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. 2023 Jan 5;13(1):36. doi: 10.1007/s13205-022-03450-6

Comparative docking studies of drugs and phytocompounds for emerging variants of SARS-CoV-2

Ananya Chugh 1,#, Ishita Sehgal 1,#, Nimisha Khurana 1,#, Kangna Verma 1, Rajan Rolta 2, Pranjal Vats 3, Deeksha Salaria 2, Olatomide A Fadare 4, Oladoja Awofisayo 5, Anita Verma 1, Rajendra Phartyal 1,✉, Mansi Verma 6,✉
PMCID: PMC9815891  PMID: 36619821

Abstract

In the last three years, COVID-19 has impacted the world with back-to-back waves leading to devastating consequences. SARS-CoV-2, the causative agent of COVID-19, was first detected in 2019 and since then has spread to 228 countries. Even though the primary focus of research groups was diverted to fight against COVID-19, yet no dedicated drug has been developed to combat the emergent life-threatening medical conditions. In this study, 35 phytocompounds and 43 drugs were investigated for comparative docking analysis. Molecular docking and virtual screening were performed against SARS-CoV-2 spike glycoprotein of 13 variants using AutoDock Vina tool 1.5.6 and Discovery Studio, respectively, to identify the most efficient drugs. Selection of the most suitable compounds with the best binding affinity was done after screening for toxicity, ADME (absorption, distribution, metabolism and excretion) properties and drug-likeliness. The potential candidates were discovered to be Liquiritin (binding affinities ranging between −7.0 and −8.1 kcal/mol for the 13 variants) and Apigenin (binding affinities ranging between −6.8 and −7.3 kcal/mol for the 13 variants) based on their toxicity and consistent binding affinity with the Spike protein of all variants. The stability of the protein–ligand complex was determined using Molecular dynamics (MD) simulation of Apigenin with the Delta plus variant of SARS-CoV-2. Furthermore, Liquiritin and Apigenin were also found to be less toxic than the presently used drugs and showed promising results based on in silico studies, though, confirmation using in vitro studies is required. This in-depth comparative investigation suggests potential drug candidates to fight against SARS-CoV-2 variants.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03450-6.

Keywords: COVID-19, Variants, Drug repurposing, Docking, Phytocompounds, MD Simulation

Introduction

It is not unusual that a Coronaviridae virus has infected humans. In 2002–03, there was a Severe Acute Respiratory Syndrome (SARS) outbreak, followed by that, in 2012 the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) emerged and affected the Middle Eastern countries (Rabaan et al. 2020). The tremendous evolutionary adaptability of coronaviruses to the environment and host specificity eventually gave rise to SARS-CoV-2, which has been inflicting chaos in the world with a series of new mutations (Zheng 2020). COVID-19 has surfed successive waves in the preceding two years, with new variants surfacing one after the other, leaving the infection vulnerable to rumours because of its poor understanding (Maher et al. 2021). According to the World Health Organisation, there have been a total of 5 Variants of Concern (VOCs), namely Alpha, Beta, Gamma, Delta and Omicron (Chugh et al. 2022).

Owing to the mutability of this virus, many variants of SARS-CoV-2 have originated and have been more infectious and transmissible than before. Therefore, one of the major obstacles in controlling the Pandemic has been the absence of an efficient therapeutic strategy against the existing and emerging variants (Aleem et al. 2022).

Computational biology has been proved to be a crucial tool in indicating the potential phytocompounds from medicinal plants and repurposed pharmaceuticals in the fight against SARS-CoV-2 (Scherman and Fetro 2020; Toor et al. 2021). Within the initial few months of the COVID-19 pandemic, there was a rise in clinical trials of repurposing drugs such as Hydroxychloroquine, Remdesivir, Ritonavir, Lopinavir, Ivermectin, Interferon and several other immunomodulators and anti-inflammatory drugs. However, most of them either had serious side effects or did not seem to affect the virus significantly (Toor et al. 2021; Hall and Ji 2020; Khanna et al. 2021; Martinez 2021; Yadav et al. 2021). In parallel, research into herbal cures (phytocompounds) also grew rapidly due to their minimal adverse effects and widespread acceptance (Basu et al. 2020; Pk et al. 2020). Furthermore, the use of plant extracts in traditional medicine and novel drugs have been productive multiple times over the past few centuries (Hakobyan et al. 2016; Rolta et al. 2021).

With the growing number of new variants, it has become necessary to analyse the differential effects of the available therapeutics and to assess their efficacy on each variant of SARS-CoV-2. Therefore, a rapid and cost effective in silico method to identify existing molecules or phytocompounds could help in building a repository for clinical trials. Moreover, it would decrease the time for discovery of new drug candidates and may provide significant help for drug development that can be interpreted into clinical applications to combat SARS-CoV-2 (Dotolo et al. 2021; Rudrapal and J. Khairnar S et al. 2020).

In this study, various drugs and phytocompounds have been selected for molecular docking with Spike glycoprotein, a trimeric club shaped structural protein of the SARS-CoV-2 virus, which facilitates viral fusion with the host cell (Chugh et al. 2022). As Spike gene has a nucleotide mutation rate of 8.066 × 10–4 substitutions per site per year, while the SARS-CoV-2 genome has a rate of 6.677 × 10–4 substitutions per site per year, so certain mutations, particularly in the Spike glycoprotein, have been believed to enhance viral infectivity and transmissibility, resulting in emergence of several variants classified as Variants of Concern (VOC) and Variants of Interest (VOI) by the World Health Organization (Hu et al. 2021; Wang et al. 2020; Singh et al. 2021a, b, c, d). Hence, in this comparative study, molecular docking was performed with Spike glycoprotein from 13 variants of SARS-CoV-2. This may prove to be beneficial in curing COVID-19 as the best phytocompound and drug, effective on all the variants have been compared and analysed using in silico studies.

