Abstract
The recent outbreak of the coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) in the last few months raised global health concern. Previous research described that remdesivir and ritonavir can be used as effective drugs against COVID-19. In this study, we applied the structure-based virtual screening (SBVS) on the high similar remdesivir- and ritonavir-approved drugs, selected from the DrugBank database as well as on a series of ritonavir derivatives, selected from the literature. The aim was to provide new potent SARS-CoV-2 main protease (Mpro) inhibitors with high stability. The analysis was performed using AutoDock VINA implicated in the PyRx 0.8 tool. Based on the ligand binding energy, 20 compounds were selected and then analyzed by AutoDock tools. Among the 20 compounds, 3 compounds were selected as high-potent anti-COVID-19.
1. Introduction
By the end of 2019, the World Health Organization (WHO) declared a pandemic of a novel coronavirus infection coined COVID-19, caused by SARS-CoV-2 virus. This disease was first detected in the city of Wuhan, China [1, 2], and has quickly spread to more than 216 countries around the world. As of July 1, 2020, and according to WHO report [3], more than 10 million (10 321 689) people were affected by COVID-19, of which more than half a million (507,435) passed away. The contagion rate and death tolls are still increasing, and no confirmed drugs or approved vaccines have yet been discovered. Development of an anti-COVID-19, hence, became a global health emergency.
Currently, there are no targeted therapeutics, and options of effective treatment remain very limited. The clinical candidates that have received attention are remdesivir [4, 5] and ritonavir [6]. Remdesivir is an adenosine triphosphate analog first described in the literature in 2016 as potential treatment for Ebola virus (EBOV) [7]. Ritonavir is an HIV protease inhibitor that interferes with the reproductive cycle of HIV [8]. Although it was initially developed as an independent antiviral agent, it has been shown to possess advantageous properties in combination regimens with low-dose ritonavir and other protease inhibitors.
Accordingly, the process of identification of new antiviral drugs is very complex, expensive, and time-consuming. Thus, computer-aided drug design (CADD) approaches have been recognized as an alternative to overcome this situation [9–11]. Among these approaches, structure-based virtual screening (SBVS) [12–15] by using molecular docking study [16, 17] has become a valuable primary step in the identification of novel lead molecules for the treatment of diseases [9] and proven to be a very efficient tool for antiviral [18] and antibacterial [19] drug discovery. In this study, an SBVS was applied on the high similar remdesivir- and ritonavir-approved drugs that are selected from the DrugBank database as well as on a series of ritonavir derivatives which are selected from the literature. The docking-based virtual screening approach was performed by using AutoDock VINA implicated in the PyRx 0.8 tool. The aim was to identify new SARS-CoV-2 main protease (Mpro) inhibitors with high binding affinity. The top-ranked compounds were then submitted to another screen by using AutoDock 4.2.
2. Materials and Methods
2.1. Protein Structure Preparation
The crystal structure of SARS-CoV-2 Mpro (PDB ID : 6LU7) was collected from Protein Data Bank [20]. The ligand was separated from the protein. The polar hydrogen atoms and Kollman charges were added to the protein, and the water molecules were eliminated. The final prepared file was minimized by UCSF Chimera software [21] and saved in PDBQT format for further analysis.
2.2. Dataset Collection and Preparation
To identify the potential drugs against SARS-CoV-2 Mpro, an SBVS was performed on the high similar remdesivir- and ritonavir-approved drugs (taken from DrugBank database) [22] as well as on a series of ritonavir analogues (taken from literature) (see Table S1 in Supplementary Materials) [23, 24]. The compounds were imported into OpenBabel within the PyRx 0.8 tool [25] and subjected to energy minimization. The energy minimization was performed with the universal force field (UFF) using the conjugate gradient algorithm. The total number of steps was set to 2000, and the number of steps for update was set to 1. In addition, the minimization was set to stop at an energy difference of less than 0.01 kcal/mol. The minimized compounds were than transformed to PDBQT format for further analysis.
2.3. Structure-Based Virtual Screening (SBVS)
In order to identify new potent SARS-CoV-2 Mpro, an SBVS using docking simulations was performed on both prepared libraries. The 6LU7 crystal structure was used as receptor and compounds libraries as ligands. The calculation of binding energies was performed using PyRx AutoDock VINA. Firstly, a grid box was set to cover the active site of crystal structure with the following dimension in Å: center (X, Y, Z) = (−9.63, 11.33, 70.50), dimensions (X, Y, Z) = (13.24, 20.25, 13.21) with an exhaustiveness of 8. The top-ranked compounds were then submitted to another screen by using AutoDock 4.2 [26]. Finally, analysis of the finding was performed using Discovery Studio [27] and PyMOL [28] programs.
