Skip to main content
Download PDF

Molecular docking analysis reveals the functional inhibitory effect of Genistein and Quercetin on TMPRSS2: SARS-COV-2 cell entry facilitator spike protein

BMC Bioinformatics volume 23, Article number: 180 (2022) Cite this article

Abstract

Background

The Transmembrane Serine Protease 2 (TMPRSS2) of human cell plays a significant role in proteolytic cleavage of SARS-Cov-2 coronavirus spike protein and subsequent priming to the receptor ACE2. Approaching TMPRSS2 as a therapeutic target for the inhibition of SARS-Cov-2 infection is highly promising. Hence, in the present study, we docked the binding efficacy of ten naturally available phyto compounds with known anti-viral potential with TMPRSS2. The aim is to identify the best phyto compound with a high functional affinity towards the active site of the TMPRSS2 with the aid of two different docking software. Molecular Dynamic Simulations were performed to analyse the conformational space of the binding pocket of the target protein with selected molecules.

Results

Docking analysis using PyRx version 0.8 along with AutoDockVina reveals that among the screened phyto compounds, Genistein shows the maximum binding affinity towards the hydrophobic substrate-binding site of TMPRSS2 with three hydrogen bonds interaction ( − 7.5 kcal/mol). On the other hand, molecular docking analysis using Schrodinger identified Quercetin as the most potent phyto compound with a maximum binding affinity towards the hydrophilic catalytic site of TMPRSS2 ( − 7.847 kcal/mol) with three hydrogen bonds interaction. The molecular dynamics simulation reveals that the Quercetin-TMPRSS complex is stable until 50 ns and forms stable interaction with the protein ( − 22.37 kcal/mol of MM-PBSA binding free energy). Genistein creates a weak interaction with the loop residues and hence has an unstable binding and exits from the binding pocket.

Conclusion

The compounds, Quercetin and Genistein, can inhibit the TMPRSS2 guided priming of the spike protein. The compounds could reduce the interaction of the host cell with the type I transmembrane glycoprotein to prevent the entry of the virus. The critical finding is that compared to Genistein, Quercetin exhibits higher binding affinity with the catalytic unit of TMPRSS2 and forms a stable complex with the target. Thus, enhancing our innate immunity by consuming foods rich in Quercetin and Genistein or developing a novel drug in the combination of Quercetin and Genistein could be the brilliant choices to prevent SARS-Cov-2 infection when we consider the present chaos associated with vaccines and anti-viral medicines.

Peer Review reports

Introduction

The dreadful global pandemic of this century caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV2) has affected over 170 million people across the world. It has taken the lives of nearly 38 million people in the past 19 months [1]. Though several vaccines are being used, notable disproportions in the vaccine manufacturing rate and world population size of approximately 7.8 billion significantly limit the speed of global vaccine administration. Moreover, developing an effective vaccine with one hundred percent protection against SARS-CoV-2 infection is tricky due to the possible mutation in the spike glycoprotein of coronavirus. Moreover, all the possible mutations supported the virus to emerge as the most virulent strains in the fore coming waves [2]. Mounting studies show that people with excess viral load and other co-morbidities, especially with diabetes and cardiovascular disease (CVD), are at high risk for COVID-19 associated mortalities [3, 4, and 5]. Thus, in the current situation identifying some methods that can either reduce or prevent the colonization and adhesion of the viruses in the lungs of humans or having the ability to increase the human immune response against viral infections would be more acceptable for the prevention of coronavirus infections.

The mechanism underlying COVID-19 viral entry into a host cell is now well understood. The SARS-CoV-2, the enveloped virus with a positive-sense RNA genome, is majorly composed of four structural glycoproteins, namely spike (S), membrane (M), envelope (E), and Nucleoplasmid (Nsp) [6]. Recently it has been shown that the host cell entry of SARS-CoV-2 is reliant on two host proteins, such as Transmembrane Serine Protease 2 (TMPRSS2) and Angiotensin-Converting Enzyme 2 (ACE2) located on the surface of epithelial cells in the target organ [4, 5, and 6]. Meanwhile, all other proteins, including M, E, and Nsp are involved in the viral particle assembly release into the host cell [7, 8]. The TMPRSS2 primes the viral spike (S) protein by cleaving it at two sites and facilitates the fusion of the viral and host membrane for the smoothening of the cellular entry of viral genome with the aid of a terminal carboxypeptidase and type I transmembrane glycoprotein, ACE2 [1]. Thus, focusing on the inhibition of either ACE2 or TMPRSS2's biological functions has become more attractive targets to prevent the viral genome's entry into the host cells. Among the two proteins, inhibition of TMPRSS2 is more convincing and has recently been shown to inhibit SARS–2-S-driven access in lung cells [9]. Therefore, developing therapeutic agents targeting the inhibition of TMPRSS2 function will have a promising impact against the current and emerging coronavirus outbreaks.

Though many natural molecules are identified against the different target proteins of SARS-COV-2, we selected ten most crucial phyto compounds that exist abundantly in the raw diet based on their profound anti-viral properties (Table 1) [6, 10]. To find the best anti-viral compound against SARS-COV-2 infection, we analyzed the binding efficacy of the selected compounds with the core function site of the human TMPRSS2 using 2 different molecular docking software. The three-dimensional model of TMPRSS2 shows three domains such as an N-terminal Low-Density Lipoprotein (LDL)-receptor class A domain (113–148), a Scavenger Receptor Cysteine-Rich (SRCR) domain (153–246), and a C-terminal peptidase S1 Catalytic domain ranging from 256 to 487 amino acids. It has three catalytic residues, such as HIS296, ASP345, and SER441, in the C terminal serine domain. The six amino acid residues that are more important in the active site make-up of TMPRSS2 are the HIS296, ASP345, and SER441, and are located at the catalytic site (catalytic triad). The remaining three are the ASP435, SER460, and GLY462 and are located at the substrate-binding site (Additional file 1: Fig. S1) [11]. The model was considered to investigate the interactions between the TMPRSS2 and the selected anti-viral phyto compounds.