Materials and methods

Ligand preparation

Based on previous studies on RNA viruses, 43 drugs and 35 phytocompounds were selected for virtual screening and molecular docking study against SARS-CoV-2 Spike glycoprotein (Toor et al. 2021; Hall and Ji 2020; Khanna et al. 2021; Poratti and Marzaro 2019). The 3-dimensional structures of all the ligands were retrieved from DrugBank (https://go.drugbank.com/k) and PubChem database (https://pubchem.ncbi.nlm.nih.gov/). Further, the files were converted into protein data bank (PDB) format using OpenBabel and all the selected ligands were prepared using AutoDock Vina 1.5.6 tools. Table 1 shows the selected 78 drugs and phytocompounds (see Table 2).

Table 1.

List of 46 drugs and 36 phytocompounds

S. No Drugs Phytocompounds
1 N-acetylcysteine Emodin
2 2-mercaptoethane sulfonate, sodium salt (MESNA) Thymol
3 Tiopronin Carvacrol
4 Cysteamine Artemisinin
5 Amifostine (parent drug) WR-1065 (active metabolite) Aloe-emodin
6 Erdosteine (parent drug) Met I (active metabolite) Anthrarufin
7 Penicillamine Alizarine
8 Glutathione Dantron
9 Cangrelor 1,8 dihydroxy-3-carboxyl-9,10-anthraquinone or rhein
10 Dpnh (NADH) Cucurbitacin B (−112.09)
11 Flavin adenine dinucleotide (FAD) adeflavin Cardiofoliolide (−111.5)
12 Iomeprol Apigenin (−98.84)
13 Coenzyme A Pyrethrin (−92.98)
14 Tiludronate Zingiberene
15 Flavin adenine dinucleotide (FAD) adeflavin Vasicine
16 Azithromycin Andrographolide
17 Remdesivir Carvacol
18 Peramivir Costunolide
19 Abacavir Eugenol
20 Didanosine Pyrethrin
21 Tenofovir Chalcone
22 Colistin 6-Shogaol
23 Eugenol Myristicin
24 Liquiritin Bis (3, 5, 5-trimethylhexyl) phthalate
25 Emblicanin A Tangeretin
26 3-Carene Nelfinavir
27 Allicin Griffithsin
28 Glycyrrhizic acid Kamferol
29 Nafamostat Curcumin
30 Oseltamivir Pterostilbene
31 Telbivudine Fisetin
32 Zanamavir Quercetin
33 Stavudine Isorhamnetin
34 Raltegravir Genistein
35 Zalcitabine Luteolin
36 Favipiravir Resveratrol
37 Ribavirin –
38 Galidesivir –
39 Lopinavir –
40 Ritonavir –
41 Azadirachtin –
42 Camostat –
43 Doxycycline –
44 Ivermectin –
45 abemaciclib –
46 2-deoxy- Glucose –
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(Source: elaborated in Table S3)

Table 2.

Summary of SARS-CoV-2 variants

WHO label Pango Lineage Emergence Spike mutation
RBD region mutations Other S1and S2 mutations Furin
Alpha B.1.1.7 UK, Sep 2020 N501Y DelH69, DelV70, ∆Y144, A570D, D614G, T716I, S982A, D1118H P681H
Beta B.1.351 South Africa, May-2020 K417N, E484K, N501Y L18F, D80A, D215G, ∆L242, ∆A243, ∆L244, R246I, D614G, A701V N/A
Gamma P.1 Brazil, Nov-2020 K417T, E484K, N501Y

L18F, T20N, P26S, D138Y, R190S,

D614G, H655Y, T1027I, V1176F

 N/A
Delta B.1.617.2

India,

Oct-2020

 L452R, T478K, G142D, D614G, T19R, 156del, 157del, R158G, D950N P681R
Epsilon B.1.427/B.1.429 United States of America, Mar-2020 L452R S13I, W152C, D614G N/A
Zeta P.2 Brazil, Apr-2020 E484K D614G, F656, V1176F, T859I N/A
Eta B.1.525 Multiple countries, Dec-2020 E484K O52R, Q677H, F888L, D614G, A67V N/A
Theta P.3 Philippines, Jan-2021 E484K, N501Y D614G, E1092K, H1101Y, V1176F P681H
Iota B.1.526 United States of America, Nov-2020 E484K, S447N L5F, T95I, D253G, D614G, A701V N/A
Kappa B.1.617.1 India, Oct-2020 E484Q, L452R, G142D, D614G, T95I, E154K, Q1071H, M153I P681R
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(Source: https://viralzone.expasy.org/9556, https://cov-lineages.org/)

Retrieval of Spike sequences of SARS-CoV-2 variants

The crystal structures of Spike glycoprotein region for 4 different SARS-CoV-2 variants were downloaded from the protein data bank (Wild Type (6VYB) (Walls et al. 2020), Alpha (7LWT) (Gobeil et al. 2021), Beta (7LYQ) (Gobeil et al. 2021), Omicron (7QO7) (Ni et al. 2021)) (https://www.rcsb.org/). Remaining 9 were modelled using Swiss Model based on their amino acid sequences in FASTA format (Gamma (MW642248), Delta (QWO57033), Zeta (QVE55301.1), Iota (QTP80309.1), Theta (QVR41797.1), Epsilon (QPJ72086.1), Eta (QWO17721.1), Kappa (QTY54081.1), Delta Plus (QWS06686.1)), retrieved from NCBI (National Center for Biotechnology Information) virus (https://www.ncbi.nlm.nih.gov/). The information about all the selected variants and their mutations is summarized in Supplementary Table 2 and Table S1, respectively. The Ramachandran plots of the strains are shown in Supplementary Fig. S1.

Target protein preparation

The AutoDock Vina tool 1.5.6 was used for preparation of the protein structures. The binding site for protein–ligand interaction of the target Spike protein from different variants were determined through grid box generation by adjusting the grid parameter x, y, z coordinates value. The grid values of all the 13 variants are provided in Supplementary Table S2.

Virtual screening and molecular docking

Molecular docking study of the selected ligands (43 drugs and 35 phytocompounds) against Spike protein of 13 variants was done using AutoDock Vina tool 1.5.6, following the protocol described by Verma et al. (Verma et al. 2021). After the completion of the docking search, the best conformation with the lowest docked energy was chosen and the protein–ligand complex was analyzed using Discovery Studio (https://discover.3ds.com/d) to examine the list of interactions within the complex. Following that, the suitable compounds were selected for ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis.

