Identification of a conserved neutralizing epitope in Seneca Valley virus VP2 protein: new insight for epitope vaccine designment
Bibliographic record
Abstract
Abstract Background Seneca Valley virus (SVV) is a picornavirus that causes vesicular disease in swine. Clinical characteristics of the disease are similar to common viral diseases such as foot-and-mouth disease virus, porcine vesicular disease virus, and vesicular stomatitis virus, which can cause vesicles in the nose or hoof of pigs. Therefore, developing tools for detecting SVV infection is critical and urgent. Methods The neutralizing antibodies were produced to detect the neutralizing epitope. Results Five SVV neutralizing monoclonal antibodies (mAb), named 2C8, 3E4, 4C3, 6D7, and 7C11, were generated by immunizing mouses with ultra-purified SVV-LNSY01-2017. All five monoclonal antibodies exhibited high neutralizing titers to SVV. The epitopes targeted by these mAbs were further identified by peptide scanning using GST fusion peptides. The peptide 153 QELNEE 158 is defined as the smallest linear neutralizing epitope. The antibodies showed no reactivity to VP2 single mutants E157A. Furthermore, the antibodies showed no neutralizing activity with the recombinant virus (SVV-E157A). Conclusions The five monoclonal antibodies and identified epitopes may contribute to further research on the structure and function of VP2 and the development of diagnostic methods for detecting different SVV strains. Additionally, the epitope recognized by monoclonal antibodies against VP2 protein may provide insights for novel SVV vaccines and oncolytic viruses development.
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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 | 0.001 | 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 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".