Table 1 List of phyto compounds
Full size table

Materials and methods

Homology modelling

The 3D structure of TMPRSS2 was obtained from Brookhaven protein databank (PDB ID: 7 MEQ) [12]. The TMPRSS2 FASTA sequence (accession number: O15393) was extracted from the UniProt database and modelled with Swiss model server using the crystal structure (PDB ID: 7MEQ) as a template because of the presence of some missing loop regions in the resolved crystal structure [13].

Structure validation

The quality of the TMPRSS2 3D modelled structure was assessed using the PROCHECK Structure Verification Method. The PROCHECK analysis provides a Ramachandran plot of each residue's bond angles (Phi and Psi angles) and confirms the predicted secondary structure's reliability and 3D conformations. Also, a G-Factor score is generated to quantify the error/deviation probability of the predicted structure. The quality of the developed homology model of TMPRSS2 was assessed using the Ramachandran plot [14].

Molecular docking using PyRx

Protein preparation

The modelled protein structure was prepared for docking using AutoDockTools. First, the Gasteiger charges of the side chain amino acids were computed, followed by the addition of polar hydrogens and the merging of the nonpolar hydrogens.

Ligand preparation

The chemical structure of the elected phyto compounds was obtained from the PubChem compound database. The chemicals were imported into OpenBabel for 3D format conversion and were saved in mol format for Argus lab. The spatial optimization was carried out with the help of the Argus Lab 4.0.1 software. The Molecular Mechanics (MM) method UFF has been used in ArgusLab with the "Clean Geometry" option for preliminary refining geometries. Hydrogens have been added through the "Add Hydrogens" feature in the edit column of ArgusLab 4.0.1.

Molecular docking

The molecular docking has been performed with AutodockVina using PyRx V.0.8 GUI [15, 16]. The 3D modelled protein was imported into PyRx software, which generated a PDBQT file of the protein structure with all polar hydrogens included. All ligand bonds were considered rotatable. The Lamarckian Genetic Algorithm (LGA) was used to perform all calculations by considering rigid-protein and flexible-ligand docking. The binding site on the receptor was described by creating a grid box with the dimensions of Centre X: − 5.99220259237; Centre Y: − 7.10950986462; Centre: 16.4125813637 Å, with a grid spacing of 0.375 Å, and size of X: 19.0610 Å; Y: 21.9221 Å; Z: 29.8639 Å. Following the completion of the docking study, the ligand docked pose with the least binding energy was chosen. In each case, the eight runs with AutoDockVina were executed, and each runner's best pose was saved. The final affinity value was determined by taking the average affinity for the best stances. The binding pattern of docked complexes, hydrogen bond details, and bond length were studied using the discovery studio visualizer.

Molecular docking using Schrodinger

Ligand preparation

The LigPrep (Schrodinger, LLC, NY, USA, 2009) was used to create the conformation structure of the ligands by removing salt, adding hydrogen molecules, and ionizing at pH (7.0 ± 2.0) [17]. Energy minimization has been performed with the support of the OPLS3e force field by using the regular energy capacity of atomic mechanics and RMSD slice of 0.01 Ǻ to create the minimal energy compound isomer.

Preparation of protein

The modelled three-dimensional structure of TMPRSS2 was prepared using the Protein preparation wizard panel in the Schrödinger platform. The protein preparation methods involve many stages such as adding of protons, resolving bond orders, optimisation of protonation states and hydrogen-bond networks and conducting protein structure minimization. The hydrogens were added to ensure the structural requirements, and the side chains were optimized either close to the binding cavity or too near the active site or the salt bridges. The hydrogen atoms were added to the structure, most likely in hydroxyl and thiol hydrogen atoms, protonation states, and tautomers of His residue and Chi 'flip' assignment for ASN, GLN, and HIS residues. The optimized structure was minimized with the OPLS-AA force field until the average root mean square deviation of the non-hydrogen atoms reached 0.3 Å [18].

Grid-based molecular docking

The docking study was carried out with the Grid-Based Ligand docking method to analyse the interaction of selected phyto compounds with TMPRSS2. The receptor grid is generated to ensure the involvement of other amino acids in the phyto compound's interactions with TMPRSS2. For receptor, a grid box of 30—×—30—×—30 Å3 with a default inner box (10—×—10— ×—10 Å3) was centred on the corresponding ligand (placed on the appropriate ligand. After the grid generation, all of the prepared conformations of the selected compounds have been docked against the binding site utilising 'extra precision' glide docking (Glide XP), which docks compounds freely [19]. The compounds were chosen for further evaluation based on the different docking parameters such as docking score, glide energy, and physical parameters like hydrogen bonding interactions. The Discovery studio visualizer was used as the Visualization tool for docked ligands.

