Development of SYK NanoBRET cellular target engagement assays for gain–of–function variants
Bibliographic record
Abstract
Spleen tyrosine kinase (SYK) is a non-receptor tyrosine kinase that is activated by phosphorylation events downstream of FcR, B-cell and T-cell receptors, integrins, and C-type lectin receptors. When the tandem Src homology 2 (SH2) domains of SYK bind to phosphorylated immunoreceptor tyrosine-based activation motifs (pITAMs) contained within these immunoreceptors, or when SYK is phosphorylated in interdomain regions A and B, SYK is activated. SYK gain-of-function (GoF) variants were previously identified in six patients that had higher levels of phosphorylated SYK and phosphorylated downstream proteins JNK and ERK. Furthermore, the increased SYK activation resulted in the clinical manifestation of immune dysregulation, organ inflammation, and a predisposition for lymphoma. The knowledge that the SYK GoF variants have enhanced activity was leveraged to develop a SYK NanoBRET cellular target engagement assay in intact live cells with constructs for the SYK GoF variants. Herein, we developed a potent SYK-targeted NanoBRET tracer using a SYK donated chemical probe, MRL-SYKi, that enabled a NanoBRET cellular target engagement assay for SYK GoF variants, SYK(S550Y), SYK(S550F), and SYK(P342T). We determined that ATP-competitive SYK inhibitors bind potently to these SYK variants in intact live cells. Additionally, we demonstrated that MRL-SYKi can effectively reduce the catalytic activity of SYK variants, and the phosphorylation levels of SYK(S550Y) in an epithelial cell line (SW480) stably expressing SYK(S550Y).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".