This research project aims to identify potential drug candidates for COVID-19 through computational molecular docking of secondary metabolites from natural products against SARS-CoV-2 viral proteins, followed by immunochemistry experiments. Over 100 secondary metabolites isolated from plants are being evaluated based on their known antiviral activities. Preliminary molecular docking and dynamics simulations have identified several compounds including Cordifolioside-A, Palmitoside-G and Tinosinenoside-A that show strong binding to SARS-CoV-2 spike and envelope proteins.

1. COVID-19: Investigation on Secondary Metabolites from
Natural Products through Computational Chemistry and
Immunochemistry Approach
Collaborative Research Project-UGC
Thematic areas
Major biological and medical aspects of a SARS-CoV-2
Innovative and Therapeutic values
Prof. Dr. Ram Chandra Basnyat
Principal Investigator
Central Department of Chemistry
Tribhuvan University
Kirtipur
May 26, 2021
Khag Raj Sharma, PhD
Co-Investigator
Assistant Professor
Central Dept. of
Chemistry
Tribhuvan University
Bishnu P Marasini, PhD
Co-Investigator
Head, Dept. of
Biotechnology
National College
Madan Kumar Paudel,
PhD
Co-Investigator
Director
Phytomax Care Intl Pvt
Ltd
Binod Rayamajhee
Co-Investigator
Ph.D. Candidate
UNSW, Australia

2. Summary
⮚ COVID-19, declared as a global emergency by WHO, emerged in China and widespread all over the world
in short period.
⮚ Now, second wave of new variants are documented but till this date no specific treatment has been
revealed.
⮚ Natural products (secondary metabolites) can be introduced as preventive and therapeutic agents in the
fight against the Corona Virus.
⮚ Several molecules, including, Glycyrrhizin, Amentoflavone, baicalin, daidzein, epicatechin, galangin,
herbacetin, hesperidin, luteolin, cordifolioside A, cordifolioside B, Palmitoside F, tinosinenoside A,
borapetoside A, etc. isolated from plants exhibiting promising inhibitory effects against influenza-
parainfluenza viruses, respiratory syncytial virus, SARS-CoV, MERS-CoV, and SARS-CoV-2.
⮚ This project has planned to consider 136 molecules for molecular docking and MD simulations and till
now more than 78 secondary metabolites are in study. Among them computational investigation of 26
molecules have been completed with a well-known target protein spike - protein receptor-binding
domain (RBD) and Envelope (TMD) protein.

3. Conti…
⮚ We used GOLD along with MOE and Discovery studio for analysis of 26 secondary metabolites from Tinospora
species, widely cultivated in Nepal. The molecular docking of Cordifolioside-A (Gold score 58.27, ΔG bind -9.8
kcal/mol), Tinosinenoside-A (Gold score 54.86, ΔG bind -9.2 kcal/mol), and Borapetoside-C (Gold score 54.52, ΔG bind -
8.3 kcal/mol) displayed that the drug has effectively bound at the competitive site of spike protein.
⮚ Similary, Cordifolioside-A (Gold score 62.72, ΔG bind -9.6 kcal/mol), Palmitoside-G (Gold score 52.41, ΔG bind -8.7
kcal/mol), Tinosinenoside-A (Gold score 60.40, ΔG bind -9.1 kcal/mol) also displayed strong competitive interation
between ligand and envelope protein.
⮚ By molecular dynamics, we revealed that Cordiofolioside-A, Palmitoside-G, Tinosinenoside-A leads to a
conformational change in target proteins and hinders the binding interaction.
⮚ In silico ADMET analysis has shown that these molecules were optimal within the categorical range compared to
commercially available drug Remdevisir. Our study thus recommends Cordiofolioside-A, Palmitoside-G, and
Tinosinenoside-A as potent drug candidates against SARS-CoV-2.
⮚ Outcomes of the molecular docking and MD simulations will be applied to immunochemistry experiments.

