Key dimer interface residues impact the catalytic activity of 3CLpro, the main protease of SARS-CoV-2
Bibliographic record
Abstract
3C-like protease (3CLpro) processes and liberates functional viral proteins essential for the maturation and infectivity of severe acute respiratory syndrome coronavirus 2, the virus responsible for COVID-19. It has been suggested that 3CLpro is catalytically active as a dimer, making the dimerization interface a target for antiviral development. Guided by structural analysis, here we introduced single amino acid substitutions at nine residues at three key sites of the dimer interface to assess their impact on dimerization and activity. We show that at site 1, alanine substitution of S1 or E166 increased by twofold or reduced relative activity, respectively. At site 2, alanine substitution of S10 or E14 eliminated activity, whereas K12A exhibited ∼60% relative activity. At site 3, alanine substitution of R4, E290, or Q299 eliminated activity, whereas S139A exhibited 46% relative activity. We further found that the oligomerization states of the dimer interface mutants varied; the inactive mutants R4A, R4Q, S10A/C, E14A/D/Q/S, E290A, and Q299A/E were present as dimers, demonstrating that dimerization is not an indication of catalytically active 3CLpro. In addition, present mostly as monomers, K12A displayed residual activity, which could be attributed to the conspicuous amount of dimer present. Finally, differential scanning calorimetry did not reveal a direct relationship between the thermodynamic stability of mutants with oligomerization or catalytic activity. These results provide insights on two allosteric sites, R4/E290 and S10/E14, that may promote the design of antiviral compounds that target the dimer interface rather than the active site of severe acute respiratory syndrome coronavirus 2 3CLpro.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".