Phylogeny analysis of Pakistani SARS-CoV2 strains and screening of a list of natural compounds as the antiviral targets for viral M pro protein

doi:10.21203/rs.3.rs-1976970/v1

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Phylogeny analysis of Pakistani SARS-CoV2 strains and screening of a list of natural compounds as the antiviral targets for viral M pro protein

https://doi.org/10.21203/rs.3.rs-1976970/v1

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The outbreak of COVID-19 has become a global health concern. The vaccines against SARS-CoV-2 are unable to barricade the reinfection in fully vaccinated individuals. Considering this dilemma, the recent research strategies are focused on the new candidates having antiviral potential with significant and consistent efficacies as well as the least side effects. In this study, we have screened plant-derived phytochemicals, antiviral compounds from PubChem, and natural compounds from the Hamdard products for identification of antiviral therapeutics against Spike (S) glycoprotein and main protease (M^pro) of SARS-CoV-2. All these compounds were screened based on their binding affinities as predicted by molecular docking analysis and compounds having binding affinity values ≤ -10 kcal/mol were considered for analysis. Furthermore, from physicochemical assessment, drug-likeness initially nine compounds were identified as the antiviral targets for the selected viral proteins. Finally, after ADMET analysis and MD simulations, the compound 9064 with the lowest Root Mean Square Deviations (RMSD), Coul-SR interaction energy (-71.53 kJ/mol), and LJ-SR energy (-95.32 kJ/mol) was selected as the most stable drug candidate against COVID-19 main protease M^pro. The selected antiviral compound 9064 is an antioxidant flavonoid (Catechin or Cianidanol), which is previously known to have significant immunomodulatory, anti-inflammatory, and antioxidant properties.

ADMET Analysis

COVID-19

Drug Design

Main protease (Mpro)

Natural compounds

Pakistan

Phytochemicals

SARS-CoV-2

Spike glycoprotein (S).

Coronaviridae is the family of enveloped, positive-sense, single-stranded RNA viruses (Comas-Garcia, 2019). These viruses affect the respiratory tract of mammals, including humans leading mild to severe respiratory tract infections (Lim, Ng, Tam, & Liu, 2016). During the first two decades of the 21st century, two members of this family, Severe Acute Respiratory Syndrome (SARS) and the Middle East Respiratory Syndrome (MERS) coronaviruses were responsible for the major outbreaks in China (Peiris et al., 2003; Xie et al., 2015).

Recently, from December 2019, another member of this family SARS-CoV-2, is spreading throughout the world and become a global pandemic with a continuous increase in confirmed cases, and mortalities (J. Yang et al., 2020). Despite active researches across the globe, there is no specific therapeutic drug available for this virus, as a result, the outbreak is still continually ongoing and has become a tremendous threat to public health and economics (Liu et al., 2020). The development of novel antiviral compounds presents several challenges and requires significant efforts and time for validation. Hence, exploring the repurposing of already-approved pharmaceuticals or the use of natural compounds can provide cost-effective management for COVID-19 infections (Rabaan et al., 2020).

Nature provides a vast library of chemicals having the potential to treat various ailments, including viral infections (Gu et al., 2013). A number of the plant-derived secondary metabolites, including alkaloids, steroids, diterpenoid lactones, aliphatics, and glycosides, have proved to induce antiviral effects in humans (Mishra, Sharma, Diwaker, Ganju, & Singh, 2013). Pakistan is a rich source of medicinal plants, and like other developing countries, 70–80% of people in Pakistan are dependent on herbal drugs for their primary healthcare because of the easy access, cost effectiveness, less side effects of these folk medicines (Bibi, Nisa, Chaudhary, Zia, & medicine, 2011; Mustafa, Arif, Atta, Sharif, & Jamil, 2017; Shinwari & Qaiser, 2011). Pakistan also has an ancient Unani Tibb system of medicine, which is in use today for the treatment of many diseases (Subbarayappa, 2001). Keeping in view the efficacy of natural products; many researches have started exploring anti-CoV agents from natural products (Mani et al., 2020; Orhan, Deniz, & Bioprospecting, 2020). The use of natural antiviral agents against novel coronavirus would be an economical way to overcome the epidemiology of this viral disease with relatively fewer side effects and toxicity.

In silico approaches contribute significantly to the field of drug development (Terstappen & Reggiani, 2001). Multiple computational programs are available, which determine the therapeutic potential of selected natural compounds and, therefore, help to reduce the library of natural compounds for wet lab practices, making the drug development procedure faster, cheaper, and safer (Boruah et al., 2013).

The present study involves the selection of the phytochemicals with known antiviral or antioxidant activities, from Pakistani medicinal plants and natural Hamdard products, and antiviral compounds, from PubChem, followed by their interaction with viral drug targets. Two proteins from SARS-CoV-2, main protease (M^pro) and spike glycoprotein (S), were selected as the potential drug targets. M^pro protein plays a pivotal role in viral gene expression and replication through the proteolytic processing of replicase polyproteins, while the S protein controls host invasion via its interaction with host angiotensin-converting enzyme 2 (ACE2) (C. Wu et al., 2020). The binding affinities and molecular interactions of selected natural compounds with SARS-CoV-2 proteins (M^pro and S), are assessed by molecular docking, followed by physicochemical analysis, drug-likeness and ADMET analysis, Molecular dynamics simulations of the putative compounds. We expect that the present study will offer a new dimension for managing the ongoing infections of SARS-CoV-2.