4. TABLE OF CONTENTS
❑ Background

5. Background
⮚ COVID-19 causing pathogen, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), emerged at
the end of 2019 in Wuhan, China has high transmissibility and infectivity.
⮚ Due to the lack of robust pre-existing or acquired immunity by the hosts (Petersen, et al., 2020; Tortorici,
et al., 2020) has caused COVID-19 cases surge to more than 162 million over the world, resulting in over
3.35 million deaths by 15 May 2021.
⮚ Among 7 types of CoV are identified that can cause human infections, SARS-CoV-2 has shown a superior
adaptation in human cells compared to other CoV strains (Dilucca et al., 2020).
⮚ SARS-CoV-2 is a positive-sense, single stranded RNA virus which encodes 16 non-structural proteins (NSP1-
NSP16) four structural proteins (spike, membrane, envelope and nucleocapsid) and nine accessory
proteins.
⮚ Proteins or some drugs responsible for the virus cycle (adsorption, penetration, uncoating, transcription,
replication, assembly, and release) could be the targets for the development of antiviral agents against the
virus (Milovanovic et al., 2017).
⮚ Hence, plant-based natural compounds could be an excellent alternative for the screening of antiviral
drugs that can inhibit multiple steps of the viral replication cycle.

6. ⮚ Natural plant secondary metabolites are a major source of antiviral drugs and found to belong to
various types such as saponins, alkaloids, flavonoids, isoflavonoids, diterpenes, quinones and
coumarins.
⮚ In this research, antiviral properties of some plant metabolites are studied and some are under study
for the inhibition of SARS-CoV-2.
⮚ In silico screening of natural products against a membrane protein of COVID-19 could provide a reliable
alternative for in vitro, in vivo testing and clinical trials.
⮚ Natural compounds identified as membrane protein inhibitors through molecular docking could speed up the drug
discovery process for the treatment of COVID-19 patients.
⮚ Here, we carried out the molecular docking and MD simulations of the 26 antiviral compounds from three
different medicinal plants (Tinospora cordifolia, Tinospora crispa, and Tinospora sinensis) against RBD and TMD of
S- and E-proteins of SARS-CoV-2 providing aid in drug discovery programs.
⮚ In addition, more than 100 molecules' ADMET value and toxicity have been analysed and are on the table to carry
out Molecular docking and MD simulations.

7. Research Objectives
General objective
To identify potent antiviral drug candidate(s) for COVID-19 by molecular docking and
immunoassays.
Specific objectives
o Identification of potent drug candidate(s) from plant-based secondary metabolites.
o Molecular docking with spike receptor-binding domain, Envelope (TMD), Mpro, RdRP of
SARS-CoV-2 and helicase nsp13.
o Selection of drug candidates from computational chemistry and correlate them with
literature.
o Immunochemistry of drug candidate(s).
o Identification of potent drug candidate(s).

8. Research hypothesis
⮚ Molecules isolated from plants, which show promising inhibitory effects against viruses,
such as influenza-parainfluenza viruses, respiratory syncytial virus, SARS, MERS-CoV,
could also be potential drug candidate(s) against COVID-19.
⮚ We have identified 136 antiviral drug candidates which could be a potential area of
research in computational chemistry followed by immunochemistry approach.
(Source of 136 candidates: Recently accepted manuscript on Phytotherapy Research)

9. Literature review
⮚ The natural compounds isolated from various medicinal plants have already proven
effective against the COVID-19 (Luo et al., 2020).
⮚ Plant-based secondary metabolites could be an excellent alternative for the invention of
antiviral drugs that can inhibit multiple steps of the virus replication cycle.
⮚ Polyphenols, glucosides, flavonoids, proanthocyanidins, monoterpenoids,
triterpenoids, sesquiterpenes, saponins, and alkaloids extracted and isolated from
various medicinal plants have exhibited diverse antiviral activities.