3. Results and Discussion
3.1. Structure-Based Virtual Screening
In order to find new potential approved drugs for treating SARS-CoV-2 Mpro, an SBVS of both selected libraries was performed. AutoDock VINA implicated in the PyRx tool generated 9 different conformations for each ligand which are classified by binding affinity (kcal/mol). The top 20 ranked compounds displaying the free energy of binding in the range −8.7 to −9.6 kcal/mol are presented in Table 1, and the others are presented in Tables S2 and S3 (in Supplementary Materials).
The top hit selected drugs including methotrexate, dihydroergocornine, dihydroergocristine, relacorilant, irinotecan, lifirafenib, ergotamine, laniquidar, saquinavir, dihydro-alpha-ergocryptine, fostemsavir, imatinib, PF-03715455, fenebrutinib, simeprevir, and umbralisib as well as Mol181, Mol1184, Mol187, and Mol188 were then submitted to another screen by the AutoDock 4.2 tool (Table 1). As shown in Table 1, all selected ligands have a binding free energy greater than remdesivir (−7.3 kcal/mol) and ritonavir (−6.9 kcal/mol) (Figure 1). Similarly, Arul et al. screened the DrugBank database against vital targets of SARS-CoV-2, and among the screened compounds, it was observed that anticancer drugs such as regorafenib, sorafenib, and lifirafenib have a high binding affinity score for the spike protein through molecular docking experiments [29].
3.2. Molecular Interaction and Binding Mode
In order to evaluate the binding site interactions between the screened compounds and the SARS-CoV-2 Mpro, PF-03715455, lifirafenib, and Mol181 were selected (Figures 2–4).
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As described in Figure 2, the PF-03715455 drug was fixed in the binding pocket of SARS-CoV-2 Mpro via conventional hydrogen bond with GLU 166, CYS 145, and LEU 141; hydrophobic interactions (Pi-Sigma, Pi-Pi-T-shaped, Alkyl and Pi-Alkyl) with HIS 41, MET 165, HIS 163, CYS 145, and PRO 168; electrostatic interaction (Pi-anion) with PHE 140; and miscellaneous interaction (Pi-sulfur) with CYS 145. However, lifirafenib (Figure 3) interacted by forming the conventional hydrogen bond interactions with ALA 191; carbon hydrogen bond and Pi-donor hydrogen bond interactions with THR 190 and GLU 166; hydrophobic interactions (Pi-Pi-T-shaped, amide-Pi-stacked, alkyl, and Pi-alkyl) with HIS 41, MET 49, CYS 145, and PRO 168; and halogen (fluorine) interaction with GLN 189. Also, the Mol181 compound (Figure 4) was fixed in the binding pocket of SARS-CoV-2 Mpro by forming the same type of interactions, and the most important are the hydrogen bond interactions with CYS 145 and HIS 41 (catalytic dyad residues). From these results, we can conclude that the screened compounds are more likely to be anti-COVID-19 compared to the remdesivir and ritonavir drugs (Figures 5 and 6).
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4. Conclusion
In this study, a structure-based virtual screening (SBVS) was applied on the high similar remdesivir- and ritonavir-approved drugs, selected from the DrugBank database as well as on a series of ritonavir derivatives, selected from literature. The SBVS was performed by using AutoDock VINA implicated in PyRx 0.8 software. The top 20 hits based on their highest binding free energy were then verified by using AutoDock tools. Among the top 20 hit selected compounds, PF-03715455, lifirafenib, and Mol181 exhibited the highest binding affinity along with strong and stable interactions with the binding pocket residues of SARS-CoV-2 Mpro.
Data Availability
The dataset compounds of this work (format: SDF, MOL2, and PDBQT) are available at https://github.com/ELAISS-OUQ/SARS-CoV-2-inhibitors
Ethical Approval
This study does not involve any study with human or animal subjects.
Conflicts of Interest
The authors declare no conflicts of interest.
Acknowledgments
The authors thank the Moroccan Association of Theoretical Chemistry (MATC) for its relevant help regarding software.
Supplementary Materials
Table S1: chemical structures of ritonavir analogues selected from the literature. From MOL 1 to MOL 177, they are synthetized by Kempf DJ et al., and from Mol178 to Mol188, they are synthetized by Kaye PT et al. The chemical structures of all compounds were designed and optimized using ChemDraw and Chem3D software. Table S2: binding free energy of DrugBank compounds. The calculation was realized using AutoDock VINA, implicated in the PyRx tool. Each drug was calculated using 9 conformations which are classified by binding affinity (kcal/mol). Table S3: binding free energy of ritonavir derivatives. The calculation was realized using AutoDock VINA, implicated in the PyRx tool. Each drug was calculated using 9 conformations which are classified by binding affinity (kcal/mol). (Supplementary Materials)