Synthetic peptide vaccine development: measurement of polyclonal antibody affinity and cross‐reactivity using a new peptide capture and release system for surface plasmon resonance spectroscopy
Bibliographic record
Abstract
A method has been developed for measurement of antibody affinity and cross-reactivity by surface plasmon resonance spectroscopy using the EK-coil heterodimeric coiled-coil peptide capture system. This system allows for reversible capture of synthetic peptide ligands on a biosensor chip surface, with the advantage that multiple antibody-antigen interactions can be analyzed using a single biosensor chip. This method has proven useful in the development of a synthetic peptide anti-Pseudomonas aeruginosa (PA) vaccine. Synthetic peptide ligands corresponding to the receptor binding domains of pilin from four strains of PA were conjugated to the E-coil strand of the heterodimeric coiled-coil domain and individually captured on the biosensor chip through dimerization with the immobilized K-coil strand. Polyclonal rabbit IgG raised against pilin epitopes was injected over the sensor chip surface for kinetic analysis of the antigen-antibody interaction. The kinetic rate constants, k(on) and k(off), and equilibrium association and dissociation constants, KA and KD, were calculated. Antibody affinities ranged from 1.14 x 10(-9) to 1.60 x 10(-5) M. The results suggest that the carrier protein and adjuvant used during immunization make a dramatic difference in antibody affinity and cross-reactivity. Antibodies raised against the PA strain K pilin epitope conjugated to keyhole limpet haemocyanin using Freund's adjuvant system were more broadly cross-reactive than antibodies raised against the same epitope conjugated to tetanus toxoid using Adjuvax adjuvant. The method described here is useful for detailed characterization of the interaction of polyclonal antibodies with a panel of synthetic peptide ligands with the objective of obtaining high affinity and cross-reactive antibodies in vaccine development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".