All steps adopted in this study are listed sequentially in Fig. 1.

1- Selection of potential SARS-CoV-2 proteins as antiviral targets

The comprehensive literature study helps in identifying two proteins, main proteinase (M^pro/3CL^pro) and spike glycoprotein (S), from SARS-CoV-2 as potential drug targets (Gil et al., 2020; Kliger, Levanon, & Gerber, 2005; Kumar, Singh, Patel, & health, 2020; Rani, Singh, Pareek, & Tomar, 2020; Salvatori et al., 2020; F. Wu et al., 2020; J. Yang et al., 2020).

2- Phylogenetic analysis

Till now, three strains of SARS-CoV-2, are reported from Pakistan, which includes MT500122 from Karachi, MT240479 from Gilgit, and MT262993 from the Manga region of the country. To determine the phylogeny of these three strains the whole-genome sequences were retrieved, through GenBank followed by the homologs searching via NCBI BLAST. The top fifty hits, for each strain, were considered for multiple sequence alignment and phylogenetic tree construction, which helped in tracing the origin of these strains in Pakistan (Pickett et al., 2012). CLUSTAL Omega (https://www.ebi.ac.uk/Tools/msa/clustalo/) was used for this purpose.

3- Predictions of 3D structures of targeted viral proteins

The amino acid sequences of S protein, from Pakistani SARS-CoV-2 strains, MT240479, MT500122 and MT262993, were retrieved from NCBI GenBank, with the accession numbers, QIQ22760.1, QJX57853.1 and QIS60276.1, respectively (Benson, Lipman, & Ostell, 1993). The sequences of M^pro were, however, fetched from ORF1ab polyprotein (QIQ22758.1, QJX57851.1 and QIS60274.1) after aligning with a reference protein (PDB ID: 6Y2E) (Berman et al., 2000). The amino acid sequences of M^pro and S proteins from three strains were aligned using CLUSTAL Omega to evaluate the conservation of these proteins among Pakistani strains of SARS-CoV-2 (Sievers & Higgins, 2014). After sequence retrieval the 3D structure of the M^pro was retrieved from PDB, with PDB ID 6Y2E; while the model of viral S protein, was built using I-TASSER (Zhang, 2008). The protein models were verified using SAVES v5.0, followed by the refinement of the models removing, water molecules, ions, and ligands via Discovery Studio software (Server; Visualizer, 2013). Moreover, hydrogen atoms were added, via Protonate-3D, and energies of protein models were minimized, by Molecular Operating Environment (Gercke, Regel, Singh, & Moerschbacher) software (Labute, 2007; Vilar, Cozza, & Moro, 2008). All these models were saved in the PDB format.

4- Screening of phytochemicals and natural antiviral compounds

The phytochemicals in this study, are selected by a combination of three different sources. First, an extensive literature search resulted in the retrieval of the plant-derived antiviral/antioxidant compounds from Pakistani medicinal plants (Anilakumar, Kumar, Ilaiyaraja, & Sciences, 2015; Ashfaq & Idrees, 2014; Benso, 2016; Bodeker, Burford, Chamberlain, Chamberlain, & Bhat, 2001; Böhme, Barros-Velázquez, Calo-Mata, & Aubourg, 2014; Budrat & Shotipruk, 2008; Hussain et al., 2017; Joshi et al., 2020; JURCA, Pallag, Marian, & EUGENIA, 2019; Maurya, Kumar, Prasad, Bhatt, & Saxena, 2020; Muhammad & Dewettinck, 2017; Mukhtar et al., 2008; Shah et al., 2011; Soleimany, Banaee, Mohiseni, Nematdoost Hagi, & Mousavi Dehmourdi, 2016; Tallei et al., 2020; Tariq). Secondly, due to the remarkable efficacy of multiple herbal products, in common cough, cold, and other respiratory infections, the Hamdard products, were also considered (Additional file 1).

Finally, the putative antiviral compounds reported in the COVID-19 data section of the PubChem database, were also included in this list (Kim et al., 2016). The 3D structures of all these compounds were downloaded from the PubChem and saved in the SDF file format. Like protein targets, ligands were also prepared for docking by adding hydrogen atoms via Protonate-3D, followed by the energy minimization by Molecular Operating Environment (Gercke et al.) software (Labute, 2007; Vilar et al., 2008).

5- Molecular docking analysis of the antiviral compounds

The selected antiviral compounds were docked with catalytic and receptor-binding domains of viral M^pro and S proteins, respectively, using MOE software (Vilar et al., 2008). The ligand-protein docking helps to evaluate the molecular interactions and binding affinities among all antiviral compounds and viral proteins. For selecting ligands having significant binding affinities with the viral proteins; binding energy (B.E) values of ≤ -10 kcal/mol and, the Root Mean Square Deviation (RMSD) of ≤ 1, were applied for the final selection of the ligands.

6- Assessments of Drug-likeness and ADMET properties

The physicochemical properties and drug-likeness of the selected antiviral compounds were investigated, via the Swiss-ADME online tool ((Daina, Michielin, & Zoete, 2017). The compounds were screened on the basis of Lipinski’s, Veber’s, Ghose’s, Egan’s, and Muegge’s rules. The compounds violating two or more of these five rules were removed from the final list. Moreover, the pharmacokinetic properties including, absorption, distribution, metabolism, excretion, and toxicity (ADMET) of the selected drug-like compounds, were also determined using admetSAR 2.0 and pkCSM online tools (Pires, Blundell, & Ascher, 2015; Hongbin Yang et al., 2019).

