Differential interaction of PRMT1 with RGG‐boxes of the FET family proteins EWS and TAF15
Bibliographic record
Abstract
The FET sub-family (FUS/TLS, EWS, TAF15) of RNA-binding proteins have remarkably similar overall structure but diverse biological and pathological roles. The molecular basis for FET protein specialization is largely unknown. Gly-Arg-Rich regions (RGG-boxes) within FET proteins are targets for methylation by Protein-Arginine-Methyl-Transferase-1 (PRMT1) and substrate capture is thought to involve electrostatic attraction between positively charged polyRGG substrates and negatively charged surface channels of PRMT1. Unlike FUS and EWS, a high proportion of TAF15 RGG-boxes are embedded within neutrally charged YGGDR(S/G)G repeats, suggesting that they might not bind well to PRMT1. This notion runs contrary however to a report that YGGDR(S/G)G repeats are methylated by PRMT1. Using peptide-based polyRGG substrates and a novel 2-hybrid binding assay, we find that the Asp residue in YGGDR(S/G)G repeats confers poor binding to PRMT1. Our results therefore indicate that YGGDR(S/G)G repeats may contribute to TAF15 specialization by enabling differential interactions with PRMT1 and reduced overall levels of TAF15 methylation compared with other FET proteins. By analogy with molecular recognition of other disordered polyvalent ligands by globular protein partners, we also propose a dynamic polyelectrostatic model for substrate capture by PRMT1.
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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 | 0.000 | 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 | 0.001 |
| 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".