key: cord-0798230-zdljt79k authors: Gyebi, Gideon A.; Ogunyemi, Oludare M.; Adefolalu, Adedotun A.; Rodríguez-Martínez, Alejandro; López-Pastor, Juan F.; Banegas-Luna, Antonio J.; Pérez-Sánchez, Horacio; Adegunloye, Adegbenro P.; Ogunro, Olalekan B.; Afolabi, Saheed O. title: African derived phytocompounds may interfere with SARS-CoV-2 RNA capping machinery via inhibition of 2’-O-ribose methyltransferase: an in silico perspective date: 2022-04-12 journal: J Mol Struct DOI: 10.1016/j.molstruc.2022.133019 sha: ea2ecb9fe12feb673df9d572248e353f9f47b0f6 doc_id: 798230 cord_uid: zdljt79k Despite the ongoing vaccination against the life-threatening COVID-19, there is need for viable therapeutic interventions. The S-adenosyl-L-Methionine (SAM) dependent 2-O’-ribose methyltransferase (2′-O-MTase) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents a therapeutic target against COVID-19 infection. In a bid to profile bioactive principles from natural sources, a custom-made library of 226 phytochemicals from African medicinal plants with especially anti-malarial activity was screened for direct interactions with SARS-CoV-2 2′-O-MTase (S2RMT) using molecular docking and molecular dynamics (MD) simulations as well as binding free energies methods. Based on minimal binding energy lower than sinefungin (a reference methyl-transferase inhibitor) and binding mode analysis at the catalytic site of S2RMT, a list of 26 hit phytocompounds was defined. The interaction of these phytocompounds was compared with the 2′-O-MTase of SARS-CoV and MERS-CoV. Among these compounds, the lead phytocompounds (LPs) viz: mulberrofuran F, 24-methylene cycloartenol, ferulate, 3-benzoylhosloppone and 10-hydroxyusambarensine interacted strongly with the conserved KDKE tetrad within the substrate binding pocket of the 2′-O-MTase of the coronavirus strains which is critical for substrate binding. The thermodynamic parameters analyzed from the MD simulation trajectories of the LPs-S2RMT complexes presented an eminent structural stability and compactness. These LPs demonstrated favorable druggability and in silico ADMET properties over a diverse array of molecular computing descriptors. The LPs show promising prospects in the disruption of S2RMT capping machinery in silico. However, these LPs should be validated via in vitro and in vivo experimental models. The coronavirus disease-19 (COVID- 19) , was classified a worrisome global pandemic by the World Health Organization (WHO), following the virulent infection rate of Severe Acute Respiratory Syndrome Coronavirus 2 -(SARS-CoV-2) in humans [1] . Recent epidemiological findings present a cumulative total of about over 246 million confirmed cases and 5 million deaths have been reported since the start of the outbreak. [2] . The SARS-CoV-2 belongs to one of the two zoonotic coronaviruses, the other ones being the Middle East Respiratory Syndrome Coronavirus (MERS-CoV). MERS-CoV and SARS-CoV have engendered severe respiratory disorder in mankind since the 21st century commenced [3] . SARS-CoV-2 has been described to be part of the most virulent viruses of this century, with the most fatalities till date [4] . Coronaviruses are described as rapidly evolving viruses, with a high rate of genomic mutation [5] . Recently, several variants of SARS-CoV-2 have been identified: the United Kingdom (UK), South Africa and Brazil variants are cited in the several literatures as (B.1.1.7 for UK, 501Y.V2 or 20C/501Y.V2B.1.351 for South Africa and P.1 for Brazil variants) [6] . This, along with a high infection rate has made the development of drugs quite elusive. Like the earlier coronaviruses, SARS-CoV-2 makes use of its cell environment for its replication and survival [7] . The viral RNA maintains its integrity through the "cap", a unique organization towards the 5' end of the RNA molecule which comprise of a C-2′-O-methyl-ribosyladenine and N-methylated guanosine triphosphate; an arrangement similar to the host cell's RNA [8, 9] . The "cap" structure plays significant function in pre-mRNA splicing, mRNA export, RNA stability and escaping the cellular innate immune system [10] . However, in humans, the cap is established in the nucleus of the cell, on the newly transcribed RNA to which the virus has no access. Consequently, they have to own their specified cap-synthesizing enzymes [9] . The last methylation step that cumulates into the RNA cap requires two enzymes, non-structural proteins (nsp) 14 and 16. The nsp 14 for GTP nucleobase, N-7 methylation while