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Record W4300002445 · doi:10.1002/cmdc.202200399

Effects of the 5′‐Triphosphate Metabolites of Ribavirin, Sofosbuvir, Vidarabine, and Molnupiravir on CTP Synthase Catalysis and Filament Formation: Implications for Repurposing Antiviral Agents against SARS‐CoV‐2

2022· article· en· W4300002445 on OpenAlexafffund
Thomas D. Gillis, Stephen L. Bearne

Bibliographic record

VenueChemMedChem · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiochemical and Molecular Research
Canadian institutionsDalhousie University
FundersResearch Nova ScotiaIWK Health CentreDalhousie UniversityDalhousie Medical Research FoundationQEII FoundationMitacs
KeywordsBiochemistrySofosbuvirATP synthaseNucleotideChemistryEnzymeNucleotide salvageAllosteric regulationBiologyRibavirin

Abstract

fetched live from OpenAlex

Abstract Repurposing of antiviral drugs affords a rapid and effective strategy to develop therapies to counter pandemics such as COVID‐19. SARS‐CoV‐2 replication is closely linked to the metabolism of cytosine‐containing nucleotides, especially cytidine‐5′‐triphosphate (CTP), such that the integrity of the viral genome is highly sensitive to intracellular CTP levels. CTP synthase (CTPS) catalyzes the rate‐limiting step for the de novo biosynthesis of CTP. Hence, it is of interest to know the effects of the 5′‐triphosphate (TP) metabolites of repurposed antiviral agents on CTPS activity. Using E. coli CTPS as a model enzyme, we show that ribavirin‐5′‐TP is a weak allosteric activator of CTPS, while sofosbuvir‐5′‐TP and adenine−arabinofuranoside‐5′‐TP are both substrates. β‐ d ‐ N 4 ‐Hydroxycytidine‐5′‐TP is a weak competitive inhibitor relative to CTP, but induces filament formation by CTPS. Alternatively, sofosbuvir‐5′‐TP prevented CTP‐induced filament formation. These results reveal the underlying potential for repurposed antivirals to affect the activity of a critical pyrimidine nucleotide biosynthetic enzyme.

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 score0.009
Threshold uncertainty score0.475

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 head0.016
GPT teacher head0.278
Teacher spread0.263 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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