key: cord-1033990-fys3hywo
authors: Poustforoosh, Alireza; Hashemipour, Hassan; Tüzün, Burak; Pardakhty, Abbas; Mehrabani, Mehrnaz; Nematollahi, Mohammad Hadi
title: Evaluation of potential anti-RNA-dependent RNA polymerase (RdRP) drugs against the newly emerged model of COVID-19 RdRP using computational methods
date: 2021-02-20
journal: Biophys Chem
DOI: 10.1016/j.bpc.2021.106564
sha: 42f8dc8752455d596f8078fd9f867be5240e2f13
doc_id: 1033990
cord_uid: fys3hywo

INTRODUCTION: Despite all the efforts to treat COVID-19, no particular cure has been found for this virus. Since developing antiviral drugs is a time-consuming process, the most effective approach is to evaluate the approved and under investigation drugs using in silico methods. Among the different targets within the virus structure, as a vital component in the life cycle of coronaviruses, RNA-dependent RNA polymerase (RdRP) can be a critical target for antiviral drugs. The impact of the existence of RNA in the enzyme structure on the binding affinity of anti-RdRP drugs has not been investigated so far. METHODS: In this study, the potential anti-RdRP effects of a variety of drugs from two databases (Zinc database and DrugBank) were evaluated using molecular docking. For this purpose, the newly emerged model of COVID-19 (RdRP) post-translocated catalytic complex (PDB ID: 7BZF) that consists of RNA was chosen as the target. RESULTS: The results indicated that idarubicin (IDR), a member of the anthracycline antibiotic family, and fenoterol (FNT), a known beta-2 adrenergic agonist drug, tightly bind to the target enzyme and could be used as potential anti-RdRP inhibitors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These outcomes revealed that due to the ligand-protein interactions, the presence of RNA in this structure could remarkably affect the binding affinity of inhibitor compounds. CONCLUSION: In silico approaches, such as molecular docking, could effectively address the problem of finding appropriate treatment for COVID-19. Our results showed that IDR and FNT have a significant affinity to the RdRP of SARS-CoV-2; therefore, these drugs are remarkable inhibitors of coronaviruses.

reported so far, including the 229E and NL63 strains of HCoVs (Alphacoronaviruses), and the OC43, HKU1, SARS, MERS, and SARS-CoV-2 HCoVs (Betacoronaviruses) [4] , [5] . Two groups of proteins characterize HCoVs: structural proteins like spikes marking all coronaviruses; and non-structural proteins, such as RNA-dependent RNA polymerase (RdRP) [6] . As a viral enzyme, RdRP is a vital component in the life cycle of RNA viruses, which has been targeted in various viral infections, including the Zika virus (ZIKV), hepatitis C virus (HCV), and coronaviruses (CoVs) [7] [8] [9] [10] [11] . Nucleotide and nucleoside analogs with polymerase inhibition activity are considered as an essential group of antiviral agents [12] . The active site of RdRP is thoroughly protected with two surface-accessible and successive aspartates in a beta-turn structure [13]- [15] . The structure of nsp12, the RdRP of SARS-CoV-2, which is bound to nsp7 and nsp8 with an overall view on the complex architecture has been previously specified [16] , but the mechanism of RNA recognition and synthesis has yet to be determined. One of the most significant barriers to finding nucleotide-based drugs and their reliable evaluation is the absence of molecular detail on the substrate identification through the replication process. To address this problem, Wang et al. have specified the near-atomic-resolution arrangement of SARS-CoV-2 polymerase in complex with RNA when it is in the catalytic condition [17] , which provides a considerable opportunity for the optimization of drug design and finding dependable drug candidates.

In the battle against coronavirus, scientists have utilized three different approaches for developing drugs [18] . The first approach is to use the current wide range of antivirals [19] .