Molecular dynamic simulation

The molecular dynamics simulations of Quercetin and Genistein complexes were performed for 50 ns (ns) using NAMDV.2.14 (with NVIDIA CUDA acceleration) [20,21,22]. The input files for the simulations were prepared using the CHARMM-GUI web server with the CHARMM force field [23, 24]. The ligand topology and parameter files were prepared using the CgenFF program [25]. Cubic periodic boxes with a minimum distance of 2 nm box edges were assigned. The boxes were filled with water (TIP3P model), and the system was neutralized and saturated with 0.15 M NaCl. Then, the energy minimization and equilibration were performed (NVT and NPT for 250 ps each). The equilibrated complexes were subjected to the production run for 50 ns with CHARMM parameters and topologies generated in CHARMM-GUI. Further, the trajectory analysis (RMSD, RMSF, number of hydrogen bonds, and SASA) was performed using VMD [26]. The ligand ensemble cluster analysis and ligand interaction analysis were performed with UCSF Chimera and DS Visualizer [27]. Post-convergence, MM-PBSA binding free energy analysis was performed with 100 snapshots spanning a region of 5 ns using the CaFE binding energy VMD plugin [28].

Results

Homology modeling and validation

The quality of the modeled TMPRSS-2 was analyzed using the PROCHECK program for further structural validation. The graphical representation of the target protein's predicted tertiary structure and Ramachandran plot analysis are shown in Additional file 2: Fig. S2. The Ramachandran plot indicates that 86.9% of the residues of the modeled protein were located within the Most Favored Region, 12.1% in Additionally Allowed Region, and 0.7% of the residues in the disallowed region. Thus the data suggested that the predicted model is highly reliable for further analysis.

Molecular docking analysis using PyRx

The molecular docking studies were carried out using PyRx to understand the binding affinity of the selected phyto compounds with the modeled TMPRSS2 in terms of measuring the binding energy. The molecular docking studies were carried out in the catalytic site, which comprises the catalytic triad HIS296, ASP345, and SER441, and with the substrate binding site consisting of key substrate interacting residues (ASP435, SER460, and GLY462). Though the catalytic site and the substrate-binding site are positioned adjacent to each other, the hydrophobicity of the pocket was found to be different. The catalytic site was found to be more hydrophilic, consisting of residues such as HIS-274, CYS-281, CYS-297, LEU-302, LYS-342, LYS-392, and CYS-465. In contrast, the substrate-binding site was found to be relatively hydrophobic with amino acids like TRP-461, GLY-472, VAL-473, and TYR-474. The results show that most of the selected phyto compounds exhibit high binding interaction with the catalytic and substrate binding sites of TMPRSS2 protein (Fig. 1). The compounds were ranked based on the binding energy and are listed in Additional file 3: Table S1. The compound Genistein exhibits high binding affinity with TMPRSS2 with a − 6.7 kcal/mol score among the analyzed compounds. The compound could form hydrogen bond interactions with CYS-437, GLY-464 A, and CYS-465 residues located in the substrate-binding site, close to the catalytic site, and thus demonstrated its significant affinity with the catalytic domain of TMPRSS2 (Fig. 2). The rest of the compounds form hydrogen bonds, pi-pi interactions, and other electrostatic interactions with the catalytic sites (HIS296, ASP345, and SER441) and substrate binding sites (ASP435, SER460, and GLY462). All compounds' interaction details and binding energy were shown in the Additional file 3: Table S1 and demonstrated in Additional file 4: Fig. S3.

Fig. 1
figure 1

Comparative analysis of binding energy of all compounds obtained from PyRx

Full size image
Fig. 2
figure 2

Molecular interaction of TMPRSS2—Genistein (Green colour dotted line—hydrogen bond)

Full size image

Molecular docking using Schrodinger

The glide XP docking was performed to confirm the molecular basis of interaction and conformation of the selected phyto compounds to the catalytic and substrate binding sites of TMPRSS2. The details of the molecular docking of all the compounds were enlisted in Supplementary Table 2. Results of XP docking studies indicated that the phyto compounds could exert several potential hydrogen bonds (HB) and non-bonding interactions with the core functional residues of the target protein TMPRSS2. Binding energy analysis of all the ten compounds obtained from the Schrödinger was shown in Fig. 3. The data indicate that compared to other compounds, Quercetin shows the most favorable binding affinity with TMPRSS2 protein and could form strong hydrogen bond interaction with the core functional amino acids such as LYS390, GLN438, SER436, and CYS-465 with a docking score of -7.847 kcal/mol. The compound also exhibits the Pi–Pi interaction with CYS437 and TRP461 amino acids near the catalytic site (Fig. 4). Thus the molecular docking analysis using glide XP indicates that among the analyzed phyto compounds, the Quercetin could fit into the pocket of the serine protease catalytic domain of TMPRSS2 and could competitively inhibit the binding of TMPRSS2 with the viral genome effectively. The detailed interacting residue and all compound’s corresponding binding energy values were listed in Additioanl file 5: Table S2 and Additional file 6: Fig. S4.

Fig. 3
figure 3

Comparative analysis of binding energy of all compounds obtained from Schrodinger

Full size image
Fig. 4
figure 4

Molecular interaction of TMPRSS2- Quercetin (Green colour dotted line—hydrogen bond; Pink colour—Pi–Pi interaction)

Full size image

Molecular dynamics simulation

To assess the binding stability and interaction of Quercetin and Genistein against TMPRSS-2 receptor, the molecular dynamic simulation was performed for 50 ns with NAMDv.2.14 using the CHARMM force field parameters. The protein backbone RMSD of TMPRSS2 alone (Protein alone) and TMPRSS2-Quercetin complex (Quercetin-complex) and TMPRSS2-Genstein complex (Genistein-complex) reached convergence at 30 ns and remained stable till 50 ns (Fig. 5A). Post-convergence, the ligand backbone RMSD of Quercetin was found to show a slight increase at 36 ns by 0.61 Å and continue to remain stable till 50 ns (Fig. 5C). Conformational cluster analysis of Quercetin revealed that the ligand adopted two major conformation ensembles, ensemble-1 (22.6%) and ensemble-2 (54.3%) (Fig. 5D). The structural alignment and ligand interaction analysis showed significant difference between the ensemble-1 and 2 arose due to the orientation of the 3, 4-Dihydroxyphenyl group (side group). In contrast, the orientation and interaction of the core, benzopyran-one ring, was found to remain the same in both the ensembles. In ensemble-1, the side group was protruding outside by interacting with the solvent, whereas in ensemble-2, the side group had re-oriented itself by a rotation of 84.2°around the axis to fit inside the pocket (Fig. 5D). This increased the total number of H-bonds interactions in ensemble-2 concerning ensemble-1 (Fig. 5B).

