key: cord-0835941-1wyj8w8b
authors: Rudrapal, Mithun; Celik, Ismail; Chinnam, Sampath; Azam Ansari, Mohammad; Khan, Johra; Alghamdi, Saad; Almehmadi, Mazen; Zothantluanga, James H.; Khairnar, Shubham J.
title: Phytocompounds as potential inhibitors of SARS-CoV-2 Mpro and PLpro through computational studies
date: 2022-02-25
journal: Saudi J Biol Sci
DOI: 10.1016/j.sjbs.2022.02.028
sha: 197be24669be62205456c0627da425050a7717c8
doc_id: 835941
cord_uid: 1wyj8w8b

The inhibition of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) and papain-like protease (PLpro) prevents viral multiplications; these viral enzymes have been recognized as one of the most favorable targets for drug discovery against SARS-CoV-2. In the present study, we screened 225 phytocompounds present in 28 different Indian spices to identify compounds as potential inhibitors of SARS-CoV-2 Mpro and PLpro. Molecular docking, molecular dynamics simulation, molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) binding free energy calculations, and absorption, distribution, metabolism, excretion and toxicity (ADMET) studies were done. Based on binding affinity, dynamics behavior, and binding free energies, the present study identifies pentaoxahexacyclo-dotriacontanonaen-trihydroxybenzoate derivative (PDT), rutin, and dihyroxy-oxan-phenyl-chromen-4-one derivative (DOC), luteolin-7-glucoside-4’-neohesperidoside as promising inhibitors of SARS-CoV-2 Mpro and PLpro, respectively.

Among many disease outbreaks caused by RNA viruses, coronavirus disease 2019 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is by far, the deadliest of all (Rudrapal et al., 2021a; Mohamadian et al., 2021; Khan et al., 2021a) . Since its initial in December 2019, COVID-19 has afflicted human lives worldwide (Liu et al., 2020; Dong et al., 2020) . Current pharmacotherapies available for COVID-19 have their share of controversy with some studies reporting their effectiveness while others are reporting the opposite (Cascella et al., 2021) . The emergence of different new variants, different reports on vaccine side effects, and the global issue of vaccine hesitancy are some other serious issues that have put the world in a state of dilemma as they had crippled our fight against COVID-19 to a certain degree (Cascella et al., 2021; Lucia et al., 2021; Riad, 2021) . These problems mandate the urgent need to find an alternative treatment strategy that will supplement the current treatment regimens of COVID-19. Viral enzymes such as the main protease (Mpro) and papain-like protease (PLpro) are significantly responsible for the replication of SARS-CoV-2 . As inhibition of Mpro and PLpro prevents viral multiplication, they have been recognized as the most favorable targets for anti-SARS-CoV-2 drugs (Dror et al., 2020; Bhat et al., 2020; Kumar et al., 2021a) . Medicinal plants and their phytoconstituents have been proposed as potential phytotherapy for COVID-19 (Prajapati et al., 2021) . The polyphenolic-rich extract of Vitis vinifera (grapevine) has been reported to act (in vitro) against SARS-CoV-2 (Zannella et al., 2021) . It is safe to state that medicinal plants hold tremendous potential to be developed as alternative phytotherapy for COVID-19. Nowadays, computational techniques are being extensively used to investigate the inhibitory potential of phytocompounds against SARS-CoV-2 (Sachdeva et al., 2020; Shah et al., 2020) . Several studies conclude that phytocompounds can potentially inhibit the Mpro and PLpro of SARS-CoV-2 (Gowrishankar et al., 2021; Kumar et al., 2020) . In the drug discovery process, computational techniques including docking and molecular dynamics (MD) simulation offer several advantages owing to their efficiency, accuracy, robustness, and eco-friendly approach (Hassan Baig et al., 2016) .

Spices are used to enrich the flavor and aroma of our daily diet. Ginger, rosemary, onion, turmeric, cloves, fennelflower, black pepper, garlic, clove, cinnamon, coriander, and basil are a few examples among many other consumed spices in India. Spices contain several bioactive compounds that are used for the management of inflammatory diseases, cardiovascular diseases, cancer, metabolic diseases and viral infections (Srinivasan, 2005; Embuscado, 2019; Opara and Chohan, 2014; Yashin et al., 2017) . In the study, a total of 225 phytocompounds present in 28 Indian spices were computationally investigated for their inhibitory action against SARS-CoV-2 by carrying out molecular docking, MD simulation, and binding free energy calculations against Mpro and PLpro.

