Epitope mapping of Ly-49G and G-like receptors: CK-1 antibody defines a polymorphic site of functional interaction with class I ligand
Bibliographic record
Abstract
Ly-49 receptors regulate mouse natural killer cell functions. Members of the polymorphic Ly-49 multigene family recognize specific alleles of major histocompatibility complex class I (MHC I) or MHC I-like proteins. Previous studies have provided insight into the nature of Ly-49A and -C interaction with their high-affinity MHC I ligands, H-2Dd and Kb, respectively. Unlike Ly-49C, recognition of MHC I by Ly-49A is regulated in part by residues within the beta4-beta5 loop of its ectodomain. Ly-49A and -G are within the same Ly-49 subfamily, and both receptors recognize Dd. However, there have been no studies that define specific sites on Ly-49G that mediate class I MHC recognition. The Ly-49G receptors of different inbred mouse strains can differ as a result of amino acid polymorphisms within their ectodomains. In this report, we have generated a novel antibody, CK-1, which recognizes Ly-49G(B6) and a Ly-49G(B6)-like receptor, Ly-49M(nonobese diabetic), but not Ly-49G(BALB/c). By exploiting the differences within ectodomains of C57BL/6 and BALB/c Ly-49G allele products, we identified epitopes recognized by the Ly-49G-specific antibodies CK-1 and Cwy-3, whose epitopes mapped within the beta4-beta5 loop and the beta1 strand, respectively, and were nonoverlapping. Although both antibodies specifically recognized the Ly-49G(B6) ectodomain, Cwy-3 was unable to block its interaction with MHC I, and CK-1 significantly inhibited it. The importance of residues within the beta4-beta5 loop in Ly-49G recognition demonstrates that its interaction with MHC I is similar to that of Ly-49A but not Ly-49C.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".