Fig. 5
figure 5

Molecular Dynamic simulation results—A Protein backbone RMSD plot of TMPRSS-2 (saffron), Quercetin complex (blue) and Genistein complex (green); B The total number of hydrogen bonds of Quercetin complex; C Ligand RMSD plot of Quercetin complex; D Quercetin complex showing the two different ligand conformation ensemble, ensemble-1 (red, t = 23–32 ns), ensemble-2 (green, t = 33–50 ns)

Full size image

The binding pocket's solvent-accessible surface area (SASA) plot shows that the SASA decreases over time as the ligand fits more stably inside the binding pocket (Fig. 6A). The ligand interaction analysis plot confirms that at t = 0 ns, the side group of Quercetin is solvent-exposed, and at t = 50 ns the compound has undergone conformational re-arrangement (ensemble 2) to fit inside the binding pocket (Fig. 6. B and 6. C). The critical interacting amino acids were LYS340, THR421, LEU419, ILE420, and TRP461 with b-occupancy of 21.3%, 4.2%, 12.7%, 6.2%, and 7.8%, respectively. TRP461 and ILE420 were found to be involved in the hydrophobic and pi-pi stacking interaction. The MM-PBSA binding free energy calculated with the CaFE VMD plugin was found to be with -22.37 kcal/mol, with major energy contribution from Vander Waal (ΔE{VdW}) and electrostatic energies (ΔE{Elec}) (Fig. 6.D). Moreover, Quercetin bound closer towards the hydrophilic catalytic site of TMPRSS2.

Fig. 6
figure 6

A Solvent Accessible Surface Area (SASA) analysis plot of the active site of Quercetin complex; B Quercetin complex—showing the decrease in SASA upon re-orientation of a ligand inside the binding pocket; C Ligand interaction analysis of Quercetin complex (i) at t = 0 ns and (ii) t = 50 ns; D MM-PBSA energy analysis of Quercetin complex

Full size image

In the case of the Genistein complex, it was observed that the ligand had exited the binding pocket after 26 ns which reflected in an abnormal deviation in the ligand backbone RMSD of Genistein. This may be due to the weak interaction formed by the Genistein with the hydrophobic substrate-binding site of TMPRSS2. To further understand this phenomenon, the iMODS webserver has been used to understand the deformable and highly mobile residues/regions of the Genistein complex. The main-chain deformability is the measure of the capability of a given molecule to deform at each of its residues under dynamic conditions. The iMODS analysis of the Genistein complex revealed that the vital interacting residues of Genistein (CYS465, GLY-465, GLY-462, and CYS-437) have high deformability (Additional file 7: Fig. S5). This deformability may be due to the presence of these residues in the loop region. In addition, the major portion of the benzopyran-4-one core of Genistein was exposed to the solvent and is not involved in the interaction with the protein. The weak interaction of Genistein with the loop residues might be one reason for its unstable binding against TMPRSS2. Moreover, Genistein was bound closer to the Site S2- substrate binding site, which is relatively more hydrophobic in nature. Whereas, Quercetin was bound closer to Site S1, which is hydrophilic (Additional file 8: Fig. S6). Thus, the molecular dynamics study reveals that the compound Quercetin can be a lead molecule with more stable ligand-binding towards TMPRSS2 than Genistein and hence can be taken further to design a novel drug against the current corona virus infection.

Discussion

The immediate hit of the second corona wave in developing countries such as India has increased the rate of COVID-19 associated mortalities and co-morbidities and more than 2,10,000 deaths within three months [29, 30]. Despite the rapid and massive spread of the SARS-COV2 virus across the world, no specific anti-viral drugs or completely effective vaccines have been developed to date to overcome this global pandemic situation. Moreover, the world is threatened by many waves of SARS-CoV-2 hit with much more genetic variations in the viral RNA structure. In this scenario, developing or boosting the body's self-defence system against viral attack is the right choice to prevent SARS-CoV-2 viral infections [31]. Here we performed a computational approach to identify potential anti-viral phyto molecules that could inhibit the SARS-CoV-2 entry to the host cells. A handful of recent studies have suggested many potential anti-SARS-CoV-2 molecules based on computational results [32, 33, and 34].

It was noted that the SARS-CoV-2 virus uses the ACE2 receptor for cell entry in synergy with the host's TMPRSS2 [35]. Access to the cytosol portion of the host genome is accomplished by acid-dependent proteolytic cleavage of TMPRSS2. It could efficiently activate the S protein to induce fusion of the virus with the host cell membrane for viral activation [10]. Thus, TMPRSS2 represents an essential host factor for SARS-CoV-2 pathogenicity [35]. Hence, developing therapeutic agents targeting TMPRSS2 could be a good measure against the current emerging SARS-CoV-2 outbreak.

