Quantitative affinity measurement of small molecule ligand binding to major histocompatibility complex class-I–related protein 1 MR1
Bibliographic record
Abstract
The Major Histocompatibility Complex class I–related protein 1 (MR1) presents small molecule metabolites, drugs, and drug-like molecules that are recognized by MR1-reactive T cells. While we have an understanding of how antigens bind to MR1 and upregulate MR1 cell surface expression, a quantitative, cell-free, assessment of MR1 ligand-binding affinity was lacking. Here, we developed a fluorescence polarization–based assay in which fluorescent MR1 ligand was loaded into MR1 protein in vitro and competitively displaced by candidate ligands over a range of concentrations. Using this assay, ligand affinity for MR1 could be differentiated as strong (IC 50 < 1 μM), moderate (1 μM < IC 50 < 100 μM), and weak (IC 50 > 100 μM). We demonstrated a clear correlation between ligand-binding affinity for MR1, the presence of a covalent bond between MR1 and ligand, and the number of salt bridge and hydrogen bonds formed between MR1 and ligand. Using this newly developed fluorescence polarization–based assay to screen for candidate ligands, we identified the dietary molecules vanillin and ethylvanillin as weak bona fide MR1 ligands. Both upregulated MR1 on the surface of C1R.MR1 cells and the crystal structure of a MAIT cell T cell receptor–MR1–ethylvanillin complex revealed that ethylvanillin formed a Schiff base with K43 of MR1 and was buried within the Aʹ-pocket. Collectively, we developed and validated a method to quantitate the binding affinities of ligands for MR1 that will enable an efficient and rapid screening of candidate MR1 ligands.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".