquitoes, thereby preventing onward transmission. Pfs48/45 is a leading clinical TBV candidate antigen and is recognized by the most potent transmission-blocking monoclonal antibody (mAb) yet described; still, clinical development of Pfs48/45 antigens has been hindered, largely by its poor biochemical characteristics. Here, we used structure-based computational approaches to design Pfs48/45 antigens stabilized in the conformation recognized by the most potently inhibitory mAb, achieving >25°C higher thermostability compared with the wild-type protein. Antibodies elicited in mice immunized with these engineered antigens displayed on liposome-based or protein nanoparticle-based vaccine platforms exhibited 1-2 orders of magnitude superior transmission-reducing activity, compared with immunogens bearing the wild-type antigen, driven by improved antibody quality. Our data provide the founding principles for using molecular stabilization solely from antibody structure-function information to drive improved immune responses against a parasitic vaccine target.
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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) | 0.001 | 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".