Exploring the active constituents of Oroxylum indicum in intervention of novel coronavirus (COVID-19) based on molecular docking method

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13721-020-00279-y.

Keywords: COVID-19, Oroxylum indicum, Molecular docking, Molecular dynamics, ADMET study

Protein preparation

In silico analysis of phytoconstituents of Oroxylum indicum was performed on 2.16 Å crystal structure of COVID-19 M^pro: main protease in complex with an inhibitor N3 (PDB ID: 6LU7) also a key CoV enzyme which plays a pivotal role in mediating viral replication and transcription, making it an attractive drug target for this virus (Isah 2019), which was retrieved from protein data bank (https://www.rcsb.org). The 6LU7 protein contains two chains, A and B, which form a homodimer. Chain A was used for macromolecule preparation (Fig S1).

The protein was visualised using PyMol (http://www.pymol.org) and unwanted chain residues, bound ligand N3, water molecules were removed. Additionally to prepare protein, charges added and minimized the energy of the protein using Autodock Vina in PyRx open source software and subsequently converting it to pdbqt format.

Ligand preparations

For this investigation, chemical structures of chemical constituents of Oroxylum indicum were retrieved in a SDF format from chemical database PubChem available on NCBI website (https://pubchem.ncbi.nlm.nih.gov/). The ligand N3 (N-[(5-methylisoxazol-3yl) carbonyl] alanyl-l-valyl-n ~ 1 ~ -((1R, 2Z)-4-(benzyloxy)-4-oxo-1-{[(3R)-2-oxopyrrolidin-3-yl] methyl} but-2-enyl)-l-leucinamide) was obtained from database of chemspider. CSID:4883311, http://www.chemspider.com/ChemicalStructure.4883311.html (accessed 04:56, May 12, 2020). Using OpenBabel control all ligands were loaded into the PyRx virtual screening software. The OpenBabel software converts the SDF files of the ligands to PDB format (O’Boyle et al. 2011). Further, the ligands are prepared by detecting the torsion root, correcting the torsion angles, assigning charges, optimizing using UFF (Universal force field) (Jaillet et al. 2017) and finally converting them to pdbqt format to generate 3D atomic coordinates of the molecules. The chemical structures of all the ligands is depicted in Fig. 1.

Receptor grid generation and molecular docking

A primary objective in molecular docking was the ability to estimate the scoring function and to evaluate interactions between a protein and small molecules based on the geometry in order to predict the binding affinity and activity of the ligand molecule (Verdonk et al. 2003; Leach et al. 2006). In this study, docking was performed using Autodock Vina in PyRx virtual screening open source software (Leach et al. 2006). The protein and ligand molecules to be docked are selected under the Vina wizard control. A grid appears over the protein structure. The grid size can be adjusted according to the active site residues that are selected and Autodock Vina is run. A grid box with the size 32.803 × 27.870 × 26.639 with coordinates (x,y,z) of − 9.8336, 11.1638, 69.5594 having the grid spacing of 0.375 Å was used. The docking Lamarckian Genetic Algorithm (LGA) was allowed to produce 10 docked positions for each ligand (Fuhrmann et al. 2010). The docking results can be viewed under ‘analyse results’ tab. Although, the final results were analyzed and visualized on the basis of docking scores using Discovery Studio 2020 Client (Biovia 2016) and PyMol software’s (DeLano 2002).

Molecular dynamics (MD) studies

To perform molecular dynamics simulations, CABS Flex 2.0 server were used. It is based on coarse-grained simulations of protein motion (Kurcinski et al. 2019) for 50 cycles, 50 trajectory frames for 10 ns with some additional distance restraints with a global weight of 1.0, built with Poisson-Boltzmann/Generalized Born (PB/GB) molecular mechanics the solvent probe radius was set as 1.4 Å, minimum atomic radius 1 Å, salt radius 2 Å, ionic strength 0.15, temperature of simulation 1.4, in order to analyze the conformational stability of the receptor-ligand complex system.

ADME and toxicity studies

The ligands were further screened for Lipinski rule of five by following certain criteria such as molecular weight ≤ 500, logP ≤ 5, hydrogen bond donor ≤ 5, hydrogen bond acceptor ≤ 10 and topological polar surface area ≤ 500. During drug development, safety is always the most important issue, therefor Toxicology prediction of small molecules is important to predict amount of tolerability of the small molecule before being ingested into the human and animal models. Two main approaches for assessment of phytoconstituents toxicity, webserver based and quantitative structure activity relationship.

pkCSM (http://biosig.unimelb.edu.au/pkcsm/prediction) is an online server database in which the small molecule can be analyzed for calculating pharmacokinetic and toxicological properties by uploading the SMILES of molecules. We can obtain the details of toxicological effects in the fields of AMES Toxicity, human maximum tolerance dose, hERG-I inhibitor, hERG-II inhibitor, LD50, LOAEL, Hepatotoxicity, Skin Toxicity, T. pyriformis toxicity and Minnow toxicity(Pires et al. 2015).

