key: cord-0776984-zansdb6k
authors: Sajid Jamal, Qazi Mohammad; Alharbi, Ali H.; Ahmad, Varish
title: Identification of doxorubicin as a potential therapeutic against SARS-CoV-2 (COVID-19) protease: a molecular docking and dynamics simulation studies
date: 2021-04-07
journal: Journal of biomolecular structure & dynamics
DOI: 10.1080/07391102.2021.1905551
sha: 253a6c9e315ae065b5e22a8baf278f6d7d2a9d7e
doc_id: 776984
cord_uid: zansdb6k

After one year, the COVID-19 pandemic caused by SARS-CoV-2 is still the largest concern for the scientific community. Of the many recognized drug targets of SARS-CoV-2, the main protease is one of the most important target due to its function in viral replication. We conducted an in silico study with repurposing drugs of antibiotics class against virus protease and peptidase using AutoDock tool. The following significant binding energy interaction was observed with protease (PDB: 6LU7) like piperacillin –7.25; tobramycin –9.20 and doxorubicin (Doxo) –10.04 kcal/mol and with peptidase (PDB: 2GTB) piperacillin –7.08; tobramycin –8.54 and Doxo –9.89 kcal/mol. Furthermore, the interaction and stability behavior of the Doxo–protease and Doxo–peptidase complexes were analyzed for a 100-nanosecond (ns) time. Calculated RMSD values observed using molecular dynamics simulation (MDS) were found to be 0.15–0.25 nm, RMSF calculation per residues showed a value near 0.2 nm and R(g) values remained approximately 2.25 nm. MM-PBSA analysis of total binding energy calculation of Doxo–protease and Doxo–peptidase complexes are found to be –148.692 and –105.367 kJ/mol, respectively. Moreover, amino acid residue ASP-197 showed the lowest contribution binding energy i.e. –18.1185 kJ/mol, and amino acid residue ASP-187 showed –17.0267 kJ/mol contribution energy. Thus, significant docking interaction and stable dynamicity of Doxo–protease complex with time was suggested that Doxo could be a choice to inhibit potentially the viral proteases that could prevent the entry inside the host cell to control the COVID-19 disease. Communicated by Ramaswamy H. Sarma

Recently, the population of the world has encountered a challenge from a dangerous risk named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is popularly known as Coronavirus Disease 2019 (COVID-19) (Paraskevis et al., 2020) . The disease is caused by a member of genera b-Coronaviridae which is later described as an RNA virus (Pal et al., 2020; WHO, 2008) . WHO released data on COVID-19, as on 14 May 2020 that 4,179,479 infected persons and 287,525 human deaths have been confirmed worldwide in 215 countries (https://www.who.int/emergencies/ diseases/novel-coronavirus-2019/situation-report). The different infected persons have been recorded from different countries with a prompt rate of transmission of disease (Khaerunnisa et al., 2020) .

The disease is characterized by sore throat, cough, headache, myalgia, fatigue, fever and breathlessness (Lovato et al., 2020) . In 2003, the SARS-CoV came into scientist knowledge which has been reported with a much higher mortality rate (12%) and infected more the 8098 persons across 26 countries.

The COVID-19 has been reported with less (9%) mortality rate than the SRAS-CoV (2003) (Shereen et al., 2020) .

The incubation of the disease is 1-14 days and after 6-7 days, the disease can progress to pneumonia, complexes and even death also. The health complication is due to extreme release of Interleukin (IL) such as IL-10, IL7, IL2, macrophage inflammatory protein 1A, monocyte chemoattractant protein 1, inducible protein 10, granulocyte-macrophage colony-stimulating factor and tumor necrosis factor . Moreover, many cases of asymptomatic or no fever have been reported (Lu et al., 2021) .

It has also been reported that a high level of creatinine, prothrombin time, Lactate dehydrogenase (LDH), Alanine transaminase (ALT)/Aspartate transaminase (AST), and Creatine kinase (CPK) are associated with severe conditions of disease. The patients are diagnosed with molecular and immunological tests using respiratory samples like sputum/ throat swab/bronchoalveolar lavage/and endotracheal aspirates (Abbasi-Oshaghi et al., 2020) . Imaging technologies like X-ray and CT scans have also been used in the preliminary examination of the disease conditions (Singhal, 2020) .

