Kinetic analysis of human protein arginine N-methyltransferase 2: formation of monomethyl- and asymmetric dimethyl-arginine residues on histone H4
Bibliographic record
Abstract
Protein arginine N-methyltransferases (PRMTs) methylate arginine residues within proteins using S-adenosyl-L-methionine (AdoMet) to form S-adenosyl-L-homocysteine and methylarginine residues. All PRMTs produce omega-NG-monomethylarginine (MMA) residues and either asymmetric omega-N(G),N(G)-dimethylarginine (aDMA) or symmetric omega-N(G),N'(G)-dimethylarginine (sDMA) residues, referred to as Type I or Type II activity respectively. Here we report methylation activity from PRMT2 and compare it with PRMT1 activity using UPLC-MS/MS (ultra-performance liquid chromatography-tandem MS), gel electrophoresis, and thin-layer chromatography. We show that PRMT2 is a Type I enzyme and that the ratio of aDMA to MMA produced by PRMTs 1 and 2 is dependent on the substrate, regardless of rate or K(m), suggesting that the reactions for both enzymes are distributive rather than processive. Using UPLC-MS/MS we find that, for PRMT2, the dissociation constant (KAs) and K(m) of AdoMet and the Km of histone H4 are similar to values for PRMT1, whereas the PRMT2 k(cat) is 800-fold less than the PRMT1 k(cat). Although PRMT2 activity is substantially lower than PRMT1 in vitro, the fact that both enzymes selectively methylate histone H4 suggest that PRMT2, like PRMT1, may act as a transcription co-activator through this modification.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".