Structure-based discovery of inhibitors of the SARS-CoV-2 Nsp14 <i>N7</i> -methyltransferase
Bibliographic record
Abstract
Abstract An under-explored target for SARS-CoV-2 is non-structural protein 14 (Nsp14), a crucial enzyme for viral replication that catalyzes the methylation of N7 -guanosine of the viral RNA at 5′-end; this enables the virus to evade the host immune response by mimicking the eukaryotic post-transcriptional modification mechanism. We sought new inhibitors of the S-adenosyl methionine (SAM)-dependent methyltransferase (MTase) activity of Nsp14 with three large library docking strategies. First, up to 1.1 billion make-on-demand (“tangible”) lead-like molecules were docked against the enzyme’s SAM site, seeking reversible inhibitors. On de novo synthesis and testing, three inhibitors emerged with IC 50 values ranging from 6 to 43 μM, each with novel chemotypes. Structure-guided optimization and in vitro characterization supported their non-covalent mechanism. In a second strategy, docking a library of 16 million tangible fragments revealed nine new inhibitors with IC 50 values ranging from 12 to 341 μM and ligand efficiencies from 0.29 to 0.42. In a third strategy, a newly created library of 25 million tangible, virtual electrophiles were docked to covalently modify Cys387 in the SAM binding site. Seven inhibitors emerged with IC 50 values ranging from 3.2 to 39 μM, the most potent being a reversible aldehyde. Initial optimization of a second series yielded a 7 μM acrylamide inhibitor. Three inhibitors characteristic of the new series were tested for selectivity against 30 human protein and RNA MTases, with one showing partial selectivity and one showing high selectivity. Overall, 32 inhibitors encompassing eleven chemotypes had IC 50 values <50 μM and 5 inhibitors in four chemotypes had IC 50 values <10 μM. These molecules are among the first non-SAM-like inhibitors of Nsp14, providing multiple starting points for optimizing towards antiviral activity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".