Although the metabolic features, employed dosages, potential effectiveness, and adverse effects of these therapies are evident, they cannot destroy coronaviruses in a targeted way because of their broad spectrum; also their unfavorable effects cannot be neglected [20] . The second approach is to find molecules that could have therapeutic effects on coronaviruses by screening the molecules present in the existing molecular databases [21] , [22] . Due to the high-throughput screening of this method, many new operations for therapeutic agents could be determined. In

There are also some state-of-the-art developments in discovering drugs against COVID-19.

These approaches could affect this area of research considerably. One of these approaches was utilized by Hu et al. They conducted a normal mode analysis (NMA) of spike protein to comprehend the connection between their nanomechanical features and virus lethality as well as infection rate. Their investigation revealed that epidemiological virus properties could be directly connected to pure nanomechanical features [24] . Panda et al. used a structure-based drug designing and immunoinformatics approach to evaluate the effects of different antiviral drugs against the spike glycoprotein, main protease, and receptor-binding domain (RBD) of SARS-CoV-2 [25] . Another crucial approach to manage the COVID-19 pandemic is preparing sensitive diagnostic systems. Electrochemical SARS-CoV-2 biosensing supported by artificial intelligence is one consequential part of these systems, which was adequately reviewed by Kaushik et al. [26] . Furthermore, the role of nanomaterials and antimicrobial nano-coatings against COVID-19 is indisputable and was thoroughly addressed by Rai et al. [27] . In another study, the CRISPR/Cas13 system as a potential approach for the treatment of COVID-19 was discussed, which could attract considerable attention in the near future [28] .

The fastest way to find therapeutic agents against coronaviruses is to identify the potential therapeutics among the approved or developing drugs. For this purpose, computational studies such as molecular docking or molecular dynamics could be used as beneficial strategies [17] , [29] , [30] .

In this study, we used molecular docking to evaluate the potential of different drugs to target the post-translocated catalytic complex of COVID-19 RdRP (PDB ID: 7BZF), which is the reaction

To gain a better insight into the recently emerged COVID-19 RdRP post-translocated catalytic complex, the sequence of this practical model was aligned with the structure of SARS-CoV-2 RdRP in complex with cofactors in reduced conditions (PDB ID: 7BTF), which is the template of RdRP post-translocated catalytic complex [17] .

The structure of COVID-19 RdRP post-translocated catalytic complex (PDB ID: 7BZF) with an electron microscopy method and the resolution of 3.26 Å was downloaded from the PDB database (http://www.rcsb.org/pdb). Maestro molecular modeling platform (version 12.2) by Schrödinger program was used for these calculations [32] . To prepare the structure, the protein preparation module [33] from the Schrödinger Suite was used to add the hydrogen atoms, remove the waters beyond 5 Å from the binding sites, and optimize the structure for creating an H-bond network. Finally, the energy was minimized using optimized potentials for liquid simulations (OPLS3e) force field with a default setting of 0.30 Å root mean square deviation (RMSD). The overall stability and stereochemical quality of the protein in the 3D structure were determined using the Ramachandran plot [34] .

Before performing docking, the identification and characterization of the binding site should be carried out for a more reliable and accurate molecular docking. The active sites for target-ligand binding interaction were predicted using the SiteMap module of Schrödinger software (SiteMap, Maestro molecular modeling platform (version 12.2) by Schrödinger program). SiteMap can determine the binding sites in a large-scale validation with best results for the sites that bind ligands with the subnanomolar association, and in addition, it can propose an adjusted version of the score that accurately classifies the druggability of proteins [35] .

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The receptor grid was developed using Glide (Glide, Schrödinger, generated LLC, New York, NY, 2020) by preferring the predicted active sites. The box was centroid of the active site of the protein receptor with the default parameters of van der Waals scaling factor of 1Å, partial charge cut-off at 0.25, and docked ligand length of 20 Å.

Twenty-eight compounds from different databases and literature were identified as test ligand molecules against the COVID-19 RdRP post-translocated catalytic complex. The preparation of ligands was carried out utilizing the LigPrep module of Schrödinger [36] , and the OPLS3e force field was used for generating the ionization and tautomeric states at pH 7.0 ± 2.0 using the Epik module of LigPrep. The name and structures of the chosen drugs are presented in Table 1 .

