3.1. Identification of hits targeting the JAK-STAT pathway from a natural source
We screened 25 different herbal medicines; among them, 17 were chosen for further study i.e. Abutilon indicum, Crescentia alata, Caesalpinia bonduc, Datura metel, Durio zibethinus, Evolvulus alsinoides, Garcinia mangostana, Indigofera tinctoria, Jatropha curcus, Leucas aspera, Orobanche corymbosa, Ricinus communis, Trichodesma indicum, Vitex trifolia, Wedelia Chinensis, Woodfordia fruticosa, and Wrightia tinctoria; that were reported to possess anti-viral activity from which total 174 multiple phytoconstituents were retrieved from ChEBI database. Among them, 96 phytoconstituents were identified to modulate the proteins involved in the JAK-STAT pathway. Likewise, a total of 174 phytoconstituents were identified to regulate 807 genes; among them, 15 were from the JAK-STAT signaling pathway. Similarly, network analysis identified β-maaliene as a hit molecule to possess high gene expression from the JAK-STAT pathway and TRAF5 was identified to be majorly targeted (Figure 1). Similarly, GO gene analysis of proteins modulated in the JAK-STAT pathway is represented in Figure 2 reflects the highest number of gene regulation in biological process and were from the cytoplasm.
3.2. Enrichment analysis of JAK-STAT modulated proteins
Enrichment analysis of proteins relating to KEGG pathway database identified few pathways i.e. JAK-STAT signaling pathway, viral carcinogenesis, chronic myeloid leukemia, ErbB signaling pathway, apoptosis, Hepatitis B, Necroptosis, HTLV-I infection, NF-kappa B signaling pathway, Th17 cell differentiation, autophagy - animal, mTOR signaling pathway, chemokine signaling pathway, and Ras signaling pathway which; get regulated in viral infections (Table 1).
3.3. Druglikeness and ADMET profile of lead hits
Among 39 different phytoconstituents targeting TRAF5, vitexilactone was predicted to possess the highest druglikeness score i.e. 0.88. Among them, 18 compounds were predicted to possess a non-druglikeness character based on the rule of five. The druglikeness score of each phytoconstituent targeting TRAF5 is summarized in Table 2. Similarly, the ADMET profile of each phytoconstituent is represented in figure 3 as a heat map.
3.4. In silico molecular docking
Among 39 different phytoconstituents that interacted with TRAF5, sesaminol 2-O-β-D-gentiobioside was predicted to have the highest binding affinity i.e. -8.6 kcal/mol binding energy and five hydrogen bond interactions with GLN431, PHE429, TRP408 amino acids. However, vicenin-3 was predicted to have the highest number of hydrogen bond interactions i.e. nine with LYS500, ASP502, SER505, SER506, SER507, GLY520, SER519 amino acids; though scored comparatively lower binding affinity than sesaminol 2-O-β-D-gentiobioside i.e. binding energy: -6.6 kcal/mol (Table 3). The interaction of sesaminol 2-O- β -D-gentiobioside and vicenin-3 are represented in Figure 4.
Likewise, sesaminol 2-O-β-D-glucoside was predicted to have the highest binding affinity with 3clpro i.e. -9.2 kcal/mol with two hydrogen bond interactions with GLU166, THR190. Similarly, vicenin-3 was predicted to have a binding affinity with 3clpro (binding energy: -8.2 kcal/mol) by forming five hydrogen interactions with GLY143, SER144, GLU166 (Table 4). The interaction of sesaminol 2-O-β-D-glucoside and vicenin-3 is represented in Figure 5.
Similarly, sesaminol 2-O-β-D-gentiobioside was predicted to possess binding energy of -8.5 kcal/mol with PLpro by four hydrogen bond interactions with THR75 and ASP77. However, taxine B was predicted to have the highest hydrogen bond interactions i.e. six with THR171, ARG167, GLU204, MET207, GLN233 though it scored comparatively lower binding energy i.e. -6.9 kcal/mol with PLpro (Table 5);interaction is represented in Figure 6.
Likewise, sesaminol 2-O-β-D-gentiobioside was predicted to have the highest binding affinity i.e. -9.7 kcal/mol binding energy with spike protein with the highest number of hydrogen bond interaction i.e. eight by interacting with THR47, LEU48, GLN825, ALA801, ALA803, PHE805, LYS807, and ASP811 (Table 6); interaction is represented in Figure 7.
3.5. Phylogeny comparison of COVID-19 3clpro, PLpro and spike protein
The blast result identified three different proteins (3C-like proteinase of porcine transmissible gastroenteritis coronavirus strain Purdue, replicase of transmissible gastroenteritis virus and Mpro Transmissible gastroenteritis virus) which are very similar to 3clpro based on maximum score, query cover, E-value and percentage identity. The alignments were also matched with some other proteins like Pedv 3c-like protease of diarrhea virus, replicative polyprotein 1ab of murine hepatitis virus strain A59 and purine nucleoside phosphorylase of Toxoplasma gondii. However, maximum matching was found with multiple strains of the coronavirus from different organisms. Similarly, the sequence of PLpro was found to be similar to previously reported papain-like protease from coronavirus in England and Jordan. We identified the matching of PLpro sequence with papain-like protease (Murine hepatitis virus strain A59) by 29.45%, replicase polyprotein 1ab (Murine hepatitis virus strain A59) by 29.13%, replicase polyprotein 1ab (Avian infectious bronchitis virus; strain Beaudette) by 27.11%, and nonstructural protein 3 (Avian infectious bronchitis virus; strain Beaudette) by 26.76%. Likewise, spike proteins from other viruses like Murine hepatitis virus strain A59, feline infectious peritonitis virus, infectious bronchitis virus, and porcine epidemic diarrhea virus CV777 were found to be similar with spike protein of coronavirus. Similarly, 47.06% of spike protein was found to be matched with Hepcidin; however, the E-value for this match was 5. Similarly, the dendrogram (Figure 8) represents the hierarchical clustering relationship of 3clpro, PLpro and spike protein with respective proteins.