key: cord-0701129-4qm53hv5
authors: Joshi, Tanuja; Bhat, Sunaullah; Pundir, Hemlata; Chandra, Subhash
title: Identification of Berbamine, Oxyacanthine and Rutin from Berberis asiatica as anti-SARS-CoV-2 compounds: An in silico study
date: 2021-10-11
journal: J Mol Graph Model
DOI: 10.1016/j.jmgm.2021.108028
sha: 1eb0a4dda1cc239e10e8f387ae237344a2d78f7e
doc_id: 701129
cord_uid: 4qm53hv5

Owing to the shortage of specific medicines, the global pandemic of COVID-19 caused by SARS-CoV-2 has been the greatest challenge for the science community. Researchers from all over the world developed some drugs which failed to completely suppress the contiguous disease. SARS-CoV-2 main protease (Mpro), an important component in viral pathogenesis, is considered as a prospective drug target to stop SARS-CoV-2 infection. Since identification of phytochemicals with anti-Mpro activity has been carried out to develop the potential drugs against SARS-CoV-2. Therefore, the present study was conducted to screen phytochemicals of Berberis asiatica for anti-SARS-CoV-2 activity. Through text mining, thirty phytochemicals were reported from B. asiatica, of which, three phytochemicals (Berbamine, Oxyacanthine, and Rutin) show high affinity with the SARS-CoV-2 Mpro and exhibited favorable intermolecular interactions with the catalytic residues (His41 and Cys145) and other essential residues. The molecular dynamics simulation showed that Mpro-phytochemical complexes are more stable, less fluctuating, more compact, and moderately extended than the Mpro-X77 (Reference) complex. The number of H-bonds and MMPBSA results also demonstrates that Berbamine, Oxyacanthine, and Rutin are potent Mpro inhibitors having free energy of −20.79, −33.35, and −31.12 kcal mol(−1) respectively. The toxicity risk prediction supports all phytochemicals for drug-like and non-toxic nature. From the result, we propose that binding of these phytochemicals could hamper the function of Mpro. This work suggests that selected phytochemicals could be used as novel anti-COVID-19 drug candidates, and might act as novel compounds for in vitro and in vivo study.

In December 2019 an unidentified case of pneumonia caused by 2019-nCoV was noticed among some person, in Wuhan city, Hubei Province, China, later on, declared as a global pandemic by the world health organization (WHO) [1] [2] [3] . The International Committee on Taxonomy of Viruses (ICTV) and the WHO permanently name the 2019-nCoV pathogen as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and the causing disease as Coronavirus disease 2019 (COVID-2019), after whole-genome submitted by different laboratories and regions to GISAID database [4, 5] . COVID-19 is a severe and often fatal respiratory tract infection caused by a transmissible human pathogenic SARS-CoV-2. COVID-19 is responsible for medical emergencies globally as well as in India. While comparing the genomic of SARS-CoV-2, it was found to belong to the Beta coronavirus family and is phylogenetically very similar to SARS-CoV-1, which was accountable for acute pneumonia that occurred in November 2002 in Guangdong Province, China. COVID-19 began in Wuhan in Hubei Province, People's Republic of China, in December 2019, and became a worldwide pandemic [2, 3, 6] . The infection was extremely contagious which led to the global dissemination of the virus in the following months, thus causing the COVID-19 outbreak [7] . COVID-19 is responsible for a lot of deaths all over the world [8, 9] . As per the online data in World meter's report, about 211,487,519 people have been infected with COVID-19, and 4,426,500 deaths occurred at world-wise, till August 20, 2021, (https://www.worldomet ers.info/coronavirus/), and in India, the cases are increasing on daily basis at an enormous rate with 3,23,92,506 confirmed cases and 4,33, 998 persons being deceased due to COVID-19. As far as India is concerned 57, 22, 81,488 doses of vaccine have been administrated till 20, August 2021 (https://www.COVID19india.org/). With the emergence of the global pandemic of COVID-19, not only the health care system, but the global economy was a decline at an exponential rate. The COVID-19 being a new and largely unknown disease, has provided doctors with the need to investigate and try different methodologies and interventions.

COVID-19 infection causes acute respiratory distress symptoms such as fever, dry cough, and breathing difficulty with an incubation time of around five days (average 2-14 days) [10] . Possible COVID-19 treatments include lopinavir, a coronavirus protease inhibitor [11] , ribavirin, a guanosine analog that targets RdRp and was developed to fight the Ebola virus [12] , and chloroquine, an antimalarial drug that has displayed antiviral efficacy by disrupting viral fusion with the cell due to increased endosomal pH [13] . In a retrospective study, Xu et al. [3] examined the efficacy of tocilizumab (atlizumab, an immunosuppressive drug) and found that it lowered fever, oxygen requirement, radiological characteristics, and C-reactive protein levels (CRP). Bian et al. [14] , in an open-labeled clinical trial (concurrent controlled add-on clinical trial) of meplazumab, identified a median virus clearance rate, discharge time, and improved repair time. The effects of some corticosteroids on coronavirus have also been investigated [15] . Convalescent plasma transplant is still being used now, and it has been shown to lower mortality rates [16] . Two antimalarial agents, chloroquine and hydroxychloroquine's are also being used for emergency coronavirus care [17] .

