Single-Molecule Assay Reveals Binding Dynamics of SARS-CoV-2 Polymerase Components and Provides a New Tool to Distinguish Polymerase Inhibitors
Bibliographic record
Abstract
The genome replication of SARS-CoV-2, the causative agent of COVID-19, involves a multisubunit replication complex consisting of nonstructural proteins (nsps) 12, 7, and 8. While the structure of this complex is known, the dynamic behavior of the subunits interacting with RNA is missing. Here we report a single-molecule protein induced fluorescence enhancement (SM-PIFE) assay to monitor binding dynamics between the reconstituted or coexpressed replication complex and RNA. Increasing binding times were observed, in this order, with nsp7 (none), nsp8, and nsp12, in nsp8 nsp12 mixtures and in reconstituted mixtures bearing all three proteins. Unstable, unstable→stable, and stable binding modes were recorded in the latter case, indicating that complexation is dynamic and the correct conformation must be achieved before stable RNA binding can occur. Notably, the coexpressed protein yields mostly stable binding even at low concentrations, while the reconstituted proteins exhibit unstable binding indicating inefficient complexation with reduced protein. The SM-PIFE assay distinguishes inhibitors that impact protein binding from those that prevent replication, as demonstrated with suramin and remdesivir, respectively. The data reveals a correlation between binding lifetime/affinity and protein activity and underscores differences between coexpressed vs reconstituted mixtures, suggesting the existence of trapped conformations that may not evolve to productive binding.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".