7- MD simulation analysis of the antiviral compounds

Finally, the binding affinities of the selected antiviral compounds with the viral Mpro and S proteins were inspected at the atomic level using the molecular dynamics simulation approach. For this purpose, GROMACS version 5.0.5 was used to follow the dynamic pattern of docked complexes applying the CHARMM36 force field on proteins and CGenFF on drug-like compounds (Abraham et al., 2015; Yu, He, Vanommeslaeghe, & MacKerell Jr, 2012). The system was equilibrated for 100 ps followed by a 50 ns production phase. Eventually, MD trajectories were analyzed for the calculation of root mean square deviation (RMSD), root mean square fluctuations (RMSF), the number of hydrogen bonds (HB), and compound interaction energies (Coul-SR and LJ-SR). Furthermore, we used MMGBSA method to estimate ΔG value of ligand and protein complex formation. The ΔG energy values are calculated using following two equations.

ΔG_bind = G_complex – G_protein – G_ligand (eq-1)

ΔE_MM + ΔG_GB + ΔG_nonpolar – TΔS (eq-2)

In Eq. 1, G_protein and G_ligand are individual free energies of protein and ligand, respectively while G_complex is free energy of ligand-protein complex. These energies are estimated using the values of Eq. 2. In equation-2, ΔE_MM is value of binding energy between ligand and protein in gas-phase. ΔG_GB and ΔG_nonpolar are de-solvation free energy of polar and non-polar components of system, respectively. TΔS is change of entropy upon binding of ligand with protein during complex formation.

1- Phylogenetic analysis and undermining the molecular relationships among different strains of SARS-CoV-2

The phylogenetic analysis revealed that two of the three strains of Pakistani SARS-CoV-2, MT500122, and MT240479, are closely related to the viral strains in Japan and China. The third strain, MT262993, however, is close to the USA, as is evident from the common clade shared by both strains (Fig. 2).

2- Modelling 3D structures of targeted viral proteins and model validations

The 3D structure of the spike glycoprotein predicted via I-TASSER and verified using SAVES v5.0 and of M^pro retrieved from PDB (6Y2E) are shown in Fig. 3.

3- Selection of phytochemicals and natural antiviral compounds

A library of a total of 579 antiviral compounds, was constructed using phytochemicals retrieved using a combination of three selection parameters. Of these 579 antiviral compounds, 71 were retrieved, via the literature survey, 128 from natural Hamdard products, and 380 from COVID-19 data, available in the PubChem database (Additional file 2 and 3).

4- Docking of antiviral compounds with M^pro and S proteins

The ligand-protein docking helped to screen the antiviral compounds having significant binding affinities for the M^pro and S proteins, the drug targets from the SARS-CoV-2. Initially the compounds with B.E ≤ -6 kcal/mol and RMSD ≤ 2 were considered relevant, all these compounds are enlisted in additional file 4. To further screen the best binders of these viral proteins, compounds having B.E ≤ -10 kcal/mol and RMSD ≤ 1 were selected, which results in the selection of seven compounds for M^pro and four compounds for the S protein.Among these seven compounds selected for M^pro, five were from the literature search and Hamdard products, while the reaming two were from COVID-19 data of the PubChem. Likewise, the two of the four compounds selected for S protein, were common in the literature review and Hamdard products, while the remaining two were those selected from COVID-19 data of the PubChem database. The names, sources, PubChem IDs, binding energies, and RMSD value of these selected compounds, are mentioned in Tables 1 and 2. However, the 2D representations of the binding of these with the viral proteins, are given in Figs. 4 and 5.

5- Assessment of physicochemical properties and drug-likeness

The physicochemical properties of the selected antiviral compounds, for M^pro and S proteins, are reported in Tables 3 and 4, respectively. Among the seven compounds having significant binding affinities for M^pro protein, only five were found to have drug-like properties, the other two (5281672 and 5281855), were not satisfying the selection criteria, therefore, rejected. On the other hand, the four compounds selected as the best binders for S protein were also having drug-like properties, thus considered for the ADMET analysis (Tables 5 and 6).

6- ADMET analysis

The admetSAR results revealed that all four compounds selected for the S protein and four of the five proteins for M^pro were exhibiting a good probability of intestinal absorption (Guan et al., 2019). The compound with Ligand ID: 85379763, cinnzeylanine, have shown poor intestinal absorption. The selected compounds, being able to distribute among various tissue and organs, guaranteed better treatment strategies (Chillistone & Hardman, 2017). The drug metabolism analysis suggested that these compounds are either non-substrates or non-inhibitors of CYP1A2, CYP3A4, CYP2D6, CYP2C9, CYP2C19, and CYP3A4 enzymes (Zanger, Schwab, & therapeutics, 2013). Moreover, most of the drug molecules were non-inhibitor of OCT2 and MATE1 except 5280445, which was inhibiting MATE1 and 6741 inhibiting OCT2, but none of them were inhibiting both MATE1 and OCT2 at the same, assuring the excretion of these compounds from the body (König, Zolk, Singer, Hoffmann, & Fromm, 2011; Masuda et al., 2006; Omote, Hiasa, Matsumoto, Otsuka, & Moriyama, 2006; Otsuka et al., 2005; Tanihara et al., 2007; Terada & Inui, 2008). The toxicity analysis is among the most crucial parameters for the selection of potential drug molecules (Joshi et al., 2020). All drug candidates selected for the viral M^pro and S proteins were non-carcinogenic. Two compounds, one selected for M^pro (Ligand ID: 9064) and one for S protein, (Ligand ID: 460612), were showing Ames mutagenesis. The two compounds selected for M^pro, (6741 and 44137675), have hERG inhibition. Furthermore, only two compounds selected for the M^pro (Ligand IDs: 44137675 and 5280445) and two for S protein, (Ligand IDs: 2806372 and 5280445), were considered hepatotoxic (Additional file 5). The confirmation of all these properties via the PkCSM pharmacokinetics tool, showed that none of these compounds have hERG inhibition, only one compound 44137675, was hepatotoxic, and two compounds, (3314 and 460612), were possessing Ames inhibition. Moreover, all these compounds were having more than 60% of intestinal absorption. Considering these points, three compounds for Mpro, 9064, 85379763, and 6741, and two compounds for S protein, 2806372, and 3314, were considered as the most reliable drug targets (Additional file 5). The docked models of these selected compounds, with the respective viral proteins, are shown in Fig. 6.

