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Kinetic Analysis of PRMT1 Reveals Multifactorial Processivity and a Sequential Ordered Mechanism

2018· article· en· W3176888819 on OpenAlexaffabout
Jennifer I. Brown, Jolinde van Strien, Nathaniel I. Martin, Adam Frankel

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsUniversity of British Columbia
FundersUniversiteit Utrecht
KeywordsProcessivityMethyltransferaseBiochemistryChemistryEnzymeStereochemistryBinding siteMethylationPeptideBiophysicsBiologyDNAPolymerase

Abstract

fetched live from OpenAlex

BACKGROUND Protein arginine N ‐methyltransferases (PRMTs) are responsible for the transfer of one or two methyl groups from the methyl donor S ‐adenosyl‐L‐methionine (SAM) to produce mono‐ or dimethylated arginine residues and the spent cofactor S ‐adenosyl‐L‐homocysteine (SAH). Methylated arginines in eukaryotic cells are involved in several crucial biological processes, including transcription control, DNA damager repair, and RNA processing. However, contrasting findings for two major questions in the field have generated debate regarding PRMT mechanisms. The purpose of this research was to elucidate the PRMT1 mechanism and answer these two highly debated questions: do PRMTs dimethylate their substrates processively or distributively, and do PRMTs bind their substrates using a random or sequential method of substrate binding? METHODS To explore these mechanisms, we assayed PRMT1 with well‐characterized peptide substrates. We used tandem mass spectrometry to quantitate the ratios of mono‐ and dimethylarginine species produced to assess PRMT1 processivity. We determined the mechanism of bisubstrate binding using radioactive SAM in enzyme assays to measure methyl transfer to a target peptide in the presence of various concentrations of product inhibitors. RESULTS We demonstrate that PRMT1 processivity differs depending on the substrate sequence, which is congruent with previous reports. However, we show for the first time that as cofactor and/or enzyme concentration increases, the ratio of dimethylarginine to monomethylarginine substantially increases, suggesting the enzyme becomes more processive. Further, the steady‐state inhibition patterns favour a sequential ordered mechanism over a random mechanism, indicating that for catalysis to occur, SAM binding must precede peptide binding. CONCLUSIONS The degree of PRMT1 processivity is a multifactorial effect that is heavily influenced by experimental design considerations, so previous conclusions about processivity may require reassessment. We also find that PRMT1 uses a sequential ordered substrate binding mechanism, in contrast with recent findings. Use of nonlinear regression and presentation of our data using three linear plots strengthens our conclusions. Considering the conserved active sites within the human PRMT family, we anticipate that our results regarding PRMT1 kinetic and processive mechanisms may extend to other human PRMTs. Support or Funding Information This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) RGPIN‐2015–04450 Discovery Grant (A.F.), NSERC CGS‐M (J.I.B.), the Shaughnessy Hospital Volunteer Society Fellowship in Health Care (J.I.B.), and the Walter C. Koerner Fellowship (J.I.B.). Additional support provided by Utrecht University and the Netherlands Organization for Scientific Research is acknowledged (T.K., J.v.S., and N.I.M.). This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.266
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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