High-throughput virtual screening and molecular docking of the chosen ligands against the target protein was done using the Glide module of the Schrödinger Suite. The prepared output files of LigPreb were used as input files for screening and combined with redistribution for sub-jobs.

The unique compounds were identified by generating unique properties for each input compound, and the Epik state penalties were used for docking. Standard-precision (SP) and extra-precision (XP) docking calculations were performed. The flexible method was utilized for docking, and 10% of the best compounds were kept after each step with retaining all good scoring states. The values of the scaling factor and partial charge cut-off were set at 0.80 and 0.15, respectively. The ligands that had a sufficient association with the RdRP were authorized.

In the final step, the pose viewer was used for analyzing the interactions of the selected ligands and the docked protein complex.

Considering the fact that rigid docking has some limitations as the protein is kept rigid [37] , [38] , the accuracy of its results has to be further evaluated. The induced-fit docking module of J o u r n a l P r e -p r o o f Journal Pre-proof Schrödinger was utilized to accomplish induced-fit docking [39] , [40] . In brief, the ligand is docked into the active site of the protein utilizing the Glide protocol (SP) [41] , while the active site residues of the protein are maintained rigid. Next, the protein side chains or backbone are refined using the prime refinement module [42] , [43] . Finally, the ligand is redocked into the refined protein conformation, and the score is calculated to rank the protein-ligand complex. The centroid of the residues was selected for the box center option, and the residues of active sites were selected. For the conformational sampling option, the sample ring conformation was chosen with the energy window of 2.5 kcal/mol. The receptor van der Waals scaling and the ligand van der Waals scaling were set to 0.7 and 0.5, respectively. The residues were refined within 5 Å of the ligand poses, and Glide redocking was performed on the structures within 30 kcal/mol of the best structure. Remdesivir [17] Favipiravir [17] Ribavirin [6] Galidesivir [6] Tenofovir [6] Setrobuvir [6] J o u r n a l P r e -p r o o f 

Not surprisingly, as shown in Fig. 1 , the COVID-19 RdRP post-translocated catalytic complex was highly conserved between SARS-CoV-2 RdRP in complex with cofactors in reduced conditions. The homology of these two structures was as high as 97%. 

The quality and the reliability of the target protein in the 3D form are of importance for drug design. The Ramachandran plot was obtained using the Schröodinger Suite to display the allowed and disallowed regions. As can be seen in Fig. 2 , more than 98.9% of the residues were in the allowed regions (95% in the favored regions and 3.9% in the allowed regions), and only 1.1% of the residues were present in the disallowed regions implying the reliability of the structure.

The active sites of the protein are presented in Fig. 3A . Since the site score of 1 and above confirms the high druggability of a site [44] , the first two sites with the site scores of 1.040 and 1.019 were chosen as the target regions. The details of the five active sites are given in Table 2 .

In Fig. 3B and C the second active site with the size of 551 that indicates different parts of the second site is presented. The green regions show the hydrophilic domains of the site, while the blue and red regions are hydrogen-bond donors and hydrogen-bond acceptors, respectively. The residues that are included in the active sites are presented in Table 3 .

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Two active sites of the protein were selected to be used as the target regions. The first active site was chosen because it had the highest activity (site score: 1.040), and the next one was chosen due to its size (size: 551). However, the site score of the second site was heightened too. The results of molecular docking are presented in Table 3 . The top three docking scores of rigid docking in the first site belonged to idarubicin (IDR) (-6.424), fludarabine (-6.401), and cromolyn (-6.110). The top three docking scores in the second site belonged to IDR (-8.611), fenoterol (FNT) (-8.562), and silybin (-7.786). As can be seen, IDR had the most binding affinity to both sites. The ligand among the residues of the active site is shown in Fig. 4 . The ligand interaction of IDR is presented in Fig. 5 . As can be seen, the ligand had direct contact with Arg 555 and Lys 500. The docking scores of induced-fit docking revealed that FNT is comparable to IDR because the docking score of FNT is higher than that of IDR in the second site. In Fig. 6 FNT is presented among the residues and in Fig. 7 the interactions of FNT with RdRP are described. 