ADMET prediction of drugs and phytocompounds

ADMET screening was done to determine the absorption, toxicity, and drug-likeliness properties of ligands (Dong et al. 2018). The 3D structures of ligands were uploaded on SWISSADME (Molecular Modeling Group of the Swiss Institute of Bioinformatics), Molinspiration cheminformatics (a spin-off of Bratislava University), and ProTox-II (Prediction of TOXicity of chemicals) web servers (Charite University of Medicine, Institute for Physiology, Structural Bioinformatics Group, Berlin, Germany) for ADMET screening. ProTox-II web server was used to predict toxicity profile of the chemical (http://tox.charite.de/protox_II) (Singh et al. 2021a, b, c, d; Banerjee et al. 2018). The toxicity of a ligand is measured in terms of toxicity endpoints such as mutagenicity, carcinogenicity, etc. It can also be measured both quantitatively such as LD50 (lethal dose) values, where Class I (LD50 ≤ 5) and II (5 < LD50 ≤ 50) are considered fatal if swallowed and Class VI (LD50 > 5000) is non-toxic, and qualitatively, such as binary (active or inactive) for certain cell types and assays or indication area such as cytotoxicity, immunotoxicity and hepatotoxicity (Parasuraman 2011).

Molecular descriptors and drug likeliness properties of compounds were analyzed using the tool Molinspiration server (http://www.molinspiration.com), based on Lipinski’s Rules of five (Frey and Bird 2011).

Molecular dynamics simulation

MD simulation was done with GROMACS 2018.3 (simulation in duplicate) (Abraham et al. 2015) software which was installed in ubuntu 18.04 LTS, to study the stability of protein–ligand complexes over the period of 100 ns. Docked structures of the protein–ligand complexes (Apigenin with Delta plus Variant) were used in the simulation study. The target protein was processed and the topology file was prepared using pdb2gmx and GROMOS54a7_atb. Force field was downloaded from the automated topology builder website and incorporated into GROMACS. The ligand topology file was prepared using the automated topology builder (ATB) version 3.0. The solvent addition was done in a cubic box using a box distance 1.0 nm from closest atom in the protein. To neutralise the device, the Cl- ions calculated from the genion module for each protein were used. The energy was minimised using the steepest descent algorithm with 50,000 steps and a cumulative force of 5 kJ mol-1, as well as the Verlet cut-off scheme with Particle Mesh Ewald (PME) columbic interactions. During the equilibration process, position restraints were used. Following that, NVT equilibration was performed at 300 K with 100 ps in 50,000 steps, using the leapfrog integrator and NPT equilibration was performed with Parrinello-Rahman (pressure coupling), 1 bar reference pressure, and 100 ps in 50,000 steps. The LINCS algorithm was used to constrain the length of all bonds. For long-range electrostatics, the Particle-mesh Ewald (PME) algorithm was used. The protein–ligand complex's MD was run for 100 ns (in duplicate). Following efficient completion of Molecular dynamic simulation, the root mean square deviation (RMSD) of backbone residues, the number of hydrogen bonds, root mean square fluctuations (RMSF), Radius of gyration (ROG) & Solvent accessible surface area (SASA) were calculated (Verma et al. 2021; Darden et al. 1999; Hess et al. 1997; Páll et al. 2015).

Estimation of free energy of binding

The free energy of binding calculation was done using the standalone program, G-MMPBSA (Rolta et al. 2021; Egan et al. 2000; Kumari and Kumar 2014; Kushwaha and Kaur 2021; Pant et al. 2020) based on the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method. The average binding energy calculations were done by a python script provided in G-MMPBSA program.

Results

Molecular docking of drugs and phytocompounds with different variants of SARS-CoV-2

A total of 35 phytocompounds and 43 drugs were used for molecular docking with the Spike protein of all 13 SARS-CoV-2 variants (Table 1). On the basis of their binding affinities with all the variants, 10 drugs were chosen, namely Dpnh (NADH), Flavin Adenine Dinucleotide (FAD) Adeflavin, Liquiritin, Glycyrrhizic acid, Raltegravir, Ritonavir, Doxycycline, Ivermectin, Abemaciclib and Nafamostat (Table 3), out of which Liquiritin showed comparable affinities with all the 13 variants (between -7.0 and -8.1 kcal/mol). Similarly, 15 phytocompounds were chosen, namely, Emodin, Artemisinin, Aloe-emodin, Anthrarufin, Alizarine, Dantron, Rhein, Cucurbitacin B, Apigenin, Curcumin, Fisetin, Quercetin, Isorhamnetin, Genistein and Luteolin (Table 4), out of which Apigenin showed similar binding affinities for all variants (between −6.8 and −7.3 kcal/mol). List of chosen drugs and phytocompounds with their respective Accession Numbers/Pubchem ID is summarized in supplementary Table S3.

Table 3.

Docking score of drugs with 13 variants of SARS-CoV-2

Drugs Binding energy (kcal/mol)
1 2 3 4 5 6 7 8 9 10 11 12 13
Dpnh (NADH) − 8.3 − 7.3 − 8.2 − 9 − 8.5 − 8.3 − 9 − 9 − 8.7 − 9 − 8.3 − 8.5 − 7.4
Flavin adenine dinucleotide (fad) adeflavin − 8.5 − 8.8 − 8.4 − 9 − 8.4 − 9.3 − 8.9 − 8.3 − 9.1 − 9.5 − 8.5 − 8.9 − 7.0
Liquiritin − 7.5 − 7.2 − 7 − 7.7 − 7.7 − 8 − 7.4 − 7.3 − 7.5 − 8.1 − 7.3 − 7.3 − 7.2
Glycyrrhizic acid − 7.5 − 7.3 − 6.9 − 7.6 − 7.3 − 8.1 − 7.6 − 7.8 − 7.5 − 8 − 7.5 − 7.8 − 9.2
Raltegravir − 7.6 − 6.7 − 7.2 − 7.6 − 7.3 − 8 − 7.5 − 7.3 − 7.4 − 8 − 7.3 − 7.4 − 7.9
Ritonavir − 8.2 − 7 − 6.5 − 8.2 − 8 − 7.8 − 8.2 − 8.2 − 7.9 − 7.6 − 6.5 − 8.4 − 7.5
Doxycycline − 8.1 − 7.5 − 8.1 − 8.2 − 8.2 − 8.5 − 8.1 − 8.2 − 8.2 − 8.6 − 8.2 − 8.2 − 7.5
Ivermectin − 10.4 − 9.7 − 9.5 − 10.2 − 10.2 − 9.7 − 10.2 − 10.2 − 10.2 − 9.8 − 10.3 − 10 − 9.6
Abemaciclib − 7.7 − 7.2 − 7.4 − 7.8 − 7.7 − 7.6 − 7.8 − 7.6 − 7.7 − 7.6 − 7.6 − 7.6 − 7.1
Nafamostat − 7.4 − 6.8 − 7.5 − 7.8 − 7.2 − 7.7 − 7.6 − 7.7 − 7.6 − 7.5 − 7.6 − 6.8 − 7.1
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1 Wuhan (Wild type), 2 Alpha, 3 Beta, 4 Eta, 5 Zeta, 6 Theta, 7 Gamma, 8 Delta, 9 Epsilon, 10 Iota, 11 Kappa, 12 Delta Plus, 13 Omicron