7- MD simulation analysis of the antiviral compounds

Considering the results of molecular docking and ADMET properties, a total of five antiviral compounds were selected for the MD simulations analysis, three for Mpro (9064, 85379763, and 6741) and two for S protein (2806372, and 3314). The RMSD values of two compounds selected as drug candidates for the viral S protein (3314 and 2806372) were greater than 0.1 nm. The high fluctuations indicate the least thermodynamic and kinetic stabilities of these compounds and therefore, these two compounds were ruled out of the analysis (Sakano, Mahamood, Yamashita, Fujitani, & physicobiology, 2016), (Wang, Kleerekoper, Revtovich, Kang, & Kirienko, 2020). Similarly, the RMSD values of three compounds selected for Mpro protein (6741 and 83579763) were also more than 0.1 nm, indicating higher structural fluctuations. Only 9064 showed the RMSD values less than 0.1 nm (Fig. 7 & Fig. 9). Therefore, it was retained for further analysis. RMSF values of free and docked Mpro protein with 9064 were also calculated (Fig. 8 (A)). The values showed small flexibility changes in Mpro protein upon binding of the ligand with it. Furthermore, the total number of hydrogen bonds formed by the ligand with Mpro were estimated. The ligand maintained the hydrogen bonds with the protein through the 50 ns simulations (Fig. 8 (B)). This phenomenon shows the effective binding of the ligand with the protein. Interestingly, 9064 showed high negative Coul-SR (-71.53 kJ/mol) and LJ-SR (-95.32 kJ/mol) interaction energies (Fig. 8 (C)). These values demonstrated the strong interaction of the ligand with protein. Finally, we employed MMGBSA to calculate binding energy (ΔG) of ligand-protein complex. Interestingly, the ΔG value was − 6.3 Kcal/mol (Fig. 10). The low negative values of ΔG shows the strong binding of ligand protein complex. It also showed that the ligand may have spontaneous binding with the Mpro in natural system. The MD simulations data is convincing that 9064 can be used as potential drug to inhibit the viral growth in living system.

The COVID-19 spreads in Pakistan from the initial of two cases, on 26th February 2020, to more than 2 million cases at the end of July 2020 (Ali, Shah, & Siddiqui, 2020; 2020). A total of three strains of SARS-CoV2, MT240479, MT500122, and MT262993, are reported from Gilgit, Karachi, and Manga, respectively. The phylogenetic analysis helped to trace the molecular footprints of these strains, which indicate the closest similarities of Pakistani strains to Japan, China, Israel, and the USA (Fig. 2). These findings suggest that the origin of SARS-CoV2 in Pakistan may be from these countries, probably due to travelers (Shafique, Ihsan, & Liu, 2020). As the recent outbreak of COVID-19 has caused immense human and economic losses, there are increasing demands for effective cure and therapeutics for this disease (C. Wu et al., 2020). Numerous research groups have targeted viral M^pro/3CL^pro and S protein for drug development and several inhibitors of these proteins have been reported during the last few months (Calligari, Bobone, Ricci, & Bocedi, 2020; Rani et al., 2020). For M^pro, Yang et al. and Jin et al. separately identified a mechanism-based inhibitor (N3) (Haitao Yang et al., 2005; J. Yang et al., 2020). In another study, Khaerunnisa et al. reported nelfinavir and lopinavir as the potential inhibitor of this protein (Khaerunnisa, Kurniawan, Awaluddin, Suhartati, & Soetjipto, 2020). Adem et al. found natural polyphenols including, hesperidin, rutin, diosmin, apiin, diacetylcurcumin, to be more effective than nelfinavir (Adem, Eyupoglu, Sarfraz, Rasul, & Ali, 2020). Ebselen was found an excellent inhibitor by Jin et al. (Jin et al., 2020). Likewise, multiple studies have reported that the epitopes from S protein, can be used as the peptide vaccines for treating the COVID-19 infections (Bhattacharya et al., 2020; Dagur, Dhakar, & Gupta; Ka et al., 2020; Kalita, Padhi, Zhang, & Tripathi, 2020).

