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Biochemical and Structural Studies of Conserved Maf Proteins Revealed Nucleotide Pyrophosphatases with a Preference for Modified Nucleotides

2013· article· en· W2064643666 on OpenAlexafffund
Anatoli Tchigvintsev, Dmitri Tchigvintsev, Robert Flick, Ana Popovic, Aiping Dong, Xiaohui Xu, Greg Brown, Wenyun Lu, Hong Wu, Hong Cui, Ludmila Dombrowski, Jeong Chan Joo, Natalia Beloglazova, Jinrong Min, Alexei Savchenko, Amy A. Caudy, Joshua D. Rabinowitz, Alexey G. Murzin, Alexander F. Yakunin

Bibliographic record

VenueChemistry & Biology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiochemical and Molecular Research
Canadian institutionsStructural Genomics ConsortiumUniversity of Toronto
FundersNational Cancer InstituteCanadian Institutes of Health ResearchNational Institute of General Medical SciencesWellcome Trust
KeywordsNucleotideBiologyBiochemistryGTP'PyrophosphataseCell biologyEnzymeGene

Abstract

fetched live from OpenAlex

Maf (for multicopy associated filamentation) proteins represent a large family of conserved proteins implicated in cell division arrest but whose biochemical activity remains unknown. Here, we show that the prokaryotic and eukaryotic Maf proteins exhibit nucleotide pyrophosphatase activity against 5-methyl-UTP, pseudo-UTP, 5-methyl-CTP, and 7-methyl-GTP, which represent the most abundant modified bases in all organisms, as well as against canonical nucleotides dTTP, UTP, and CTP. Overexpression of the Maf protein YhdE in E. coli cells increased intracellular levels of dTMP and UMP, confirming that dTTP and UTP are the in vivo substrates of this protein. Crystal structures and site-directed mutagenesis of Maf proteins revealed the determinants of their activity and substrate specificity. Thus, pyrophosphatase activity of Maf proteins toward canonical and modified nucleotides might provide the molecular mechanism for a dual role of these proteins in cell division arrest and house cleaning.

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 score0.004
Threshold uncertainty score0.798

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 head0.037
GPT teacher head0.293
Teacher spread0.255 · 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

Citations26
Published2013
Admission routes2
Has abstractyes

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