Fig 1.
Workflow of the study.
Table 1.
Data description.
Fig 2.
Cross-validation of DEGs by WGCNA.
(A) Illustration of soft-thresholding powers based on the scale-free tropology model fit (left) and the mean connectivity (right). (B) The dendrogram of all DEGs clustered based on a dissimilarity measure (1-TOM). (C) The dendrogram of eigengene module and cluster analysis of eigengene network by heatmap summarize the modules yielded in the clustering analysis. (D) Median rank preservation (left) and Z summary preservation (Right); the black, red, yellow and turquoise indicate the strong preservation above dashed lines Z = 2 and Z = 10.
Fig 3.
PPI network of validated DEGs to identify hub-genes.
Table 2.
Significantly enriched top-ranked 6 GO-terms with hub-genes.
Table 3.
Significantly enriched top-ranked 6 biological pathways with hub-genes in different databases.
Fig 4.
Hub-genes regulatory network analysis with (A) Transcription factors (TFs) and (B) micro RNAs (miRNAs).
Fig 5.
Matrix of binding affinity scores between receptors and ligands computed by molecular docking.
(A) Row indicates ordered 13 proposed receptor proteins and column indicates the top-ordered 50 drug agents out of 158, where red colors indicate the strong binding affinities, (B) Row indicates top-ranked 8 receptor proteins obtained through published literature, and column indicates the proposed top-ordered 10 drug agents out of 158, where red colors indicate the strong binding affinities.
Fig 6.
The 2D view of strong binding interactions between targets and drugs are shown by Ligplot.
(A) AURKA vs. Nilotinib, (B) AURKB vs. Tegobuvir, and (C) OAS1 vs. Proscillaridin.
Fig 7.
Lead compound (left side) and three complexes of three-dimensional chemical interactions (right side) obtained from molecular docking. (a) AURKA vs. Nilotinib, (b) AURKB vs. Tegobuvir, and (c) OAS1 vs. Proscillaridin.
Table 4.
Docking results of interacting proteins and drugs.
The last row shows key interactions of amino acids and their binding types with potential targets.
Fig 8.
MD simulations of top-ranked three complexes.
(A) Time evolution of RMSDs for each of the top-ranked three complexes. (B) Binding free energy (kcal/mol) of each snapshot was calculated by MM-PBSA, representing the change in binding stability of each complex during simulations; positive values indicate better binding. Complexes: blue AURKA vs. Nilotinib, red AURKB vs. Tegobuvir, and green. OAS1 vs. Proscillaridin.
Fig 9.
Validation of hub genes and candidate drugs in favor of SARS-CoV-2 by the literature review.
(A) Validation of the proposed HubGs: circles with node color indicates hub genes, and each connected network with number(s) indicates the reference(s) of gene(s) of SARS-CoV-2 (B) Validation of the proposed candidate drugs: circles with green color indicate FDA approved, light blue color indicates investigational drugs and purple color indicates experimental drugs and red color indicates unapproved drugs, and each connected network with number(s) indicates the reference(s) of drug(s) of SARS-CoV-2.