Currently, there is no well-tested treatment method discovered yet to control the disease CoV. For the progress of significant treatment against this disease, the targeting active sites or ligand interacting proteins must be explored for therapeutic purpose. Currently, the genome of the virus COVID-19 and probable cell-interacting protein have been explored, which will be helpful in the designing of the therapeutic intervention for the control of the disease. Many phytoconstituents, previously used FDA-approved drugs, including antiviral drugs have been tested against this virus (Zhou et al., 2020) .

Many therapeutics molecules have been used for the management of different kinds of diseases. The therapeutics uses of herbal medicines, chemical entities, antibiotics, antifungal antiviral, anticoagulant, therapeutics proteins and vaccines are well explored. Antimalarial drug Artemisinin from the herb Artemisia annua (Pan et al., 2013) , antibacterial and antifungal Withafarin A recovered from Withania somniferum, polyacetylene active against bacteria, fungi and virus have been reported from Arctium lappa (Cowan, 1999) . Moreover, monoclonal antibodies such as adalimumab (Humira) have been well explored for the treatment of various cancers (Lu et al., 2020) . Each drug has a different mechanism of action such as beta-lactam antibiotics including penicillin (PCNs), carbapenems monobactam (Aztreonam) and cephalosporins inhibit cell wall synthesis, Macrolideserythromycin, azithromycin, and clarithromycin inhibit protein synthesis through binding with 50S ribosomal, while tetracyclinestetracycline, doxycycline, and minocycline act as 30S ribosomal bacterial protein synthesis inhibitors, aminoglycosides like tobramycin, streptomycin, amikacin, and gentamicin also inhibit protein synthesis mediated by 30S ribosomes.

The role of viral proteins (spike protein, protease and peptidase) in the pathogenesis of COVID-19 disease is well reported. Ribosomes mainly 30S and 50S units are universal translational mechinery of the living cells involved in protein synthesis. The selected repressor drugs of this study bind to 30S or 50S ribosomal units, inhibit protein synthesis and mediate their antimicrobial effect. More negative docking energies of these drugs observed with protease and peptidase of viral enzymes showing the significant binding interaction with the viral enzymatic proteins. Moreover, the dynamics simulation study of these drugs with studied target enzymes is found to be significant and could interfere with the viral pathogenesis.

Fluoroquinolones including ciprofloxacin, ofloxacin, gatifloxacin inhibit DNA gyrase as well as topoisomerase (Zumla et al., 2016) . The Coronavirus is an RNA virus, and antiviral drugs commonly inhibit reverse transcriptase like zidovudine, stavudine, zalcitabine, lamivudine, abacavir, tenofovir, didanosine and emtricitabine (Yan et al., 2020) . The genome of virus SARS-CoV synthesize many proteins by the gene expression of replicase which is an important segment of the CoV genome expressed proteases for the release of 16 non structural proteins (NSPs) namely polyprotein PP1a/PP1ab. The enzyme chymotrypsin-like main proteases (M pro ) belong to cysteine protease, cleaves the C-terminal part of the enzyme, whereas PLpro papain-like protease possessed N-terminal part of the enzyme and these could be a target to control the virus infection (Barretto et al., 2005; Lindner et al., 2005; Ullrich & Nitsche, 2020) .

The in silico screening studies have been conducted and signified the interaction COVID-19 target molecules. Moreover, scientific efforts are being contributed for the search for effective treatment of COVID-19 disease but to date, no effective molecules are available to control the virus. Also, molecular dynamics simulations (MDS) are one of the emerging computational tools to study the time-dependent behavior of molecules in a biological system. Protein and nucleic acids are key biomolecules of the cells that regulate most of the life-supporting processing of the cell. Biological molecules such as protein, DNA or RNA produce responses through a complex process and changes dynamically with time as the responses proceed. Thus, molecules behave differently with different molecules of the cellular environment and result in disease conditions and pathological conditions. Moreover, cell surfaces including extracellular molecules as well as internal molecules of extra cellular matrix (ECM) have also been reported to behave differently in the presence of therapeutics or drug molecules being prescribed to manage the disease condition (Ahmad, 2020) .

