Structure, Mechanism and Crystallographic fragment screening of the SARS-CoV-2 NSP13 helicase
Bibliographic record
Abstract
Abstract The global COVID-19 pandemic is caused by the SARS-CoV-2 virus and has infected over 100 million and caused over 2 million fatalities worldwide at the point of writing. There is currently a lack of effective drugs to treat people infected with SARS-CoV-2. The SARS-CoV-2 Non-structural protein 13 (NSP13) is a superfamily1B helicase that has been identified as a possible target for anti-viral drugs due to its high sequence conservation and essential role in viral replication. In this study we present crystal structures of SARS-CoV-2 NSP13 solved in the APO form and in the presence of both phosphate and the non-hydrolysable ATP analogue (AMP-PNP). Comparisons of these structures reveal details of global and local conformational changes that are induced by nucleotide binding and hydrolysis and provide insights into the helicase mechanism and possible modes of inhibition. Structural analysis reveals two pockets on NSP13 that are classified as “druggable” and include one of the most conserved sites in the entire SARS-CoV-2 proteome. To identify possible starting points for anti-viral drug development we have performed a crystallographic fragment screen against SARS-CoV-2 NSP13 helicase. The fragment screen reveals 65 fragment hits across 52 datasets, with hot spots in pockets predicted to be of functional importance, including the druggable nucleotide and nucleic acid binding sites, opening the way to structure guided development of novel antiviral agents.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".