Production of recombinant soluble dimeric C-type lectin-like receptors of rat natural killer cells
Bibliographic record
Abstract
Working at the border between innate and adaptive immunity, natural killer (NK) cells play a key role in the immune system by protecting healthy cells and by eliminating malignantly transformed, stressed or virally infected cells. NK cell recognition of a target cell is mediated by a receptor "zipper" consisting of various activating and inhibitory receptors, including C-type lectin-like receptors. Among this major group of receptors, two of the largest rodent receptor families are the NKR-P1 and the Clr receptor families. Although these families have been shown to encode receptor-ligand pairs involved in MHC-independent self-nonself discrimination and are a target for immune evasion by tumour cells and viruses, structural mechanisms of their mutual recognition remain less well characterized. Therefore, we developed a non-viral eukaryotic expression system based on transient transfection of suspension-adapted human embryonic kidney 293 cells to produce soluble native disulphide dimers of NK cell C-type lectin-like receptor ectodomains. The expression system was optimized using green fluorescent protein and secreted alkaline phosphatase, easily quantifiable markers of recombinant protein production. We describe an application of this approach to the recombinant protein production and characterization of native rat NKR-P1B and Clr-11 proteins suitable for further structural and functional studies.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".