Virtual High-Throughput Screening Identifies Mycophenolic Acid as a Novel RNA Capping Inhibitor
Bibliographic record
Abstract
The RNA guanylyltransferase (GTase) is involved in the synthesis of the (m7)Gppp-RNA cap structure found at the 5' end of eukaryotic mRNAs. GTases are members of the covalent nucleotidyl transferase superfamily, which also includes DNA and RNA ligases. GTases catalyze a two-step reaction in which they initially utilize GTP as a substrate to form a covalent enzyme-GMP intermediate. The GMP moiety is then transferred to the diphosphate end of the RNA transcript in the second step of the reaction to form the Gppp-RNA structure. In the current study, we used a combination of virtual database screening, homology modeling, and biochemical assays to search for novel GTase inhibitors. Using this approach, we demonstrate that mycophenolic acid (MPA) can inhibit the GTase reaction by preventing the catalytic transfer of the GMP moiety onto an acceptor RNA. As such, MPA represents a novel type of inhibitor against RNA guanylyltransferases that inhibits the second step of the catalytic reaction. Moreover, we show that the addition of MPA to S. cerevisiae cells leads to a reduction of capped mRNAs. Finally, biochemical assays also demonstrate that MPA can inhibit DNA ligases through inhibition of the second step of the reaction. The biological implications of these findings for the MPA-mediated inhibition of members of the covalent nucleotidyl superfamily are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".