High Throughput Screening of Protein Arginine <i>N</i> ‐Methyltransferases – Filter Binding and Phosphor Screening (FBAPS) assay
Bibliographic record
Abstract
Study Objective We seek to develop a cost‐effective assay to screen protein arginine N‐ methyltransferase (PRMT) enzyme activity in a high throughput manner by combining P81 filter binding with phosphor imaging. Hypothesis A combination of the P81 filter binding assay and phosphor screening (FBAPS) will provide improved throughput for testing inhibitors against PRMTs while eliminating the economic and environmental costs incurred with current PRMT assays. Statement of methods Recombinantly‐expressed PRMT1 and coactivator‐associated arginine methyltransferase 1 (CARM1) were used to develop the FBAPS assay using GST fusions of polyA binding protein 1 (PABP1(437‐488)) and glycine‐ and arginine‐rich (GAR) protein as substrates, respectively, and radiolabelled S ‐adenosyl‐L‐[ methyl ‐ 14 C]‐methionine as cofactor. Methylation reactions were spotted onto P81 filter paper in a dot blot apparatus and radioactive signal was measured both by phosphor imaging and liquid scintillation counting. Kinetic parameters (K M , k cat ) for enzymes and substrates were determined, and IC 50 values were obtained for well‐characterized inhibitors S ‐adenosyl‐L‐homocysteine (SAH), MS023, EPZ020411, and Diamine 12 using FBAPS. Summary of Results FBAPS yielded kinetic parameters with no statistically significant differences to what was obtained using liquid scintillation counting. The IC 50 values obtained by the FBAPS assay for PRMT1 and CARM1 were comparable to values reported in literature. Statement of Conclusions The FBAPS assay is a modification to the P81 filter binding assay with a dot blot apparatus that allows for parallel processing of samples in a multi‐well format, dramatically increasing throughput. Signal detection by phosphor imaging offers a cost effective and quantitative method that can be used to screen several inhibitors simultaneously against PRMT enzymes with high accuracy.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".