Quantitative Methods to Study Protein Arginine Methyltransferase 1-9 Activity in Cells
Bibliographic record
Abstract
Protein methyltransferases (PRMTs) catalyze the transfer of a methyl group to arginine residues of substrate proteins. The PRMT family consists of nine members that can monomethylate or symmetrically/asymmetrically dimethylate arginine residues. Several antibodies recognizing different types of arginine methylation of various proteins are available; thus, providing tools for the development of PRMT activity biomarker assays. PRMT antibody-based assays are challenging due to overlapping substrates and motif-based antibody specificities. These issues and the experimental setup to investigate the arginine methylation contributed by individual PRMTs are discussed. Through the careful selection of the representative substrates that are biomarkers for eight out of nine PRMTs, a panel of PRMT activity assays were designed. Here, the protocols for cellular assays quantitatively measuring the enzymatic activity of individual members of the PRMT family in cells are reported. The advantage of the described methods is their straightforward performance in any lab with cell culture and fluorescent western blot capabilities. The substrate specificity and chosen antibody reliability were fully validated with knockdown and overexpression approaches. In addition to detailed guidelines of the assay biomarkers and antibodies, information on the use of an inhibitor tool compound collection for PRMTs is also provided.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".