The proteins structures of SARS-CoV-2 were retrieved from the RCSB-PDB website. The Mpro (PDB id: 6W63, 2.1 Å) and PLpro (PDB id: 7JIW, 2.3 Å) bear the co-crystal inhibitors of X77 and VBY, respectively.

The preparation of proteins was initiated with the removal of water molecules using the Schrodinger suite Maestro (version 2021-2). The residues involving side chain amino acids were filled, the missing hydrogen atoms were added, and the H-bonds were optimized. The OPLS4 force field was applied for minimization of energy until the root-mean-square deviation (RMSD) value averages at 0.30 Å (Roos et al., 2019; Kumar et al., 2021b) .

The chemical structures of phytocompounds present in Indian spices were obtained from an online database, COCONUT (https://coconut.naturalproducts.net/). The LipPrep module of Schrodinger was used to prepare the 3-dimensional (3D) coordinates of all the phytocompounds (Chen and Foloppe, 2010) . The Epik module of Schrodinger suite version 2021-2 was used to achieve an ionization state of pH 7.4, and the tautomer and chirality were also predicted. Energy minimization was carried out with OPLS4 force field and the process was allowed to proceed until the conformation obtained was energetically stable (Roos et al., 2019) .

A compound library of 225 phytocompounds found to be present in Indian spices was studied for their binding affinity towards Mpro and PLpro by molecular docking study (MDS). The glide docking module of Schrodinger Maestro which uses the extra precision (XP) protocol was used for MDS (Friesner et al., 2006; Chen and Foloppe, 2010) . Considering the position of the co-crystallized ligand (Kalita et al. 2020) , the binding site in the protein was identified with the application of default settings to the glide-receptor grid generation module (Halgren et al., 2004) .

Following the same protocol as described above, the native ligands were re-docked into the same binding pocket where they were originally present. This was done to validate the docking protocol to examine if the specified methods used in the present MDS will be able to dock the test compounds into the same binding pocket where the native ligand was initially present. The RMSD value between the original docked ligand and the re-docked ligand was calculated (Hevener et al., 2009 ).

Molecular dynamics (MD) simulation was carried out with GROMACS 2019.2 software (Abraham et al., 2015; Rath et al., 2021; Ghosh et al., 2021; Pasala et al., 2022) . The GROMOS96 54a7 force field was then applied for energy minimization. The SCP water model was used for salvation of protein-ligand complexes. The 'gmxgenion' script was used to electrically neutralize the solvated model by adding chloride ions and sodium ions. Following this, it was equilibrated with 0.3 ns isothermal-isobaric (NPT) and 0.3 ns isothermal-isochoric (NVT) ensembles. The simulation period was for 100 ns. The motion equation was integrated into triplicates with the leapfrog algorithm at 2 femtoseconds time step. GROMACS scripts were used to generate the trajectories such as RMSD, RMSF, and radius of gyration (Rg) (Swargiary et al., 2020; Junejo et al., 2021) . Visual molecular dynamics 1.9.3 was used for visualization and analysis of trajectories (Humphrey et al., 1996) .

The MM-PBSA was used to quantify the binding (free) energies of complexes using the g_mmpbsa script (Kumari et al., 2014; James et al., 2022) . Energies of Van der Waals, polar solvation, electrostatic, and non-polar energy i.e., solvent-accessible surface area (SASA) were calculated.

Lipinski's rule of 5, bioavailability score, and synthetic accessibility score were studied with the SwissADME web tool (Daina et al., 2017) . The median lethal dose (LD 50 ) and toxicity class of the phytocompounds were computed with ProTox-II web tool (Banerjee et al., 2018) . The drug score was obtained using DataWarrior v.5.2.1 software.

MDS revealed the binding affinity of the studied phytocompounds for the active sites of the target enzymes. A high binding affinity indicated by low binding energy involvement is considered as an important factor for selecting compounds to be further studied. The XP glide score of 10 phytocompounds that showed the best binding affinity towards the Mpro and PLpro is given in Table 1 . The glide score of the native ligands originally bound to the target protein is also given in Table 1 . PDT (COCONUT ID: CNP0412082), and rutin (COCONUT ID: CNP0268715) have the best docking score for Mpro. The glide score of PDT and rutin are -15.367 and -12.360, respectively, while that of the native ligand (X77) is -5.966. DOC (COCONUT ID: CNP0301186), and luteolin-7-glucoside-4'-neohesperidoside (COCONUT ID: CNP0359835) have the best docking score for PLpro. The glide score of DOC and luteolin-7-glucoside-4'-neohesperidoside are -12.801 and -9.226, respectively, while that of the native ligand (VYB) is -6.538.

