Kinase Inhibitor Selectivity Profiling Using Differential Scanning Fluorimetry
Bibliographic record
Abstract
Fast, robust, and inexpensive screening methods are the heart of drug discovery processes. Moreover, it is useful to have access to several established assay formats, for validation purposes. If a targeted protein is an enzyme, the logical and widely used approach is the direct measurement of the effect of the added ligands on its activity. A variety of enzymatic assay formats have been successfully applied for inhibitor screening of protein kinases. However, enzymatic assays require an active enzyme with a known substrate and often time-consuming assay optimization. Several alternative approaches have been recently developed that detect binding of ligands to proteins. This chapter overviews and provides the experimental protocol of the successful application of differential scanning fluorimetry (DSF) in our laboratory for fast and robust screening of medium-sized (<10,000) inhibitor libraries. DSF monitors the thermal stabilization of the native protein structure upon ligand binding. It allows selectivity profiling of any protein kinase without prior knowledge of either substrate or activity of the kinase under investigation. Comparative studies revealed that generated data is highly reproducible and correlates well with the results from other ligand binding methodologies, direct binding constants as well as enzymatic assays.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".