Integrative In Silico Investigation Reveals the Host-Virus Interactions in Repurposed Drugs Against SARS-CoV-2
Bibliographic record
Abstract
The ongoing COVID-19 outbreak have posed a significant threat to public health worldwide. Recently Toll-like receptor (TLR) has been proposed to be the drug target of SARS-CoV-2 treatment, the specificity and efficacy of such treatments remain unknown. In the present study we performed the investigation of repurposed drugs via a framework comprising of Search Tool for Interacting Chemicals (STITCH), Kyoto Encyclopedia of Genes and Genomes (KEGG), molecular docking, and virus-host-drug interactome mapping. Chloroquine (CQ) and hydroxychloroquine (HCQ) were utilized as probes to explore the interaction network that is linked to SARS-CoV-2. 47 drug targets were shown to be overlapped with SARS-CoV-2 network and were enriched in TLR signaling pathway. Molecular docking analysis and molecular dynamics simulation determined the direct binding affinity of TLR9 to CQ and HCQ. Furthermore, we established SARS-CoV-2-human-drug protein interaction map and identified the axis of TLR9-ERC1-Nsp13 and TLR9-RIPK1-Nsp12. Therefore, the elucidation of the interactions of SARS-CoV-2 with TLR9 axis will not only provide pivotal insights into SARS-CoV-2 infection and pathogenesis but also improve the treatment against COVID-19.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".