Development of a non-biased, high-throughput ELISA for the rapid evaluation of immunogenicity and cross-reactivity
Bibliographic record
Abstract
Traditional ELISA-based protein analysis has been predicated on the assumption that proteins bind randomly to the solid surface of the ELISA plate polymer (polystyrene or polyvinyl chloride). Random adherence to the plate ensures equal access to all faces of the protein, an important consideration when evaluating immunogenicity of polyclonal serum samples as well as when examining the cross-reactivity of immune serum against different antigenic variants of a protein. In this study we demonstrate that the soluble form of the surface lipoprotein transferrin binding protein B (TbpB) from three different bacterial pathogens (Neisseria meningitidis, Actinobacillus pleuropneumoniae, and Mannheimia haemolytica) bind the ELISA plate in a manner that consistently obscures the transferrin binding face of the proteins' N-lobe. In order to develop a non-biased ELISA where all faces of the protein are accessible, the strong interaction between biotin and avidin has been exploited by adding a biotin tag to these proteins during Escherichia coli-based cytoplasmic expression and utilizing streptavidin or neutravidin coated ELISA plates for protein capture and display. The use of avidin coated ELISA plates also allows for rapid purification of biotin-tagged proteins from crude E. coli lysates, removing the requirement of prior affinity purification of each protein to be included in the ELISA-based analyses. In proof of concept experiments we demonstrate the utility of this approach for evaluating immunogenicity and cross-reactivity of serum from mice and pigs immunized with TbpBs from human and porcine pathogens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".