In this study, we have used in silico strategies to evaluate the therapeutic potential of multiple natural and herbal compounds, from Pakistani herbal plants, Hamdard products, and anti-COVID-19 data from the PubChem database. Initially, a total of 579 compounds were selected from three categories, which were then screened for their binding affinities with the selected viral proteins (M^pro and S), followed by the assessment of drug-likeness and ADMET properties of these compounds. As a useful drug molecule must possess significant binding affinities for the drug targets, therefore, the first step was to find the binding affinities of all drug candidates with the viral proteins (M^pro and S) by molecular docking (Additional file 4). The seven compounds for M^pro, and four for S protein, having B.E ≤ -10 kcal/mol and RMSD of ≤ 1, were screened as the best ligands (Tables 1 and 2). The binding affinity of a drug molecule depends on the molecular interactions of these compounds with the amino acid residues of the viral proteins (Homans, 2006). In case of M^pro protein, the active amino acid residues interacting with the selected drug molecules include, Thr-25, His-41, Cys-44, Thr-45, Ser-46, Met-49, Phe-140, Leu-141, Asn-142, Gly-143, Ser-144, Cys-145, His-163, Met-165, Glu-166 and Gln-189. Likewise,Arg-403, Val-445, Gly-447, Asn-448, Tyr-449, Leu-452, Lys-458, Ser-459, Glu-471, Ile-472, Tyr-473, Gln-474, Ala-475, Ser-494, Tyr-495, Gly-496, Phe-497, Gln-498, Asn-501 and Tyr-505 are the active amino acid residues from the receptor-binding domain of the viral S protein, interacting with the selected drug candidate molecules (Figs. 3 and 4).

After assessing binding affinities, the physicochemical properties of selected drug molecules, including molecular weight, number of hydrogen bond donors, hydrogen bond acceptors, number of rotatable bonds (ROTBs), topological polar surface area (TPSA), lipophilicity and aqueous solubility, were predicted using Swiss-ADME web tool (Tables 3 and 4). A likely drug molecule should have TPSA less than 90 Å², and it should not more than 140 Å², the highest value at which oral absorption occurs. At higher TPSA values, the compounds tend to be poor at penetrating cell membranes (Palm, Stenberg, Luthman, & Artursson, 1997). Likewise, to penetrate the blood-brain barrier, the TPSA of compounds must be lower than 90 Å² (Pajouhesh & Lenz, 2005). The drug candidates selected in this study were having TPSA values in the range of 29 to 151 Å². The lipophilic character of a drug, relates to its biological activity, as it determines the possibility that it can cross the cell membrane. All drug candidates selected in this study were mildly lipophilic (Lobo, 2020). Log S values report the aqueous solubilities of drug molecules, with values of -10 (insoluble), -6 (poorly soluble), -4 (soluble), -2 (very soluble) and 0 (highly soluble) (Jorgensen & Duffy, 2002). A drug should have good aqueous solubility for oral bioavailability and absorption. Almost all drug molecules, selected in this study, were readily soluble with the log S values in the range of -3.71 to -1.56 mol/L.

For determining the drug-likeness of the compounds, most drug designing studies use Lipinski, Veber, Ghose, Egan, and Muegge's rules as the standard drug-like classifiers (Egan, Merz, & Baldwin, 2000; Ghose, Viswanadhan, & Wendoloski, 1999; C. A. Lipinski, Lombardo, Dominy, & Feeney, 1997 o. p. Lipinski & methods, 2000; Muegge, Heald, & Brittelli, 2001). Of the seven compounds selected for the M^pro two compounds, 5281855 and 5281672, violating more than one of these five rules were excluded. After this filter, a total of nine compounds, five for M^pro and four for S protein, were considered as the potential drug molecules (Tables 5 and 6). The pharmacokinetic parameters, including absorption, distribution, metabolism, excretion, and toxicity, of these molecules, were analyzed via ADMET analysis. These properties are the important determinants of the success of the drug designing strategies (Zhong, 2017). The absorption and distribution are crucial factors for the assessment of any pharmacological agent. As most of the drugs are administered orally, therefore, they should efficiently absorb into the intestine. The drug permeability is tested in-vitro on Caco-2 cell lines, which indicates that the drug is easily absorbed in the intestine. The drugs having poor intestinal or oral absorption, should be administered intramuscularly or intravenously (M. P. J. J. o. m. c. Gleeson, 2008). For distribution to the target site, the drug molecules should interact with the surface proteins of the cells. P-glycoprotein and Organic Anion Transporting Polypeptide proteins (OATPs) in the cell membrane aids in transporting many drugs, and their inhibition affects the drug distribution (Abdullahi, Davis, & Ronaldson, 2017). The drug molecules should, therefore, be the non-inhibitors of these proteins. Ideally, drugs are broken down into harmless soluble metabolites that are easily excreted through urine or bile. Most of the drugs are metabolized by the Cytochrome P450 family of enzymes. Inhibition of these enzymes may, therefore, affect the biodegradation of the drugs (Furge, Guengerich, & Education, 2006). The excretion of degraded drug molecules depends on organic cation transporter 2 (OCT2) expressed on the renal tubule cells, which plays a key role in the disposition and clearance of drugs and endogenous compounds. This activity mostly depends on the multidrug and toxin extrusion transporter 1 (MATE1), which facilitates the elimination of degraded compounds into the urine (Motohashi & Inui, 2013). The toxicity assessment of any pharmacological agent depends on carcinogenicity, Ames mutagenesis, hepatotoxicity, hERG inhibition, and acute oral toxicity (Kramer, Sagartz, & Morris, 2007). The drug candidates having poor absorption, distribution, metabolism, excretion, or having toxic effects on the body are not promising therapeutic agents (P. Gleeson, Bravi, Modi, Lowe, & chemistry, 2009). Considering all these parameters, we hypothesize that three compounds; 9064, 85379763, and 6741 may be the reliable drug candidates for the viral M^pro. Similarly, two compounds, 2806372 and 3314, might be the most suitable drugs for S protein (Additional file 5).

