Probing the SAM Binding Site of SARS-CoV-2 Nsp14 In Vitro Using SAM Competitive Inhibitors Guides Developing Selective Bisubstrate Inhibitors
Bibliographic record
Abstract
The COVID-19 pandemic has clearly brought the healthcare systems worldwide to a breaking point, along with devastating socioeconomic consequences. The SARS-CoV-2 virus, which causes the disease, uses RNA capping to evade the human immune system. Nonstructural protein (nsp) 14 is one of the 16 nsps in SARS-CoV-2 and catalyzes the methylation of the viral RNA at N7-guanosine in the cap formation process. To discover small-molecule inhibitors of nsp14 methyltransferase (MTase) activity, we developed and employed a radiometric MTase assay to screen a library of 161 in-house synthesized S -adenosylmethionine (SAM) competitive MTase inhibitors and SAM analogs. Among six identified screening hits, SS148 inhibited nsp14 MTase activity with an IC 50 value of 70 ± 6 nM and was selective against 20 human protein lysine MTases, indicating significant differences in SAM binding sites. Interestingly, DS0464 with an IC 50 value of 1.1 ± 0.2 µM showed a bisubstrate competitive inhibitor mechanism of action. DS0464 was also selective against 28 out of 33 RNA, DNA, and protein MTases. The structure–activity relationship provided by these compounds should guide the optimization of selective bisubstrate nsp14 inhibitors and may provide a path toward a novel class of antivirals against COVID-19, and possibly other coronaviruses.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".