Discovery of Rift Valley fever virus natural pan-inhibitors by targeting its multiple key proteins through computational approaches
Bibliographic record
Abstract
The Rift Valley fever virus (RVFV) is a zoonotic arbovirus and pathogenic to both humans and animals. Currently, no proven effective RVFV drugs or licensed vaccine are available for human or animal use. Hence, there is an urgent need to develop effective treatment options to control this viral infection. RVFV glycoprotein N (GN), glycoprotein C (GC), and nucleocapsid (N) proteins are attractive antiviral drug targets due to their critical roles in RVFV replication. In present study, an integrated docking-based virtual screening of more than 6000 phytochemicals with known antiviral activities against these conserved RVFV proteins was conducted. The top five hit compounds, calyxin C, calyxin D, calyxin J, gericudranins A, and blepharocalyxin C displayed optimal binding against all three target proteins. Moreover, multiple parameters from the molecular dynamics (MD) simulations and MM/GBSA analysis confirmed the stability of protein-ligand complexes and revealed that these compounds may act as potential pan-inhibitors of RVFV replication. Our computational analyses may contribute toward the development of promising effective drugs against RVFV infection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".