Despite all the efforts since the emerging of new COVID-19, no particular drug with a direct effect against this coronavirus has been discovered or created so far. The transient nature of the cause of this global disaster could be considered as a practical reason for this setback. Since developing antiviral treatment is time-consuming, the screening of approved drugs could be a practical approach for finding a probable treatment for coronavirus. In recent years, in silico screening of bioactive components based on molecular docking has become a common strategy in the pharmaceutical industry [45] . Moreover, the high-speed development of computer technology and the rapid increase of biological data on therapeutic targets, make it understandable that the use of computational methods and in silico approaches has increased remarkably [46] [47] [48] [49] . In fact, in silico approaches enable the possibility of virtual screening among millions of compounds, which leads to an affordable cost of discovering the desired drug candidates [50] . In this study, potential anti-RdRP drugs and some antiviral drugs were chosen to be evaluated against the newly emerged COVID-19 RdRP post-translocated catalytic complex.

The active sites of the structure were distinguished to find more reliable results, which could indicate whether a candidate site could make a tight ligand binding or not. The SiteMap module of Schrödinger software could provide this opportunity. In addition, the SiteMap results could categorize the sites into the druggable and undruggable sites [51] , which gives a better insight into the physical basis of classification. The sequence alignment of 7BZF and its template, 7BTF, showed that the gaps between these two structures were less than 3%. The significant gap J o u r n a l P r e -p r o o f Journal Pre-proof was from glycine 897 to aspartate 910, which consisted of Gly897, His898, Met899, Leu900, Asp901, Met902, Tyr903, Ser904, Val905, Met906, Leu907, Thr908, Asn909, and Asp910.

Theses residues were the same in SARS-COV-2, Bat-coronavirus-RaTG13, SARS-COV, Pangolin-coronavirus, and BtRs-BetaCoV/YN2018D, but the residues 908 and 909 in MERS-CoV were cysteine and glycine, respectively [17] . For the molecular docking, the Glide module of the Schrödinger Suite was utilized, which provides more reliable results than a variety of different docking programs such as AutoDock, GOLD, and FlexX [52] . After a high-throughput screening and considering the docking score of different drugs, IDR exhibited a tight binding affinity to the host protein. IDR is a member of the anthracycline antibiotic family, which is usually used for the treatment of acute myeloid leukemia [53] . It has been reported that IDR can interrupt the DNA topoisomerase IIA subunits [54] and effectively block the viral protein and RNA synthesis [55] . Charak and Mehrotra, using UV-visible spectroscopy, observed that IDR gets intercalated between the DNA bases [56] . The docking results against two different sites of 7BZF showed that IDR could tightly bind to the RdRP, especially at the second active site. There were different interactions between IDR and 7BZF. IDR created hydrogen bonds with two residues Lys500 and Arg555, with the bond lengths of 2.80 Å and 3.11 Å, respectively. The only protein residue that had a hydrophobic interaction with IDR was Arg836. The most important thing about this site was that it contained RNA, and IDR could form multiple bonds with RNA.

IDR and RNA created four hydrogen bonds and a hydrophobic interaction. Bases A0 and C1

formed hydrogen bonds with the lengths of 2.84 Å and 2.80 Å, respectively. Base A4 formed two hydrogen bonds with the lengths of 3.01 and 3.06 Å, respectively. The only base with hydrophobic interaction was C3. It is worth noting that the hydrogen bond of A4, hydrophobic interaction of C3, and hydrogen bond of Arg555 were created with oxygen number 9 of IDR.

The position of this oxygen is presented in Fig. 5 . FNT formed six hydrogen bonds, and only one of these hydrogen bonds was with residues. The rest of the hydrogen bonds were formed

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The authors declare that the investigators in this study have no conflict of interest.

Supplementary material