Table 4.

Docking score of phytocompounds with 13 variants of SARS-CoV-2

Phytocompounds Binding Energy (kcal/mol)
1 2 3 4 5 6 7 8 9 10 11 12 13
Emodin − 7.2 − 6.1 − 7.1 − 6.7 − 6.3 − 6.9 − 6.8 − 6.6 − 6.7 − 6.8 − 6.3 − 6.6 − 6.6
Artemisinin − 6.9 − 5.9 − 6.9 − 7.0 − 7 − 7.5 − 7.4 − 7 − 7.4 − 7.5 − 7.1 − 7.2 − 7.6
Aloe-emodin − 7.1 − 6.3 − 7 − 7.0 − 6.8 − 6.6 − 6.9 − 6.9 − 6.8 − 6.7 − 6.9 − 6.8 − 7.4
Anthrarufin − 7 − 6.6 − 7.1 − 7.5 − 7.5 − 6.9 − 7.2 − 7.5 − 7.3 − 6.8 − 7.5 − 7.4 − 7.0
Alizarine − 7.3 − 6.5 − 7.3 − 7.6 − 6.9 − 7.2 − 7.4 − 7.6 − 7.4 − 7.1 − 7.6 − 7.5 − 6.8
Dantron − 6.8 − 6.9 − 7.1 − 6.9 − 6.8 − 6.5 − 6.7 − 6.8 − 6.8 − 6.4 − 6.9 − 6.8 − 7.5
Rhein − 7.2 − 6.5 − 7.3 − 7 − 7.2 − 7 − 7 − 7.2 − 7 − 7.0 − 7.2 − 7.1 − 7.5
Cucurbitacin B − 7.1 − 6.3 − 6.8 − 8.3 − 7.3 − 7.8 − 8.2 − 8.2 − 8.2 − 7.9 − 7.3 − 8.3 − 7.6
Apigenin − 7.1 − 6.8 − 7.1 − 7.0 − 7 − 7 − 7.2 − 7 − 7.2 − 7.0 − 7.0 − 7.1 − 7.3
Curcumin − 6.6 − 6.5 − 6.9 − 6.7 − 6.3 − 6.5 − 6 − 6.6 − 6.7 − 6.4 − 6.3 − 6.7 − 6.1
Fisetin − 7.2 − 6.6 − 7.5 − 6.9 − 6.8 − 7.1 − 6.8 − 6.8 − 7.4 − 7.2 − 6.8 − 6.8 − 6.7
Quercetin − 7 − 7.1 − 7.2 − 7.0 − 6.9 − 7.1 − 7 − 7 − 7 − 7.3 − 7.0 − 7.0 − 7.0
Isorhamnetin − 6.7 − 6.4 − 7 − 6.9 − 6.5 − 6.8 − 7 − 6.9 − 7 − 6.6 − 6.3 − 6.9 − 7.1
Genistein − 6.8 − 6.5 − 7.3 − 6.8 − 6.8 − 7.1 − 6.7 − 6.7 − 6.8 − 7.1 − 6.8 − 6.8 − 7.2
Luteolin − 7.1 − 6.8 − 7.4 − 7.2 − 7.1 − 7.1 − 7.4 − 7.2 − 7.4 − 7.2 − 7.1 − 7.3 − 7.3
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1 Wuhan (Wild type), 2- Alpha, 3- Beta, 4- Eta, 5- Zeta, 6- Theta, 7- Gamma, 8- Delta, 9-Epsilon, 10-Iota, 11- Kappa, 12- Delta Plus, 13- Omicron

The binding affinity of all 35 phytocompounds and 43 drugs along with their interacting amino acids were visualised using Discovery Studio as mentioned in supplementary Table S4.

Toxicity prediction of drugs and phytocompounds

The 10 drugs and 15 phytocompounds were analysed by Molinspiration, Protox II and SWISSADME to check for Lipinski’s rule, toxicity and ADME respectively. ADME data showed that most of the selected drugs were water-soluble, but only a few had significant GI absorption as shown in Table 5. In the case of phytocompounds, all of them showed good water solubility and high GI absorption except Cucurbitacin B (Table 5).

Table 5.

