A split luciferase system for studying coronavirus Mpro dimerization in vitro and in living cells
Bibliographic record
Abstract
The main protease enzyme (M pro ) of coronaviruses cleaves the viral polyprotein into functional units essential for virus replication.Prior work has demonstrated that M pro functions as a homodimer.However, studies on the mechanism of dimerization have been challenging because the purified protease is mostly dimeric, dimerization-defective mutants lack proteolytic activity, and robust cell-based assays have yet to be reported.To enable work on M pro dimerization, we have developed a quantitative luciferase-based SARS-CoV-2 (SARS2) M pro biosensor that accurately reports protein dimerization in living cells and, upon purification, also in vitro.Cotransfection of cells with a construct expressing M pro fused to the 18 kDa LargeBiT of luciferase (LgBiT) and a second construct with M pro fused to the 1 kDa SmallBiT of luciferase (SmBiT) results in a reconstitution of luciferase activity in a dose-dependent manner that requires conserved residues within the dimerization interface.Proteolytic activity is dispensable for dimerization and, uniquely, a C145A catalytically inactive mutant exhibits enhanced dimerization signal likely due to lower cytotoxicity.M pro enzymes from multiple different coronaviruses also dimerize in this system, indicating mechanistic conservation.Interestingly, this dimerization biosensor also provides a quantitative read-out of inhibitor-facilitated dimerization.Covalent SARS2 M pro inhibitors such as nirmatrelvir cause a 3-to 5-fold increase in luciferase activity.Together with corroborating structural, biophysical, and molecular dynamics experiments, our studies support a model in which covalent M pro inhibitors such as nirmatrelvir simultaneously block catalytic activity and induce allosteric stabilization of the dimeric complex.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".