Design of novel and highly selective SARS-CoV-2 main protease inhibitors
Bibliographic record
Abstract
ABSTRACT We have synthesized a novel and highly selective severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease peptide mimetic inhibitor mimicking the replicase 1ab recognition sequence -Val-Leu-Gln- and utilizing a cysteine selective acyloxymethyl ketone as the electrophilic warhead to target the active site Cys145. Utilizing a constrained cyclic peptide that locks the conformation between the P3 (Val) and P2 (Leu) residues, we identified a highly selective inhibitor that fills the P2 pocket occupied by the leucine residue sidechain of PF-00835231 and the dimethyl-3-azabicyclo-hexane motif in nirmatrelvir (PF-07321332). This strategy resulted in potent and highly selective Mpro inhibitors without inhibiting essential host cathepsin cysteine or serine proteases. The lead prototype compound 1 (MPro IC 50 = 230 ± 18 nM) also inhibits the replication of multiple SARS-CoV-2 variants in vitro , including SARS-CoV-2 variants of concern, and can synergize at lower concentrations with the viral RNA polymerase inhibitor, remdesivir, to inhibit replication. It also reduces SARS-CoV-2 replication in SARS-CoV-2 Omicron-infected Syrian golden hamsters without obvious toxicities, demonstrating in vivo efficacy. This novel lead structure provides the basis for optimization of improved agents targeting evolving SARS-CoV-2 drug resistance that can selectively act on Mpro versus host proteases and are less likely to have off-target effects due to non-specific targeting. Developing inhibitors against the active site of the main protease (Mpro), which is highly conserved across coronaviruses, is expected to impart a higher genetic barrier to evolving SARS-CoV-2 drug resistance. Drugs that selectively inhibit the viral Mpro are less likely to have off-target effects warranting efforts to improve this therapy.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".