The re-docking method used for the validation of docking study was successful. This process was carried out to see the accuracy and efficiency of the docking method adopted for the study. For Mpro and PLpro, the computed RMSD values between the original inhibitor and the re-docked inhibitor were 2.498 Å and 0.385 Å, respectively.

The 2D and 3D ligand interactions of X77, PDT, and rutin against Mpro are given in Fig. 1 . The co-crystal inhibitor X77 formed conventional H-bonds with ASN142, GLY143, HIS163, and GLU166; hydrophobic attractions with HIS41 and LEU27; electrostatic interactions with MET49 and CYS145; and Van der Waals interactions were shown by different amino acid residues ( Fig.   1 a, b) . PDT formed conventional H-bonds with THR24, CYS44, GLY143, CYS145, GLU166, ARG188, and GLN189; hydrophobic attractions with HIS41, MET49, and MET165; and van der Waals interactions were shown by different amino acid residues ( Fig. 1 c, d) . Rutin formed Hbonds with THR26, GLY143, CYS145, GLU166, and ARG188; hydrophobic interaction with MET165; electrostatic interaction with MET49; and Van der Waals interactions were shown by HIS41 along with different amino acid residues ( Fig. 1 e, f) .

The 2D and 3D ligand interactions of VYB, DOC, and luteolin-7-Glucoside-4'-Neohesperidoside against PLpro are given in Fig. 2 . The co-crystal inhibitor VYB formed conventional H-bonds with ASP164, GLU167, and GLN269; hydrophobic attractions with PRO247, PRO248, TYR264, and TYR273; and Van der Waals interactions were shown by different amino acid residues (Fig.   2 a, b) . DOC formed conventional H-bonds with ASP164, GLU167, PRO248, GLY266, and ASN267; hydrophobic attractions with PRO247; and van der Waals interactions were shown by different amino acid residues (Fig. 2 c, d) . Luteolin-7-Glucoside-4'-Neohesperidoside formed conventional H-bonds with LYS157, GLU161, ASP164, ARG166, GLU167, and THR301; hydrophobic attractions with TYR171 and TYR268; and van der Waals interactions were shown by different amino acid residues (Fig. 2 e, f) .

From results of simulation studies, the conformational stability as well as dynamics complex formation was obtained for 100 ns period.

The RMSD, RMSF, and Rg of the co-crystal inhibitor (X77), and the two ligand (PDT and rutin) complexes were plotted as graphs in Fig. 3 . The RMSD trajectory of the Mpro-X77 complex showed signs of stability as it fluctuates between 0.1nm and ~0.3 nm, and the majority of the fluctuations occurred at ~0.2 nm (Fig. 3a) . The majority of the RMSD trajectory of the Mpro-rutin complex seemed to fluctuate between 0.2nm and 0.3 nm and the observed data was indicative of a stable protein-ligand complex (Fig. 3a) . After 20 ns, the RMSD trajectory of the Mpro-PDT complex fluctuates around 0.3 nm and ends at 0.4 nm which was suggestive of a stable conformation for the ligand on the binding site of Mpro (Fig. 3a) .

The RMSF data of each amino acid residue of SARS-CoV-2 Mpro is plotted in Fig. 3b . During the 100 ns MD simulation, the fluctuation of each amino acid of Mpro-rutin and Mpro-PDT were similar to Mpro-X77. As homogeneity of RMSF was maintained in all complexes, the results obtained from this parameter support and correlate with the RMSD trajectory of the complexes.

The compactness of protein molecule during the simulation period was determined by the Rg. The observed Rg data is given in Fig. 3c . 

The RMSD, RMSF, and Rg of the co-crystal inhibitor (VBY) and the two (luteolin-7-glucoside-4'-neohesperidoside and DOC) complexes were plotted as graphs in Fig. 4 . The PLpro-VBY complex RMSD trajectory showed fluctuation between the range of 0.2nm and 0.4nm; while the majority of the fluctuations occurred at ~0.3nm (Fig. 4a) . The RMSD trajectory of the PLproluteolin-7-glucoside-4'-neohesperidoside complex fluctuates from 0.2 nm up to 0.4 nm for 20 ns and then stabilized between 0.3 nm and 0.4 nm for the rest of the period (Fig. 4a) . After 20 ns, the majority of the RMSD fluctuations of the PLpro-DOC complex occurred between 0.2 5 nm and 0.35 nm, which suggested optimal stability conformation of the ligand while complex formation with PLpro (Fig. 4a) .