There are currently 172 vaccines in pre-clinical development and 61 vaccines in clinical development, respectively [18] . Some of the most prominent vaccines worldwide are crmirnaty (first vaccine approved by FDA) with an efficacy of about 88% against the delta variant, Spikevav the second vaccine given nods by FDA, GRAd-COV-2, SputnikV (https://www.nytimes.com/interactive/2020/science/coronavirusvaccine-tracker.htm). Seven vaccines viz., Covaxin, Covishield, Sput-nikV, ZyCov-D, Moderma mRNA-1273, AD26-COV2.S, AZD 1222 have been given emergency approval for India's immunization program (htt ps://COVID19.trackvaccines.org/country/india/), and of them, the two most commonly used to vaccinate Indian people are Covishield and Covaxin with the effectiveness of 70.42% and 50%, respectively.

To overcome the viral infection, inhibition of replication of the viral genome is a well-known strategy [19] . The virus infection in the host cells can be stopped by inhibiting the cleavage process. Some researchers [20] reported that SARS-CoV-2 genome codes for several non-structural proteins (NSP) including 3-chymotrypsin-like protease (3CLpro), RNA-dependent RNA polymerase (RdRp), and its helicase, papain-like protease (papLpro), the structural glycoprotein, and accessory proteins (12) . Among various proteins encoded by SARS CoV-2, Main protease (Mpro) has been reported as a lead potential target for any drug, because it cuts the two replicate polyproteins required to mediate viral replication and transcription [21] . Therefore, Mpro becomes the principal target for drug discovery to identify novel inhibitors of SARS-CoV-2 [21, 22] . In a recent study, Nelfinavir was reported to be a COVID-19 drug candidate as the best potential inhibitor of Mpro on the basis of molecular dynamics simulation of docked protein-ligand complex [9, 23, 24] .

As per the WHO report, 80% of the population in developing countries depend on conventional plants for health needs [25] . Herbal medicines which occur naturally offer an extensive variety of natural products, which can serve as a supplementary guide for unscrambling many mysteries behind human illnesses [25, 26] . The role of traditional medicine to combat COVID-19 has recently been reported in the literature [3] . Indeed, medicinal plants may be the source of compounds that can have the potential to fight SARS-CoV-2 [27] [28] [29] [30] .

Berberis asiatica which belongs to the Bereridaceae family has a long history in traditional remedy as the root of B. asiatica are used in indigenous system of medicine for treating various ailments such as rheumatism, jaundice, diabetes, fever, stomach disorders, skin disease, malarial fever, and as a tonic, and so forth [31, 32] . It is used as a single plant remedy or in polyherbal formulations, predominantly in specified systems of medicine such as Ayurveda, Siddha, and Unani. This plant has great importance to fight against pneumococcal infection [33] . The major alkaloid of B. asiatica has been reported to be berberine (C20H18NO4+) [34] , which is quaternary ammonium salt from the protoberberine group of isoquinoline alkaloids and is differentially found in the roots, rhizomes, stems, bark, and berries of this plant [28, [35] [36] [37] . Different pharmacological properties of berberine including anticonvulsant [38] , antidepressant [28, 38] , anti-Alzheimer [28] , anti-arrhythmic [38] , anti-inflammatory [38] , antiviral [39] , antibacterial [39] , antineoplastic [35] and anti-diabetic [40, 41] have been reported in both in vivo and in vitro studies. Berberine besides having anticancer properties, is an essential therapeutic phytochemical agent [28, 33] with anti-diabetic, anti-malaria, anti-AIDS, anti-jaundice, anti-cholera, anti-diarrhea, anti-leprosy, and anti-inflammation effects [35, 37, [42] [43] [44] [45] [46] [47] [48] [49] .

For drug designing and discovery, the computational technique for screening natural inhibitors is gaining attention among researchers [50] . Researchers have investigated many phytochemicals of known and unknown biological function using computer-based approaches to find out lead compounds that can effectively inhibit the therapeutic targets in SARS-CoV-2 including 3CLpro and PLpro [51, 52] . For this, present study was carried out to find out whether B. asiatica phytochemicals can also stop viral replication within those infected by SARS-CoV-2or not, therefore virtual screening was carried out to find out potential natural anti-SARSCoV-2 agents. Therefore, Main Protease (306 amino acids) was adopted as a target which is essential for the survival and function of polypeptide generation in the host cell. In order to find novel SARS-CoV-2 Mpro inhibitors, researchers studied the docking score of B. asiatica phytochemicals with SARS-CoV-2 Mpro using text mining, molecular docking, molecular dynamics simulation, drug-likeness, and toxicity prediction approaches. Furthermore, we also investigated the in-silico toxicity of the screened phytochemicals.

B. asiatica was undertaken in this study due to its various therapeutic roles against several diseases. Text mining analysis showed that various phytochemicals of B. asiatica have antiviral properties. Hence to screen antiviral compounds against SARS-CoV-2, a dataset of B. asiatica phytochemicals was built in-house by collecting information from the scientific literature. DLAD4U (Disease List Automatically Extracted For You), PubTator, and Carrot2 servers were used for creating a dataset of B. asiatica phytochemicals by text mining analysis. The scientific name of the plant (B. asiatica) and COVID-19 were used as keywords for the search. The pieces of literature cited were focused largely on the COVID-19 resources that were made publicly available to the science community (COVID-19 open research dataset https://pages.semanticscholar. org/coronavirus-research), but also on specific databases such as Pubmed and Google scholar. Here, we conducted molecular docking of B. asiatica phytochemicals with SARS-CoV-2 Mpro to screen antiviral phytochemicals against coronavirus.

