key: cord-0842077-70o89p0l authors: Hosseini, Maryam; Chen, Wanqiu; Xiao, Daliao; Wang, Charles title: Computational molecular docking and virtual screening revealed promising SARS-CoV-2 drugs date: 2021-01-18 journal: Precis Clin Med DOI: 10.1093/pcmedi/pbab001 sha: 6a284111997039987980866bab17225623059a9f doc_id: 842077 cord_uid: 70o89p0l The pandemic of novel coronavirus disease 2019 (COVID-19) has rampaged the world with more than 58.4 million confirmed cases and over 1.38 million deaths across the world by November 23, 2020. There is an urgent need to identify effective drugs and vaccines to fight against the virus. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to the family of coronaviruses consisting of four structural and 16 non-structured proteins. Three non-structural proteins such as main protease (Mpro), papain-like protease (PLpro), and RNA-dependent RNA polymerase (RdRp) are believed to play a crucial role in the virus replication. We applied a computational ligand-receptor binding modeling and performed a comprehensive virtual screening on the FDA-approved drugs against these three SARS-CoV-2 proteins using AutoDock Vina, Glide, and rDock. Our computational studies identified six novel ligands as potential inhibitors against SARS-CoV-2, including antiemetics Rolapitant and Ondansetron for Mpro; Labetalol and Levomefolic acid for PLpro; and Leucal and antifungal Natamycin for RdRp. Molecular dynamics simulation confirmed the stability of the ligand-protein complexes. The result of our analysis with some other suggested drugs indicated that chloroquine and hydroxychloroquine had high binding energy (low inhibitory effect) with all three proteins—Mpro, PLpro, and RdRp. In summary, our computational molecular docking approach and virtual screening identified some promising candidate SARS-CoV-2 inhibitors that may be considered for further clinical studies. Respiratory Syndrome 2) also belongs to the betacoronavirus (beta-CoV) family, RNA viruses with crown-like spikes on their surface of the coronavirus particles. The fatality rate of the new SARS-CoV-2 seems lower than that of SARS and MERS. An estimate of the overall fatality for SARS-CoV-2 is 2-3% 4 , whereas the World Health Organization (WHO) estimated the fatality rate of ~14-15% for SARS 5 and ~35% for MERS 6 . However, SARS-CoV-2 has the features of rapid transmission from personto-person, asymptomatic transmission 7 and prolonged symptomatic development, as well as substantial increased fatalities in the aged group 8 . The pandemic of COVID-19 has caused a surge in the need for intensive care, which has given rise to tremendous pressure on the healthcare systems in many countries. Substantial efforts have been made for treating SARS-CoV-2 infected patients [9] [10] [11] . The anticoronaviral strategies include preventing the synthesis of viral RNA, inhibiting virus replication, blocking the virus binding to human cell receptors, or inhibiting the virus's self-assembly process 12 . The SARS-CoV-2 genome encodes for four structural and 16 non-structural proteins (NSP) 13 . Among these translated NSPs, the main protease (Mpro, EC 3.4.22.69) , also called as the chymotrypsin-like protease (3C-like protease), and the papain-like protease (PLpro, EC 3.4.19.12) are the two essential proteases for proteolytic processing of the coronavirus replicase polyprotein therefore generating functional replication complex of the virus 14, 15 ; whereas RNA-dependent RNA polymerase (RdRp, EC 2.7.7.48) is the central enzyme for RNA-synthesizing in all positive-strand RNA virus replication 16 . These three NSP proteins play crucial roles in coronavirus replication, making them attractive targets for anti-coronaviral drug design. Targeting one or multiple NSP proteins including Mpro, RdRp, and PLpro, may lead to potential treatment for COVID-19. Dozens of potential therapies for SARS-CoV-2 have been suggested during the COVID-19 outbreaks. The WHO launched a trial, SOLIDARITY, to focus on testing the four most promising COVID-19 treatments -Remdesivir; Chloroquine and hydroxy-chloroquine; Lopinavir plus Ritonavir; and Lopinavir plus Ritonavir and interferon-beta. It is worth mentioning that the four therapies against SARS-CoV-2 are somewhat targeting one of the three NSPs proteins of coronavirus -Mpro, RdRp, and PLpro. Chloroquine/hydroxy-chloroquine and Lopinavir/Ritonavir were removed from the COVID- are studied in clinical trials 17, 18 . Remdesivir (Veklury) was the first and only drug that was approved by FDA on October 22, 2020 to treat COVID-19 19 . [27] [28] [29] [30] [31] . Some potential candidates for SARS-CoV-2 have been identified, many of which are anti-HIV or hepatitis C (HCV) drugs. In this study, we aim to screen the FDA-approved drugs that may have inhibiting activity against one or more of the three SARS-CoV-2 proteins Mpro, RdRp, and PLpro, and attempt to identify other drug candidates that may potential higher inhibitory activity and lower binding energies with three SARS-CoV-2 proteins than Remdesivir. In this regard, we