The RMSF data of PLpro are plotted in Fig. 4b . During the simulation, the fluctuation of each amino acid of PLpro-luteolin-7-glucoside-4'-neohesperidoside and PLpro-DOC were similar to PLpro-VBY. As the homogeneity of RMSF was maintained in all studied complexes, the findings of the RMSF supports and correlate with the RMSD trajectory of all the complexes (Khan et al., 2021b) .

The observed Rg data for PLpro and its ligand complexes is given in Fig. 4c 

The binding free energy represents the amount of energy generated as a result of protein-ligand interaction (Othman et al., 2021) . A more negative binding free energy suggests more effective protein-ligand binding.

The binding energies of complexes with Mpro are given in Table 2 . Among the selected complexes, Mpro-PDT (-280.170±18.945 kJ/mol) showed more negative binding energy followed by Mpro-X77 (-160.625±15.840 kJ/mol), and Mpro-rutin complex (-146.820±15.448 kJ/mol). In terms of binding free energy, PDT outperforms X77, while rutin failed to outperform X77.

The binding free energies of complexes with PLpro are given in Table 3 . The binding energy of PLpro-DOC (-148.323±15.338 kJ/mol) is more negative followed by PLpro-luteolin-7-glucoside-4'-neohesperidoside (-142.425±22.452 kJ/mol), and PLpro-VBY (-141.573±13.971 kJ/mol). Both DOC and luteolin-7-glucoside-4'-neohesperidoside outperform the native ligand of the protein.

The ADMET parameters of the studied compounds are given in Table 4 . All of the selected compounds violated 3 rules of Lipinski's rule of 5 (molecular weight > 500, hydrogen acceptor > 10, hydrogen donor > 5), and each has a bioavailability score of 0.17. A low synthetic accessibility score means the compound will be easy to synthesize. Rutin (6.52) has the lowest synthetic accessibility score followed by DOC (6.54), luteolin-7-glucoside-4'-neohesperidoside (7.48), and PDT (8.43). The LD 50 of PDT was 2250 mg/kg body weight and 5000 mg/kg was the LD 50 for rutin, DOC, and luteolin-7-glucoside-4'-neohesperidoside. All compounds belonged to toxicity class 5 that suggests their safety for oral consumption. Rutin (0.546604) has the highest drug score followed by PDT (0.2016708), DOC (0.1766791), and luteolin-7-glucoside-4'-neohesperidoside (0.1573803).

In an attempt to find out molecules that could control the multiplication of SARS-CoV-2, several computational studies have been carried out by different researchers to active molecules against Mpro and PLpro (Choudhary et al., 2020; Ghosh et al., 2020; Jamalan et al., 2020; Mirza et al., 2020; Nogara et al., 2021) . Medicinal plants are proposed as a favorable antiviral therapy for COVID-19 as they are reported to contain bioactive molecules that can be effective against COVID-19 (Adhikari et al., 2021; Benarba & Pandiella, 2020) . Previous literature have highlighted the potential impact of medicinal plants and their phytocompounds against SARS-CoV-2 (Zannella et al., 2021; Rudrapal et al., 2021a; Rudrapal et al., 2021b) . Therefore, the present study investigates a total of 225 phytocompounds present in 28 different Indian spices for their inhibitory potential against Mpro and PLpro of SARS-CoV-2 through computational investigations.

The catalytic sites of Mpro and Plpro are essential for active residues of protein molecule and and chemical groups of ligands (Ismail et al., 2021; Osipiuk et al., 2021; . Studies reported that thioflavonoids, epicatechin-3-O-gallate, dobutamine and masoprocol interacted with (binding sites) Mpro and PLpro, and were considered as SARS-CoV-2 inhibitors (Kneller, Phillips, Weiss, et al., 2020; Liu et al., 2020; Ismail et al., 2021) . Thus, bioactive molecules bind to or interact with the active residues which are present at the binding cavity of Mpro and PLpro.