A) Protein preparation: More than a dozen proteins are encoded by the SARS-CoV-2 genome, the most studied of which is the 3CLpro. The main enzyme of the SARS-CoV-2 virus is a protease (Mpro or 3CLpro) which is vital CoV enzyme and plays a significant role in promoting viral replication and transcription, thus making it a most critical drug target [21] . The Mpro crystal structures (PDB ID: 6W63) attached with its inhibitor (X77) was collected from the Protein Data Bank and imported into the PyMol to visualize the binding domain and to identify the amino acids in the binding site pocket. The protein was added with hydrogen atoms to fix the ionization and tautomeric states of the amino acids using the AutoDockTools (ADT). Furthermore, prior to the docking, the water molecules and ligand bound to the receptor molecule were eliminated by utilizing PyMol. In addition, the protein was subjected to energy minimization by using the AMBER 14SB force field with a maximum number of 200 steps at 0.02 RMS gradients. The optimized protein structure was then saved in pdbqt format and imported to PyRx for molecular docking.

A) Ligand preparation: The X77 (N-(4-tertbutylphenyl)-N-[(1R)-2-(cyclohexylamino)-2-oxo-1-(pyridin-3-yl) ethyl]-1H-imidazole-4carboxamide) was used as reference compound in this study. The three-dimensional (3D) structure of the X77 co-crystallized with Mpro was retrieved for the respective protein structure from Protein Data Bank [http://www.rcsb.org/pdb/home/home.do]. PubChem database were used to retrieve the SDF files of each phytochemical (htt p://pubchem.ncbi.nlm.nih.gov/). The files were converted into a PDB file using the OpenBabel tool [53] . The Polar hydrogen charges were assigned and the non-polar hydrogens were integrated by using ADT [54] . Finally, for docking the reference ligand (X77), as well as other ligands (phytochemicals), were converted to pdbqt format.

A) Molecular docking studies: Molecular docking was performed using AutoDock Vina [55] once the target and ligands were prepared. The potential to estimate the scoring function and analysis of protein-ligand interactions to figure out the ligand's binding affinity and activity determination is the foremost objective of molecular docking [56] . Autodock Vina in the PyRx platform was used to generate the binding pose of phytochemicals in the active site of SARS-CoV-2 Mpro. The amino acid residues of Mpro interacting with their co-crystallized ligand i. e. X77 was taken as the active site residues and docking grid parameters were set accordingly (Fig. 1C) . The active site pocket contains amino acid residues Thr25, Thr26, His41, Phe140, Leu141, Asn142, Gly143, Ser144, Cys145, His163, His164, Met165, Glu166, Asp187, and Gln189. The parameters for the grid box were set as x, y, z size, and center coordinates: − 20. 11, 18.79, -27.35 , and 25, 25, and 25 respectively. B) Validation of the docking protocol: The validation of the docking procedure was carried out by docking the co-crystallized ligand at the active site of Mpro. The docking algorithm was then carried out by keeping the exhaustiveness 8. Hit phytochemicals with the lowest binding energy (kcal/mol) than X77 and anticipated interactions with the essential amino acids present at the active site of the protein can exhibit powerful antagonist properties against SARS-CoV-2 Mpro. The program Discovery studio visualizer was used to visualize hydrogen and hydrophobic contacts at the SARS-CoV-2 Mpro inhibitor site.

MD simulation was implemented to validate the docking analysis and quantify the change in protein conformation. The MD simulation package GROMACS 5.0.7 [57] was used to simulate the systems (protein-ligand complex and apo-protein structure) wherein the CHARMM 36 force field was used for building the topology of each system [58] . Using transferable intermolecular potential water molecules (TIP3Pmodel) [59] , the water molecules were added, and then neutralization of the system was achieved by adding 4 Na ions at a temperature of 310 K. For energy minimization of the system, the periodic boundary condition was retained where the Particle Mesh Ewald (PME) approach [60] with the steepest descent algorithm was used for the measurement of long-range electrostatic interaction using the Verlet cutoff scheme at 10 kJ mol − 1 . A dodecahedral simulation box was developed to simulate the system that was 10 Å greater than the size of system. The Berendsen thermostat [61] has been used to monitor the temperature of the simulation system. Initially, each system were cleaned and equilibrated in two stages by the steepest gradient approaches [62] (5000 ps); NVT and NPT ensemble.

Lastly, constant temperature and pressure of 300 K and 1 atm, were maintained for all the systems subjected to the production MD of 250 ns. The simulation time was maintained using the Parrinello-Rahman with a time step of 2fs for constant pressure simulation. To evaluate the result, the simulation trajectory was saved for every 100 ps.