Thus, in search of a new drug molecule, in this study, we conducted the computational-based studies of the different class of antibiotics with the COVID-19 viral protease and peptidase proteins and validated the best complex through MDS (Barretto et al., 2005) .

The two-dimensional (2D) structures of drug compounds penicillin, sulbactam, piperacillin, tazobactam, azithromycin, ceftriaxone, tobramycin, vancomycin, trimethoprim, doxorubicin (Doxo) and artemisinin (Table 1) were downloaded from PubChem Database provided by National Center for Biotechnology Information (NCBI) (https://pubchem.ncbi.nlm. nih.gov/). The 2D drug compounds chemical structure cannot be used directly to run docking studies it should be further converted to .pdb files using CORINA online three-dimensional (3D) structure generator (https://www.mn-am.com/ online_demos/corina_demo) after providing drug compounds SMILES IDs as an input and further energy minimization was done after implementing CHARMm forcefield (Brooks et al., 2009) available in Discovery Studio visualizer 2019 (Biovia et al., 2000) .

We have downloaded the crystal 3D structures of COVID-19 Protease (PDB: 6LU7) (Figure 1 (a)) bounded with N3 inhibitor and COVID-19 main peptidase (PDB: 2GTB) (Figure 1 (b)) bounded with substrate-like aza-peptide epoxide from Protein Data Bank (PDB) (www.rcsb.org) . To prepare the downloaded 3D crystal structure for docking studies, we have removed the water molecules and HETATM 

from the published structures and CHARMm force field applied for energy minimization (Brooks et al., 2009) using Discovery Studio Visualizer 2019 (Biovia et al., 2000) . Also, we have analyzed the active site of the pre-bounded ligand molecules in both structures. Furthermore, the active site of each receptor was identified by considering the active site of the inhibitor crystallized with the proteins, for instance, selected all atoms/bonds of any residue in selection zone of within <5 Å and obtained the amino acid residues information available in the active site to implement docking analysis on the same binding pocket. For all the structural visualization, Discovery Studio Visualizer 2019 (Biovia et al., 2000) was used.

MGL tool Autodock 4.2 tools protocols were used to predict the interaction energies between drug compounds and COVID-19 proteins. Interaction analysis was executed through the Lamarckian genetic algorithm (LGA). AutoDock calculate the ligand and receptor interaction binding energy (DG) on the basis of the following formula:

Here, DG gauss : attractive term for dispersion of two gaussian functions, DG repulsion : square of the distance if closer than a threshold value, DG hbond : ramp functionalso used for interactions with metal ions, DG hydrophobic : ramp function, DG tors : proportional to the number of rotatable bonds (Morris et al., 1998) .

Furthermore, water (HOH) was removed during editing of native pdb files of the selected 3D structure of COVID-19 proteins, i.e. main protease (PDB: 6LU7) and peptidase (PDB: 2GTB). The hydrogen atoms, Kollman united charges, and the default solvation parameters were added to both proteins. Gasteiger charge is also assigned to the drug compounds. The Grid box was set to cover the maximum part of both proteins and drug compounds (Jamal et al., 2015) . The values were set to 60 Â 60 Â 60 in the X-, Y-and Z-axis of a grid point. The default grid point spacing was 0.375 Å. Lamarckian Genetic Algorithm (LGA) was used for proteinsdrug compounds flexible docking calculations (Morris et al., 2009) . The default LGA parameters like population size (ga_pop_size), energy evaluations (ga_num_generation), mutation rate, crossover rate and step size were set to 150, 2,500,000, 27,000, 0.02, 0.8 and 0.2 Å, respectively. The LGA runs were set at 10 runs. After successful execution of docking steps, obtained conformations of selected SARS-CoV-2 proteins and drug complexes were subjected to further analysis and were deeply analyzed for the interaction energy 

Doxo-protease and Doxo-peptidase complexes docking results need to be further evaluated through advanced computational techniques. Therefore, the MDS environment was set to execute 100 nanoseconds (ns) simulation using GROningen MAchine for Chemical Simulations (GROMACS) tool 2018 version (Van Der Spoel et al., 2005) developed by the University of Groningen, The Netherlands. Main protease (PDB: 6LU7) simulation in water was performed using GROMACS standard protocol. For selected complexes simulation initially, pdb2gmx module was used to generate the required main protease (PDB: 6LU7) and peptidase (PDB: 2GTB) topology file followed by CHARMM27 all-atom force field selection. In the next step, ligand (Doxo) topology files were generated from the SwissParam server (Zoete et al., 2011) .