The TMPRSS2 possesses a catalytic domain at its C-terminus, typical of chymotrypsin family serine proteases [34]. The TMPRSS2 C-terminal peptidase S1 domain is anticipated to interact with the SARS-CoV-2 spike protein. The amino acids located at the triad of the catalytically active site (HIS296, ASP345, and SER441), and substrate binding sites (ASP435, SER460, and GLY462) are more critical for SARS-CoV-2 spike protein entry into the host cells [33]. Thus, the compounds that could bind with the mentioned core functional unit of TMPRSS2 can potentially restrict the viral access to the host cell. The molecular docking analysis supported to identify the best anti-viral phyto compounds with potential binding affinity with the substrate-binding site of TMPRSS2 protein among the chosen. Based on the scoring parameters and interaction, both Genistein (PyRx) and Quercetin (Schrödinger) could potentially interact with the substrate binding and catalytic site amino acid residues of the TMPRSS2. In addition, the compounds also exhibited interaction with several neighboring residues of the catalytic site and highlighted the importance of neighboring residues in the establishment of the molecular complex between TMPRSS2-Genistein and TMPRSS2-Quercetin complexes [11]. Apart from this, the identification of charged residues at the intermolecular interactions and calculation of binding affinity values supported the complex's structural conformation reliability.

Quercetin (C15H10O7) is one of the most abundant dietary flavonoids found in many fruits, vegetables, leaves, grains, seeds, and red onions and is noted with enriched anti-oxidant properties. Mounting studies have proven the therapeutic effects of Quercetin against various dreadful diseases, including diabetes, cancer, cardiovascular diseases, etc. [35, 36]. Most importantly, many in vitro and in vivo studies have supported potent anti-viral properties of Quercetin [11, 37, 38]. The phytoestrogen Genistein (C15H10O5) is a natural isoflavaone found abundantly in several plants, including lupin, fava beans, soybeans, kudzu, Psoralea, etc. Studies have shown that Genistein possesses enormous anti-viral, anti-oxidant, and anthelmintic properties [39, 40]. Like Quercetin, Genistein is also proven to be an ideal drug to treat various dreadful viral infections, including coronaviridae, HIV, Epsein-Barr virus, herpes simplex virus etc. [38, 40]. Our in silico analysis clearly shows that both Quercetin and Genistein could bind to the catalytic and substrate binding sites of TMPRSS2. Quercetin has a higher binding affinity with a -7.847 kcal/mol binding energy towards the catalytic site, which is more hydrophilic than the substrate-binding site. On the contrary, Genistein showed higher binding energy with the substrate-binding site. This may be because the compound Quercetin (XLogP = 1.5) is more hydrophilic than Genistein (XLogP = 2.7). Overall, our research data show that both the phyto compounds inhibit the principal function of the TMPRSS2 protein and thus prevent the fusion of the SARS-CoV-2 viral genome into the host cell.

We also performed a molecular dynamics simulation to understand the binding affinity of Genistein and Quercetin with TMPRSS-2. The molecular dynamics simulation reveals that the Quercetin-TMPRSS complex is stable until 50 ns and the ligand forms a stable interaction with the protein, whereas the Genistein-TMPRSS2 complex was found to suffer unstable binding. It was observed that the Quercetin binds to the S1-hydrophilic site with average MMPBSA binding free energy − 22.37 kcal/mol (at 50 ns) with significant energy contribution from Vander Waal and electrostatic energies. The binding of Quercetin with TMPRSS-2 was also found to decrease the solvent-accessible surface area, indicating a better fit of molecule in the binding pocket. On the other hand, Genistein was found to bind at the S2-hydrophobic site, forming weak interactions with the loop residues and suffering unstable binding, thereby exiting from the binding pocket. This suggests that the catalytic-S1 site of TMPRSS-2, including the residues LYS340, THR421, LEU419, ILE420, and TRP461, can be a potential site for targeting and designing inhibitors. On the contrary, the ligand interaction with loop region residues in the S2-site (substrate binding site), CYS465, GLY465, GLY462, and CYS437, should be avoided to have stable binding. Therefore, Quercetin which has stable and higher binding affinity with S1-site, compared to Genistein, can be considered as a potential molecule for lead optimization and drug development. The ability of Quercetin to exhibit a strong binding affinity with the core functional unit of TMPRSS2 indicates that the compound can effectively inhibit the binding of the virus with the host TMPRSS2 receptor and thereby prevent the viral genome entry into the host cells.

Conclusion

In summary, viruses pose a danger among the many infectious threats that people face nowadays. Considering all the possibilities of today's pandemic scenario, depending on naturally occurring substances continue to be one of the primary sources of preventing the infectious rate of SARS-CoV-2. Centuries ago, it was proved that a healthy food intake positively influences a person's health, and instead of pills, functional foods can be consumed as part of a regular diet. Improving our natural immunity against SARS-CoV-2 cell entry through consuming healthy foods rich in phyto compounds, especially with anti-viral activities, is the safest and easier way to fight against all kinds of coronavirus infection, including the present global pandemic SARS- CoV-2 spread. In the present study, the molecular docking analysis prioritized Quercetin and Genistein as the best phyto compounds against SARS-Cov-2 infection based on the interaction energy with the binding site of TMPRSS2. But when compared to the Genistein ability, the compound Quercetin has the strongest binding affinity with the core functional unit of TMPRSS2. Hence, the analysis concludes that the phyto compound Quercetin can be used as an effective lead molecule to control the novel coronavirus-2 entry into the human cells. Further studies have to be carried out to prove the molecule's efficacy before setting a therapeutic application target on the same axis.

Availability of data and materials

All data generated or analyzed during this study are included in this published article. The data will be provided on request, rejimanjunath@gmail.com.