ADME prediction of drugs and phytocompounds by swiss ADME server

SwissADME
Consensus log PO/W Water solubility GI absorption TPSA (Å2) Lipinski’s rule Ghose rule Veber rule Egan rule Muegge rule
Name of compounds
 Dpnh (NADH) − 4.19 Highly soluble Low 337.24 Å2 No; 3 violations No; 4 violations No; 2 violations No; 1 violation No; 5 violations
 Flavin Adenine Dinucleotide (FAD) Adeflavin − 2.89 Very soluble Low 382.55 Å2 No; 3 violations No; 4 violations No; 2 violations No; 1 violation No; 5 violations:
 Liquiritin 0.34 Soluble Low 145.91 Å2 Yes; 0 violation Yes No; 1 violation No; 1 violation Yes
 Glycyrrhizic acid 1.49 Poorly soluble Low 267.04 Å2 No; 3 violations No; 3 violations No; 1 violation No; 1 violation No; 4 violations
 Raltegravir 1.38 Soluble Low 152.24 Å2 Yes; 1 violation Yes No; 1 violation No; 1 violation No; 1 violation
 Ritonavir 5.03 Poorly soluble Low 202.26 Å2 No; 2 violations No No No No
 Doxycycline − 0.34 Soluble Low 181.62 Å2 Yes; 1 violation Yes No No No
 Ivermectin 6.68 Insoluble Low 340.12 Å2 No; 3 violations No No No No
 Nafamostat 2.16 Soluble High 138.07 Å2 Yes; 0 violation Yes Yes No; 1 violation Yes
 Abemaciclib 4.04 Moderately soluble High 75.00 Å2 Yes; 1 violation No; 2 violations Yes Yes Yes
Name of Phytocompounds
 Emodin 1.87 Soluble High 94.83 Å2 Yes; 0 violation Yes Yes Yes Yes
 Artemisinin 2.49 Soluble High 53.99 Å2 Yes; 0 violation Yes Yes Yes Yes
 Aloe-emodin 1.5 Soluble High 94.83 Å2 Yes; 0 violation Yes Yes Yes Yes
 Anthrarufin 2.16 Moderately soluble High 74.60 Å2 Yes; 0 violation Yes Yes Yes Yes
 Alizarine 2.02 Soluble High 74.60 Å2 Yes; 0 violation Yes Yes Yes Yes
 Dantron 2.04 Soluble High 74.60 Å2 Yes; 0 violation Yes Yes Yes Yes
 1,8 dihydroxy-3-carboxyl-9,10-anthraquinone or rhein 1.47 Soluble High 111.90 Å2 Yes; 0 violation Yes Yes Yes Yes
 Cucurbitacin B (-112.09) 3.17 Moderately soluble Low 138.20 Å2 Yes; 1 violation: No; 3 violations Yes No; 1 violation: Yes
 Apigenin 2.11 Soluble High 90.90 Å2 Yes; 0 violation Yes Yes Yes Yes
 Curcumin 3.03 Soluble High 93.06 Å2 Yes; 0 violation Yes Yes Yes Yes
 Fisetin 1.55 Soluble High 111.13 Å2 Yes; 0 violation Yes Yes Yes Yes
 Quercetin 1.23 Soluble High 131.36 Å2 Yes; 0 violation Yes Yes Yes Yes
 Isorhamnetin 1.65 Soluble High 120.36 Å2 Yes; 0 violation Yes Yes Yes Yes
 Genistein 2.04 Soluble High 90.90 Å2 Yes; 0 violation Yes Yes Yes Yes
 Luteolin 1.73 Soluble High 111.13 Å2 Yes; 0 violation Yes Yes Yes Yes
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Toxicity data generated using the Protox II online server showed that among all the drugs, Ivermectin and Raltegravir are Class II and III drugs respectively, while the other drugs belong to Class IV–VI. As for phytocompounds, Cucurbitacin B is a Class II drug, Fisetin and Quercetin are Class III drugs, and the others are categorised as Class IV-VI drugs. Also, Apigenin and Genistein are both non-toxic and each has an LD50 value of 2500. Toxicity data of drugs and phytocompounds are summarised in Table 6. Drug likeliness estimation of active drugs and phytocompounds was done by Molinspiration online server. According to, in-silico druglikeliness prediction Liquiritin showed zero violoations; while Raltegravir, Doxycycline, Nafamostat, Abemaciclib, were found to violate only one of the rules making them suitable candidates for further analysis. On the contrary, in phytocompounds only Cucurbitacin B showed 1 violation, whereas the other phytocompounds followed all rules of drug-likeliness data as summarised in Table 7.

Table 6.

Toxicity prediction of antiviral drugs and phytocompounds using Protox II server

Protox II
LD50 (mg/kg) Hepato-toxicity Carcino-genecity Immuno toxicity Muta-genicity Cyto-toxicity Predicted toxicity class
Name of compound/antibiotic
 Dpnh (NADH) 11,250 Inactive Inactive Active Inactive Inactive Class 6
 Coenzyme A 11,250 Inactive Inactive Inactive Inactive Inactive Class 6
 Flavin Adenine Dinucleotide (FAD) Adeflavin 7000 Inactive Inactive Active Inactive Inactive Class: 6
 Azithromycin 2000 Inactive Inactive Active Inactive Inactive Class 4
 Colistin 836 Inactive Inactive Active Inactive Inactive Class 4
 Liquiritin 2300 Inactive Inactive Active Inactive Inactive Class 5
 Glycyrrhizic acid 1750 Inactive Inactive Active Inactive Inactive Class 4
 Raltegravir 200 Active Inactive Inactive Inactive Inactive Class 3
 Lopinavir 5000 Inactive Inactive Inactive Inactive Inactive Class 5
 Ritonavir 1000 Active Inactive Inactive Inactive Inactive Class 4
 Doxycycline 1007 Active Inactive Active Inactive Inactive Class 4
 Ivermectin 27 Inactive Inactive Active Inactive Inactive Class 2
 Cangrelor 5000 Inactive Inactive Active Inactive Inactive Class 5
 Nafamostat 1190 Active Inactive Active Inactive Inactive Class 4
 Abemaciclib 2000 Inactive Inactive Inactive Inactive Inactive Class 4
Name of phytocompounds
 Emodin 5000 Inactive Inactive Inactive Active Inactive Class 5
 Artemisinin 4228 Inactive Inactive Active Inactive Inactive Class 5
 Aloe-emodin 5000 Inactive Inactive Active Active Inactive Class 5
 Anthrarufin 5000 Inactive Inactive Active Active Inactive Class 5
 Alizarine 7000 Inactive Active Active Active Inactive Class 5
 Dantron 7000 Inactive Inactive Active Active Inactive Class 6
 1,8 dihydroxy-3-carboxyl-9,10-anthraquinone or rhein 5000 Inactive Inactive Inactive Active Inactive Class 5
 Cucurbitacin B 14 Inactive Active Active Inactive Inactive Class 2
 Apigenin 2500 Inactive Inactive Inactive Inactive Inactive Class 5
 Costunolide 3140 Inactive Inactive Active Inactive Inactive Class 5
 Nelfinavir 600 Inactive Inactive Active Inactive Inactive Class 4
 Kaempferol 3919 Inactive Inactive Inactive Inactive Inactive Class 5
 Curcumin 2000 Inactive Inactive Active Inactive Inactive Class 4
 Fisetin 159 Inactive Active Inactive Inactive Inactive Class 3
 Quercetin 159 Inactive Active Inactive Active Inactive Class 3
 Isorhamnetin 5000 Inactive Inactive Active Inactive Inactive Class 5
Genistein 2500 Inactive Inactive Inactive Inactive Inactive Class 5
Luteolin 3919 Inactive Active Inactive Active Inactive Class 5
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Table 7.

