The Role of the HLA Class I α2 Helix in Determining Ligand Hierarchy for the Killer Cell Ig-like Receptor 3DL1
Bibliographic record
Abstract
Abstract HLA class I molecules that represent ligands for the inhibitory killer cell Ig-like receptor (KIR) 3DL1 found on NK cells are categorically defined as those HLA-A and HLA-B allotypes containing the Bw4 motif, yet KIR3DL1 demonstrates hierarchical recognition of these HLA-Bw4 ligands. To better understand the molecular basis underpinning differential KIR3DL1 recognition, the HLA-ABw4 family of allotypes were investigated. Transfected human 721.221 cells expressing HLA-A*32:01 strongly inhibited primary human KIR3DL1+ NK cells, whereas HLA-A*24:02 and HLA-A*23:01 displayed intermediate potency and HLA-A*25:01 failed to inhibit activation of KIR3DL1+ NK cells. Structural studies demonstrated that recognition of HLA-A*24:02 by KIR3DL1 used identical contacts as the potent HLA-B*57:01 ligand. Namely, the D1–D2 domains of KIR3DL1 were placed over the α1 helix and α2 helix of the HLA-A*24:02 binding cleft, respectively, whereas the D0 domain contacted the side of the HLA-A*24:02 molecule. Nevertheless, functional analyses showed KIR3DL1 recognition of HLA-A*24:02 was more sensitive to substitutions within the α2 helix of HLA-A*24:02, including residues Ile142 and Lys144. Furthermore, the presence of Thr149 in the α2 helix of HLA-A*25:01 abrogated KIR3DL1+ NK inhibition. Together, these data demonstrate a role for the HLA class I α2 helix in determining the hierarchy of KIR3DL1 ligands. Thus, recognition of HLA class I is dependent on a complex interplay between the peptide repertoire, polymorphisms within and proximal to the Bw4 motif, and the α2 helix. Collectively, the data furthers our understanding of KIR3DL1 ligands and will inform genetic association and immunogenetics studies examining the role of KIR3DL1 in disease settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".