For the solvation step, unit cell triclinic box filled with water was created. Na þ and Cl-ions were added for stabilization of the system leading to the energy minimization execution. Equilibrium setup for the (Doxo-protease complex) system was essential and done followed by two-step ensembles NVT (constant number of particles, volume, and temperature) and NPT (constant number of particles, pressure, temperature). Both ensembles provide control over temperature, pressure coupling resulting in constancy and stabilization of the system through complete simulation (Gupta et al., 2020) .

GORMACS contain several packages, for Doxo-protease and Doxo-peptidase complexes MDS analysis, we used gmx rms for root mean square deviation (RMSD) (Kufareva & Abagyan, 2012) , gmx rmsf for root mean square fluctuation (RMSF), gmx gyrate for the calculation of radius of gyration (R g ) (Kuzmanic & Zagrovic, 2010) and gmx hbond for the calculation of numbers of hydrogen-bond formed during the interaction. Finally, after a successful 100 ns simulation run, trajectories files and graphical plots were generated by xmgrace program (Turner, 2005) .

The molecular mechanic/Poisson-Boltzmann surface area (MM-PBSA) methods were implemented for the calculation of binding free energy of Doxo-protease and Doxo-peptidase complexes. This calculation validates the extensive analysis of ligand-protein interaction.

The MM-PBSA binding free energies like potential energy, polar and non-polar solvation energies were calculated by the 'g_mmpbsa' (Baker, 2001; Kumari et al., 2014) packages using GROMACS. MM-PBSA calculations use the following equation:

The DG binding shows the total binding energy of the complex, while the binding energy of the free receptor is G receptor , and that of unbounded ligand is represented by G ligand .

After analyzing molecular interaction results obtained from docking experimentation with selected drugs, we observed that these drugs showed significant interaction efficiency with COVID-19 proteins. Molecular docking results could be evaluated based on final intermolecular energy, inhibition constants and hydrogen bond formation during the interaction between drugs and receptor molecules (Ferreira et al., 2015) . 

Among the docked compounds, piperacillin, tobramycin and Doxo exhibited strong binding with the protease (PDB: 6LU7) than the other compounds. A total of 12 protease residues were found to interact hydrophobically, specifically, Leu141 was found to form alkyl, and a pi-sulfur bond with piperacillin was formed by Cys145 and Met165 (Figure 2(a,b) ; Table  2 ), while 16 residues of protease were found to be involved in the interaction with tobramycin in which Leu141, Phe140, Glu166 and His41 formed H bond; His172, Met 165, His163, His164, Asn142, Cys145, Gln189, Met49, Thr25, Thr26, Leu27 were involved in van der waals interaction (Figure 2(c,d) ; Table 2 ). Furthermore, Phe140, Leu141, Asn142, Ser144, Cys145, His163, His164, Met165, Glu166, Leu167, Pro168, His172, Gln189, Thr190, Ala191, Gln192 residues of protease were found in the formation of hydrophobic (Gln189 formed amide-pi stacked, Leu167 and Cys145 formed alkyl/pi-alkyl) interaction with Doxo (Figures 2(e,f) and 3; Table 2 ). The binding energy and inhibition constant for 'piperacillin-protease', 'tobramycin-protease' and 'Doxo-protease' complexes were found as '-7.25 kcal/mol and 16.49 mM', '-9.20 kcal/mol and 8.23 mM', and '-10.04 kcal/mol and 225.44 nM', respectively. Furthermore, the intermolecular energy for the aforementioned complexes was found as -7.25 kcal/mol, -9.20 kcal/mol and -10.04 kcal/mol, respectively.