VEGA-QSAR ((http://www.vega-qsar.eu/) is integrating In silico QSAR models and read-across method for a number of toxicological data outcomes(Rogiers et al. 2020). The molecules can be analyzed for calculating toxicological properties by either inserting SMILES notations or uploading SDF files and then selecting toxicological models for generating numerous information about structure related effects. The results also show structural alerts in chemical structure based on known mutagenic and carcinogenic structural analogy (Benfenati et al. 2019).

Molecular docking studies

The main aim of the study was to prospect active chemical constituents of Oroxylum indicum to a highly conserved protein, M^pro of SARS-CoV-2, therefore we performed molecular docking studies of all chemical constituents of Oroxylum indicum followed by identification of top hits which is discussed below. Furthermore, to investigate pharmacokinetics and toxicity properties of the molecules showing promising results so that they can be proposed as potential drugs candidates.

Autodock Vina is used to determine molecular interactions and binding energy between phytoconstituents of Oroxylum indicum and COVID-19 M^pro. Phytoccosntituents of Oroxylum indicum has shown antiviral actions (Mohamat et al. 2018; Antonio et al. 2020). Therefore we selected 18 chemical constituents from this plant to investigate their inhibition potential for COVID-19 M^pro. Remdesivir is shown potential in treatment of Marburg virus, Paramyxoviridae (such as parainfluenza type 3, Nipah, Hendra, measles and mumps viruses) and Pneumoviridae (respiratory syncytial virus) (Ko et al. 2020) that can represent possible treatment options against corona (Mahmood et al. 2020) it was selected as standard drug (Pizzorno et al. 2020).

The binding potential energy or Vina fitness score obtained from docking 6LU7 with the native ligand (N3) Fig S2, Remdesivir and Oroxylum indicum compounds are given in Table S1. The binding energy of the best docked pose of ligand is compared with co-crystalized ligand and reference standard drug. The docked binding energy of the N3 and Remdesivir is placed in the fifth and sixth position, a bit lower than those of the four best ligands. The binding affinity values range from − 9.1 to − 6.2 kcal/mol. There is no doubt that the Baicalein-7-O-diglucoside exhibits the higher binding affinity (− 9.1 kcal/mol), higher to Chrysin-7-O-glucuronide (− 8.6 kcal/mol), Oroxindin (− 8.1 kcal/mol) and Scutellarein (− 8.0 kcal/mol).

According to the analysis of docking results, the interactions (listed in Table 1) between Baicalein-7-O-diglucoside, Chrysin-7-O-glucuronide, Oroxindin and Scutellarein are highly consistent with that of Remdesivir and even they represent the most promising inhibitors of the SARS-CoV-2 M^pro. The results of the molecular docking showed that the tested compound baicalein-7-O-diglucoside gives the lowest binding energy (− 9.1 kcal/mol) in complex with 6LU7, which is the best score when compared to other docked compounds. However, it is reasonably disapproving to obey Lipinski’s rule of five as it is characterized by two violations (Table 2).

The docking of Chrysin-7-O-glucuronide with main protease is accompanied by an affinity of − 8.6 kcal/mol by forming five hydrogen bonds with THR 26 A, LEU 141 A, GLY 143A, SER 144A, CYS 145A along with hydrophobic interaction with MET 165 A, GLN 189 A. It shows favorable drug-like properties by following Lipinski's rule of five without any violation. Both Oroxindin and Scutellarein exhibit drug-likeness as they were found to obey Lipinski’s rule of five. Oroxindin was found to exhibit a hydrogen bond with THR 26 A, ASN 142A, GLY 143A, CYS 145A, ASP 187A and residue of the main protease with an affinity of − 8.1 kcal/mol. It also forms pi–pi T-shape interactions with His 41A, pi-alkyl interactions with PRO 168A (Fig S3). Similarly, Scutellarein can be recognized as promising drug candidate on the basis of qualifying Lipinski’s rule five. Scutellarein was found to show carbon-hydrogen bonding with TYR 54A, HIS 163A, HIS 164A, GLU 166A, ASP 187A and pi-alkyl interaction with MET 49A.

Hydrogen bonds formed by OH and C=O groups stabilizes ligand-receptor complex in which ligand plays dual role of acceptor and donor (Matondo et al. 2018) (Fig. 3 and Table 1). Also, stability of complexes are directed by dispersion forces (Trujillo and Sánchez-Sanz 2016), π–π interactions(Kasende et al. 2017), and hydrophobic interactions (Muya et al. 2019) types of interactions. It is the interaction (both lipophilic and H-bonds) between polar amino acid and hydrophobic residues are responsible to inhibit COVID-19 M^pro (Figs. 2, 3) (Khaerunnisa et al. 2020).

