Crystal structure of <scp>SARS‐CoV</scp>‐2 nsp10–nsp16 in complex with small molecule inhibitors, <scp>SS148</scp> and <scp>WZ16</scp>
Bibliographic record
Abstract
SARS-CoV-2 nsp10-nsp16 complex is a 2'-O-methyltransferase (MTase) involved in viral RNA capping, enabling the virus to evade the immune system in humans. It has been considered a valuable target in the discovery of antiviral therapeutics, as the RNA cap formation is crucial for viral propagation. Through cross-screening of the inhibitors that we previously reported for SARS-CoV-2 nsp14 MTase activity against nsp10-nsp16 complex, we identified two compounds (SS148 and WZ16) that also inhibited nsp16 MTase activity. To further enable the chemical optimization of these two compounds towards more potent and selective dual nsp14/nsp16 MTase inhibitors, we determined the crystal structure of nsp10-nsp16 in complex with each of SS148 and WZ16. As expected, the structures revealed the binding of both compounds to S-adenosyl-L-methionine (SAM) binding pocket of nsp16. However, our structural data along with the biochemical mechanism of action determination revealed an RNA-dependent SAM-competitive pattern of inhibition for WZ16, clearly suggesting that binding of the RNA first may help the binding of some SAM competitive inhibitors. Both compounds also showed some degree of selectivity against human protein MTases, an indication of great potential for chemical optimization towards more potent and selective inhibitors of coronavirus MTases.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".