the nsp 16 for C-2′-O methylation of the following nucleotide. Both enzymes are methyltransferases (MTases) that depend on Sadenosylmethionine (SAM) [11, 12] . Nsp14 when complexed to nsp10 has been reported to reduce cases of mismatched nucleotides via its exoribonuclease domain (ExoN) [13] . The 2'-Oribose methyltransferase (2'-O-MTase) activity of nsp16 is also influenced by the enzyme's association with nsp10 [13] , The activity of nsp16 functioning as a 2'-O-ribose methyltransferase (2'-O-MTase) is also influenced by its association with nsp10 [14, 15] . These properties indicate nsp14 and nsp16 as promising therapeutic targets for SARS-CoV-2, especially nsp16 being a very promising molecular target for structural drug design. The 2′-O methyltransferase (MTase) is also essential for coronaviruses replication (in cell cultures) [16, 17] . Identifying bioactive compounds with therapeutic activities against these targets is a necessary step to designing potent antiviral agents. Initial large-scale screening of bio-active agents capable of inhibiting target proteins, using bioinformatics tools have been variously reported [18] [19] [20] [21] [22] [23] . The use of plants and their parts, 'herbal remedies', in traditional medicine has been well documented. These plants are used as concoctions, decoctions, infusions etc. Indeed, the efficacy of many of these remedies has been attributed to their bioactive phyotcompounds [24] . Compounds derived from plants have been known to possess enormous structural diversity that has served as good starting points for investigating new drug [25] . There are several reports that focuses on the use of computation methods to screen different databases and libraries of natural compounds for potential inhibitors of several targets of SARS-CoV-2, this information have been compiled in some reviews [26, 27] . Though there are few reports that targets 2'-O-MTase as a viable therapeutic target [28, 29] , there is no report on the repurposing of antimalarial compounds against SARS-CoV-2 2'-O-MTase The inhibitory potential of phytocompounds against viral methyltransferases have been well documented [30, 31] . This approach can be exploited in the quest for inhibitors of important targets against the novel SARS-CoV-2. In the wake of the ravaging (and still evolving) effect of the COVID-19 pandemic, the dearth of effective anti-viral drugs, and the relatively long process of drug discovery, computational simulation techniques has been a viable tool employed to study the evolving mutations [32, 33] , and for screening possible novel drug candidates [34] [35] [36] [37] [38] . In this study, we employ computational techniques to predict the interactions of a list of bioactive phytocompounds (BP) that were compiled from literature search and known to be derived from African medicinal plants against SARS-CoV-2 and other coronaviruses 2'-O-MTase. The 3D structure of nsp-16/10 of SARS-CoV-2 complexed with its native substrate (PDBID: 6WRZ), and previous viruses SARS-CoV (PDB ID: 3R24) and MERS-CoV (PDB ID: 5YNB), were retrieved from the Protein Data Bank (http://www.rcsb.org). Existing ligands and water molecules associated with the protein structures were removed and missing hydrogen atoms were added. Using MGL-AutoDockTools (ADT, v1.5.6), the Kollamn charges were added as the partial atomic charge [39] . The nonpolar hydrogens were merged while the polar hydrogens were added to the proteins. This procedure was applied to all proteins and then saved into a dockable pdbqt format for docking calculations. The structure data format (SDF) of the reference inhibitors (sinefungin and S-adenosyl-Lhomocysteine (SAH)) and 226 phytocompounds were downloaded from the PubChem database (www.pubchem.ncbi.nlm.nih.gov). These ligands were converted to mol2 using Open babel [40] . Compounds that were unavailable on the database were drawn using ChemDraw version 19, the same was converted to mol2 chemical format. The screening of the 226 bioactive compounds against SARS-CoV-2 2'-O-ribose methyltransferase (S2RMT) was performed using AutoDock Vina [41] . ]. Based on the docking scores, interaction in the catalytic site and binding poses, 26 hit phytocompounds were selected. These hit phytocompounds were docked for interaction with the active pockets of the S2RMT of other strains (SARS-CoV and MERS-CoV). For all the docking In OpenBabel that is incorporated into PyRx 0.8. the Universal Force Field (UFF) was used as the energy minimization parameter and