The pharmacokinetic analysis has shown that these five compounds are reliable drug candidates having sufficient drug-like properties. Two of these five compounds, 9064 and 85379763, do not absorb orally with the probability score of 0.7 for 9064, and 0.5 for 85379763. Since the probabilities of poor oral absorption of these compounds are quite low, therefore, this may not be considered as a drawback, moreover, the alternative routes of drug administrations, intravenous, and intramuscular may prove more helpful (Additional file 5). Furthermore, the molecular docking analysis has suggested that selected drug candidates develop multiple stable interactions with the amino acid residues of the respective viral proteins. The two compounds selected for S protein, 3314 and 2806372, have binding energies of -10.0284 and − 10.1429 kcal/mol respectively (Table 2). The compound 3314, is stabilized by the carbon-hydrogen bonds with the Asn-448 (2.2 Å), Gly-447 (2.84 Å), Phe-497 (2.19 Å) and Tyr-449 (2.27 Å), pi-pi interaction with Tyr-449 (4.11 Å) and H-bond with Tyr-495 (2.84 Å). The second compound, 2806372 forms three carbon-hydrogen bonds; two with Arg-403 (2.63 Å and 2.75 Å) and one with Tyr-495 (2.8 Å), one pi-sulfur interaction with Tyr-505 (5.76 Å), one pi-alkyl interaction with Tyr-449 (4.2 Å) and three hydrogen bonds; one with Arg-403 (2.14 Å) and two with Gly-496 (2.74 Å and 1.99 Å) (Fig. 5). Similarly, the three potential inhibitors selected for the viral M^pro protein have binding energies of -10.9852 kcal/mol for 9064, -10.6675 kcal/mol for 85379763, and − 11.4281 kcal/mol for 6741 (Table 1). The first compound 9064, forms one pi-anion interaction with Cys-145 (4.78 Å) and three hydrogen bonds with the His-41 (2.15 Å), Asn-142 (2.23 Å) and Glu-166 (2.03 Å). The second compound, 85379763, forms alkyl interactions with three amino acid residues; Leu-27 (4.43 Å), Met-49 (4.57 Å), and Met-165 (4.51 Å), two pi-alkyl interactions with His-41 (4.6 Å and 4.81 Å), one carbon-hydrogen bond with Asn-142 (2.44 Å), four hydrogen-bonds; two with Gly-143 (2.06 Å and 2.76 Å) and remaining two with the Ser-144 (2.43 Å) and Cys-145 (2.44 Å). The third compound, 6741, forms alkyl interactions with Leu-27 (5.04 Å), Cys-44 (5.04 Å) and Met-49 (5.04 Å), two pi-alkyl interactions with His-41 (4.05 Å), two carbon-hydrogen bonds with Asn-142 (2.45 Å) and Ser-144 (2.71 Å), and four hydrogen bonds with Leu-141 (2.62 Å), Gly-143 (2.68 Å), Ser-144 (2.02 Å) and Cys-145 (1.73 Å) (Fig. 6).

Although the docking and ADMET analysis has been extensively used identify the novel drug target but this approach holds some major limitations (Llanos et al., 2021). The one major limitation of the approach related to confirmational changes in protein triggered upon binding of ligands. Therefore, it is very important to couple the docking approach to molecular dynamics simulations to validate the findings. In this study, we performed 50 ns of molecular dynamics simulations to understand the dynamic binding of our selected ligands with M^pro and S protein of SARS-CoV2.

The MD trajectory analysis revealed the higher RMSD values of four (3314, 2806372, 6741, 6741, 83579763) compounds among five after 10 ns. This shows the week binding of the compounds with the proteins. Along with the RMSD values, the interaction energy analysis of these compounds revealed 9064 as the most stable drug candidate with the average Coul-SR interaction energy (-71.53 kJ/mol) and LJ-SR energy (-95.32 kJ/mol) compared to the other two compounds. The selected (Ligand ID: 9064) is a polyphenolic flavonoid (Catechin or Cianidanol), which is previously well characterized for its immunomodulatory, anti-inflammatory, and antioxidant properties (Bae, Kim, Shin, Kim, & Kim, 2020) and according to this study, this compound has a great potential to become COVID-19 main protease M^pro inhibitor. Nevertheless, for their medicinal use, further experimental validation is necessary.

Acknowledgements

Authors are thankful for the online available open access databases which were used in this research. The research was self-supported by the authors.

Funding

The study was self-supported.

Conflicts of interests

Authors declare no conflicts of interests.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Availability of data and material

All data is available in manuscript and supporting files.

Code availability

Not applicable.

Authors Contributions

All authors substantially contributed to the study. MSM and NMA designed the study. AJ collected the prime data for the study and MSM analyzed it. HB and SB wrote the first draft of the manuscript. MA, MS and AK contributed to molecular dynamics simulation. All authors analyzed the results and proofread the manuscript.