References

  1. World Health Organization. Corona Vituse Dashboard. Available from: https://covid19.who.int/. July 2021.

  2. Wang W, Tang J, Wei F. Updated understanding of the outbreak of 2019 novel coronavirus (2019-nCoV) in Wuhan, China. J Med Virol. 2020;92(4):441–7. https://doi.org/10.1002/jmv.25689.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Nishiga M, Wang DW, Han Y, Lewis DB, Wu JC. COVID-19 and cardiovascular disease: from basic mechanisms to clinical perspectives. Nat Rev Cardiol. 2020;17(9):543–58. https://0-doi-org.brum.beds.ac.uk/10.1038/s41569-020-0413-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Kruglikov IL, Shah M, Scherer PE. Obesity and diabetes as comorbidities for COVID-19: underlying mechanisms and the role of viral-bacterial interactions. Elife. 2020;15(9): e61330. https://0-doi-org.brum.beds.ac.uk/10.7554/eLife.61330.

    Article  Google Scholar 

  5. Callender LA, Curran M, Bates SM, Mairesse M, Weigandt J, Betts CJ. The impact of pre-existing comorbidities and therapeutic interventions on COVID-19. Front Immunol. 2020;11(11):1991. https://0-doi-org.brum.beds.ac.uk/10.3389/fimmu.2020.01991.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Bhardwaj VK, Singh R, Sharma J, Rajendran V, Purohit R, Kumar S. Identification of bioactive molecules from tea plant as SARS-CoV-2 main protease inhibitors. J Biomol Struct Dyn. 2021;39(10):3449–58. https://0-doi-org.brum.beds.ac.uk/10.1080/07391102.2020.1766572.

    Article  CAS  PubMed  Google Scholar 

  7. Donoghue M, Hsieh F, Baronas E, Godbout K, Gosselin M, Stagliano N, et al. A novel angiotensin-converting enzyme-related carboxypeptidase (ACE2) converts angiotensin I to angiotensin 1–9. Circ Res. 2000;87(5):E1-9. https://0-doi-org.brum.beds.ac.uk/10.1161/01.res.87.5.e1 (PMID: 10969042).

    Article  CAS  PubMed  Google Scholar 

  8. Tipnis SR, Hooper NM, Hyde R, Karran E, Christie G, Turner AJ. A human homolog of angiotensin-converting enzyme. Cloning and functional expression as a captopril-insensitive carboxypeptidase. J Biol Chem. 2000;275(43):33238–43. https://0-doi-org.brum.beds.ac.uk/10.1074/jbc.M002615200.

    Article  CAS  PubMed  Google Scholar 

  9. Iwata-Yoshikawa N, Okamura T, Shimizu Y, Hasegawa H, Takeda M, Nagata N. TMPRSS2 contributes to virus spread and immunopathology in the airways of murine models after coronavirus infection. J Virol. 2019;93(6):e01815-e1818. https://0-doi-org.brum.beds.ac.uk/10.1128/JVI.01815-18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Sharma J, Kumar Bhardwaj V, Singh R, Rajendran V, Purohit R, Kumar S. An in-silico evaluation of different bioactive molecules of tea for their inhibition potency against non-structural protein-15 of SARS-CoV-2. Food Chem. 2021;1(346): 128933. https://0-doi-org.brum.beds.ac.uk/10.1016/j.foodchem.2020.

    Article  Google Scholar 

  11. Hussain M, Jabeen N, Amanullah A, Baig AA, Aziz B, Shabbir S, Raza F, Uddin N. Molecular docking between human TMPRSS2 and SARS-CoV-2 spike protein: conformation and intermolecular interactions. AIMS Microbiol. 2020;6(3):350–60. https://0-doi-org.brum.beds.ac.uk/10.3934/microbiol.2020021.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kishk SM, Kishk RM, Yassen ASA, Nafie MS, Nemr NA, ElMasry G, Al-Rejaie S, Simons C. Molecular insights into human transmembrane protease serine-2 (TMPS2) inhibitors against SARS-CoV2: homology modelling, molecular dynamics, and docking studies. Molecules. 2020;25(21):5007. https://0-doi-org.brum.beds.ac.uk/10.3390/molecules25215007.

    Article  CAS  PubMed Central  Google Scholar 

  13. Bairoch A, Apweiler R, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ, Natale DA, O’Donovan C, Redaschi N, Yeh LS. The Universal Protein Resource (UniProt). Nucleic Acids Res. 2005;33:D154–9. https://0-doi-org.brum.beds.ac.uk/10.1093/nar/gki070.

    Article  CAS  PubMed  Google Scholar 

  14. Lovell SC, Davis IW, Arendall WB 3rd, de Bakker PI, Word JM, Prisant MG, et al. Structure validation by Calpha geometry: phi, psi and Cbeta deviation. Proteins. 2003;50(3):437–50. https://0-doi-org.brum.beds.ac.uk/10.1002/prot.10286.

    Article  CAS  PubMed  Google Scholar 

  15. Morris GM, Huey R, Olson AJ. Using AutoDock for ligand-receptor docking. Curr Protoc Bioinform. 2008. https://0-doi-org.brum.beds.ac.uk/10.1002/0471250953.bi0814s24.

    Article  Google Scholar 

  16. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455–61. https://0-doi-org.brum.beds.ac.uk/10.1002/jcc.21334.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Vijayalakshmi P, Nisha J, Rajalakshmi M. Virtual screening of potential inhibitor against FtsZ protein from Staphylococcus aureus. Interdiscip Sci. 2014;6(4):331–9. https://0-doi-org.brum.beds.ac.uk/10.1007/s12539-012-0229-3.

    Article  CAS  PubMed  Google Scholar 

  18. Daisy P, Vijayalakshmi P, Saipriya K. Insilico structure prediction of SaeR protein from Staphylococcus aureus. Int J Curr Res. 2011;3:027–31.

