Development of a time-resolved fluorescence resonance energy transfer ultra-high throughput screening assay targeting SYK and FCER1G interaction
Bibliographic record
Abstract
The spleen tyrosine kinase (SYK) and high affinity immunoglobulin epsilon receptor subunit gamma (FCER1G) interaction has a major role in the normal innate and adaptive immune responses, but dysregulation of this interaction is implicated in several human diseases, including autoimmune disorders, hematological malignancies, and Alzheimer's Disease. Development of small molecule chemical probes could aid in studying this pathway both in normal and aberrant contexts. Herein, we describe the miniaturization of a time-resolved fluorescence resonance energy transfer (TR-FRET) assay to measure the interaction between SYK and FCER1G in a 1536-well ultrahigh throughput screening (uHTS) format. The assay utilizes the His-SH2 domains of SYK, which are indirectly labeled with anti-His-terbium to serve as a TR-FRET donor and a FITC-conjugated phosphorylated ITAM domain peptide of FCER1G to serve as an acceptor. We have optimized the assay into a 384-well HTS format and further miniaturized the assay into a 1536-well uHTS format. Robust assay performance has been achieved with a Z' factor > 0.8 and signal-to-background (S/B) ratio > 15. The utilization of this uHTS TR-FRET assay for compound screening has been validated by a pilot screening of 2,036 FDA-approved and bioactive compounds library. Several primary hits have been identified from the pilot uHTS. One compound, hematoxylin, was confirmed to disrupt the SYK/FECR1G interaction in an orthogonal protein-protein interaction assay. Thus, our optimized and miniaturized uHTS assay could be applied to future scaling up of a screening campaign to identify small molecule inhibitors targeting the SYK and FCER1G interaction.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".