Recognition of Class I MHC by a Rat Ly49 NK Cell Receptor Is Dependent on the Identity of the P2 Anchor Amino Acid of Bound Peptide
Bibliographic record
Abstract
Members of the rodent Ly49 receptor family control NK cell responsiveness and demonstrate allele specificity for MHC class I (MHC-I) ligands. For example, the rat Ly49i2 inhibitory NK cell receptor binds RT1-A1(c) but not other rat MHC class Ia or Ib molecules. RT1-A1(c) preferentially binds peptides with proline at the second, or P2, position, which defines it as an HLA-B7 supertype MHC-I molecule. Previously, our laboratory showed that mutations within the MHC-I supertype-defining B-pocket of RT1-A1(c) could lead to alterations in P2 anchor residues of the peptide repertoire bound by RT1-A1(c) and loss of recognition by Ly49i2. Although suggestive of peptide involvement, it was unclear whether the peptide P2 anchor residue or alteration of the RT1-A1(c) primary sequence influenced Ly49i2 recognition. Therefore, we directly investigated the role of the P2 anchor residue of RT1-A1(c)-bound peptides in Ly49i2 recognition. First, fluorescent multimers generated by refolding soluble recombinant RT1-A1(c) with individual synthetic peptides differing only at the P2 anchor residue were examined for binding to Ly49i2 NK cell transfectants. Second, cytotoxicity by Ly49i2-expressing NK cells toward RMA-S target cells expressing RT1-A1(c) bound with peptides that only differ at the P2 anchor residue was evaluated. Our results demonstrate that Ly49i2 recognizes RT1-A1(c) bound with peptides that have Pro or Val at P2, whereas little or no recognition is observed when RT1-A1(c) is complexed with peptide bearing Gln at P2. Thus, the identity of the P2 peptide anchor residue is an integral component of MHC-I recognition by Ly49i2.
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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".