Contribution of the catalytic dyad of SARS-CoV-2 main protease to binding covalent and noncovalent inhibitors
Bibliographic record
Abstract
The effect of mutations of the catalytic dyad residues of SARS-CoV-2 main protease (MPro WT ) on the thermodynamics of binding of covalent inhibitors comprising nitrile [nirmatrelvir (NMV), NBH2], aldehyde (GC373), and ketone (BBH1) warheads to MPro is examined together with room temperature X-ray crystallography. When lacking the nucleophilic C145, NMV binding is ∼400-fold weaker corresponding to 3.5 kcal/mol and 13.3 °C decrease in free energy (ΔG) and thermal stability (T m ), respectively, relative to MPro WT . The H41A mutation results in a 20-fold increase in the dissociation constant (K d ), and 1.7 kcal/mol and 1.4 °C decreases in ΔG and T m , respectively. Increasing the pH from 7.2 to 8.2 enhances NMV binding to MPro H41A , whereas no significant change is observed in binding to MPro WT . Structures of the four inhibitor complexes with MPro 1-304/C145A show that the active site geometries of the complexes are nearly identical to that of MPro WT with the nucleophilic sulfur of C145 positioned to react with the nitrile or the carbonyl carbon. These results support a two-step mechanism for the formation of the covalent complex involving an initial non-covalent binding followed by a nucleophilic attack by the thiolate anion of C145 on the warhead carbon. Noncovalent inhibitor ensitrelvir (ESV) exhibits a binding affinity to MPro WT that is similar to NMV but differs in its thermodynamic signature from NMV. The binding of ESV to MPro C145A also results in a significant, but smaller, increase in K d and decrease in ΔG and T m , relative to NMV.
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.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".