Mechanism of Nucleic Acid Unwinding by SARS-CoV Helicase
Bibliographic record
Abstract
The non-structural protein 13 (nsp13) of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) is a helicase that separates double-stranded RNA (dsRNA) or DNA (dsDNA) with a 5' → 3' polarity, using the energy of nucleotide hydrolysis. We determined the minimal mechanism of helicase function by nsp13. We showed a clear unwinding lag with increasing length of the double-stranded region of the nucleic acid, suggesting the presence of intermediates in the unwinding process. To elucidate the nature of the intermediates we carried out transient kinetic analysis of the nsp13 helicase activity. We demonstrated that the enzyme unwinds nucleic acid in discrete steps of 9.3 base-pairs (bp) each, with a catalytic rate of 30 steps per second. Therefore the net unwinding rate is ~280 base-pairs per second. We also showed that nsp12, the SARS-CoV RNA-dependent RNA polymerase (RdRp), enhances (2-fold) the catalytic efficiency of nsp13 by increasing the step size of nucleic acid (RNA/RNA or DNA/DNA) unwinding. This effect is specific for SARS-CoV nsp12, as no change in nsp13 activity was observed when foot-and-mouth-disease virus RdRp was used in place of nsp12. Our data provide experimental evidence that nsp13 and nsp12 can function in a concerted manner to improve the efficiency of viral replication and enhance our understanding of nsp13 function during SARS-CoV RNA synthesis.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".