<scp>V<sub>H</sub></scp> and <scp>V<sub>L</sub></scp> Domains of Polyspecific IgM and Monospecific IgG Antibodies Contribute Differentially to Antigen Recognition and Virus Neutralization Functions
Bibliographic record
Abstract
We analysed contributions of variable heavy (FdVH ) and variable light (FdVL ) domains in comparison to scFv (FdVH +FdVL ) of naturally occurring polyspecific bovine IgM with an exceptionally long CDR3H and an induced monospecific bovine herpes virus-1 (BoHV-1) neutralizing IgG1 antibody in the context of to antigen-binding site and antibody function. Various recombinant FdVH , FdVL and scFv were constructed and expressed in Pichia pastoris from the bovine IgM and IgG1 antibody encoding cDNA. The scFv1H12 showed polyspecific antigen binding similar to parent IgM antibody, though subtle differences, for example, higher thyroglobulin recognition. Such differences reflect influence of the constant region on the antigen-binding site configuration. Unlike, variable light domain FdVL 1H12, the variable heavy domain FdVH 1H12 alone recognized multiple antigens that differed from the recognition pattern of scFv1H12 (FdVH +FdVL ) and the parent IgM antibody. Nonetheless, role of FdVL 1H12 in providing structural support to FdVH in antigen recognition is noted, apart from its intrinsic antigen recognition ability. Surface plasmon resonance analysis revealed low to moderate affinity of scFv1H12 to IgG antigen. By contrast, the individual FdVH 073 and FdVL 074, originating from induced BoHV-1 neutralizing IgG1 antibody, recognized target epitope on BoHV-1 weakly when compared to FdVH +FdVL (scFv3-18L). Interestingly, both the FdVH and FdVL domains of induced IgG antibody are required to achieve BoHV-1 neutralization. To conclude, there exist subtle functional differences in the contribution of FdVH and FdVL to antigen-binding site generation of polyspecific IgM and monospecific IgG antibodies relevant to antigen recognition and virus neutralization functions.
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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".