From the present computational investigations, we found that PDT showed the best binding affinity (XP glide score: -15.367) for the binding cavity of Mpro. PDT interacted with the binding site of Mpro as it produced a conventional H-bond with CYS145 and hydrophobic interaction with HIS41. Among the residues present at the active site cavity, PDT also bonded through H-bonds with GLU166, and GLN189, while Van der Waals interactions were shown by SER46, ASN142, PRO168, and THR190. From MD simulation, it was observed that PDT showed stable conformation at the catalytic site throughout the entire simulation as evidenced by small fluctuations in the RMSD, RMSF, and Rg values. However, it showed the highest binding free energy (-280.170±18 .945) among the tested compounds against Mpro. Based on ADMET studies, PDT was predicted to have low oral bioavailability. However, PDT was computed to be safe for oral consumption as its LD 50 was predicted to be 2260 mg/kg. The synthetic accessibility score of PDT was computed to be 8.43 which suggest that the compound might be difficult to synthesize.

A score of 1 indicates easier synthesis, while a score of 10 indicates difficulty in synthesis (Daina et al., 2017) .

With an XP glide score of -12.360, the present computational investigation found rutin as a phytocompound of Indian spices that showed the second most binding affinity for the active site of Mpro. Rutin formed bonds with the residues from the binding cavity of Mpro as it produced Hbonds with CSY145 and a C-H bond with HIS41. Among the other amino acids present at the catalytic site cavity, rutin was reported to form a conventional H-bond with GLU166; C-H bond with LEU141 and ASN142; and Van der Waals interactions were shown by SER46, GLN189, and THR190. The RMSD, RMSF, and Rg data generated from the 100 ns MD simulation indicate that the docked ligand (rutin) is stable throughout the entire simulation because the observed fluctuations were minimal (Prajapati et al., 2021) . The binding free energy released during Mprorutin interaction was computed to be -146.820±15.448 kJ/mol. Rutin showed a lower binding free energy as compared to PDT (-280.170±18.945 kJ/mol) which implies that the MM-PBSA calculation for rutin supports the results of the MD simulation that had previously suggested a stable Mpro-rutin binding. Moreover, the binding free energy of rutin was also found to be lower than the co-crystal ligand 'X77' (-160.625±15.840 kJ/mol). Rutin was computed by SwissADME (Daina et al., 2017 ) as a phytocompound with low bioavailability (Bioavailability score = 0.17).

However, rutin (6.53) was predicted to be easier to be synthesized as compared to PDT (8.43).

ProTox-II (Banerjee et al., 2018) predicted rutin as safe for oral consumption with an LD 50 of 5000 mg/kg body weight. DataWarrior software-generated overall drug score for rutin was 0.546604 and was the highest among all the studied phytocompounds, even higher than that of PDT (0.2016708).

The XP glide score of DOC against PLpro was -12.801. DOC had the best binding affinity towards PLpro among all the compounds present in Indian spices. Among the amino acids at the active binding site, TYR268 and GLN269 showed Van der Waals's interaction with DOC. DOC also formed conventional H-bonds with ASP164, GLU167, PRO248, GLY266, and ASN267.

Minimum fluctuations are desirable with the parameters generated from MD simulation. The RMSD, RMSF, and Rg trajectories of the PLpro-DOC complex showed small fluctuations. This is indicative of a stable protein-ligand interaction. Considering the binding free energy, the PLpro-DOC (148.323±15.338 kJ/mol) outperforms the protein-native ligand complex (141.573±13.971 kJ/mol). The low binding free energy of the PLpro-DOC complex supports the findings from the MD simulation. DOC violated 3 parameters (mw more than 500, H-bond acceptor more than 10, H-bond donor more than 5) in rule of 5 defined by Lipinski (Lipinski, 2004) and showed a low bioavailability score of 0.17. DOC has a synthetic accessibility score of 6.54. The LD 50 and toxicity class of DOC were 5000 mg/kg and 5, respectively. This suggests that although DOC will be safe for oral consumption, it will have low bioavailability. The overall drug score of DOC as computed by Data Warrior software was 0.1766791. The drug score of DOC was lower than rutin (0.546604) and PDT (0.2016708).

Luteolin-7-glucoside-4'-neohesperidoside had a binding affinity score of -9.226 with PLpro. It was the phytocompound that showed the second most binding affinity towards PLpro next to DOC.