conjugate gradient descent as the optimization algorithm. The energy of all the ligands were minimized using conjugate gradient descent as the optimization algorithm in OpenBabel that is incorporated into PyRx 0.8. The active sites of the three enzymes were defined by the grid boxes and presented in Table 1 . All other parameters were kept as default. Desmond module of Schrodinger 2019-4 was employed for the MD simulation of the LPs-S2RMT. Water boxes were added to the proteins subsequent to addition of the missing hydrogen atoms and removal of any ligand in the TIP3P molecules solvent system [42] under orthorhombic periodic boundary conditions for 10 Å, buffer region with OPLS3 force field. An isothermal-isobaric ensemble (constant number of particles N, constant pressure P and constant temperature T) which is an ensemble of Nose-Hoover thermostat [43] and barostat was applied to maintain the constant temperature (310 K) and pressure (1 bar) of the systems, respectively. An energy minimization of 1000 steps with steepest descent followed by conjugate gradient algorithms was utilized. The Parameters such as temperature, salt concentration, and pH were set snapshots (one every 10 ns as shown in figure S19: supplementary data). PCA and FEL analysis and covariance matrix generation were performed through covar and anaeig GROMACS modules with Desmond MD trajectories and represented by matplotlib Python library. The LPs for S2RMT were submitted for drug-likeness and ADMET filtering analysis. The SwissADME webserver (http://www.swissadme.ch/index.php) was used to analysis the druglikeness using the Lipinski and Veber filtering tools [44] . Several molecular descriptors on the SuperPred webserver (http://lmmd.ecust.edu.cn/admetsar1/predict/) was used to analysis the predicted Absorption, Distribution, Metabolism, Excretion and toxicity (ADME/tox [45] . The canonical SMILES of the LPs were used for the analysis. The The interactions of the LPs with the amino acid residues of coronaviruses 2'-O-MTase is given in Table 3 An in-depth 100 ns MD simulation was performed on the LPs complexed with S2RMT. In other to access the stability of the bound system and the structural integrity upon the binding of the phytochemicals, the MD simulation trajectories of the complex systems were compared to that of the unbound systems. The following thermodynamic parameters (RMSD, RMSF SASA, RoG, and number of H-bonds) protein secondary structure, ligand properties and protein-ligand contacts were computed from the trajectories, the plots were presented as a function of time frame. The ro Protein secondary structure elements (SSE) of the S2RMT such as the alpha-helices and betastrands were monitored throughout the simulation. Figure 8a shows the SSE distribution by residue index. Figure 8b summarizes the SSE composition, while figure 8c monitors each residue and its SSE assignment over time. The result of the analysis showed that 19.75 % was Helix, 15 .28% was strands, while the Total SSE was 35.05% (Figure 5) The RMSD plots for the five systems show that they were equilibrated before 10 ns. The systems exhibited the same progression of RMSD with minimal fluctuation with average RMSD values of 6.83043, 5.674218, 6.124726, 6.369042 and 5.989651 Å for the unbound enzyme, and S2RMT complexed to 10-Hydroxyusambarensine, Mulberrofuran F, 3-Benzoylhosloppone, 24-Methyono cycloartenol respectively. The binding of the lead phytochemicals reduced the fluctuation in the phytochemical-enzyme complex system; this indicates a more compacted structure upon the binding of the phytochemicals ( Figure 6 ). The LP-S2RMT systems were further analysed in Figure S1 -S4 (suplemantery data) The Cα shows the RMSD evolution of a protein (left Y-axis). The ligand RMSD (right Y-axis) indicates how stable the LPs are with respect to the S2RMTP and its binding pocket. The RMSF plots reveal the flexibility of the amino acid residues of the protein. Figure 7) . The RMSF plots of the LP-S2RMT systems were analysed to reveal the secondary structure elements (alpha-helical and beta-strand) regions that interacted with the LP. For the 4 LP-S2RMT systems, the highest fluctuation was observed with the amino acid residues close to residue no. 300 and after residue no. 350. These residues weren't involved in interaction with the ligand. A minimal fluctuation was observed with the interacting amino acid residues before amino acid residue no. 150. The catalytic and substrate binding residue were stable throughout the simulation