Kinetic Characterization of SARS-CoV-2 nsp13 ATPase Activity and Discovery of Small-Molecule Inhibitors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
SARS-CoV-2 non-structural protein 13 (nsp13) is a highly conserved helicase and RNA 5′-triphosphatase. It uses the energy derived from the hydrolysis of nucleoside triphosphates for directional movement along the nucleic acids and promotes the unwinding of double-stranded nucleic acids. Nsp13 is essential for replication and propagation of all human and non-human coronaviruses. Combined with its defined nucleotide binding site and druggability, nsp13 is one of the most promising candidates for the development of pan-coronavirus therapeutics. Here, we report the development and optimization of bioluminescence assays for kinetic characterization of nsp13 ATPase activity in the presence and absence of single-stranded DNA. Screening of a library of 5000 small molecules in the presence of single-stranded DNA resulted in the discovery of six nsp13 small-molecule inhibitors with IC50 values ranging from 6 ± 0.5 to 50 ± 6 μM. In addition to providing validated methods for high-throughput screening of nsp13 in drug discovery campaigns, the reproducible screening hits we present here could potentially be chemistry starting points toward the development of more potent and selective nsp13 inhibitors, enabling the discovery of antiviral therapeutics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it