10. Table 1: Possible antiviral drug candidates from natural products
Plant sources Isolated molecules Active against virus reference
Scutettaria
baicalensis
Scutellarein, Wogonin SARS-CoV, SARS-CoV-2 https://doi.org/10.1016/j.bmcl.2012.04.
081;
https://doi.org/10.1080/14756366.2021.187
3977
Ipomoea batatas Myricetin SARS-CoV https://doi.org/10.1016/j.bmcl.2012.04.08
1
Citrus aurantium Hesperetin SARS-CoV https://doi.org/10.1016/j.antiviral.2005.
07.002
Alpinia officinarum Galangin SARS-CoV-2 doi:
10.20944/preprints202003.0214.
v1
Aglaia foveolata Silvesterol MERS-CoV, HCoV-229E http://dx.doi.org/10.1016/j.antiviral.201
7.12.010
Silybum marianum Silymarin SARS-CoV-2 https://scholar.google.com/scholar?cite
s=14526448380890328370&as_sdt=20
05&sciodt=0,5&hl=en
Morus alba Morin MERS-CoV, SARS-CoV and SARS-
CoV-2
https://doi.org/10.1080/07391102.2021.
1871863
Sambucus nigra Cyanidin SARS-CoV-2 https://arxiv.org/abs/2005.11767
Camellia sinensis Epigallocatechin gallate SARS-CoV-2(3CL-Protease) https://doi.org/10.1155/2020/5630838
Glycyrrhiza glabra Katsumadain A, Glycyrrhizin SARS-CoV, SARS-CoV-2 https://doi.org/10.1007/s13659-020-00250-4)
17https://doi.org/10.1021/jm0493008
18doi: 10.1016/j.pharmthera.2020.107618
19https://doi.org/10.1038/s41477-020-0628-0 ; doi:
10.20944/preprints202001.0358.v3
Artemisia annua Artemisinin SARS-CoV-2 https://doi.org/10.1021/acsinfecdis.0c0
0522
Calophyllum lanigerum Calanolide A SARS-CoV https://scholar.google.com/citations?us
er=7nmbC3QAAAAJ&hl=en&oi=sra
Ginko biloba Amentoflavone SARS-CoV https://scholar.google.com/citations?us
er=YsSgefgAAAAJ&hl=en&oi=sra

11. Materials and Methods
• All the computational docking studies were performed on a Microsoft Windows workstation
(Intel Core i7 processor and system memory 6GB RAM).
• Molecular Operating Environment (MOE) version 3.12 (Wolber & Langer, 2005) was used for
protein preparation, and binding site analysis.
• GOLD (Genetic Optimization for ligand docking) version 4.0.1 based on a genetic algorithm
was used to examine the binding of the ligands to the target proteins (G. Jones et al., 1997).
• Protein-ligand interactions were visualized on Biovia Discovery Studio Visualizer (Pettersen et
al., 2004) for graphical analysis and final processing.
• Computational analyses were performed to check the stability of the complex with binding free
energy (ΔG bind) calculation for the selection of compounds from the docking process.

12. Protein Preparation
• Spike RBD protein (PDB: 6MOJ, 2.45Å) and Envelope protein TMD (PDB:
7K3G, 2.1Å) were retrieved from RCSB protein data bank and prepared using
MOE protein preparation wizard.
• Small molecules were removed from the crystal structures using BIOVA
Discovery Studio Software (Dassault Systems BIOVIA).
• Energy minimization was done using parameters (Force Field:
AMBER99+Solvation, gradient: 0.05, and Chiral Constraint: Current Geometry).

13. Preparation of ligands
• Secondary metabolites from Tinospora species were selected as ligands for molecular
docking with receptors on the basis of their antiviral activities were accessed from
PubChem and Chemspider and literature ((Lam et al., 2018), Lagunin et al., 2011;
Maunz et al., 2013)
• Ligands preparation was carried out using ligand preparation module of molecular
modeling package.
• Suitable parameters like optimization, ring conformation, 2D to 3D conversion, and
determination of protomers, tautomers, and ionization states at pH 7.0; along with partial
atomic charges using OPLS3e force field (Roos et al., 2019).
• Finally, the ligands were processed into mol2 file format and minimized for molecular
docking using the MOE Lig-Prep module (I.-J. Chen & Foloppe, 2010).

16. Active site prediction
• Amino acids of receptors involved in active pocket formation were determined
using Site-Finder (Del Carpio et al., 1993).
• Further, the site number, size and residues of the active site (Table 1-2) were
calculated using Site-Finder.
Figure 1: Binding cavity of spike RBD region showing
surface map and main active site
Figure 2: Binding cavity of envelope TMD region showing
surface map and main active site

17. Molecular docking and virtual screening
• First of all, Molecular docking and virtual screening was performed using MOE
(https://www.chemcomp.com) in a local Window machine.
• Second Molecular docking is achieved using GOLD (Verdonk et al., 2003) using
water toggle procedure in flexible mode.
• Finally the docked conformations that have a higher GOLD fitness score was
taken for analyzing the binding mode.

