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Record W4306855683 · doi:10.1016/j.sbi.2022.102489

Starting a new chapter on class Ia ribonucleotide reductases

2022· review· en· W4306855683 on OpenAlexfundno aff
Talya S. Levitz, Catherine L. Drennan

Bibliographic record

VenueCurrent Opinion in Structural Biology · 2022
Typereview
Languageen
FieldChemistry
TopicMetal-Catalyzed Oxygenation Mechanisms
Canadian institutionsnot available
FundersCanadian Institute for Advanced ResearchNational Institutes of HealthNational Science Foundation
KeywordsRibonucleotide reductaseRibonucleotideDeoxyribonucleotidesEscherichia coliBiologyDNABiochemistryChemistryGeneticsComputational biologyNucleotideGene

Abstract

fetched live from OpenAlex

Ribonucleotide reductases (RNRs) use radical-based chemistry to convert ribonucleotides into deoxyribonucleotides, an essential step in DNA biosynthesis and repair. There are multiple RNR classes, the best studied of which is the class Ia RNR that is found in Escherichia coli, eukaryotes including humans, and many pathogenic and nonpathogenic prokaryotes. This review covers recent advances in our understanding of class Ia RNRs, including a recent reporting of a structure of the active state of the E. coli enzyme and the impacts that the structure has had on spurring research into the mechanism of long-range radical transfer. Additionally, the review considers other recent structural and biochemical research on class Ia RNRs and the potential of that work for the development of anticancer and antibiotic therapeutics.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0160.000

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 head0.163
GPT teacher head0.418
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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations11
Published2022
Admission routes1
Has abstractyes

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