A Refined Model of the HCV NS5A Protein Bound to Daclatasvir Explains Drug-Resistant Mutations and Activity against Divergent Genotypes
Bibliographic record
Abstract
Many direct-acting antiviral agents (DAAs) that selectively block hepatitis C virus (HCV) replication are currently under development. Among these agents is Daclatasvir, a first-in-class inhibitor targeting the NS5A viral protein. Although Daclatasvir is the most potent HCV antiviral molecule yet developed, its binding location and mode of binding remain unknown. The drug exhibits a low barrier to resistance mutations, particularly in genotype 1 viruses, but its efficacy against other genotypes is unclear. Using state-of-the-art modeling techniques combined with the massive computational power of Blue Gene/Q, we identified the atomic interactions of Daclatasvir within NS5A for different HCV genotypes and for several reported resistant mutations. The proposed model is the first to reveal the detailed binding mode of Daclatasvir. It also provides a tool to facilitate design of second generation drugs, which may confer less resistance and/or broader activity against HCV.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".