A mass spectrometry based method for distinguishing between symmetrically and asymmetrically dimethylated arginine residues
Bibliographic record
Abstract
The arginine methylation of proteins is involved in several important cellular activities, most notably transcriptional control. Arginine dimethylation can take two distinct forms, symmetric and asymmetric, catalyzed by different classes of enzymes. To establish a method for the mass spectrometric identification and characterization of this post-translational modification, we analyzed synthetic peptides with symmetrically or asymmetrically methylated arginine residues by electrospray ionization tandem mass spectrometry. We observed abundant characteristic ions at [M+nH-31](n+) and [M+nH-70](n+) in spectra of symmetrically methylated peptides and at [M+nH-45](n+) in spectra of asymmetrically methylated peptides. We speculate these ions arise from neutral loss of monomethylamine, dimethylcarbodiimide, and dimethylamine, respectively. These characteristic ions allowed the rapid identification of a symmetrically arginine-dimethylated peptide from myelin basic protein and a symmetrically arginine-dimethylated peptide from SmD3 co-immunoprecipitated with the methyltransferase-associated protein pICln, suggesting that this method may provide a rapid means to screen for and characterize dimethylarginine sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".