Dimers and multimers of monoclonal IgG1 exhibit higher in vitro binding affinities to Fcγ receptors
Bibliographic record
Abstract
The in vitro binding of monomeric, dimeric and multimeric forms of monoclonal IgG1 molecules, designated mAb1 and mAb2, to the extracellular domains of Fcgamma receptors RI, RIIA and RIIIB were investigated using a surface plasmon resonance (SPR) based biosensor technique. Stable noncovalent and covalent dimers of mAb1 and mAb2, respectively, were isolated from CHO cell expressed materials. The dissociation constants of monomeric mAb1 and mAb2 were determined to be 1 nM for the FcgammaRI-binding and 6-12 microM for the FcgammaRIIA- and FcgammaRIIIB-binding. Dimeric mAb1 and mAb2 exhibited increased affinities, by 2-3 fold for FcgammaRI and 200-800 fold for FcgammaRIIA and FcgammaRIIIB. Further increases in binding were observed when the antibodies formed large immune complexes with multivalent antigens, but not in a linear relation with size. The binding properties of monomeric mAb2 were identical with and without a bound monovalent antigen, indicating that antigen-binding alone does not induce measurable change in binding of antibodies to Fcgamma receptors. Dimerization is sufficient to show enhancement in the receptor binding. Given the wide distribution of the low-affinity Fcgamma receptors on immune effector cells, the increased affinities to aggregated IgG may lead to some biological consequences, depending on the subsequent signal transduction events. The SPR-based in vitro binding assay is useful in evaluating Fcgamma receptor binding of various species in antibody-based biotherapeutics.
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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) | 0.001 | 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 | 0.001 |
| 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".