We have also performed the docking analysis of selected drugs with COVID-19 peptidase (PDB: 2GTB), in which piperacillin, tobramycin and Doxo was found to be strongly bound with COVID-19 peptidase. The observed interaction analysis results have showed that piperacillin interacted with significant binding energy and observed inhibition constant value of were -6.50 kcal/mol and 101.77 mM, respectively. His41, Asn142, Gly143, Ser144, Cys145, His163, His164, Met165, Glu166, Leu167, Pro168, Arg188, Gln189, Thr190 and Gln192 residues of peptidase were involved in hydrophobic interaction with piperacillin, in which His41 and Met165 have involved Pi-sigma and Pi-alkyl interactions (Figure 4(a,b) ; Table 3 ).

Further, His41, Phe140, Leu141, Asn142, Gly143, Ser144, Cys145, His163, His164, Met165, Glu166, His172 and Gln189 residue of peptidase were found to form hydrophobic interaction, specifically Met165 formed alkyl interaction with tobramycin (Figure 4(c,d) ; Table 3 ), while Doxo was observed to formed hydrophobic interaction with Phe140, Leu141, Asn142, Gly143, Ser144, Cys145, His163, His164, Met165, Glu166, Leu167, Pro168, Gln189, Thr190 residues of peptidase, in which Pro168 formed alkyl interaction ( Figures  4(e,f) and 5). The binding energy and inhibition constant for 'tobramycin-peptidase', and 'Doxo-peptidase' complexes were found as '-8.54 kcal/mol and 46.74 mM', and '-9.89 kcal/mol and 451.51 nM', respectively (Table 3) . Thus, the interaction energies analyzed from the in silico study against COVID-19 main protease (PDB: 6LU7) with repurposing drugs studied in this study were found to be high [7-8 (<4-5.5 kcal/mol)] as compared to previously reported in silico studies with antiviral drugs ritonavir, oseltamivir, remdesivir, ribavirin, chloroquine, hydroxychloroquine, and favipiravir (<4-5.5 kcal/mol) (Narkhede et al., 2020) .

After analyzing the data (Tables 2 and 3 ) obtained from the docking analysis, it was found that piperacillin, tobramycin and Doxo drugs could counter COVID-19 protease and peptidase. Thus, from the above results of this study, it was found that Doxo and tobramycin are found to potentiate the inhibition of protease enzyme of the virus. Finally, it is concluded from this study that Doxo showed potential interaction with both viral enzymes protease and peptidase that could be involved to inhibit the pathogenesis of the disease.

Furthermore, after MDS total experimentation of 100 ns run, the analysis was done based on obtained data from RMSD, RMSF, R g and formation of hydrogen bond plots interpretation revealed deviation, fluctuation and stability of main protease, Doxo-protease and Doxo-peptidase complexes during the whole simulation period. The RMSD values for selected simulated molecules was ranged between 0.1 and 0.5 nm (Figure 6(a) ). The observed RMSD values for protease, Doxo-protease and Doxo-peptidase complexes were between 0.1 and 0.15 nm, 0.15 and 0.25 nm and 0.1 and 0.5 nm, respectively. In comparison with protease, the Doxo-protease complex showed fluctuation of 40 ns until 55 ns but after that regained stability with an average value 0.25 nm. Although Doxo-peptidase complex was stable until 20 ns but after that shows a sudden increase in the RMSD value, further after 25 ns remained stable until 100 ns ( Figure  6(a) ). Doxo-protease complex average RMSD value was higher than protease and Doxo-peptidase. RMSF calculation per residue value was ranged between 0.1 and 0.9 nm for protease, Doxo-protease and Doxo-peptidase complexes and it was observed that for most of the residues RMSF value remains near 0.1 nm for protease and Doxo-protease complex while 0.2 nm for Doxo-peptidase (Figure 6(b) ).

Furthermore, few fluctuations were observed at 50-60, 140-170 and 180-210 amino acid residue regions. It shows that amino acid residues at 140-170 and 180-210 regions are important during Doxo-protease interaction. Amino acid residues present in these regions are also found in the formation of hydrogen bond and hydrophobic interaction during docking analysis (Table 2 ; Figures 2(f) and 6(b)).

