MétaCan
Menu
Back to cohort
Record W2775330731 · doi:10.1002/pro.3354

Differential interaction of PRMT1 with RGG‐boxes of the FET family proteins EWS and TAF15

2017· article· en· W2775330731 on OpenAlexfundno aff
Kim K. C. Li, Bess Ling Chau, Kevin A. W. Lee

Bibliographic record

VenueProtein Science · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsnot available
FundersVrije Universiteit BrusselUniversité de StrasbourgUniversity of TorontoUtah State University
KeywordsMethylationChemistryArginineGeneticsBiologyBiophysicsComputational biologyBiochemistryAmino acidDNA

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.260
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueProtein ScienceSame topicCancer-related gene regulationFrench-language works237,207