Evolution of protective human antibodies against <i>Plasmodium falciparum</i> circumsporozoite protein repeat motifs
Bibliographic record
Abstract
SUMMARY Circumsporozoite protein of the human malaria parasite Plasmodium falciparum (PfCSP) is the main target of antibodies that prevent the infection and disease. Protective antibodies recognize the central PfCSP domain, but our understanding of how parasite inhibition is associated with recognition of this domain and with the evolution of potent antibodies remains scattered. Here, we characterized the epitope specificity of 200 human monoclonal PfCSP antibodies. We show that the majority of PfCSP antibodies bind to NANP and NANP-like motifs with different preferences and define the molecular basis for recognition. Epitope cross-reactivity evolved with increasing antibody affinity around a conserved (N/D)P-NANP-N(V/A) core. High affinity to this motif, but not binding to NANP-like motifs, was associated with parasite inhibition and protection. Thus, NANP drives the development of potent PfCSP antibodies independently of their cross-reactivity profile, a finding with direct implications for the design of a second-generation PfCSP-based malaria vaccine. HIGHLIGHTS The majority of human PfCSP antibodies recognize multiple epitopes NANP affinity maturation drives the evolution of cross-reactive PfCSP antibodies Preferential PfCSP antibody binding to a conserved core motif High affinity not epitope specificity is associated with PfCSP antibody potency
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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".