Drug likeliness prediction of antiviral drugs and phytocompounds

miLogP TPSA Natoms MW nON nOHNH nviolations
Name of compound/antibiotic
Dpnh (NADH) − 3.59 317.64 44 665.45 21 10 3
Coenzyme A − 4.44 346.58 48 767.54 23 10 3
Flavin Adenine Dinucleotide (FAD) Adeflavin − 2.69 362.96 53 785.56 24 10 3
Azithromycin 2.73 180.09 52 749 14 5 2
Colistin − 5.74 490.65 81 1155.45 29 23 3
Liquiritin 0.41 145.91 30 418.4 9 5 0
Glycyrrhizic acid 1.97 267.04 58 822.94 16 8 3
Raltegravir − 0.81 152.25 32 444.42 11 3 1
Lopinavir 5.69 119.99 46 628.81 9 4 2
Ritonavir 7.51 145.78 50 720.96 11 4 3
Doxycycline − 0.87 181.61 32 444.44 10 7 1
Ivermectin 4.58 170.09 62 875.11 14 3 2
Cangrelor 1.33 255.92 44 776.37 17 7 3
Nafamostat 2.29 138.08 26 347.38 7 7 1
Abemaciclib 3.94 75 37 506.61 8 1 1
Name of Phytocompound
Emodin 3.01 94.83 20 270.24 5 3 0
Artemisinin 3.32 54.01 20 282.34 5 0 0
Aloe-emodin 2.42 94.83 20 270.24 5 3 0
Anthrarufin 3.13 74.6 18 240.21 4 2 0
Alizarine 2.9 74.6 18 240.21 4 2 0
Dantron 3.13 74.6 18 240.21 4 2 0
1,8 dihydroxy-3-carboxyl-9,10-anthraquinone or rhein 3 111.9 21 284.22 6 3 0
Cucurbitacin B 2.83 138.2 40 558.71 8 3 1
Apigenin 2.46 90.89 20 270.24 5 3 0
Costunolide 2.89 26.3 17 232.32 2 0 0
Nelfinavir 5.47 101.89 40 567.8 7 4 2
Kaempferol 2.17 111.12 21 286.24 6 4 0
Curcumin 2.3 93.07 27 368.38 6 2 0
Fisetin 1.97 111.12 21 286.24 6 4 0
Quercetin 1.68 131.35 22 302.24 7 5 0
Isorhamnetin 1.99 120.36 23 316.26 7 4 0
Genistein 2.27 90.89 20 270.24 5 3 0
Luteolin 1.97 111.12 21 286.24 6 4 0
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Based on our comparative study, Liquiritin (between −7.0 to −8.1 kcal/mol) and Apigenin (between −6.8 and −7.3 kcal/mol) passed the toxicity prediction, drug likeliness and also have a consistent binding affinity to each of the the 13 variants (Tables 3, 4).

Liquiritin showed hydrogen bonding with Thr300, Ser50, Asn315, Arg317, Gln626 and hydrophobic interactions with Cys299, Ala290, Cys289, Glu296, Lys302, Thr628, Thr272, Ser314, Trp631, Gln319 in Delta variant; and in case of Delta plus variant it makes hydrogen bonds with Gln626, Leu627, Ser314, Thr300, Thr272, Ser50 and hydrophobic interactions with Arg271, Cys299, Ala290, Thr272, Thr628, Pro629, Glu296, Lys302, Cys289. Similarly, Apigenin made hydrogen bonds with Arg1012 and hydrophobic interaction with Thr959, Ala956, Tyr1005, Leu960, Ser1001, Gln963, Thr1004, Gln1008, Gln952, Gln955 in delta strains in case of delta plus apigenin showed only hydrophobic interactions with Thr 959, Gln 1008, Gln 952, Gln 955, Ala 956, Arg 1012, Tyr 1005, Leu 960, Ser 1001, Gln 963, Thr 1004 amino acids the most important variants, are summarized in Table 8 and Figs. 1, 2,3 and 4.

Table 8.

Interactions of apigenin and liquiritin with delta and delta plus variants of SARS-CoV-2

Phytocompound/ drug Delta Delta plus
Binding energy H-bonding Hydrophobic interactions Binding energy H-bonding Hydrophobic interactions
Apigenin − 7 2Arg1012 Thr959, Ala956, Tyr1005, Leu960, Ser1001, Gln963, Thr1004, Gln1008, Gln952, Gln955 − 7.1 - Thr 959, Gln 1008, Gln 952, Gln 955, Ala 956, Arg 1012, Tyr 1005, Leu 960, Ser 1001, Gln 963, Thr 1004
Liquiritin − 7.3 5Thr300, Ser50, Asn315, Arg317, Gln626 Cys299, Ala290, Cys289, Glu296, Lys302, Thr628, Thr272, Ser314, Trp631, Gln319 − 7.3 Gln626, Leu627, Ser314, Thr300, Thr272, Ser50 Arg271, Cys299, Ala290, Thr272, Thr628, Pro629, Glu296, Lys302, Cys289
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Fig. 1.

Fig. 1

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Interactions of Apigenin with delta variant of SARS-CoV-2 Variant: in close view of delta in complex with Apigenin, purple colour is showing target protein, green colour is showing hydrophobic interactions, yellow colour is showing hydrogen bonding and red colour ligand

Fig. 2.

