Computational study on the molecular mechanisms of drug resistance of Narlaprevir due to V36M, R155K, V36M+R155K, T54A, and A156T mutations of HCV NS3/4A protease
Bibliographic record
Abstract
Narlaprevir is a novel NS3/4A protease inhibitor of hepatitis C virus (HCV), and it has been tested in a phase II clinical trial recently. However, distinct drug-resistance of Narlaprevir has been discovered. In our study, the molecular mechanisms of drug-resistance of Narlaprevir due to the mutations V36M, R155K, V36M+R155K, T54A, and A156T of NS3/4A protease have been investigated by molecular dynamics (MD) simulations, free energy calculations, and free energy decomposition analysis. The predicted binding free energies of Narlaprevir towards the wild-type and five mutants show that the mutations V36M, R155K, and T54A lead to low-level drug resistance and the mutations V36M+R155K and A156T lead to high-level drug resistance, which is consistent with the experimental data. The analysis of the individual energy terms indicates that the van der Waals contribution is important for distinguishing the binding affinities of these six complexes. These findings again show that the combination of different molecular modeling techniques is an efficient way to interpret the molecular mechanism of drug-resistance. Our work mainly elaborates the molecular mechanism of drug-resistance of Narlaprevir and further provides valuable information for developing novel, safer, and more potent HCV antiviral drugs in the near future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".