After the advancement in Bioinformatics, tools like computer aided drug design and virtual screening has been used extensively to recognize novel lead compounds [57]. The major advantages of these tools are that they permit us to lessen experimental time and cost by downsizing biological target [57]. In addition to these, methods like molecular dynamics, molecular docking, virtual screening have also become vital part in computer aided drug design for their reliability and accuracy in prediction [58, 59]. Virtual screening processes based on plant derived phytocompounds have been effective to predict the potential inhibitor of the biological target [58].
Among different structural proteins of SARS CoV2, the main protease of SARS-CoV-2 has become a good target for different therapeutic methods [57]. Mainly it consists of three domains namely domain I with residues 10 to 99, domain II with residues 100 to 182 and domain III with residues 198 to 303 [60–61, 22]. Two catalytic residues such as Cys145 and His41 among three domains, are stated to induce activation via dimerization method [22]. As a result, by blocking the catalytic site can help to prevent the function of the main protease [22].
1000 plant compounds are analysed for their protease inhibitor activity against SARS COV2 main protease. ADME-Tox property analysis is important to study the absorption, distribution, metabolism, excretion and toxicity that gives an idea about pharmacokinetics of a lead molecule as they are the key source of failures for leads in drug design [62]. Among the 1000 given compounds, 95 compounds having decent ADME-Tox properties are selected for molecular docking. Major properties taken into consideration includes water solubility where a soluble molecule greatly facilitates in drug development activities. Two methods to predict water solubility is ESOL model [63], the second one is from Ali et al [64]. Other predictor for solubility is developed by SILICOS-IT. Drug likeness qualitatively assesses the chance for a molecule to become an oral drug with respect to bioavailability [62]. SWISSADME provides 5 different rule-based filters for drug likeness. The Lipinski’s rule [65] is considered as the pioneer rule-of-five implemented from the Ghose, Veber, Egan and Muegge. The selected 95 compounds have molecular weight less than 500 are soluble with high gastrointestinal absorption and show no BBB permeability that indicates no expected neurological side effects. These compounds also showed significant druglikeness based on Lipinski’s rule of five. They also showed significant bioavailability which means that the molecules could be absorbed and delivered throughout the body if it is used as a drug [62].
The binding site we predicted consist of THR24, THR26, PHE140, ASN142, GLY143, CYS145, HIS163, HIS164, GLU166 and HIS172. But while going through some literatures it was found that the substrate-binding pocket of main protease consists of amino acid residues His41, Phe140, Asn142, Gly143, Ser144, Cys145, Tyr161, His163, Glu166 and His172 [66]. There are two intensely buried subsites (S1 & S2) and three shallow subsites (S3, S4, S5), where the S1 include His163, Glu166, Cys145, Gly143, His172, Phe140 amino acid residues and S2 include Cys145, His41 and Thr25 amino acid residues which are mainly involved in hydrophobic and electrostatic interactions [67]. The shallow subsites consist of Met49, His41, Met165, Glu166 and Gln189 amino acid residues that can carry out diverse functionalities [67]. Molecular Docking analysis of the selected 95 compounds with the predicted binding site showed that all the compounds interacted with the receptor with a minimum of -5.0 kcal/mol affinity. The highest interaction is shown by the compound Acanthocarpan with − 8.1kcal/mol binding affinity. The compound showed three conventional hydrogen bonds with the residues HIS 41 and HIS 163. In addition to this, 8 van der waals bonds with the residues THR26, THR25, GLN189, HIS164, MET165, PHE140, ASN142, LEU141 as well as 4 alkyl and pi-aklyl bonds and one carbon-hydrogen bond is observed. This interaction is then followed by other compounds such as Abacopterin F (-7.9 kcal/mol), Caesalpiniaphenol G (-7.9 kcal/mol), Abacopterin C (-7.9 kcal/mol) etc. Some compounds like Talosin A showed − 7.9 kcal/mol binding affinity but the compound doesn’t bind with the concerned binding site residues. The ADME property analysis of the top interacting compounds showed that all are soluble with no violations in Lipinski’s rule and no BBB permeability. Acanthocarpan is predicted to have a molecular weight of 328.27 with 7 hydrogen bond acceptor and 1 hydrogen bond donor. The TPSA value is found to be 75.61 with 2.92 iLogP. In addition to Lipinski’s rule, this compound doesn’t show any violations in Ghose, Veber, Egan, and Muegge rule. The bioavailability of the compound is 55%. The toxicology analysis of the compound showed that it doesn’t show avian toxicity with zero binary carcinogenicity. However, a slight trinary carcinogenicity is observed. Similar observations are done on top interacting compounds showed that compounds such as Acanthocarpan, Abacopterin F and Caesalpiniaphenol G can be considered as best three compounds.