    Google Scholar 

  19. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem. 2004;47(7):1739–49. https://0-doi-org.brum.beds.ac.uk/10.1021/jm0306430.

    Article  CAS  PubMed  Google Scholar 

  20. Gupta A, Zhou HX. Profiling SARS-CoV-2 main protease (MPRO) binding to repurposed drugs using molecular dynamics simulations in classical and neural network-trained force fields. ACS Comb Sci. 2020;22(12):826–32. https://0-doi-org.brum.beds.ac.uk/10.1021/acscombsci.0c00140.

    Article  CAS  PubMed  Google Scholar 

  21. Phillips JC, Hardy DJ, Maia JDC, Stone JE, Ribeiro JV, Bernardi RC, et al. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys. 2020;153(4): 044130. https://0-doi-org.brum.beds.ac.uk/10.1063/5.0014475.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Mitra K, Ghanta P, Acharya S, Chakrapani G, Ramaiah B, Doble M. Dual inhibitors of SARS-CoV-2 proteases: pharmacophore and molecular dynamics based drug repositioning and phytochemical leads. J Biomol Struct Dyn. 2020;22:1–14. https://0-doi-org.brum.beds.ac.uk/10.1080/07391102.2020.1796802.

    Article  CAS  Google Scholar 

  23. Jaiswal G, Kumar V. In-silico design of a potential inhibitor of SARS-CoV-2 S protein. PLoS ONE. 2020;15(10): e0240004. https://0-doi-org.brum.beds.ac.uk/10.1371/journal.pone.0240004.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Lee J, Cheng X, Swails JM, Yeom MS, Eastman PK, Lemkul JA, Wei S, et al. CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J Chem Theory Comput. 2016;12(1):405–13. https://0-doi-org.brum.beds.ac.uk/10.1021/acs.jctc.5b00935.

    Article  CAS  PubMed  Google Scholar 

  25. Anommeslaeghe K, MacKerell AD Jr. Automation of the CHARMM General Force Field (CGenFF) I: bond perception and atom typing. J Chem Inf Model. 2012;52(12):3144–54. https://0-doi-org.brum.beds.ac.uk/10.1021/ci300363c (Epub 2012 Nov 28).

    Article  CAS  Google Scholar 

  26. Humphrey W, Dalke A, Schulten K. VMD: visual molecular dynamics. J Mol Graph. 1996;14(1):33–8. https://0-doi-org.brum.beds.ac.uk/10.1016/0263-7855(96)00018-5.

    Article  CAS  PubMed  Google Scholar 

  27. Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, Ferrin TE. UCSF Chimera–a visualization system for exploratory research and analysis. J Comput Chem. 2004;25(13):1605–12. https://0-doi-org.brum.beds.ac.uk/10.1002/jcc.20084.

    Article  CAS  PubMed  Google Scholar 

  28. Liu H, Hou T. CaFE: a tool for binding affinity prediction using end-point free energy methods. Bioinformatics. 2016;32(14):2216–8. https://0-doi-org.brum.beds.ac.uk/10.1093/bioinformatics/btw215.

    Article  CAS  PubMed  Google Scholar 

  29. Fehr AR, Perlman S. Coronaviruses: an overview of their replication and pathogenesis. Methods Mol Biol. 2015;1282:1–23. https://0-doi-org.brum.beds.ac.uk/10.1007/978-1-4939-2438-7_1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ganesan SK, Venkatratnam P, Mahendra J, Devarajan N. Increased mortality of COVID-19 infected diabetes patients: role of furin proteases. Int J Obes (Lond). 2020;44(12):2486–8. https://0-doi-org.brum.beds.ac.uk/10.1038/s41366-020-00670-9.

    Article  CAS  Google Scholar 

  31. Bhardwaj VK, Singh R, Das P, Purohit R. Evaluation of acridinedione analogs as potential SARS-CoV-2 main protease inhibitors and their comparison with repurposed anti-viral drugs. Comput Biol Med. 2021;128: 104117. https://0-doi-org.brum.beds.ac.uk/10.1016/j.compbiomed.2020.104117.

    Article  CAS  PubMed  Google Scholar 

  32. Singh R, Bhardwaj VK, Sharma J, Das P, Purohit R. Identification of selective cyclin-dependent kinase 2 inhibitor from the library of pyrrolone-fused benzosuberene compounds: an in silico exploration. J Biomol Struct Dyn. 2021;22:1–9. https://0-doi-org.brum.beds.ac.uk/10.1080/07391102.2021.

    Article  CAS  Google Scholar 

  33. Singh R, Bhardwaj V, Das P, Purohit R. Natural analogues inhibiting selective cyclin-dependent kinase protein isoforms: a computational perspective. J Biomol Struct Dyn. 2020;38(17):5126–35. https://0-doi-org.brum.beds.ac.uk/10.1080/07391102.2019.1696709.

    Article  CAS  PubMed  Google Scholar 

  34. Murakami A, Ashida H, Terao J. Multitargeted cancer prevention by Quercetin. Cancer Lett. 2008;269(2):315–25. https://0-doi-org.brum.beds.ac.uk/10.1016/j.canlet.2008.03.046.

    Article  CAS  PubMed  Google Scholar 

  35. Paez-Ribes M, González-Gualda E, Doherty GJ, Muñoz-Espín D. Targeting senescent cells in translational medicine. EMBO Mol Med. 2019;11(12): e10234. https://0-doi-org.brum.beds.ac.uk/10.15252/emmm.201810234.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Formica JV, Regelson W. Review of the biology of Quercetin and related bioflavonoids. Food Chem Toxicol. 1995;33(12):1061–80. https://0-doi-org.brum.beds.ac.uk/10.1016/0278-6915(95)00077-1.