18. Calculation of Binding free energy
• The binding free energies of receptor and ligand complex were analyzed by prime
Molecular Mechanics Generalized Born Surface Area (MM-GBSA) module of
Schrodinger suite with the OPLS force field.
• The prime MM-GBSA approach is based on the docking complex and is used to
calculate binding energy using the following equation (Ramatenki et al., 2015;
Tripathi et al., 2013) as
–∆GBFE = ∆EMM + ∆GSolv + ∆GSA

19. Pharmacokinetics study in secondary metabolites
• The pharmacokinetics (ADMET) properties and pharmacology of potential anti-
SARS-CoV-2 compounds from natural products were predicted by using
cheminformatics tool i.e pkCSM webserver (González-Medina et al., 2017).
• Potential toxicity of secondary metabolites was assessed by Pro Tox-II, where the
lethal dose (LD50) value was categorized from class 1 and 2 (fatal), class 3 (toxic),
class 4 and 5 (harmful), while class-6 (non-toxic) (Banerjee et al., 2018).

20. Parameters 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Absorption Water solubility
(log mol/L)
-1.965 -2.112 -2.88 -3.504 -3.393 -3.393 -3.079 -3.073 -4.633 -2.704 -3.823 -3.757 -4.819 -2.936
Caco2
permeability (log
Papp 10-6 cm/s)
-0.368 -0.613 -0.408 -0.307 0.591 0.591 -0.348 -0.386 1.077 -0.237 1.178 1.267 0.485 -0.404
Intestinal
absorption (%
absorbed)
31.087 46.863 52.66 49.384 69.799 69.799 59.506 54.034 94.917 43.031 92.024 95.383 100 57.067
Skin permeability
(log Kp)
-2.735 -2.737 -2.743 -2.737 -2.735 -2.735 -2.919 -2.784 -3.485 -2.735 -3.38 -3.538 -3.203 -2.784
Distribution VDss (Human, log
L/Kg)
0.009 -0.293 -0.515 0.02 -0.003 -0.003 -1.01 -0.673 0.114 -0.73 0.008 0.151 -0.079 -0.611
BBB Permeability
(logBB)
-1.636 -1.884 -1.682 -1.836 -1.435 -1.435 -1.553 -1.348 -0.364 -1.467 -0.653 -0.233 -0.922 -1.384
CNS Permeability
(log PS)
-5.353 -4.946 -4.934 -4.925 -4.591 -4.591 -5.079 -4.194 -2.906 -3.564 -3.017 -2.966 -3.05 -4.273
Metabolism CYP1A2 NO NO NO NO NO NO NO NO NO NO NO NO NO NO
CYP2C19 NO NO NO NO NO NO NO NO NO NO NO NO NO NO
CYP2C9 NO NO NO NO NO NO NO NO NO NO NO NO NO NO
CYP2D6 NO NO NO NO NO NO NO NO NO NO NO NO NO NO
CYP3A4 NO NO NO NO NO NO NO NO NO NO YES NO YES NO
Excretion Renal OCT2
substrate
clearance
NO NO NO NO NO NO NO NO NO NO NO NO NO NO
Total Clearance
(logml/min/kg)
0.846 0.768 0.857 0.541 0.574 0.574 0.637 0.605 1.247 0.978 1.058 1.021 0.878 0.755
ADMET properties of Tinospora cordifolia (1-14) by pKCSM server

21. Parameters 15 16 17 18 19 20 21 22 23 24 25 26
Absorption Water solubility (log mol/L) -5.67 -3.131 -2.763 -2.458 -2.485 -2.485 -2.478 -3.24 -3.252 -3.067 -3.399 -2.44
Caco2 permeability (log Papp 10-6 cm/s) 1.283 -0.199 -0.285 -0.341 -0.369 -0.369 -0.348 -0.613 -0.568 -0.769 -0.788 -0.672
Intestinal absorption (% absorbed) 100 60.315 50.062 35.534 43.643 43.643 58.162 61.003 65.794 56.616 62.835 12.633
Skin permeability (log Kp) -2.736 -2.736 -2.752 -2.736 -2.736 -2.736 -2.736 -2.759 -2.792 -2.743 -2.747 -2.735
Distributio
n
VDss (Human, log L/Kg) -0.73 -0.082 -0.468 -0.192 -0.225 -0.225 -0.005 -0.658 -0.791 -0.401 -0.464 0.236
BBB Permeability (logBB) -1.17 -1.625 -1.509 -1.621 -1.705 -1.705 -1.468 -1.687 -1.644 -1.716 -1.934 -2.139
CNS Permeability (log PS) -3.273 -4.978 -4.916 -5.053 -5.072 -5.072 -4.859 -4.868 -4.774 -4.949 -4.89 -5.289
Metabolis
m
CYP1A2 NO NO NO NO NO NO NO NO NO NO NO NO
CYP2C19 YES NO NO NO NO NO NO NO NO NO NO NO
CYP2C9 YES NO NO NO NO NO NO NO NO NO NO NO
CYP2D6 NO NO NO NO NO NO NO NO NO NO NO NO
CYP3A4 YES NO NO NO NO NO NO NO NO NO YES NO
Excretion Renal OCT2 substrate clearance NO NO NO NO NO NO NO NO NO NO NO NO
Total Clearance (logml/min/kg) 0.341 0.616 0.755 0.865 0.863 0.863 0.511 0.635 0.646 0.765 0.787 0.923
Toxicity Ames Toxicity NO NO NO NO NO NO NO NO NO NO NO NO
Hepatotoxicity NO NO NO NO NO NO NO NO NO NO NO NO
LD50 NO 3.74 3.689 4.181 4.202 4.501 3.287 3.904 3.999 3.865 3.906 4.084
ADMET properties of Tinospora Sinensis (15-20) and Tinospora crispa (21-26) by pKCSM server