period. (Figure S5 : Figure S6c ). Atoms of the benzoyl ring moiety of 3-Benzoylhosloppone were the most stable, while the alkyl, carbonyl and hydroxyl group on hosloppone moiety caused some level of fluctuations ( Figure S6d ). The extent of the compactness of the enzyme upon binding of the ligands is measured from the RoG plots and values. A stably folded protein structure presents a steady RoG plot. The Surface Accessible Surface Area analysis The measure of solvent accessible by the surface of the enzymes was computed from the generated SASA values for the systems. Both RoG and SASA plots indicates the level of structural unfolding of proteins with reference to its original structure. Figure 9 show the SASA plots for the enzymes systems. The average SASA values for the S2RMT systems are 20326.16, 21156.28, 21112.91, 20899.48 and 20900.15 for the unbound enzyme and the enzyme complexed to 10-Hydroxyusambarensine , Mulberrofuran F, 3-Benzoylhosloppone, 24-Methyono cycloartenol respectively (Figure 9 ). The average number of hydrogen bonds for the unbound enzyme, 10-Hydroxyusambarensine , Mulberrofuran F, 3-Benzoylhosloppone, 24-Methyono cycloartenol complexes are 53.72927, 46.89011, 48.31968, 50.53147 and 48.3956. In the AChE systems, a slight reduction in average number of hydrogen bond was observed in the complexes when compared to the unbound protein ( Figure 10 ). The S2RMT interactions or contacts with the LPs were monitored throughout the simulation. A timeline representation of the interactions and contacts (H-bonds, Hydrophobic, Ionic, Water bridges) summarized in Figures S11-S14 is presented in the supplementary data. The LPs properties analyzed on its reference conformation. From the plots ( Figure S7a-10a The computed free energy estimations for 11 snapshots (one every 10 ns) are summarized in average values and their standard deviation in Table 4 The free energy landscape representations generated by the two first principal components (PC1 and PC2) of the complexes with each one of the inhibitors show similar PCA distribution in the SARS-Cov-2 2'-O-MTase-ligand complexes. Additionally, all of them have differences with the PCA distribution for free protein system. It was observed more different metastable conformations with low-energy states, represented as free energy basins in the blue regions, for those complexes with inhibitor respect to the observed in the free protein. Besides, only one region near to the minimum energy was detected, while rest of complex show more of one metastable region with the minimum value ( Figure 15 ). Regarding the traces of covariance matrix, the most relevant evidence is the difference of trace between free protein (10.7905 nm 2 ) and protein binds with Mulberrofuran F (8.35602 nm 2 ). Thus, these results suggest SARS-Cov-2 2'-O-MTase structure obtains a greater compaction when is binding of Mulberrofuran F due to trace decrease in the complex. The rest of ligands don't show a considerable increase in complex compaction respect protein (Table 5 ). The result for the predictive druglikeness and ADMET filtering analyses for the LPs presented in table 6 . For ADMET analyses, the molecular descriptors used for the filtering included blood brain barrier (BBB) penetration, this who compounds could cross the blood brain barrier, the The three compounds had high probability of absorption, subcellular distribution, and low toxicity [47] . The ADMET analysis shows that the LPs have the ability to be absorbed in the human intestine, high aqueous solubility, low acute oral toxicity with a good bioavailability score (Table 6 ). SARS-CoV-2 is a virulent and highly evolving virus, whereas the drug discovery process has not matched the increasing therapeutic need of this viral infection [48] . Naturally existing phytocompounds from plants are potential bioactive repositories, including antiviral activity, which, if adequately explored, could provide affordable, accessible and available use as therapeutic agents against coronavirus infections [49] . from the roots of Strychnosusambarensis, previously reported as an antimalarial [54] . Mulberrofuran F isolated from Morus alba, has been used to treat hypotension [55, 56] . 