    Article  CAS  PubMed  Google Scholar 

  37. Güttner J, Veckenstedt A, Heinecke H, Pusztai R. Effect of Quercetin on the course of mengo virus infection in immunodeficient and normal mice. A histologic study. Acta Virol. 1982;26(3):148–55 (PMID: 6127014).

    PubMed  Google Scholar 

  38. Kinker B, Comstock AT, Sajjan US. Quercetin: a promising treatment for the common cold. J Anc Dis Prev Rem. 2014;02:2–1000111. https://0-doi-org.brum.beds.ac.uk/10.4172/2329-8731.1000111.

    Article  Google Scholar 

  39. Morito K, Hirose T, Kinjo J, Hirakawa T, Okawa M, Nohara T, et al. Interaction of phytoestrogens with estrogen receptors alpha and beta. Biol Pharm Bull. 2001;24(4):351–6. https://0-doi-org.brum.beds.ac.uk/10.1248/bpb.24.351.

    Article  CAS  PubMed  Google Scholar 

  40. Hwang YW, Kim SY, Jee SH, Kim YN, Nam CM. Soy food consumption and risk of prostate cancer: a meta-analysis of observational studies. Nutr Cancer. 2009;61(5):598–606. https://0-doi-org.brum.beds.ac.uk/10.1080/01635580902825639.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the use of the computing resources at HPCE, IIT Madras.

Funding

Not applicable.

Author information

Author notes

  1. Reji Manjunathan and Vijayalakshmi Periyaswami contributed equally

Authors and Affiliations

  1. Department of Genetics, Dr. ALM Post Graduate Institute of Basic Medical Sciences, Taramani Campus, University of Madras, Chennai, Tamil Nadu, India

    Reji Manjunathan

  2. Multi-Disciplinary Research Unit, Chengalpattu Government Medical College and Hospital, Chengalpattu, Tamil Nadu, 603001, India

    Reji Manjunathan

  3. Department of Biotechnology and Bioinformatics, Holy Cross College, Bharathidasan University, Trichy, Tamil Nadu, India

    Vijayalakshmi Periyaswami

  4. Bioengineering and Drug Design Lab, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India

    Kartik Mitra & Mukesh Doble

  5. Department of Bioinformatics, Bishop Heber College (Autonomous, Bharathidasan University), Tiruchirapalli, Tamil Nadu, India

    Arokiaraj Sherlin Rosita

  6. KPES Science College, Maharaja KrishnakumarSinhiji Bhavnagar University, Gujarat, India

    Medha Pandya

  7. Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India

    Jayaraman Selvaraj

  8. Department of Botany, St. Josephs College, Bangalore, Karnataka, India

    Lokesh Ravi

  9. Central Research Laboratory, Meenakshi Ammal Dental College, Maduravoyal, Chennai, Tamil Nadu, India

    Nalini Devarajan

  10. Department of Cariology, Saveetha Dental College and Hospital, Chennai, Tamil Nadu, 600077, India

    Mukesh Doble

Authors
  1. Reji Manjunathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vijayalakshmi Periyaswami
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kartik Mitra
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arokiaraj Sherlin Rosita
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Medha Pandya
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jayaraman Selvaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lokesh Ravi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Nalini Devarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mukesh Doble
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RM designed the work, analysed the data, and wrote the manuscript. VP, LR, and ASR performed molecular docking analysis. KM performed molecular dynamics. MP and JP critically evaluated the manuscript. ND designed the work and contributed towards manuscript preparation, and MD evaluated the molecular dynamics method and contributed towards manuscript preparation. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Reji Manjunathan, Nalini Devarajan or Mukesh Doble.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent of publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file1

: Figure S1. Represents the catalytic site (Pink Colour) and substrate binding site (Blue colour) of TMPRSS2.

Additional file2

: Figure S2. Homology model of protein drug target; & validation of predicted homology model.

Additional file3

: Table S1. Molecular docking results for phyto compounds- from PyRx.

Additional file4

: Figure S3. Molecular interaction of TMPRSS2 with phyto compounds obtained from PyRx.

Additional file5

: Table S2. Molecular docking results for phyto compounds -from Schrodinger.

Additional file6

: Figure S4. Molecular interaction of TMPRSS2 with compounds obtained from Schrodinger.

Additional file7

: Figure S5. A) Residue c-alpha deformability analysis of Genistein complex using iMODS. The plot shows the deformability measure of each residue of the Genistein-TMPRSS-2 complex. It can be seen that the deformability measure of GLY464, CYS-465 are high; B) (i) Genistein bound to the loop residues (GLY464, CYS-465, and CYS-437) of TMPRSS-, (ii) Genistein ligand interaction plot with TMPRSS-2.

Additional file8

: Figure S6. The figure shows the binding site of Genistein (ligand in black) and Quercetin (ligand in red) with TMPRSS2. The binding site surface mesh was coloured based on the hydrophobicity index. Genistein was bound closer to the site S2-substrate binding site, which is relatively more hydrophobic than the S1 region. Quercetin was bound closer to towards site S1 catalytic region, which is hydrophilic.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Manjunathan, R., Periyaswami, V., Mitra, K. et al. Molecular docking analysis reveals the functional inhibitory effect of Genistein and Quercetin on TMPRSS2: SARS-COV-2 cell entry facilitator spike protein. BMC Bioinformatics 23, 180 (2022). https://0-doi-org.brum.beds.ac.uk/10.1186/s12859-022-04724-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s12859-022-04724-9

Keywords

BMC Bioinformatics

ISSN: 1471-2105

Contact us