Förster resonance energy transfer measurements of cofactor‐dependent effects on protein arginine <i>N</i>‐methyltransferase homodimerization
Bibliographic record
Abstract
Protein arginine N-methyltransferase (PRMT) dimerization is required for methyl group transfer from the cofactor S-adenosyl-L-methionine (AdoMet) to arginine residues in protein substrates, forming S-adenosyl-L-homocysteine (AdoHcy) and methylarginine residues. In this study, we use Förster resonance energy transfer (FRET) to determine dissociation constant (K(D)) values for dimerization of PRMT1 and PRMT6. By attaching monomeric Cerulean and Citrine fluorescent proteins to their N-termini, fluorescent PRMTs are formed that exhibit similar enzyme kinetics to unconjugated PRMTs. These fluorescent proteins are used in FRET-based binding studies in a multi-well format. In the presence of AdoMet, fluorescent PRMT1 and PRMT6 exhibit 4- and 6-fold lower dimerization K(D) values, respectively, than in the presence of AdoHcy, suggesting that AdoMet promotes PRMT homodimerization in contrast to AdoHcy. We also find that the dimerization K(D) values for PRMT1 in the presence of AdoMet or AdoHcy are, respectively, 6- and 10-fold lower than the corresponding values for PRMT6. Considering that the affinity of PRMT6 for AdoHcy is 10-fold higher than for AdoMet, PRMT6 function may be subject to cofactor-dependent regulation in cells where the methylation potential (i.e., ratio of AdoMet to AdoHcy) is low. Since PRMT1 affinity for AdoMet and AdoHcy is similar, however, a low methylation potential may not affect PRMT1 function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 0.000 |
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 teacher head, 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".