The MD simulation results were incorporated with the GROMACS default script. Finally, MD trajectories were evaluated for the measurement of Root-mean-square-deviation (RMSD), Root-mean-squarefluctuation (RMSF), Radius-of-gyration (Rg), Solvent-accessiblesurface-area (SASA) [63] , Hydrogen bonds (H-bonds), and principal component analysis (PCA) (http://thegrantlab.org/bio3d_v2/tutorials /principal-component-analysis) [64] . This was worked out to measure the strength of the protein-ligand interaction. The researcher also calculated the non-bonded interaction energy between protein and ligands with the same parameter as MD simulation. In order to get a more accurate MD simulation result, each complex was run three times (n = 3) and the average result was used for analysis.

To calculate the binding free energy, the molecular mechanics Poisson-Boltzmann surface area (MMPBSA) approach was used [65] . The MD trajectories were processed before doing MMPBSA calculations. Binding free energy calculations include free solvation energy (polar + nonpolar solvation energies) and potential energy (electrostatic energies + van der Waals interactions). In the following equation, the whole process of MMPBSA can be summarized:

Here, the sum of van der Waals and electrostatic interaction is ΔGMM, the polar and non-polar solving energies are ΔGPB and ΔGSA respectively, and the entropic contribution is TΔS. For average binding energy measurements, the 'python' script provided in g_mmpbsa was used. The last 10 ns MD trajectory files were considered for the MM-PBSA measurement.

The phytochemicals with better binding energy and stability with the Mpro receptor were taken for the detailed toxicity analysis using the OSIRIS Property Explorer [66] . OSIRIS open-source software was used to predict the risk of drug toxicity for properties like tumorigenicity, mutagenicity, reproductive development, irritation, and drug score.

Text mining analysis using various servers (PubMed, Carrot2, and DLAD4U) was done to commence studies in different research papers. A total of 30 phytochemicals of B. asiatica were collected from various pieces of literature. Table 1 provides the name of the phytochemicals and the details of the publication. Data from text mining revealed that several pharmacological effects like antimicrobial, hepto-protective, anti-diabetic, antioxidant, anti-diarrheal, anti-inflammatory, cardiotonic, ophthalmic, skin related problems, laxative, anti-depressant, immune-modulatory, anti-tumor, neuro-protective, antifungal, and potential antiviral activities are found in B. asiatica. The plants belong to the genus Berberis have many medicinal properties due to the presence of alkaloids with different pharmacological activities [67] . The antiviral potential of B. asiatica is may be due to the antiviral activity present on various secondary metabolites (phytochemicals) of the plant. Table 1) . Table 1 suggests that B. asiatica phytochemicals can be used to develop antiviral drugs for the treatment of COVID-19.

The virtual screening of all B. asiatica phytochemicals was performed by the molecular docking approach at the active sites of Mpro using the PyRx tool. The coordinate center and size of the target protein (Mpro) were generated from the center of mass of its standard inhibitor (X77), which was estimated by using the "centerofmass" function of PyMOL.

Validation of the docking protocol: The protocol of molecular docking was validated by docking the reference ligand/standard inhibitor X77 into the active site of Mpro, before doing the virtual screening. The docked X77 was superimposed to compare with experimental X77 (Fig. 1A and B) . To validate docking, the RMSD value was calculated. The RMSD value amid the experimental and docked reference molecule X77 was 0.653 angstrom, which is perfectly acceptable. The result displayed that the docked X77 exhibited well-established hydrogen bonds and hydrophobic bonds with similar amino acid residue as the experimental X77 formed with the active pocket of the receptor ( Fig. 1C and D) . The figure also indicates the formation of four conventional hydrogen bonds with Glu166, His163, Gly143, and Cys145; three carbon-hydrogen interactions with Met165, Leu141, and Asn142; eleven van der Waals interaction with Phe140, Ser144, Leu27, Thr26, Thr25, His41, Arg188, Asp187, His164, Cys44, and Gln189; three unfavorable donor-donor interaction with Gly143, and Pi-sulfur bond with Met49 in Mpro-docked X77 (Fig. 1D ). Since our docking protocol produced a similar docked pose for X77 as found in the crystal structure of Mpro, the protocol was considered satisfactory and could reliably be used for the docking of the compounds of interest.

Molecular docking: Further, molecular docking studies were carried out between receptor SARS-CoV-2 Mpro and ligand (B. asiatica phytochemicals) using AutoDock Vina. All the 30 phytochemicals were analyzed for the binding energy with Mpro. Table 2 consists of name of all phytochemicals with their molecular formula, and binding energies (kcal mol − 1 ) with SARS-CoV-2 Mpro. After successfully docking these phytochemicals with target Mpro, the result shows 8 different poses of receptor-ligand interactions for each ligand. The compounds with docking scores less than the reference molecule were regarded as compounds of interest as they are the most stable ligands in comparison to the reference ligand. The frequency graph of all the docked compounds is given in Fig. 2 . Docking results revealed that all 30 phytochemicals had binding energy between the range of − 4 kcal mol − 1 to − 9 kcal mol − 1 , which was lower or equivalent to binding energy the reference (X77) (− 8.4 kcal mol − 1 ).

