Table 1.
List of 24 SARS-CoV2 targets along with their PDB ID, resolution, sequence length and function.
Fig 1.
Schematic workflow of screening known drugs for therapeutic indications against SARS-CoV2 selected targets.
Table 2.
Distribution of active and inactive drugs across all the 24 SARS-CoV2 proteins.
The total number of drugs that has docking score > threshold value has been considered as active (%) control, whereas the drugs that have docking score < threshold value has been considered as inactive (%).
Table 3.
List of potential top 10 FDA approved drugs for repurposing against structural SARS-CoV2 targets along with their known therapeutic indication.
Table 4.
List of potential top 10 FDA approved drugs for repurposing against non-structural SARS-CoV2 targets along with their known therapeutic indication.
Table 5.
List of potential top 10 FDA approved drugs for repurposing against accessory SARS-CoV2 targets along with their known therapeutic indication.
Fig 2.
The binding score distribution of top 10 FDA approved drugs against SARS-CoV2 proteins.
The figure depicts the distribution of top the 10 FDA approved drugs against SARS-CoV2 proteins namely, a) non-structural proteins namely NSP1, NSP2, NSP3, NSP4, NSP5, NSP6, NSP7, NSP8, NSP 9, NSP10, NSP12, NSP13, NSP14, NSP15 and NSP16, b) structural proteins namely Envelope, Spike and Nucleoprotein, and c) accessory proteins namely ORF3a, ORF6, ORF7a, ORF8, ORF9.
Fig 3.
The classification of a) top 10 and b) bottom 10 drugs based on their therapeutic areas. The figure depicts that the drugs with good docking score are mainly used for neurological disorders whereas poor docking score drugs are mainly from immune related disease.
Fig 4.
Scaffold analysis of top scored drugs across 24 SARS-CoV2 targets.
The level 2 and level 3 common scaffolds that are present among the top scored drug molecules across all the 24 SARS-CoV2 targets, existing approved drugs and drugs under clinical trials for COVID-19.
Fig 5.
Representation of drugs interacting with multiple SARS-CoV2 targets.
A total of 15 drugs are observed to have interaction with multiple targets (more than 3) and the maximum number of targets are found to be interacting with Dhydroergotamine, Ergotamine and Midostaurin.
Table 6.
List of FDA approved drugs interacting with more than three SARS-CoV2 targets along with their original indications.
Fig 6.
Representation of 2D interaction of drugs interacting with 8 SARS-CoV2 proteins.
Among the drugs interacting with multiple targets, (a) Dihydroergotamine and (b) ergotamine was observed to have interaction with the maximum number of SARS-CoV2 targets (8 targets). The figure represents an example of 2D interaction with membrane protein among the 8 proteins commonly for both the drugs. The drug molecule is seen to bound in the cavity of the protein through various non-covalent interactions with the amino acid residues that contributes to the protein-ligand binding affinity.
Fig 7.
Gene enrichment analysis for a) Biological process, b) Cellular component and c) Molecular function of genes associated with top screened drugs interacting with multiple SARS-CoV2 targets. The network represents the interaction of nodes (genes involved in biological processes, cellular components and molecular functions) connected through edges. From the enrichment plot, it can be observed that for biological processes, the maximum number of gene is involved in regulation of biological quality, for Cellular component, the maximum number of genes are involved in cell junction and for Molecular function, the maximum number of genes are involved in molecular transducer activity.
Fig 8.
Pathway analysis of genes related to the 15 drugs interacting with multiple proteins of SARS-CoV2 was carried out using online server Metascape.
a) Enriched ontology clusters represent the clusters of genes which are involved in the different pathway and the nodes of the colour represent the genes belonging to the same clusters. The nodes are linked by edges where the thickness of the edges represents the similarity score of the genes. b) Protein-protein interaction network of the genes involved in different pathways. The proteins which are most densely connected have been clustered through MCODE algorithm to identify the protein neighbourhoods. c) Enrichment analysis of genes-disease association represents the genes which are involved in different diseases. The images were generated using Metascape [71].