Among the amino acids present at the active binding site, luteolin-7-glucoside-4'-neohesperidoside interacted with TYR268 (hydrophobic interaction) and GLN269 (C-H bond). The compound also formed conventional H-bonds with LYS157, GLU161, ASP164, GLU167, ARG166, and THR301. During MD simulation, the interaction was found to be stable as fewer fluctuations were observed in the RMSD, RMSF, and Rg trajectories. Moreover, the binding free energy of luteolin-7-glucoside-4'-neohesperidoside (-142.425±22 .452 kJ/mol) was lower than the native ligand (-141.573±13.971 kJ/mol). The MM-PBSA calculations support the results of the MD simulation.

Similar to DOC, luteolin-7-glucoside-4'-neohesperidoside also showed the same violations against Lipinski's rule of 5 (Lipinski, 2004) , had a low bioavailability score of 0.17. The LD 50 was reported 5000 mg/kg with a toxicity level of 5. This reveals that luteolin-7-glucoside-4'-neohesperidoside will have low bioavailability but will be safe for oral consumption . The synthetic accessibility score of the compound was 7.48. Luteolin-7-glucoside-4'-neohesperidoside will be more difficult to be synthesized than DOC (6.54). Also, luteolin-7-glucoside-4'neohesperidoside has the lowest drug score (0.1573803) among all the studied phytocompounds.

The bioavailability issues that might arise with PDT, rutin, DOC, and luteolin-7-glucoside-4'neohesperidoside can be effectively solved with novel drug delivery systems such as nanoformulations since it was reported that bioavailability enhancement was achieved for antiviral drugs through nanoformulation strategy (Tatham et al., 2015) . The present study identifies rutin and luteolin-7-glucoside-4'-neohesperidoside as potential inhibitors of Mpro and PLpro, respectively. Other studies have also reported rutin as SARS-CoV-2 Mpro inhibitor (Huynh et al., 2020; Zothantluanga, 2021; Zothantluanga et al., 2022) . The findings of our current study support similar studies conducted earlier by other researchers.

From computational investigations, among 225 phytocompounds that are present in 28 different Indian spices, four bioactive compounds viz., PDT, rutin, DOC, and luteolin-7-glucoside-4'neohesperidoside were identified. These four compounds exhibited promising inhibitory potential against SARS-CoV-2 Mpro and PLpro. Further experimental studies with the identified phytocompounds from Indian spices can be carried out in order to further explore their anti-SARS-CoV-2 potential. 

GROMACS: High performance molecular simulation through multi-level parallelism from laptops to supercomputers

Potential roles of medicinal plants for the treatment of viral diseases focusing on COVID-19: A review

ProTox-II: a webserver for the prediction of toxicity of chemicals

Medicinal plants as sources of active molecules against COVID-19

SARS-CoV-2: insight in genome structure, pathogenesis and viral receptor binding analysis-an updated review

Drug-like bioactive structures and conformational coverage with the LigPrep/ConfGen suite: comparison to programs MOE and catalyst

In silico identification of potential inhibitors of key SARS-CoV-2 3CL hydrolase (Mpro) via molecular docking, MMGBSA predictive binding energy calculations, and molecular dynamics simulation

Allergic reactions including anaphylaxis after receipt of the first dose of Pfizer-BioNTech COVID-19 vaccine-United States

SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

An interactive web-based dashboard to track COVID-19 in real time

Vaccine hesitancy: the next challenge in the fight against COVID-19

Bioactives from culinary spices and herbs: a review

Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein− ligand complexes

Design, Molecular Docking, Drug-likeness and Molecular Dynamics Studies of 1,2,4-Trioxane Derivatives as Novel P. falciparum Falcipain-2 (FP-2)

Evaluation of green tea polyphenols as novel corona virus (SARS CoV-2) main protease (Mpro) inhibitors-an in silico docking and molecular dynamics simulation study

Promising phytochemicals of traditional Indian herbal steam inhalation therapy to combat COVID-19-An in silico study

Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening

Computer aided drug design: success and limitations

Validation of molecular docking programs for virtual screening against dihydropteroate synthase

VMD: visual molecular dynamics

Structure-based lead optimization of herbal medicine rutin for inhibiting SARS-CoV-2's main protease

Targeting multiple conformations of SARS-CoV2 Papain-Like Protease for drug repositioning: An in-silico study

Structure-Based Screening to Discover New Inhibitors for Papain-like Proteinase of SARS-CoV-2: An In Silico Study

Design, synthesis, molecular docking, molecular dynamics and in vivo antimalarial activity of new dipeptide-sulfonamides