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Table-1: List of Ligands best docked (B.E. < -10 & RMSD <1) with M^pro of SARS CoV-2 using MOE docking software

Compounds from Literature Review and Hamdard products
Sr. No.	Ligand ID	B.E. (kcal/mol)		RMSD		Pose #		Ligand Name		Plants name
1	5281672	-13.336		0.8901		1		Myricetin		Viola odorata
2	5280445	-12.3791		0.8451		1		Luteolin		*Viola odorata,* Marrubium peregrinum L., Capsicum annuum
3	9064	-10.9852		0.8631		1		Catechin (Cianidanol)		Viola odorata, Malva sylvestris, Acacia nilotica, Momordica charantia
4	5281855	-10.2523		0.4775		2		Ellagic acid		Acacia nilotica, Phyllanthus urinaria L.
5	85379763	-10.6675		0.9488		1		Cinnzeylanine		*Cinnamomum cassia* (oil)**
Anti Covid-19 Compounds from PubChem database
6	6741		-11.4281		0.7771		1		-		-
7	44137675		-11.1091		0.7047		1		-		-

Bold font is indicating Hamdard products’ plants
B.E.: Binding energy; RMSD: Root mean square deviation

Table-2: List of Ligands best docked (B.E. < -10 & RMSD <1) with Spike glycoprotein of SARS CoV-2 using MOE docking software

Compounds from Literature Review and Hamdard products
Sr. No.	Ligand ID	B.E. (kcal/mol)	RMSD	Pose #	Ligand Name	Plant name
1	5280445*	-11.2194	0.8802	1	Luteolin	Viola odorata, Marrubium peregrinum L., Capsicum annuum
2	3314	-10.0284	0.8549	1	Eugenol	Cinnamomum cassia (Cinnamon oil), Syzygium aromaticus (clove oil), Althea officinalis, Ocimum sanctum (Tulsi)
Anti Covid-19 Compounds from PubChem database
3	460612	-11.0258	0.7927	1	-	-
4	2806372	-10.1429	0.9838	1	-	-

*Common ligand best docked with both Mpro and Spike glycoprotein of SARS CoV-2
Bold font is indicating Hamdard products’ plants
B.E.: Binding energy; RMSD: Root mean square deviation

Table-3: Analysis of Physicochemical properties of above ligands for M pro SARS CoV-2 using Swiss-ADME tool

Sr.No.	Compound ID	MF	MW (g/mol)	HBD	HBA	nRB	MR	TPSA(Å²)	Lipophilicity (Log Po/w)*			Water Solubility (LogS) mol/L
Sr.No.	Compound ID	MF	MW (g/mol)	HBD	HBA	nRB	MR	TPSA(Å²)	WLogP	MLogP	XLogP	Water Solubility (LogS) mol/L
1	5281672	C₁₅H₁₀O₈	318.2	6	8	1	80.1	151.6	1.69	-1.08	1.18	-3.01
2	5280445	C₁₅H₁₀O₆	286.24	4	6	1	76	111.13	2.28	-0.03	2.53	-3.71
3	9064	C₁₅H₁₄O₆	290.3	5	6	1	74.3	110.4	1.22	0.24	0.36	-2.22
4	5281855	C₁₄H₆O₈	302.19	4	8	0	75.3	141.3	1.31	0.14	1.1	-2.94
5	85379763	C₂₂H₃₄O₈	426.5	5	8	3	105	136.7	0.22	0.63	-0.26	-2.12
6	6741	C₂₂H₃₀O₅	374.5	3	5	2	102	94.83	1.8	1.52	1.95	-3.26
7	44137675	C₂₂H₂₄N₄O₄	408.5	2	5	5	118	96.17	1.81	0.6	1.96	-3.67

MF: Molecular formula; MW: Molecular weight; HBD: Hydrogen bond donor; HBA: Hydrogen bond acceptor; nBR: Number of Rotatable bonds; MR: Molar refractivity; TPSA: Topological polar surface area
*octanol/water partition coefficient

Table-4: Analysis of Physicochemical properties of above ligands for Spike glycoprotein SARS CoV-2 using Swiss-ADME tool

Sr. No.	Compound ID	MF	MW (g/mol)	HBD	HBA	nRB	MR	TPSA(Å²)	Lipophilicity (Log Po/w)*			Water Solubility (LogS) mol/L
Sr. No.	Compound ID	MF	MW (g/mol)	HBD	HBA	nRB	MR	TPSA(Å²)	WLogP	MLogP	XLogP	Water Solubility (LogS) mol/L
1	5280445^	C₁₅H₁₀O₆	286.24	4	6	1	76.01	111.13	2.28	-0.03	2.53	-3.71
2	3314	C₁₀H₁₂O₂	164.2	1	2	3	49.06	29.46	2.13	2.01	2.27	-2.46
3	460612	C₁₃H₁₈C_l2N₂O₂	305.2	2	3	8	78.91	66.56	1.93	-0.03	0.2	-1.56
4	2806372	C₉H₁₁NO₄S	229.25	1	4	4	55.99	80.85	1.73	0.6	1.25	-2.08

F: Molecular formula; MW: Molecular weight; HBD: Hydrogen bond donor; HBA: Hydrogen bond acceptor; nBR: Number of Rotatable bonds; MR: Molar refractivity; TPSA: Topological polar surface area
*octanol/water partition coefficient
^ Common ligand best docked with both M pro and Spike glycoprotein of SARS CoV-2

Table-5: Analysis of drug-likeness of above ligands for M pro SARS Cov-2 using Swiss-ADME tool