24-Methylene cycloartenol ferulate, also called γ-Oryzanol (OZ) has been identified in various cereals, including barley, rice bran and corn [57] . It has been reported to exhibit antioxidant, anti-lipidemic, anti-diabetic and neuro-modulatory properties [57, 58] . These LPs interacted with the surface residues (Lys-46, Asp-130, Lys-170 and Glu-203) at the bottom of the central groove, thatcatalysis the transfer SAM methyl group within the substrate binding pocket [46] . In all strains of CoV, the catalytic tetrad (Lys46, Asp130, Lys170) and Glu203 are conserved [59] , this may have been responsible for the high binding potential to the three CoV understudied. Though the LPs interacted with the catalytic residues in a similar binding pattern as the SAH (the product of methylation of SAM) and sinefugine (a known inhibitor), they interacted with a stronger binding affinity than these compounds. Thus, these compounds may be able to bind to the S2RMT tightly and hence compromise the RNA methylation function of the enzyme, this will in turn, disrupt the capping machinery, prevent evasion of recognition by the host innate immune system [60] [61] [62] and preclude the viruses from resisting the IFN-mediated antiviral response [10, 16] . To further understand the dynamic behavior of the LPs at the binding site of S2RMT, MD simulation was employed [63] . The binding patterns and per-residue amino acid interactions of the LPs-S2RMT complexes in the dynamic state collaborated with those done from the static docking analysis. The various thermodynamics parameters that were analyzed from the 100 ns atomistic MDS trajectory files of the LPs-S2RMT complexes revealed stable complexes that can be adapted into other forms of experiments. The comparison of the RMSD plots for the complex systems shows that the binding of the LPs to S2RMT did not cause any structure deformation in the protein. [64] . From the RMSF plots analysis of the four systems, the higher fluctuation that was observed with the interacting residues is consistent with previous reports, where higher structural fluctuations occurred in ligand binding sites of catalytic loop regions [65] . The RoG and SASA plots of all the systems did not show fluctuation above the optimum of > 2 Å further indicating that the structural integrity of the proteins was preserved [66] . The binding free energy that is measured from the simulation trajectories provides more accurate computation of ligand binding affinities than the static docking analysis [67] . These results were calculated based on the total binding free energy of the complex. In these calculations, the binding free energy (∆G bind ) measures the affinity of a ligand to its target protein. Thus, the ∆G bind calculations are important to gain in-depth knowledge about the binding modes of the hits in drug design [68] . The results from the binding free energy calculation (MM-GBSA) agreed with that from the docking analysis; further establishing Mulberrofuran F as the most potent phytocompound. Also, from the predictive drug-likeness, pharmacokinetic and ADMET filtering analyses, the top docked phytocompounds were predicted to be druggable and non-toxic. The result from the filtering analyses showed descriptors that suggests a favorable ADMET and pharmacokinetic properties. This further indicates the druggable potential of the LPs [69, 70] . The LPs displayed properties that suggest their ability to cross the BBB, hence their potential to ensure overall viral clearance in the brain cells [47] . Also, the LPs expressed high possibility of human intestinal absorption and not susceptible to the permeabilityglycoprotein (P-gp, a drug efflux pump). Therefore, it is suggested to be well absorbed into the blood stream, subverting the restraining effect of the P-gp to pump compounds back into the intestinal lumen [71] . This work has been funded by the Fundación Séneca de la Región de Murcia under Project 20988/PI/18. This research was partially supported by the omputer resources and the technical Supercomputing Center, the e-infrastructure program of the Research Council of Norway via the supercomputer center of UiT−the Arctic University of Norway, and by the supercomputing infrastructure of the NLHPC (ECM-02), Powered@NLHPC CRediT author statement Conceptualization, Visualization, Original draft Draft Preparation, Methodology Oludare M. Ogunyemi: Methodology, Writing -review & editing Adedotun A. Adefolalu: Writing -review & editing Alejandro Rodríguez-Martínez: Methodology Juan F Methodology Horacio Pérez-Sánchez: supervision Adegbenro P. Adegunloye: Writing -review & editing Olalekan B. Ogunro: Writing -review & editing Saheed O. 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The authors declare that they have no competing interests Ethical approval Not required.