Epitope characterization of sero-specific monoclonal antibody to Clostridium botulinum neurotoxin type A.
Bibliographic record
Abstract
Botulinum neurotoxins (BoNTs) are extremely potent toxins that can contaminate foods and are a public health concern. Anti-BoNT antibodies have been described that are capable of detecting BoNTs; however there still exists a need for accurate and sensitive detection capabilities for BoNTs. Herein, we describe the characterization of a panel of eight monoclonal antibodies (MAbs) generated to the non-toxic receptor-binding domain of BoNT/A (H(C)50/A) developed using a high-throughput screening approach. In two independent hybridoma fusions, two groups of four IgG MAbs were developed against recombinant H(C)50/A. Of these eight, only a single MAb, F90G5-3, bound to the whole BoNT/A protein and was characterized further. The F90G5-3 MAb slightly prolonged time to death in an in vivo mouse bioassay and was mapped by pepscan to a peptide epitope in the N-terminal subdomain of H(C)50/A (H(CN)25/A) comprising amino acid residues (985)WTLQDTQEIKQRVVF(999), an epitope that is highly immunoreactive in humans. Furthermore, we demonstrate that F90G5-3 binds BoNT/A with nanomolar efficiency. Together, our results indicate that F90G5-3 is of potential value as a diagnostic immunoreagent for BoNT/A capture assay development and bio-forensic analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".