Fig. 2

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Interactions of Apigenin with Delta plus variant of SARS-CoV-2 Variant: in close view of Delta plus in complex with Apigenin, purple colour is showing target protein, green colour is showing hydrophobic interactions, yellow colour is showing hydrogen bonding and red colour ligand

Fig. 3.

Fig. 3

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Interactions of Liquiritin with Delta variant of SARS-CoV-2 Variant: in close view of Delta in complex with Liquiritin, purple colour is showing target protein, green colour is showing hydrophobic interactions, yellow colour is showing hydrogen bonding and red colour ligand

Fig. 4.

Fig. 4

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Interactions of Liquiritin with Delta plus variant of SARS-CoV-2 Variant: in close view of delta plus in complex with Liquiritin, purple colour is showing target protein, green colour is showing hydrophobic interactions, yellow colour is showing hydrogen bonding and red colour ligand

Furthermore, to study the stability of active Apigenin phytocompound, MD simulation for 100 ns was performed.

MD simulation of Apigenin with Delta plus mutant of SARS-CoV-2

MD simulation of Apigenin in complex with Delta plus variant of SARS-CoV-2 for 100 ns was performed to study the stability of protein–ligand complexes. MD simulation data revealed that RMSD of Apigenin, complexed with Delta plus variant of SARS-CoV-2 was stable from the start of the simulation and remained stable upto 100 ns time (Fig. 5A). RMSF of protein–ligand complex was done to study the flexibility and fluctuation in interactive residues in secondary structure of target proteins (Sivaramakrishnan et al. 2020; Kumar et al. 2014).The RMSF plot for Apigenin fit over the Delta plus protein and showed less residual fluctuation in alpha helical and beta strands. Residues ranging from 100 to 300, 730, 900 to 1150 showed the strongest interactions with Apigenin as shown in Fig. 5B. Binding free energy of protein–ligand complexes is composed of Van der Waals energy −145.285 ± 14.315 kJ/mol, Electrostatic energy −7.358 ± 6.263 kJ/mol, Polar solvation energy 60.148 ± 15.417 kJ/mol, SASA energy −14.129 ± 1.345 kJ/mol and Binding energy −106.624 ± 11.965 kJ/mol (Fig. 6).

Fig. 5.

Fig. 5

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RMSD and RMSF graph of Apigenin with delta plus variant of SARS-CoV-2 variants for 100 ns: A RMSD and B RMSF

Fig. 6.

Fig. 6

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Breakdown of free energy of binding estimated with gMMPBSA

Hydrogen bond interactions of protein–ligand complexes are shown in Fig. 7A. The Radius of gyration in the range of Apigenin in complex with Delta plus is 4.4–4.9 nm, as shown in Fig. 7B. The Radius of gyration plot establishes the compactness of the Apigenin and Delta plus protein complex and confirms their stability. Solvent accessible range of Apigenin complexed with Delta plus protein is between 650 and 560 nm2; as shown in Fig. 7C.

Fig. 7.

Fig. 7

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A Hydrogen bond interactions of ligand with protein, B Radius of gyration (ROG) and, C Solvent accessible surface area

Discussion

The mutations in the RBD region of the 18 amino acid long SARS-CoV-2 Spike glycoprotein strengthen the virus's capacity for transmission. To understand the genesis of novel variants, research has focused on the Spike glycoprotein. The Spike protein's RBD region mutations can make closer contact with hACE2, which results in a stronger binding affinity and probably enhanced VOCs infectivity (Chugh et al. 2022). Among all the previously circulating VOCs, the Delta variant, has shown adverse effects on patients and has caused twice as many hospitalizations (Edara et al. 2021). The Delta was found to be 60% more transmissible than the highly infectious Alpha variant identified in the United Kingdom in September 2020 (Duong 2021). The strain undoubtedly contributed to India's massive second wave of cases. According to data available on GISAID, it had spread to 208 countries as of December 19, 2022 (https://gisaid.org/hcov19-variants/). Delta plus, also known as the AY.1 strain which showed a rapid spread, was found to bind easily to the ACE-2 receptor, and was potentially resistant to monoclonal antibody therapy (Roy and Roy 2021). As per GISAID database, apart from these variants, the Omicron variant, which was discovered in Botswana, has now spread to 208 countries as of December 19, 2022 (https://gisaid.org/hcov19-variants/). In regional genomic surveillance, XBB, a recombinant of the BA.2.10.1 and BA.2.75 sublineages, has been reported in 35 countries with a global prevalence of 1.3%. The regional immunological landscape and COVID-19 vaccination rates appear to have an impact on establishing whether the increased immune escape of XBB is sufficient to cause new infection waves (Kurhade et al. 2022). Hence, repurposing existing drugs against potential targets of the virus could be an effective strategy to speed up the drug discovery process (Bhardwaj et al. 2021a, b, c).

Molecular docking facilitates the prediction of protein–ligand affinity and the structure of the protein–ligand complex. Additionally, it can be used to investigate the binding difference between the two molecules, which is useful information for lead optimization. In the early stages of the pandemic, Ivermectin was considered as a viable therapeutic drug against SARS-CoV-2. Despite the fact that it violated Lipinski's rule and was immunotoxic, being FDA approved for other viral infections, repurposing of this medicine became a ray of hope. It showed strongest affinity with the majority of SARS-CoV-2 variants (as justified in our study Table 3) and was found to minimise the probability of mortality in COVID-19 (Bryant et al. 2021; Caly et al. 2020; Krolewiecki et al. 2021; Zaidi and Dehgani-Mobaraki 2022; Mastrangelo et al. 2012). Also, Australia’s National COVID-19 Clinical Evidence Taskforce and the World Health Organization suggested the use of Ivermectin only in clinical trials (FAQs 2022). Later on, a review of 10 randomised controlled trials by Roman et al. concluded that Ivermectin is not a viable option for the treatment of COVID-19 patients (Roman et al. 2022). Consequently, it became a weak contender.

