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Record W2397309492

The hepatitis C virus NS5B RNA-dependent RNA polymerase activity and susceptibility to inhibitors is modulated by metal cations.

2001· article· en· W2397309492 on OpenAlexaff
Alaoui-Lsmaili Mh, Martine Hamel, Lucille L’Heureux, Olivier Nicolas, Darius Bilimoria, Patrick Labonté, Samir Mounir, Rando Rf

Bibliographic record

VenuePubMed · 2001
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsBioPhage Pharma (Canada)
Fundersnot available
KeywordsNS5BRNA polymeraseRNA-dependent RNA polymeraseChemistryCofactorEnzymeRNAHepatitis C virusPolymeraseBiochemistryMolecular biologyBiologyVirologyVirusGeneHepacivirus
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of this study was to understand the effect of metal cations on the hepatitis C virus (HCV) NS5B in vitro RNA-dependent RNA polymerase (RdRp) activity and its susceptibility to various inhibitors. METHODS: A recombinant full-length HCV NS5B protein was expressed in insect cells and purified to homogeneity. RdRp activity was assessed using standard filtration or polyacrylamide gel-based assays. RESULTS: Efficient inhibition of the HCV NS5B RdRp activity by gliotoxin, as well as by various substrate analogs, occurs in the presence of Mn2+, but not of Mg2+. Assays performed in the presence of both cofactors suggest that, in vitro, the enzyme's affinity for Mn2+ is higher than that for Mg2+. In addition, the RdRp activity, displayed in the presence of heteropolymeric templates, is significantly increased when the metal cofactor consists of Mn2+. Finally, steady state kinetics showed that the velocity of the reaction, as well as the affinity of the enzyme for its substrate, could both be affected by the nature of the divalent metal cation used.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.024
GPT teacher head0.283
Teacher spread0.258 · 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".

Quick stats

Citations22
Published2001
Admission routes1
Has abstractyes

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