A novel and efficient approach to high-throughput production of HLA-E/peptide monomer for T-cell epitope screening
Bibliographic record
Abstract
Over the past two decades, there has been a great interest in the study of HLA-E-restricted αβ T cells during bacterial and viral infections, including recently SARS-CoV-2 infection. Phenotyping of these specific HLA-E-restricted T cells requires new tools such as tetramers for rapid cell staining or sorting, as well as for the identification of new peptides capable to bind to the HLA-E pocket. To this aim, we have developed an optimal photosensitive peptide to generate stable HLA-E/pUV complexes allowing high-throughput production of new HLA-E/peptide complexes by peptide exchange. We characterized the UV exchange by ELISA and improved the peptide exchange readout using size exclusion chromatography. This novel approach for complex quantification is indeed very important to perform tetramerization of MHC/peptide complexes with the high quality required for detection of specific T cells. Our approach allows the rapid screening of peptides capable of binding to the non-classical human HLA-E allele, paving the way for the development of new therapeutic approaches based on the detection of HLA-E-restricted T cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".