Since ancient times compounds extracted from traditional medicinal plants with strong antiviral activity have been used to treat viral infections. It has been found that phytocompounds can inactivate SARS-CoV-2 variants by binding to the Spike glycoprotein and thus inhibit their function like Curcumin, a component of turmeric (Curcuma longa), is believed to have potential properties to prevent or treat diseases such as cancer and viral infections (Manoharan et al. 2020; Rattis et al. 2021; Singh et al. 2021). Artemisinin and Emodin have also been found to interact with SARS-CoV-2 and inhibit its Spike glycoprotein (Rolta et al. 2021; Nair et al. 2021; Sehailia and Chemat 2021).

All this led to investigation of binding affinity of drugs as well as phytocompounds with the Spike glycoprotein of SARS-CoV-2 using molecular docking. Results from our study show that phytocompounds exhibit the binding affinity as high as drugs. Also, Sathya et al. has reported the promising results of Liquirtin against H1N1 and H3N2 influenza A virus which further confirms its anti-viral drug property and makes it a competitive candidate for the treatment of COVID-19 (Sathya et al. 2020). Zhu et al. also proposed that Liquiritin mimics Type I IFN, which inhibits viral replication (Zhu et al. 2020). Our study also suggested Liquirtin as one of the promising drugs, as it exhibits high and uniform binding affinity with the Spike glycoprotein of all 13 variants (between −7.0 and −8.1 kcal/mol). Although it was found to be immuno-toxic, zero violations of Lipinski's Rule make it a candidate for research.

Similarly various studies have attempted to carry out in silico validation of phytocompounds to cure various diseases (Rolta et al. 2021; Mehta et al. 2021; Salaria et al. 2022). Rolta et al. 2021 (Rolta et al. 2021) also reported that phytocompounds (emodin, aloe-emodin, anthrarufin, alizarine, and dantron) of R. emodias inhibitor of nucleocapsid phosphoprotein of SARS-CoV-2. Some of the bioactive molecules from tea have also shown promising binding affinities with other proteins of SARS-CoV-2, some of them being NSP15, NSP16 and Mpro (Main protease)(Bhardwaj et al. 2021a, b, c; Singh et al. 2021a, b, c, d; Sharma et al. 2021; Chauhan et al. 2022. Bhardwaj et al. 2021). Hakobyan et al. demonstrated the in vitro effect of Apigenin on African swine fever virus infection by interfering with the viral cell cycle at an early stage in their study, implying that Apigenin could be an effective candidate for extended in vitro and in vivo studies combining dosage effectivity (Hakobyan et al. 2016). In present analysis as well Apigenin expressed the strongest and most consistent binding affinity with all strains (between -6.8 and -7.3 kcal/mol). Additionally, Apigenin exhibited no toxicity and zero violations of Lipinski’s rule.

Multiobjective optimisation in drug discovery field implies that a drug should be potent in being active, non-toxic, orally bioavailable, free of side effects, with strong binding affinity, GI absorption (Thomford et al. 2018; Lambrinidis and Tsantili-Kakoulidou 2021). These parameters aid in the screening and recommendation of the prospective drug candidates for in vitro and in vivo studies. Considering all the important parameters, we propose that Apigenin and Liquiritin could be promising options for the treatment of COVID-19 and that they should be investigated further in vitro and in vivo to see if they can be used to build therapeutic strategies to combat future SARS-CoV-2 peaks.

Conclusion

It is imperative that the drugs and phytocompounds not only pass the toxicity prediction and drug likeliness, but they should also have a consistent binding affinity to all the variants. In the present study, 43 drugs and 35 phytocompounds candidates with potential inhibitory effects towards Spike glycoprotein of SARS-CoV-2 were chosen to perform molecular docking studies. Based on our comparative binding affinity analysis, ADMET analysis and druglikeliness profile we have shortlisted Liquiritin (among the repurposing drugs) and Apigenin (among the phytocompounds). MD simulation results confirmed the stability of Apigenin with Delta plus variant. The consistent binding affinities of repurposing drugs and phytocompounds with all the existing variants of SARS-CoV-2 indicates that these maybe effective universally against upcoming variants as well, thus making it one of the largest comparative studies.

Data availablility

All data generated or analysed during this study are included in this published article (and its supplementary information files).

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 242 KB) (242KB, docx)

Acknowledgements

Authors acknowledge Prof. C. Sheela Reddy, Principal, Sri Venkateswara College,University of Delhi and Prof. Rama, Principal, Hansraj College, University of Delhi for their constant support.

Abbreviations

ADMET

Absorption, distribution, metabolism, excretion, and toxicity

ATB

Automated topology builder

COVID 19

Coronavirus disease

LD50

Lethal dose 50

MD SIMULATION

Molecular dynamics simulation

MERS

Middle East respiratory syndrome coronavirus

MMBPSA

Molecular Mechanics Poisson-Boltzmann surface area

NCBI

National Center of Biotechnology Information

PDB

Protein data bank

PROTOX II

Prediction of toxicity of chemicals

ROG

Radius of gyration

RMSD

Root mean square deviation

RMSF

Root mean square fluctuations

SARS- CoV -2

Severe Acute Respiratory Syndrome Coronavirus 2

SASA

Solvent accessible surface area

VOC

Variant of concern

VOI

Variant of interest

Author contributions

Conceptualization, MV and RP; methodology, MV, RR, RP, DS, software, MV, AC, NK, KV, RR and PV; validation, MV; formal analysis, PV, AC, NK, KV investigation, AC, NK; OA, OAF Performed MD Simulation, KV, IS, RR, PV and DS; resources, AC, NK, KV and IS; data curation, AC, NK, KV and IS; writing—original draft preparation, AC, NK, KV and IS; writing—review and editing, MV, RR, PV, RP, DS; visualization, MV, RR, PV, DS,AC, NK and KV; supervision, MV. All authors have read and agreed to the published version of the manuscript.

Funding

Not Applicable.

Declarations

Conflict of interest

The authors report there are no competing interests to declare.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Footnotes

Ananya Chugh, Ishita Sehgal and Nimisha Khurana these authors have contributed equally.

Contributor Information

Rajendra Phartyal, Email: rajendraphartyal@svc.ac.in.

Mansi Verma, Email: mansiverma20@gmail.com, Email: mansiverma@hrc.du.ac.in.

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