Modulations of anti‐D affinity following promiscuous binding of the heavy chain with naïve light chains
Bibliographic record
Abstract
BACKGROUND: It is generally accepted that the antibody heavy (H) chain is more important than the light (L) chain for determining antigen specificity. In the case of anti-D, the predominant role of H chains in antigen binding is well recognized, but much less is known about the function of L chains. In this work, the contribution of L chains from non-D-immunized donors to the specificity and reactivity of anti-Ds was studied with L-chain shuffling. STUDY DESIGN AND METHODS: A kappa L chain library was recombined with the H chain of the 43F10 anti-D in a phagemid vector system (pComb3H, Scripps Institute). D-specific F(ab) phages were selected by panning on RBCs. Soluble F(ab)s were prepared, and their reactivity was assessed by RBC agglutination. The nucleotide and amino acid sequences of the variable region of the L chains were analyzed. RESULTS: The L chains of the six D-specific 43F10 F(ab) clones studied used five different germline genes from three Vkappa families and three different Jkappa segments. The L chains were all cationic with isoelectric points ranging from 8.1 to 10.2. CONCLUSION: The 43F10 anti-D H chain could bind promiscuously to a diversity of L chains from non-D-immunized donors without losing the D-antigen specificity. Relationships between the anti-D affinity and the cationic charge of the L chain as well as with the presence of an arginine residue in the L-chain complementarity-determining region 1 were observed.
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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 | 0.001 |
| 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".