Berbamine showed the strongest affinity to Mpro while ascorbic acid showed the weakest binding energy. Three phytochemicals of B. asiatica (Berbamine, Oxyacanthine, and Rutin) show their inhibitory action by representing a lower binding energy score (higher docking scores) compared with the binding energy of X77 (− 8.4 kcal mol − 1 ). Berbamine, Oxyacanthine, and Rutin were found to bind at the active site of Mpro with docking scores of − 9.7 kcal mol − 1 , -8.5 kcal mol − 1 , and -8.4 kcal mol − 1 respectively ( Table 2 ). The molecular docking result suggested that screened compounds may have the same mechanism of action as the reference molecule. Therefore, it is quite evident that screened phytochemicals from B. asiatica have good potential against Mpro. Then, all these three compounds along with X77 were further used for molecular interaction analysis of protein-ligand complex.

The 2D interactions of the screened phytochemicals, as well as reference ligand X77, were visualized by Discovery studio visualizer. From molecular docking analysis, a total of 30 compounds were screened and following best three docked compounds with their receptor-ligand 2D interaction images are shown in Fig. 3 . The active site residues include Thr25, Thr26, Leu27, His41, Cys44, Met49, Tyr54, Phe140, Leu141, Asn142, Gly143, Ser144, Cys145, His163, His164, Met165, Glu166 Leu167, Pro168, His172, Asp187, Arg188, and Gln189 (Fig. 1C) . Table 3 showed necessary H-bond formation with possible active residues by ligands required for the inhibition of SARS-CoV-2 Mpro. The binding profile of the Mpro-Berbamine complex revealed mainly van der Waals interaction with Leu167, Glu166, Leu141, Asn142, Gly143, Arg188, Gln 189, Gln 192, and Thr190; while Met165 was involved in hydrogen bond formation with the sulfur group. Hydrophobic interaction was also predominant in the binding of Berbamine to Mpro such as π-alkyl interaction with Pro168, Met49, His41, and Cys145 and Alkyl bond with His41, Met49, and Cys44 (Fig. 3A) . Catalytic residues Met165, His41, Cys145, and Pro168 were visualized in hydrophobic interaction (π-alkyl) with Oxyacanthine where a single Conventional and Carbon hydrogen bonds were also observed with Thr190 and Asn142 respectively. Leu141, Gly143, Thr25, Cys25, Arg188, Gln192, Gln189, and Glu166 were involved in van der Waals interactions Cys145 was linked via Alkyl bond while Met49 was involved in hydrogen bond formation with the sulfur group in the Mpro-Oxyacanthine complex (Fig. 3B ). As visualized with Discovery studio, Met165 was involved in hydrophobic interactions (Pi-alkyl bond) while His41 was linked via π-π stacking in Rutin. Two conventional hydrogen bonds with Thr190 and Arg188, and a single Carbon hydrogen bond with Glu166 was also observed in Rutin's binding to Mpro protein. In addition, van der Waals interactions were formed with Asp187, Thr54, Leu167, Gly170, Pro168, Gln189, Gln 192, Ala191, Val186, His163, Cys145, and Asn142, while Cys44 and Met49 were involved in hydrogen bond formation with the sulfur group (Fig. 3C) .

From this study, it can be seen that all screened compounds, as well as reference, are generally interacted with the same residues i.e. His41, Met49, Cys145, Met165, Glu166, and Gln189, which are well known to be involved in the active site of Mpro. All these screened phytochemicals showed novel hydrogen and hydrophobic bonding interactions with active residues of the target protein. In comparison with the reference molecule, they showed lower docking scores and also have shown stronger interactions with the target protein and thus, these phytochemicals may be considered as potential inhibitors of Mpro.

The docked Mpro-ligand complexes were subsequently used to study the detailed dynamic, structural, as well as binding behaviors by MD simulations which allow investigating how the ligands interact with SARS-CoV-2's active site.

The MD simulation trajectories of 250 ns simulations were examined to study the detailed structural and dynamic mechanisms of the Mpro protein and Mpro-ligand complexes. The RMSD, RMSF, and Rg fluctuations profile of all systems during the period of 250 ns simulation are presented in Figs. 4-6. The RMSD of the backbone atoms computed over 250 ns revealed that the Mpro protein reached stability after approximately 50 ns, whereas all the Mpro-ligand complexes took only 5-10 ns to become stable (Fig. 4) . Mpro-X77 complex as well as all the Mprophytochemical complexes were stabilized until the end of the MD production run and converged overall except Mpro-Oxyacanthine complex which is stable up to 200 ns and after that, it showed a little fluctuation of about 0.1 ns and become stable immediately after this. The RMSD plot suggested that the last 10 ns were most preferable for further structural and dynamics analyses as all the complexes were stable during this time. The average RMSD values of Mpro, Mpro-X77 complex, Mpro-Berbamine complex, Mpro-Oxyacanthine complex, and Mpro-Rutin complex were found to be 0.20 ± 0.03 nm, 0.22 ± 0.04 nm, 0.16 ± 0.02 nm, 0.18 ± 0.01 nm, and 0.19 ± 0.05 nm, respectively.

