A viral immunoevasin controls innate immunity by targeting the prototypical natural killer cell receptor family (NK1.1/NKR-P1)
Bibliographic record
Abstract
Abstract Natural killer (NK) cells play a key role in innate immunity by detecting alterations in self and non-self ligands via paired NK cell receptors (NKR). Despite identification of numerous NKR-ligand interactions, physiological ligands for the prototypical NK1.1 orphan receptor remain elusive. Here, we identify a viral ligand for the inhibitory and activating NKR-P1 (NK1.1) receptors. This murine cytomegalovirus (MCMV)-encoded protein, m12, restrains NK effector function by directly engaging the inhibitory NKR-P1B receptor. However, m12 also interacts with the activating NKR-P1A/C receptors to counterbalance m12 decoy function. Structural analyses reveal that m12 sequesters a large NKR-P1 surface area via a “polar claw” mechanism. Polymorphisms in, and ablation of, the viral m12 protein and host NKR-P1B/C alleles impact NK cell responses in vivo. Thus, we identify the long-sought foreign ligand for this key immunoregulatory NK cell receptor family and reveal how it controls the evolutionary balance of immune recognition during host-pathogen interplay.
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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".