conducted molecular docking and virtual screening of 1615 FDA-approved drugs on the binding pocket of SARS-CoV-2 Mpro, PLpro, and RdRp proteins. In order to achieve the mode of interaction of the FDA-approved drugs with the binding pocket High quality model of SARS-CoV-2 PLpro built based on the SARS-CoV-2 genome and SARS-CoV PLpro (PDB ID: 3E9S, 2.6 Å) 34 crystal structure with GMQE and QMEAN scores of 0.9 and -0.29, respectively, was downloaded to be used as PLpro receptor 35 for each protein with our grid box information to confirm the chosen grid box information. The docking tools were set to generate ten poses for each of the ligands to be docked to the protein binding site. Once the docking was completed, the ligand poses with lower than -6.5, -6 and -50 kcal/mol docking scores from AutoDock Vina, Glide, and rDock, respectively, were kept. Then, the ligand poses that passed the docking score threshold from the three docking tools were selected. The RMSD values between the same docked poses of the same ligand in the selected list were calculated and those poses with a RMSD value lower than 1.5 between the ADT Vina, Glide, and rDock were considered as potential inhibitors ( Figure 1 ). The potential inhibitors were sorted based on the Glide docking scores. The docking processes were done using written in-house scripts. All visualizations were done using Schrodinger Maestro 37 and PyMOL (https://pymol.org/). The molecular dynamics (MD) simulation for six ligands resulted from virtual screening analyses on Mpro, PLpro, and RdRp were carried out using GROMACS v5.1.4 (http://gromacs.org) 40, 41 for a period of 50 ns of timescale to investigate the stability of the docked ligand-protein complexes. The complexes were solvated in a cubical box, where the minimal distance between any ligand atom and the edge of the box was 10 Å. These ligand-protein complexes were prepared using GROMOS96 53a6 force field 42 , and Na + /Cl − ions added to neutralize the system and balance the charges. The initial energy minimization of the system was conducted by using 5,000 steps of the steepest descent algorithm via force convergence with less than 1000 kcal/mol/nm. Once the initial minimization was completed, the entire system was equilibrated for 5 ns at 300 K degree and 1 bar pressure using canonical (NVT) and the isothermal-isobaric (NPT) ensembles 43, 44 . The thermostat coupling was set with a reference temperature of 300 K degree using Berendsen thermostat, and pressure coupling was set at 1.0 bar reference pressure using Parrinello-Rahman along with periodic boundary conditions with cut-offs for Lennard-Jones and Coulomb interactions. The Particle-Mesh Ewald (PME) algorithm was used for dialing with long-range interactions, and the final MD simulation was performed at 50 ns timescale for six ligand-protein complexes. The time step used for the simulation was 2 fs and coordinates were stored at every 10 fs. This was how the root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) were generated. 6XBI, 6XBH, 6XBG, 6WTT, 7BUY, 6M0K, 6LZE, and 6XFN to ensure that the docking analysis with only one specific Mpro structure (6LU7 in our case) will not affect the results of the Mpro docking analysis. The ten other Mpro structures were superimposed on 6LU7, and we found that the topologies of the structures were very similar, and the binding pocket residues were highly conserved between 6LU7 and ten Mpro structures, as shown in Supplementary Figure S1 . The superimposition of the binding pocket residues of four Mpro structures (7BGY, 6W63, 6WTT, and 6LU7) in tube and stick styles with N3 inhibitor from 6LU7 in the pocket side are illustrated in the Supplementary Figure S1B . Thus, we continued our analysis based on 6LU7 structure for Mpro. We used three docking and virtual screening tools to obtain the accuracy of docking analyses: AutoDock Vina (AD Vina) 45 , Glide 46 , and rDock 47 . We used these tools to predict the interactions of ligands with each of the three proteins. Figure 2 illustrates the cartoon structures of Mpro, PLpro, and RdRp with their binding pockets colored in blue for which we used for our docking analysis. Figure 1 illustrates the workflow that we used in this study. Before starting the docking analysis of the FDA-approved drugs, we redocked the cocrystalized ligand on its receptor to validate the docking parameters. Supplementary Figure S2 illustrates the re-docking of each co-crystalized ligands N3, TTT, and Remdesivir monophosphate Figure S3 illustrates the interactions between Mpro, PLpro, and RdRp with their co-crystalized ligands, respectively, in more details. Based on our docking analysis, the potential ligand candidates that may inhibit SARS-CoV-2 Mpro are listed in Table 1 Ondansetron, Vortioxetine, and Azelastine were three ligands that formed π-stacking interaction with His140 which lowered their binding energy with Mpro. Fluvastatin is an antifungal, which formed two H-bonds with Thr26, and one H-bond with Gly143, and was recently identified as a SARS-CoV-2 Mpro inhibitor