Antidiabetic bioactive compounds from Tetrastigma angustifolia (Roxb.) Deb and Oxalis debilis Kunth.: Validation of ethnomedicinal claim by in vitro and in silico studies

Design, Synthesis, Antimalarial Activity and Docking Study of 7-Chloro-4-(2-(substituted benzylidene)hydrazineyl)quinolines

Evolution of RNA viruses from SARS to SARS-CoV-2 and diagnostic techniques for COVID-19: a review

Structural plasticity of SARS-CoV-2 3CL M pro active site cavity revealed by room temperature X-ray crystallography

Unusual zwitterionic catalytic site of SARS-CoV-2 main protease revealed by neutron crystallography

Neurological Manifestation of SARS-CoV-2 Induced Inflammation and Possible Therapeutic Strategies Against COVID-19

Cerebroprotective Effect of Aloe Emodin: In Silico and

In silico prediction of potential inhibitors for the main protease of SARS-CoV-2 using molecular docking and dynamics simulation based drugrepurposing

g_mmpbsa-A GROMACS tool for high-throughput MM-PBSA calculations

Lead-and drug-like compounds: the rule-of-five revolution

COVID-19: The first documented coronavirus pandemic in history

COVID-19 vaccine hesitancy among medical students

Identification of novel human USP2 inhibitor and its putative role in treatment of COVID-19 by inhibiting SARS-CoV-2 papain-like (PLpro) protease

COVID-19: Virology, biology and novel laboratory diagnosis

In silico Studies on the Interaction Between Mpro and PLpro From SARS-CoV-2 and Ebselen, its Metabolites and Derivatives

Culinary herbs and spices: their bioactive properties, the contribution of polyphenols and the challenges in deducing their true health benefits

Structure of papain-like protease from SARS-CoV-2 and its complexes with non-covalent inhibitors

Toward a treatment of antibacterial and antifungal infections: Design, synthesis and in vitro activity of novel arylhydrazothiazolylsulfonamides analogues and their insight of DFT, docking and molecular dynamic simulation

In vivo immunomodulatory activity and In silico study of Albizia procera bark extract on doxorubicin induced immunosuppressive rats

Sterenin M as a potential inhibitor of SARS-CoV-2 main protease identified from MeFSAT database using molecular docking, molecular dynamics simulation and binding free energy calculation

Design, synthesis and molecular modeling studies of novel mesalamine linked coumarin for treatment of inflammatory bowel disease

Oral side effects of COVID-19 vaccine

OPLS3e: Extending force field coverage for drug-like small molecules

In silico screening of phytopolyphenolics for the identification of bioactive compounds as novel protease inhibitors effective against SARS-CoV-2

Coronavirus disease-2019 (COVID-19): An updated review

Repurposing of Phytomedicine-Derived Bioactive Compounds With Promising Anti-SARS-CoV-2 Potential: Molecular Docking, MD Simulation and Drug-Likeness/ ADMET

Identification of bioactive molecules from Triphala (Ayurvedic herbal formulation) as potential inhibitors of SARS-CoV-2 main protease (Mpro) through computational investigations

In silico potential of approved antimalarial drugs for repurposing against COVID-19

In silico studies on therapeutic agents for COVID-19: Drug repurposing approach

Role of spices beyond food flavoring: Nutraceuticals with multiple health effects

Screening of phytochemicals as potent inhibitor of 3-chymotrypsin and papain-like proteases of SARS-CoV2: an in silico approach to combat COVID-19

Nanoformulation strategies for the enhanced oral bioavailability of antiretroviral therapeutics

Antioxidant activity of spices and their impact on human health: A review

Antiviral Activity of Vitis vinifera Leaf Extract against SARS-CoV-2 and HSV-1

Molecular Docking Simulation Studies, Toxicity Study, Bioactivity Prediction, and Structure-Activity Relationship Reveals Rutin as a Potential Inhibitor of SARS-CoV-2 3CL pro

Antimalarial flavonoidglycoside from Acacia pennata against PfDHFR-TS: An in-silico study

The authors express their sincere thanks to Taif University, Taif, Saudi Arabia for their support through Taif University Researchers Supporting Project Number: TURSP-2020/80. The authorsThe authors hereby declare that there are no conflicting interests concerning the present study. There are no financial and personal relationships with other people or organizations that could inappropriately influence (bias) the outcomes of the present study.