Sr. No.	Compound ID	Number of Violations					Druglikeness					Bioavailability score
Sr. No.	Compound ID	Lipinski rule ^a	Veber rule ^b	Ghose rule ^c	Egan rule ^d	Muegge rule ^e	Lipinski rule	Veber rule	Ghose rule	Egan rule	Muegge rule	(F>10%)
1	5281672	1	1	0	1	2	yes	no	yes	no	no	0.55
2	5280445	0	0	0	0	0	yes	yes	yes	yes	yes	0.55
3	9064	0	0	0	0	0	yes	yes	yes	yes	yes	0.55
4	5281855	0	1	0	1	0	yes	no	yes	no	yes	0.55
5	85379763	0	0	0	1	0	yes	yes	yes	no	yes	0.55
6	6741	0	0	0	0	0	yes	yes	yes	yes	yes	0.55
7	44137675	0	0	0	0	0	yes	yes	yes	yes	yes	0.56

^aMW≤ 500, MLOGP ≤ 4.15, HBD (N or O) ≤ 10, HBA (NH or OH) ≤ 5^b nRB ≤ 10, TPSA ≤ 140^c160 ≤ MW ≤ 480, -0.4 ≤ WLOGP ≤ 5.6, 40 ≤ MR ≤ 130, 20 ≤ atoms ≤ 70^dWLOGP ≤ 5.88, TPSA ≤ 131.6^e200 ≤ MW ≤ 600, -2 ≤ XLOGP ≤ 5, TPSA ≤ 150, Num. rings ≤ 7, Num. carbon > 4, Num. heteroatoms >1, nRB ≤ 15, HBA ≤ 10, HBD ≤ 5

Table-6: Analysis of drug-likeness of above ligands for Spike glycoprotein SARS Cov-2 using Swiss-ADME tool

Sr. No.	Compound ID	Number of Violations					Druglikeness					Bioavailability score
Sr. No.	Compound ID	Lipinski rule ^a	Veber rule ^b	Ghose rule ^c	Egan rule ^d	Muegge rule ^e	Lipinski rule	Veber rule	Ghose rule	Egan rule	Muegge rule	(F>10%)
1	5280445*	0	0	0	0	0	yes	yes	yes	yes	yes	0.55
2	3314	0	0	0	0	1	yes	yes	yes	yes	no	0.55
3	460612	0	0	0	0	0	yes	yes	yes	yes	yes	0.55
4	2806372	0	0	0	0	0	yes	yes	yes	yes	yes	0.55

*Common ligand best docked with both M pro and Spike glycoprotein of SARS CoV-2
^aMW ≤ 500, MLOGP ≤ 4.15, HBD (N or O) ≤ 10, HBA (NH or OH) ≤ 5^b nRB ≤ 10, TPSA ≤ 140^c160 ≤ MW ≤ 480, -0.4 ≤ WLOGP ≤ 5.6, 40 ≤ MR ≤ 130, 20 ≤ atoms ≤ 70^dWLOGP ≤ 5.88, TPSA ≤ 131.6^e200 ≤ MW ≤ 600, -2 ≤ XLOGP ≤ 5, TPSA ≤ 150, Num. rings ≤ 7, Num. carbon > 4, Num. heteroatoms > 1, nRB ≤ 15, HBA ≤ 10, HBD ≤ 5

No competing interests reported.

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Phylogeny analysis of Pakistani SARS-CoV2 strains and screening of a list of natural compounds as the antiviral targets for viral M pro protein

Status:

Version 1

Abstract

Figures

Introduction

Methodology

1- Selection of potential SARS-CoV-2 proteins as antiviral targets

2- Phylogenetic analysis

3- Predictions of 3D structures of targeted viral proteins

4- Screening of phytochemicals and natural antiviral compounds

5- Molecular docking analysis of the antiviral compounds

6- Assessments of Drug-likeness and ADMET properties

7- MD simulation analysis of the antiviral compounds

Results

1- Phylogenetic analysis and undermining the molecular relationships among different strains of SARS-CoV-2

2- Modelling 3D structures of targeted viral proteins and model validations

3- Selection of phytochemicals and natural antiviral compounds

4- Docking of antiviral compounds with M^pro and S proteins

5- Assessment of physicochemical properties and drug-likeness

6- ADMET analysis

7- MD simulation analysis of the antiviral compounds

Discussion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1

Phylogeny analysis of Pakistani SARS-CoV2 strains and screening of a list of natural compounds as the antiviral targets for viral M pro protein

Status:

Version 1

Abstract

Figures

Introduction

Methodology

1- Selection of potential SARS-CoV-2 proteins as antiviral targets

2- Phylogenetic analysis

3- Predictions of 3D structures of targeted viral proteins

4- Screening of phytochemicals and natural antiviral compounds

5- Molecular docking analysis of the antiviral compounds

6- Assessments of Drug-likeness and ADMET properties

7- MD simulation analysis of the antiviral compounds

Results

1- Phylogenetic analysis and undermining the molecular relationships among different strains of SARS-CoV-2

2- Modelling 3D structures of targeted viral proteins and model validations

3- Selection of phytochemicals and natural antiviral compounds

4- Docking of antiviral compounds with Mpro and S proteins

5- Assessment of physicochemical properties and drug-likeness

6- ADMET analysis

7- MD simulation analysis of the antiviral compounds

Discussion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1

4- Docking of antiviral compounds with M^pro and S proteins