Mapping an epitope in EBNA-1 that is recognized by monoclonal antibodies to EBNA-1 that cross-react with dsDNA
Bibliographic record
Abstract
INTRODUCTION: The Epstein Barr Virus (EBV) has been associated with the autoimmune disease, Systemic Lupus Erythematosus (SLE). EBV nuclear antigen-I (EBNA-1) is the major nuclear protein of EBV. We previously generated an IgG monoclonal antibody (MAb) to EBNA-1, 3D4, and demonstrated that it cross-reacts with double stranded DNA (dsDNA) and binds the 148 amino acid viral binding site (VBS) in the carboxyl region of EBNA-1. The aim of the present study was to characterize another antibody to EBNA-1 that cross-reacts with dsDNA, compare its immunoglobulin genes to 3D4, and finely map the epitope in EBNA-1 that is recognized by these cross-reactive antibodies. METHODS: We generated an IgM MAb to EBNA-1, 16D2, from EBNA-1 injected mice and demonstrated by ELISA that it cross-reacts with dsDNA and binds the 148 amino acid VBS. We sequenced the variable heavy and light chain genes of 3D4 and 16D2 and compared V gene usage. To more finely map the epitope in EBNA-1 recognized by these MAbs, we examined their binding by ELISA to 15 overlapping peptides spanning the 148 amino acid domain. RESULTS: Sequence analysis revealed that 3D4 and 16D2 utilize different VH and VL genes but identical JH and Jk regions with minimal junctional diversity. This accounts for similarities in their CDR3 regions and may explain their similar dual binding specificity. Epitope mapping revealed 3D4 and 16D2 bind the same peptide in the VBS. Based on the crystal structure of EBNA-1, we observed that this peptide resides at the base of an exposed proline rich loop in EBNA-1. CONCLUSION: We have demonstrated that two MAbs that bind EBNA-1 and cross-react with dsDNA, recognize the same peptide in the VBS. This peptide may serve as a mimetope for dsDNA and may be of diagnostic and therapeutic value in SLE.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".