Interestingly, the RMSD values of all the systems were very similar and do not exceed 0.4 nm, which denotes the structural integrity of the Mpro protein. The RMSD profile suggested that upon phytochemical binding no significant variation or conformational changes were taking place in Table 3 Hydrogen and Non-Hydrogen bond interaction between Screened phytochemicals and Mpro. the Mpro structure. The structural flexibility was evaluated by the residue-wise RMSF in Mpro protein and Mpro-ligand complexes. RMSF specifies the flexible region of the protein and analyzes the portion that diverges from the overall structure. A higher RMSF value indicates greater flexibility (less stability) during the MD simulation while the lower value of RMSF suggests less flexibility (good stability) of the system. All the Mprophytochemical complexes exhibited overall similar or lower RMSF values than the Mpro-X77 complex during the simulation (Fig. 5) . RMSF analysis suggests that all active site residues had fluctuation less than 0.2 nm and were found to be stable throughout the simulation period, which is completely acceptable.

The Rg of the protein and protein-ligand complex indicates the degree of compactness and rigidity of the protein. Therefore, the Rg values of Mpro and Mpro-ligand complexes were investigated to evaluate their compactness during the 250ns simulation run. For this, we have calculated the Rg of Mpro and Mpro-ligand complexes during the 250 ns simulation time. The average Rg values of Mpro and Mpro-X77 complex were found to be 1.84 ± 0.22 nm and 1.73 ± 0.27 nm respectively. Similarly, Rg values were found to be 1.71 ± 0.29 nm, 1.73 ± 0.24 nm, and 1.70 ± 0.25 nm for the Mpro-Berbamine complex, Mpro-Oxyacanthine complex, and Mpro-Rutin complex, respectively, but in the case of the Mpro-Oxyacanthine complex little fluctuation was observed in between 220 ns and 225 ns. From Rg profiles, it was observed that the Mpro-ligand complex exhibited a more compact behavior than the Mpro protein without ligand and Mpro-X77 complex.

The lower RMSD, reduced residue-wise fluctuation, and higher compact nature in the Mpro phytochemical complexes are indicating their overall stability as well as convergence.

H-bonds are essential for drug specificity, metabolization, and stability. H-bond analysis of Mpro-ligand complexes performed was for the period of 250 ns simulation to understand the H-bond and its contributions to the overall stability of the system as shown in Fig. 7 . The Mpro-Rutin complex was the only one that formed a maximum of nine H-bonds while maintaining an average of five. The binding pocket residues i.e. His41, Asn142, Glu166, Gln189, Thr190, and Gln192 were involved in H-bond formation. The average H-bonds in the Mpro-Oxyacanthine complex was three, while the maximum had reached four. Gly143, Arg188, Thr190, and Gln192 were the binding site residues that had formed H-bonds with this complex. The highest H-bonds formed by the Mpro-Berbamine complex was five, and the average H-bonds formed was four. This complex formed a H-bond with the residues Glu166, Asp187, Gln189, and Thr190, which are involved in binding at the active site of Mpro protein. The Mpro-X77 complex had formed a maximum of six H-bond, with an average of three H-bonds. The binding site residues Asn142, Gly143, Ser144, Cys145, His163, and Glu166 of Mpro protein had formed H-bond with the complex. After analyzing results, it was found that all Mpro-phytochemical complexes did not deviate and almost similar numbers of H-bonds were formed between Mpro-phytochemical complexes and Mpro-X77 complex, indicating that all phytochemicals were bound to the Mpro as closely and effectively as its standard inhibitor X77. During the 250 ns simulation run, all complexes were found stable and observed within the pocket. This suggests that H-bonds probably played an important role in the stability of the Mpro-X77 complex during the MD simulation, and also indicates stability to the Mpro-phytochemical complexes. Fig. 8 showed that the SASA of Mpro-X77 complex and Mprophytochemical complexes. The average SASA values were found to be 152.58 ± 2.89 nm 2 for the Mpro-Berbamine complex, 152.03 ± 2.80 nm 2 for the Mpro-Oxyacanthine complex, and 151.16 ± 2.95 nm 2 for Mpro-Rutin complex respectively. The Mpro-X77 complex showed the average SASA value of 150.35 ± 2.86 nm 2 . However, after 40 ns Mpro-X77 complex as well as all the Mprophytochemical complexes showed almost similar surface area (Fig. 8) .

The results showed a similar assessable surface area of phytochemicals to the reference X77 in the aqueous system, which indicates equivalent stability of phytochemicals with Mpro as X77. PCA represents the average variation in motion within the protein on ligand binding as compared to the free protein [100] . ED allows the interpretation of dominant and collective modes from the overall dynamics of the MD trajectory. FEL denotes the probability of energy distribution as a function of one or more collective variables of the protein [101, 102] . Gibb's free energy landscape (FEL) also predicted the stability of each protein-ligand complex. Using the g_sham tool of the GROMACS package, the FEL (ΔG) was generated from PC1 and PC2 projections and are shown in Fig. 9 . In these plots, ΔG values ranging from 0 to 15.7 kcal mol − 1 , 0-15.8 kcal mol − 1 , 0-15 kcal mol − 1 , and 0-14.3 kcal mol − 1 for Mpro-X77 complex, Mpro-Berbamine complex, Mpro-Oxyacanthine complex, and Mpro-Rutin complex respectively. All the Mpro-phytochemical complexes represent similar or lower energies as compared to the Mpro-X77 complex, which indicates that these phytochemicals follow the energetically more favorable transitions during the MDS.

