Structure of the murine <scp>CD94</scp>–<scp>NKG2A</scp> receptor in complex with Qa‐1<sup>b</sup> presenting an <scp>MHC‐I</scp> leader peptide
Bibliographic record
Abstract
The heterodimeric natural killer cells antigen CD94 (CD94)–NKG2‐A/NKG2‐B type II integral membrane protein (NKG2A) receptor family expressed on human and mouse natural killer (NK) cells monitors global major histocompatibility complex (MHC) class I cell surface expression levels through binding to MHC class Ia‐derived leader sequence peptides presented by HLA class I histocompatibility antigen, alpha chain E (HLA‐E; in humans) or H‐2 class I histocompatibility antigen, D‐37 (Qa‐1b; in mice). Although the molecular basis underpinning human CD94–NKG2A recognition of HLA‐E is known, the equivalent interaction in the murine setting is not. By determining the high‐resolution crystal structure of murine CD94–NKG2A in complex with Qa‐1b presenting the Qa‐1 determinant modifier peptide (QDM), we resolved the mode of binding. Compared to the human homologue, the murine CD94–NKG2A–Qa‐1b–QDM displayed alterations in the distribution of interactions across CD94 and NKG2A subunits that coincide with differences in electrostatic complementarity of the ternary complex and the lack of cross‐species reactivity. Nevertheless, we show that Qa‐1b could be modified through W65R + N73I mutations to mimic HLA‐E, facilitating binding with both human and murine CD94–NKG2A. These data underscore human and murine CD94–NKG2A cross‐species heterogeneity and provide a foundation for humanising Qa‐1b in immune system models.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".