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
Bibliographic record
Abstract
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‐N4‐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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".