with low binding energy 51 . The potential inhibitors of SARS-CoV-2 PLpro, based on our docking analysis, are listed in Figure 4 shows the interactions of the potential inhibitors with PLpro binding residues. In summary, most of the ligands in the top list of PLpro formed H-bonds with Arg169, Tyr271, Gln272, and Tyr276, and the majority of the structures formed π-stacking interactions with Tyr271, Tyr267. Labetalol formed two H-bonds with each of the three residues, i.e., Asp167, Arg169, and Tyr271, with a total of 6 H-bonds. It also formed a pi-cation interaction with Tyr267 along with six hydrophobic interactions with residues Leu165, Asp167, Pro251, Tyr267, Gln272, and Thr304, resulting in a ligand-receptor complex with low binding energy. Levomefolic acid is a ligand with lower binding energy, and it formed three H-bonds with Arg169, Asp305, and Tyr276, along with a πstacking interaction with Tyr271. Levomefolic acid, Ketoprofen, Pralatrexate, and Modafinil formed up to three π-stacking interactions with Tyr271 and Tyr267. Labetalol, Levomefolic acid, Ramelteon, Modafinil, Tetrahydrobiopterin, and Bromfenac are drugs with minor side effects, and these drugs should be further studied as potential SARS-CoV-2 inhibitors. The potential inhibitors of RdRp are listed in Table 3 , and all of these drugs have minor side effects except folinic acid. The docking scores ranged from -6.016 to -7.17, -6.5 to -8.3, and -52.37 to -8.2 for Glide, AutoDock Vina, and rDock, respectively. The schematic interaction of the protein-ligand complexes is presented in Figure 5 . In summary, all structures formed at least 5 H-bonds with RdRp pocket residues, and the majority of these interactions occurred at Lys545, Arg555, Asp618, Ser682, Ser759, Asp760, Asp761, and Glu811. Leucal, one of the top hits for RdRp, formed 6 H-bonds with Ser501, Lys545, Ser682, Ser759, and Asp760 (2 bonds). Furthermore, it formed a salt bridge interaction with Lys500, lowering its docking score to -7.17 kcal/mol. A recent study suggested Leucal as a potential SARS-CoV-2 Mpro inhibitor 52 . In order to compare our results with recent suggested SARS-CoV-2 drugs, we downloaded structures of Remdesivir, Lopinavir, Ritonavir, chloroquine, and hydroxychloroquine; and we conducted docking analysis for these drugs against SARS-CoV-2 Mpro, PLpro, and RdRp proteins using three docking tools, AutoDock Vina, Glide, and rDock. Results of our docking analysis are in Table 4 and the corresponding ligand-protein interactions are presented in Figure 6 . Remdesivir, the recent FDA approved SARS-CoV-2 drug 19 , has shown to target both RdRp Lopinavir and Ritonavir were suggested inhibitors of Mpro 18, 57 . Our analysis showed slightly lower binding energy for these drugs with RdRp (-10.1 and -8.5 kcal/mol AD Vina) compared to Mpro (-9.3 and -7.6 kcal/mol AD Vina), suggesting that they could be potential inhibitors of RdRp as well. Chloroquine and hydroxychloroquine were suggested as potential inhibitors of PLpro 27 , but our docking analysis showed a high binding energy for these ligands with all three SARS-CoV-2 proteins, suggesting either there was no inhibitory activity against SARS-CoV-2 or the antiviral effects of chloroquine might be mainly at the entry-level instead of post-entry stage. During the course of our analysis, several complexes of SARS-CoV-2 Mpro became available. In order to confirm the accuracy of our docking results based on one specific Mpro structure (6LU7), we performed ensemble docking for ten Mpro structures with co-crystalized ligand inhibitors (7BGY, 6W63, 6XBI, 6XBH, 6XBG, 6WTT, 7BUY, 6M0K, 6LZE, and 6XFN), and ten potential Mpro inhibitors that were identified by our virtual screening analysis using Schrödinger Virtual Screening Workflow ( Table 1) . As shown in Supplementary Figure S1C , all ten ligand structures had low glide docking scores ranging from -6.461 to -7.843 kcal/mol. In order to validate and confirm the stability of the suggested protein-ligand complexes, we performed These six ligands can be considered as potential candidate drugs subject to further clinical studies. 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Drug repurposing to identify therapeutics against COVID 19 with SARS-Cov-2 spike glycoprotein and main protease as targets: an in silico study Targeting SARS-CoV-2 RNA-dependent RNA polymerase: An in silico drug repurposing for COVID-19 Profiling Molecular Simulations of SARS-CoV-2 Main Protease (Mpro) Binding to Repurposed Drugs Using Neural Network Force Fields Repurposing Ivermectin to inhibit the activity of SARS CoV2 helicase: possible implications for COVID 19 therapeutics Searching for target-specific and multitargeting organics for Covid-19 in the Drugbank database with a double scoring approach The authors would like to thank Ms. Diana Ho for the administrative support including coordinating the meetings involved in the project. The study was partially supported by an American Heart Association (AHA) grant (18IPA34170301) and a National Institute of Health (NIH) grant (R01/HD088039).