To determine how firmly phytochemicals bind to Mpro and their respective binding modes, the binding free energies were calculated using the MM-PBSA approach. The MD trajectories were analyzed through MM-PBSA to know the binding free energy values and their energy components. For this purpose, the last 10 ns trajectories were investigated to calculate binding energies and insights into the binding modes of phytochemicals with Mpro. Four different energy components were used to calculate the binding free energy: electrostatic, van der Waals, polar solvation, and SASA energies. The binding free energy was calculated for all protein-ligand complexes and is shown in Table 4 . The reference molecule X77 was found to display binding energy of − 17.59 ± 3.32 kcal mol − 1 for Mpro. Computation of the binding energies of phytochemicals for the Mpro revealed that Berbamine, Oxyacanthine, and Rutin had the binding energy − 20.79 ± 16.07 kcal mol − 1 , − 33.35 ± 15.28 kcal mol − 1 , and − 31.12 ± 2.57 kcal mol − 1 respectively. The detailed study of the individual energy components revealed that all components including the van der Waals energy, Electrostatic Energy, and SASA energy, except the polar solvation energy contributed to the efficient binding of phytochemicals with Mpro. In all the studied complexes the major contributing energy was van der Waals energy.

Although all complexes were bound in the same binding pocket of the enzyme, variations in energy contribution of each residue may be a major factor in the difference in binding free energy. For the last 10 ns of MD simulation trajectories, a per residue interaction energy profile was also developed using the MM-PBSA approach to identify the essential residues involved in ligand binding with Mpro protein. Fig. 10 shows a per-residue decomposition plot of the total binding energy of the Mproligand complexes. Only residues that contribute most to overall binding energy are illustrated in the figure for a better representation of the results. The plot showed that the major contributing amino acids in all complexes were Thr25, Leu27, His41, Cys44, Met49, Cys145, Met165, Asp187, and Gln189. The per-residue interaction plot revealed that the majority of residues had negative binding energy, while only a few had positive binding energy. The residues with negative binding energy were important in maintaining the stability of protein-ligand complex. When compared to other active site residues, Met49, Cys145, and Met165 play the most significant roles in Mpro-ligand stabilization. The contribution of the binding energy of each residue was different in each complex which had made a large difference in the final binding energy of each complex. The higher energy contribution from the binding site residues in case of Mpro-phytochemical complexes explains the stability of the complex.

The results of binding free energy calculations and other previous MD simulation (RMSD, RMSF, RG, H-Bond, and SASA analysis) led to the conclusion that Berbamine, Oxyacanthine, and Rutin had better stability and binding energy towards the Mpro as compared to its standard inhibitor X77.

The US Food and drug administration's toxicity risk predictor tool OSIRIS was used to predict molecular properties and toxicity of phytochemicals [66] . In addition to the drug-likeness properties including cLogP, LogS (solubility), MW, and drug-like properties, OSIRIS also predicted the various toxicity risk properties such as tumorigenicity (TUMO), mutagenicity (MUT), irritation (IRRI), and reproductive development (REP) toxicity. The results of OSIRIS prediction for Berbamine, Oxyacanthine, and Rutin are summarized in Table 5 . The drug score shows ranges between 0 and 1, where the value 1 indicates the good possibility of a compound to be a drug molecule, whereas, the score value 0 indicates that compounds having no possibilities of being a drug candidate. The result of toxicity estimation shows that reference molecules, X77 as well as Berbamine, Oxyacanthine, and Rutin have no risk of any kind of toxicity i.e. they all are non-reproductively toxic, non-irritating, non-tumorigenic, and non-mutagenic. Thus, considering the toxicity prediction results, we suggest that these three screened phytochemicals i. e. Berbamine, Oxyacanthine, and Rutin can be exploited as promising drug candidates for the development of anti-SARS-COV-2 drug molecules. 

The effective drugs and vaccines search against SARS-CoV-2 is the most urgent and challenging task for global researchers at this moment. Currently, synthetic antiviral drugs used to treat COVID-19 such as lopinavir/ritonavir [103, 104] and remdesivir [104] as well as some antimalarial drugs like hydroxychloroquine and chloroquine [104] have various side effects, and therefore, there is a great need to explore the natural resources which can boost immunity as well as cure viral disease. The practice of natural extracts from medicinal plants in the prevention of COVID-19 is highly inspired by the previous SARS treatments. According to the many reports, several plants metabolites possess the potential antiviral activity and therefore, they may be used as natural therapeutics for the treatment of COVID-19 Pandemic [88, 105] . Recent studies by Joshi et al. reported the beneficial role of natural compounds from lichen and plants against 106] .

One of the most well-known plants with various pharmacological properties is B. asiatica. To find out potential compounds against COVID-19, B. asiatica was selected in this study. B. asiatica is known for its diversity and pharmacological uses in the traditional medicine system since the ancient times [107] . Several investigations have supported the traditional role of B. asiatica. This is one of the plants used in Ayurveda and the Yunani medicine system for curing jaundice, eyesores, toothache, asthma, and skin pigmentation; drying unhealthy ulcers; like a fomentation for removing inflammation and swelling [32] .

