Structure-based engineering of a nutrient acquisition protein enhances neutralizing antibodies and protection for the development of a gonococcal vaccine
Bibliographic record
Abstract
Abstract Gonorrhea is increasingly resistant to treatment and has been labelled an urgent threat due to the diminishing effectiveness of existing therapeutics. To address this challenge, we targeted the Neisseria gonorrhoeae transferrin binding protein B (TbpB), which is critical for iron acquisition and neisserial growth, as a vaccine target. Building on previous studies investigating the application of TbpB as an immunogen against various bacterial pathogens, we aimed to optimize this antigen for a broad protective effect. We compared the efficacy of wild type TbpB immunogens with engineered TbpB mutants that do not bind human transferrin (hTf) using infection studies in transgenic mice expressing hTf, which were required because the strict specificity of neisserial TbpB precludes its complexing with non-human transferrin. Comprehensive biophysical analyses confirmed that the introduced single residue mutations abolished hTf binding without compromising antigen structure. Immunization with the mutant antigens conferred increased resistance to infection by N. gonorrhoeae relative to that provided by the wild-type antigen in the humanized mice. When considering effector functions of the humoral response, we observed that the mutated antigen elicited more effective bactericidal and function-neutralizing activity. Through strategic mutations, we therefore enhanced vaccine effectiveness in a physiologically relevant model without significantly affecting the structure or immunogenicity of the antigen. This study highlights the use of rational structure-guided antigen design to drive effective immune responses and the potential interference of immunogen binding to host factors, and reinforces the utility of targeting TbpB in a gonococcal vaccine.
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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".