A Systematic Approach to the Optimization of Substrate-Based Inhibitors of the Hepatitis C Virus NS3 Protease: Discovery of Potent and Specific Tripeptide Inhibitors
Bibliographic record
Abstract
The inadequate efficacy and tolerability of current therapies for the infectious liver disease caused by the hepatitis C virus have warranted significant efforts in the development of new therapeutics. We have previously reported competitive peptide inhibitors of the NS3 serine protease based on the N-terminal cleavage products of peptide substrates. A detailed study of the interactions of these substrate-based inhibitors with the different subsites of the serine protease active site led to the discovery of novel residues that increased the affinity of the inhibitors. In this paper, we report the combination of the best binding residues in a tetrapeptide series that resulted in extremely potent inhibitors that bind exquisitely well to this enzyme. A substantial increase in potency was obtained with the simultaneous introduction of a 7-methoxy-2-phenyl-4-quinolinoxy moiety at the gamma-position of the P2 proline and a tert-leucine as a P3 residue. The increase in potency allowed for the further truncation and led to the identification of tripeptide inhibitors. Structure activity relationship studies on this inhibitor series led to the identification of carbamate-containing tripeptides that are able to inhibit replication of subgenomic HCV RNA in cell culture with potencies below 1 microM. This inhibitor series has the potential of becoming antiviral agents for the treatment of HCV infections.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".