In this study of drug discovery, 30 phytochemicals were investigated from B. asiatica. these phytochemicals were verified for their against any potential viral disease. Thus, these compounds were explored in PubMed and DLAD4U for text mining analysis and it was found that many phytochemicals of B. asiatica show antiviral properties. Table 1 illustrates the list of phytochemicals of B. asiatica which are effective against various viral diseases. Then, the antiviral network of B. asiatica phytochemicals revealed that the 21 phytochemicals out of 30 were found to have effective inhibitory activity against a total of 31 viruses and each phytochemical is effective against more than one virus. The ability of phytochemicals to inhibit a broad spectrum of viruses may be useful in the treatment of SARS-CoV-2. Therefore, to find out potential anti-SARS-CoV-2 compounds, a phytochemical dataset of B. asiatica was prepared. These 30 phytochemicals were subjected to molecular docking against Mpro of SARS-CoV-2. Based on the molecular docking score of 30 phytochemicals, the three phytochemicals, viz. Berbamine, Oxyacanthine, and Rutin were screened which showed good binding energy with SARS-CoV-2 Mpro.

Further MD simulations were carried out on Berbamine, Oxyacanthine, and Rutin phytochemicals complexed with Mpro. The conformational changes and stability of all the Mpro-phytochemicals complexes were analyzed by RMSD, RMSF, RGS SASA, and H-bond analysis, etc from MD simulation trajectories. All these phytochemicals have shown good results and stability throughout the 250 ns simulation period. RMSD result indicates that all the phytochemicals possess better stability towards the active site of Mpro as compared to the reference, X77. RMSF analysis represents the lower atomic fluctuations in binding residues of Mpro indicating small conformation changes in Mpro after binding phytochemicals. Various MD simulation results revealed that all Mpro-phytochemicals complexes were highly stable throughout the 250 ns MD simulation run.

To validate the docking score, binding free energy calculations were performed using the last 10 ns of MD simulation trajectories. During the last 10 ns, all complexes show stable trajectories, and therefore last 10 ns trajectories were used for binding free energy calculations. The Mpro-Berbamine, Mpro-Oxyacanthine, and Mpro-Rutin complexes showed excellent binding free energy and the result of all the complexes was better than the reference, Mpro-X77 Complex. The least binding free energy indicates that Berbamine, Oxyacanthine, and Rutin can act in a significant way against the Mpro. Toxicity analysis demonstrated that these phytochemicals did not have a risk of any kind of toxicity and can be used as a drug with the value of tolerance prescribed for human consumption. The study also shows that all the screened phytochemicals also have drug scores similar to the reference and could be utilized as drug candidates against COVID-19.

The two screened phytochemicals of B. asiatica viz. Berbamine and Oxyacanthine are already reported to show effectiveness against the ACE-2 target of SARS-CoV-2 [71, 72] but, still, there is no such report against the Mpro target of SARS-CoV-2. Interestingly, the current findings emphasize that both these phytochemicals Berbamine and Oxyacanthine are also effective against Mpro. Hence, it suggests that these phytochemicals can prevent the replication of SARS-CoV-2 by targeting both ACE-2 as well as Mpro and consequently prevent the pathogenesis. Hence, these phytochemicals are likely to be developed as an orally active drug candidates.

The outbreak of COVID-19 is caused rapid deaths across the globe caused and has imposed great concern on the scientific community to develop potential drugs against it. Plants have been the natural source of the healthcare system since ancient times. SARS-CoV-2 uses Mpro to mediate the process of its replication and transcription. Targeting Mpro can lead to such changes in structural conformation in the virus which stops its replication and transcription inside the host cells. In this study, the in silico approach was applied for drug discovery in order to identify specific potential natural candidates to stop viral replication within those infected by SARS-CoV-2. Here, the phytochemicals of B. asiatica against SARS-CoV-2 Mpro were explored. In this research, for the first time various phytochemicals from B. asiatica were reported for their anti-SARS-CoV-2 activity, specifically by targeting the Mpro receptor. Molecular docking, MD simulation, binding free energy analysis indicate that three Phytochemicals viz. Berbamine, Oxyacanthine, and Rutin are not only bound strongly with the Mpro but also stabilized the 3D conformations of the protein structure after binding. Furthermore, the drugscore and toxicity profile of all phytochemicals showed their promising therapeutic potential. The results reveal that compounds from B. asiatica could have antiviral activity against SARS-CoV-2 and other viral diseases. Thus, Berbamine, Oxyacanthine, and Rutin from the B. asiatica and other Berberis species can surely be evaluated further in vitro and clinical trials to evaluate their antiviral potential.

This research is not supported by any funding source from the public, commercial, or not-for-profit sectors.

All the data cited in this manuscript is generated by the authors and available upon request from the corresponding author.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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She is working in the area of structural bioinformatics and Enzyme Technology

Sunaullah Bhat is a researcher at the Department of Zoology

Hemlata Pundir is a researcher at the Department of Botany

He is working in the area of vaccine development, data mining, artificial intelligence

The authors would like to thank the Head of the Botany Department, Kumaun University, S.S.J Campus, Almora, India, for providing the requisite facilities to conduct this research work. Kumaun University, Nainital, is also acknowledged by the authors for providing high-speed Internet facilities. We also extend our appreciation to Rashtriya Uchchattar Shiksha Abhiyan (RUSA), Ministry of Human Resource Development, Government of India, for the deployment of computer