Mutational and Structural Analysis of KIR3DL1 Reveals a Lineage-Defining Allotypic Dimorphism That Impacts Both HLA and Peptide Sensitivity
Bibliographic record
Abstract
Killer Ig-like receptors (KIRs) control the activation of human NK cells via interactions with peptide-laden HLAs. KIR3DL1 is a highly polymorphic inhibitory receptor that recognizes a diverse array of HLA molecules expressing the Bw4 epitope, a group with multiple polymorphisms incorporating variants within the Bw4 motif. Genetic studies suggest that KIR3DL1 variation has functional significance in several disease states, including HIV infection. However, owing to differences across KIR3DL1 allotypes, HLA-Bw4, and associated peptides, the mechanistic link with biological outcome remains unclear. In this study, we elucidated the impact of KIR3DL1 polymorphism on peptide-laden HLA recognition. Mutational analysis revealed that KIR residues involved in water-mediated contacts with the HLA-presented peptide influence peptide binding specificity. In particular, residue 282 (glutamate) in the D2 domain underpins the lack of tolerance of negatively charged C-terminal peptide residues. Allotypic KIR3DL1 variants, defined by neighboring residue 283, displayed differential sensitivities to HLA-bound peptide, including the variable HLA-B*57:01-restricted HIV-1 Gag-derived epitope TW10. Residue 283, which has undergone positive selection during the evolution of human KIRs, also played a central role in Bw4 subtype recognition by KIR3DL1. Collectively, our findings uncover a common molecular regulator that controls HLA and peptide discrimination without participating directly in peptide-laden HLA interactions. Furthermore, they provide insight into the mechanics of interaction and generate simple, easily assessed criteria for the definition of KIR3DL1 functional groupings that will be relevant in many clinical applications, including bone marrow transplantation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".