Differential contribution of variable heavy and variable light chain domains in viral epitope recognition and neutralization function (VAC6P.950)
Bibliographic record
Abstract
Abstract Antigen (Ag) may be recognized by an immunoglobulin via heavy chain variable domain (VH) alone, light chain variable domain (VL) alone and VH+VL pair. Little is known about relative contribution of VH and/or VL in Ag recognition in the context of virus neutralization function. To understand role of VH and VL, we examined Ag recognition and virus neutralization function of VH, VL and VH+VL of a neutralizing Ab against bovine herpesvirus-1 (BoHV-1), an important cattle pathogen. Monomeric scFv, VH and VL were expressed in Pichia pastoris and purified. The VH alone recognized BoHV-1 in ELISA but VL did not. This is consistent with previous studies that suggested significant role of variable heavy domain in Ag recognition. However, VH alone did not neutralize BoHV-1 in vitro but monomeric scFv, where VH and VL are linked via 18 amino acids, neutralized the virus. Such a divergence in VH and VL effector functions from Ag recognition to virus neutralization reflects fine structural complexities relevant to Ab functions. The VH alone though capable of recognizing the target neutralizing epitope is unable to neutralize BoHV-1 per se. These observations provide novel insight into non-Ag binding functional role of VL where it provides structural and configurational support to VH for accessing the neutralizing viral epitope. To conclude, such subtle differences in VH and VL contributions to Ab effector functions deserve consideration while designing Ab-based anti-viral therapeutics.
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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 | 0.000 | 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".