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Record W4224325804 · doi:10.1261/rna.079159.122

Metal content and kinetic properties of yeast RNA lariat debranching enzyme Dbr1

2022· article· en· W4224325804 on OpenAlexaff
Nathaniel E. Clark, Adam Katolik, Allison J. Taggart, Luke Buerer, S. Holloway, Nathan Miller, John D. Phillips, Colin Farrell, Masad J. Damha, William G. Fairbrother

Bibliographic record

VenueRNA · 2022
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsMcGill University
FundersNational Institute of General Medical SciencesNational Institutes of Health
KeywordsPhosphodiester bondBiochemistryBiologyEnzymeCofactorRNASaccharomyces cerevisiaeActive siteSpliceosomeYeastMolecular biologyRNA splicingGene

Abstract

fetched live from OpenAlex

In eukaryotic cells, intron lariats produced by the spliceosome contain a 2′5′ phosphodiester linkage. The RNA lariat debranching enzyme, Dbr1, is the only enzyme known to hydrolyze this bond. Dbr1 is a member of the metallophosphoesterase (MPE) family of enzymes, and recent X-ray crystal structures and biochemistry data demonstrate that Dbr1 from Entamoeba histolytica uses combinations of Mn 2+ , Zn 2+ , and Fe 2+ as enzymatic cofactors. Here, we examine the kinetic properties and metal dependence of the Dbr1 homolog from Saccharomyces cerevisiae (yDbr1). Elemental analysis measured stoichiometric quantities of Fe and Zn in yDbr1 purified following heterologous expression E. coli . We analyzed the ability of Fe 2+ , Zn 2+ , and Mn 2+ to reconstitute activity in metal-free apoenzyme. Purified yDbr1 was highly active, turning over substrate at 5.6 sec −1 , and apo-yDbr1 reconstituted with Fe 2+ was the most active species, turning over at 9.2 sec −1 . We treated human lymphoblastoid cells with the iron-chelator deferoxamine and measured a twofold increase in cellular lariats. These data suggest that Fe is an important biological cofactor for Dbr1 enzymes.

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 score0.075
Threshold uncertainty score0.362

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 studies0.0000.000
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 head0.055
GPT teacher head0.264
Teacher spread0.209 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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