Probing the SAM Binding Site of SARS-CoV-2 nsp14 in vitro Using SAM Competitive Inhibitors Guides Developing Selective bi-substrate Inhibitors
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has clearly brought the healthcare systems world-wide 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. Non-structural 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 (MT) activity, we developed and employed a radiometric MT assay to screen a library of 161 in house synthesized S-adenosylmethionine (SAM) competitive methyltransferase inhibitors and SAM analogs. Among seven identified screening hits, SS148 inhibited nsp14 MT activity with an IC 50 value of 70 ± 6 nM and was selective against 20 human protein lysine methyltransferases indicating significant differences in SAM binding sites. Interestingly, DS0464 with IC 50 value of 1.1 ± 0.2 μM showed a bi-substrate competitive inhibitor mechanism of action. Modeling the binding of this compound to nsp14 suggests that the terminal phenyl group extends into the RNA binding site. DS0464 was also selective against 28 out of 33 RNA, DNA, and protein methyltransferases. The structure-activity relationship provided by these compounds should guide the optimization of selective bi-substrate nsp14 inhibitors and may provide a path towards a novel class of antivirals against COVID-19, and possibly other coronaviruses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".