Molecular dynamics studies

Molecular dynamics studies are performed to validate docking results of phytoconstituents in complex with target protein 6LU7 using CABS-flex 2.0 server. The input of docking poses of phytoconstiutents complexed with protein (PDB) added with default parameters to study fluctuations of the individual amino acid residues (Jamroz et al. 2013). The conformational stability of docked poses are represented by the root mean square fluctuation (RMSF) values (Figs. 4, 5). The highest RMSF value in MD simulations reflects more flexibility of protein, whereas the lowest value implies the limited motion of the system during the simulation process (Zhao et al. 2015). Chrysin-7-O-glucuronide shows lowest RMSF values indicating limited motion of the system over period of simulation trajectories which also validates that Autodock Vina docking results are not obtained by chance. The presence of amino acid residues with fitting α-helix and β-sheet in secondary structures reflects minimal fluctuation with efficient constraints to the all atom. All the selected molecules (Baicalein-7-O-diglucoside, Chrysin-7-O-glucuronide, Oroxindin and Scutellarein) maintained the molecular interactions with the protein.

ADME and toxicity studies

The results of the pkCSM webserver shows that selected screened phytoconstituents does not have AMES toxicity, does not inhibit hERG-I, does not produce hepatotoxicity, it does not cause skin sensitivity. Only Baicalein-7-O-diglucoside inhibit hERG-II other do not. From the Lipinski and predicted ADME/tox filtering analyses (Table 2), we obtained four non-toxic, herbal compounds that bind to the receptor-binding site and catalytic site of SARS-CoV-2 M^pro.

Additionally to confirm reliability of toxicological data, QSAR modelling method performed using VEGA-QSAR (Table 3). The software incorporated algorithm provides evaluation of reliability prediction as Applicability domain index (ADI) value. We used positive results with ADI > 0.5, as indicators of reliability effect; low (0.5 < ADI < 0.6), medium (0.6 < ADI < 0.8) and high (0.8 < ADI < 1). The four phytoconstituents does not have AMES toxicity (CONSENSUS model, assessment and prediction) (Votano et al. 2004), does not have carcinogenicity (CAESAR model, assessment and prediction) (Fjodorova et al. 2010), no adverse health effects to humans and ecological species (IRFMN/COMPARA, assessment and prediction) (Kamel et al. 2020) and found to be inactive for Thyroid hormone receptor α/β (NRMEA, assessment and prediction). Unlike Scutellarine all compounds does not have skin sensitivity (CAESAR model, assessment and prediction) (Chaudhry et al. 2010). However, all compounds controversial to webserver predictions have very low reliability of hepatotoxic potential (IRFMN, assessment and prediction).

A literature review supports the docking result and reveals that extract of selected plant were reported to possess antimicrobial activities (Mat Ali et al. 1998; Suresh Babu et al. 2005) and act as antioxidant by inhibiting lipid-peroxidation (IC50 0.08 μg/ml) (Siriwatanametanon et al. 2010; Rajkumar et al. 2012). Baicalein extensively investigated with respect to their antiviral activity. It possess potent anti-DENV activity (Moghaddam et al. 2014), impaired H5N1 virus replication, prolong death rate in individual infected with H1N1 and Sendai viruses, suppress replication of HIV-I virus. Computational studies also promises effectiveness against chikungunya virus (CHIKV) nsP3 and NS5 protein (Zakaryan et al. 2017). In vitro, scutellarein found to inhibit the SARS-CoV helicase protein by affecting the ATPase activity, nsP13 (Yu et al. 2012). Scutellarin and Chrysin-7-O-glucuronide showed high antioxidant capacities in DPPH, ABTS and CAA assays, antiviral activity of Chrysin derivative against Coxsackievirus B3 (CVB3) (Li et al. 2018).

Thus, we anticipate that this selected phytoconstituents has the potential to enhance immunity along with the inhibition of COVID-19 infections (Lin and Weng 2006). Furthermore, Combination therapy often results in superior outcome in antiviral treatments (Mucsi et al. 1992; Chang et al. 2011). The screened phytoconstituents with antiviral activity, displayed higher docking scores, stronger binding energies, better interaction with the conserved catalytic residues and that may cause inhibition/blockade of the SARS-CoV-host protein pathways could be potential supportive and therapeutic candidates (Fuzimoto and Isidoro 2020).

Below is the link to the electronic supplementary material.

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

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All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

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Supplementary Materials

Data Availability Statement

The authors have no financial or proprietary interests in any material discussed in this article.