Doxo-peptidase RMSF plot shows fluctuation at 40-50, 140-170, 180-190, 220, 240 and 255 amino acid residue regions. Amino acid residue region 140-170 and 180-190 were involved in the formation of hydrogen bond including hydrophobic interaction (Table 3 ; Figures 3(f) and 6(b) ). The Doxo-protease and Doxo-peptidase complex hydrogen bond plots shown the formation of 1-6 and 1-8 hydrogen bonds, respectively. Doxo interaction with both SARS-CoV-2 molecules formed 3-4 hydrogen bonds during complete simulation period (Figure 6(c) ).

Radius of gyration (R g ) analysis is very important for the assessment of the compactness and stability of protein structure during whole simulation period due to presence of ligand molecules. Main Protease R g plot has shown average value approximately 2.2 nm. The R g plot for Doxo-protease and Doxo-peptidase have remained stable and shown average value near to 2.25 nm except few fluctuations found in Doxo-peptidase at 20,000 ps and 60,000 ps. Overall, R g analysis suggested that both complexes were stable and compactness was maintained for 100,000 ps simulation period (Figure 6(d) ).

Average binding energy and standard deviation were calculated for Doxo-protease and Doxo-peptidase complexes using a script name MmPbSaStat.py available in g_mmpbsa package (Kuzmanic & Zagrovic, 2010) under GROMACS simulation for Doxo-protease complex interaction (Table 4 ). Binding energy was obtained after cumulative energies score taken from Van der waal energy, electrostatic energy, polar solvation and solvent-accessible surface area (SASA) energy. All energetic components have shown significant binding score except polar solvation energy. The overall predicted binding energy for Doxo-protease and Doxo-peptidase complexes were -148.692 (±45.697) kJ/mol and -105.367 (±21.580) respectively (Figure 7) .

Further, we have also calculated binding energy contribution per residues using a python script MmPbSaDecomp.py provided by MM-PBSA package.

After calculation of Doxo-protease interaction contribution energy it was observed that amino acid residues GLU-14, ASP-33, ASP-44, GLU-55, ASP-92, PHE-140, MET-165, GLU-166, PRO-168, ASP-176, ASP-187, ASP-197, ASP-216, ASP-229, GLU-240, ASP-245, ASP-248, GLU-270, GLU-290, ASP-295 and GLN-306 show significant binding affinity (Estrada, 2020) . Amino acid residue ASP-197 has shown the most lowest contribution binding energy, i.e. -18.1185 kJ/mol and also amino acid residue ASP-187 has shown -17.0267 kJ/mol contribution energy (Figure 8(a) ). Both amino acid residues ASP-187 and ASP-197 have a potential catalytic site and they are important component of S3 subsite of substrate binding loop region 184-197 amino acids of SARS main protease (Gioia et al., 2020; Jin et al., 2020; Zhang et al., 2020; Zheng et al., 2007) .

Amino acid residue GLU-166 is a key component. In the protein, generally, N-terminal residues established a hydrogen bond with GLU-166 of the promotor resulting in the shaping of the S1 pocket of the main protease which makes a catalytic pocket (Anand et al., 2002; Zhang et al., 2020) . In addition, amino acid residue GLU-166 has shown -4.4742 kJ/mol contribution energy. Contribution energy analysis of Doxo-peptidase interaction plot has shown a similar pattern as Doxo-protease interaction plot. It was observed that ASP-187 and ASP-197 have shown best contribution energies of -16.4063 and -18.007 kJ/mol, respectively (Figure 8(b) ). Contribution energy analysis of amino acid residues GLU-166, ASP-187 and ASP-197 revealed that these amino acids played important role during interaction with Doxo which could be established as a repurposed drug against COVID-19 main protease.

The interaction energies analyzed from the in silico study against COVID-19 main protease (PDB: 6LU7) and peptidase (PDB:2GTB) with repurposing drug Doxo were found higher than other drugs. Doxo was found to have interacted with the main protease and peptidase significantly. Also, MDS data revealed stability of Doxo-viral biomolecules complexes and MM-PBSA analysis predicted lowest binding free energies.

Thus, the computational studies provided substantial predicted evidence that Doxo could be effective to inhibit the protease-mediated viral entry to the host cell. So, we suggest conducting further in vitro and in vivo antiviral activities of Doxo against the virus to control the COVID-19 disease. 

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The authors are thankful to Qassim University, Saudi Arabia for providing the necessary facilities to conduct the study.

The authors declare that there is no conflict of interest.