A Comprehensive Computational Analysis for the Binding Modes of Hepatitis C Virus NS5A Inhibitors: The Question of Symmetry
Bibliographic record
Abstract
Direct-acting antivirals (DAAs) form the current standard of care (SOC) against hepatitis C virus (HCV). These drugs selectively target the viral proteins, offering a unique mechanism to avoid toxicity, to increase their efficacy, and to evolve from decades of interferon- and ribavirin-based therapy. Among the promising HCV targets for DAAs is the NS5A protein, and daclatasvir (DCV) forms a first-in-class compound that selectively targets this protein. Despite the exceptional potency of DCV (∼picomolar IC 50 ) and although several DCV derivatives have been approved for human use or are close to approval, the exact mode of action of these drugs is still incomplete. This is simply due to the vast complexity of cocrystallizing DCV with NS5A in the absence of two amphipathic helices that are required for DCV binding. In this context, computational modeling provides a unique alternative to solve this problem. Here, we build upon our recent discovery of a completely symmetrical interaction between DCV and NS5A and investigate the mode of binding of six other structures similar to DCV. The selected compounds include both symmetric and asymmetric molecules. In addition, we show that our model correlates very well with mutations that can confer resistance to DCV. The current study enhances our understanding of the mode of action of this class of HCV inhibitors and helps in defining the origin of resistance to these drugs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".