22. Plant selection

23. Why Tinospora?
• Tinospora species( Tinospora cordifolia, Tinospora sinensis and Tinospora
crispa) is omnipresent in mountain area of Nepal.
• Tinospora cordifolia have ability to improve the body's resistance against
infections as immune modulators (Sinha et al., 2004).
• The ethanolic leaf and stem extract of Tinospora sinensis showed the
antimicrobial activity.
• And the compound diosgenin isolated from Tinospora sinensis showed the
anti-inflammatory activity (Punitha et al., 2013).
• Compounds isolated from Tinospora crispa possessed a broad range of
pharmacological activities such as immunomodulatory, anti-inflammatory,
anti-oxidant, cytotoxic, antimalarial, cardio protective and anti-diabetic
activities.

24. Result Analysis with Spike protein (6MOJ)
• The study showed that Cordifolioside-A (1), Tinesinenoside-A (16), Palmitoside-G (14) were
well located in the binding site of the E-protein TMD region with Gold fitness score of 62.72,
60.40, and 52.41 respectively which is much higher than commercial drug remdesivir (GOLD
fitness score 24.99).
• Cordifolioside-A (1), and Tinesinenoside (16) were surrounded by several amino acid residues
(Ala A32, Arg B38, Ala E32, Ala B32, Leu B31), which were described as active site residues.
• Hydrogen bonding with Ala A32 residue for a top-scoring Cordfolioside-A (1) and Ala E32
for the commercial drug (remdesivir) was observed with bond lengths of 2.3, and 3.5
respectively.

25. GOLD Fitness score, Binding Free energy, and Protein-Ligand interaction of
natural compounds with spike protein RBD region (6MOJ)
Compounds GOLD Fitness Score Binding Free Energy
(ΔG bind) (Kcal/mol)
Interacting Residues Bond Length (Å)
Remdesivir
(GS-441524)
28.53 -8.1 Asp 428
Thr 430
1.9
3.2
Cordifolioside-A (1) 58.27 -9.8 Thr 430
Glu 516
2.9/2.9
1.8
Tinosinenoside-A (16) 54.86 -9.2 Glu 516 2.3
Borapetoside-C (23) 54.52 -8.3 Glu 516
Thr 430
2.1
3.4/3.0
Borapetoside-B (22) 53.68 -8.3 Thr 430
Phe 515
3.1
2.2
Palmitoside-G (14) 50.80 -8.2 Asp 428 1.9/2.1
Rhumphioside-A (24) 50.77 -9.8 Arg 355
Pro 463
3.0
2.2

27. Result Analysis with Envelope Protein (7K3G)
• The study showed that cordifolioside-A (1), Tinesinenoside-A (16), Palmitoside-G
(14) were well located in the binding site of the E-protein TMD region with
GOLD fitness score of 62.72, 60.40, and 52.41 (Table 4) respectively which is
much higher than commercial drug remdesivir (Gold fitness score 24.99).
• Cordifolioside-A (1), and Tinesinenoside (16) were surrounded by several amino
acid residues (Ala A32, Arg B38, Ala E32, Ala B32, Leu B31), which were
described as active site residues.
• Hydrogen bonding with Ala A32 residue for a top-scoring Cordfolioside-A (1)
and Ala E32 for the commercial drug (remdesvisir) was observed with bond
lengths of 2.3, and 3.5 respectively.

28. GOLD Fitness score, Binding Free energy, and Protein-Ligand interaction of
envelope protein TMD region (7K3G)
Compounds GOLD Fitness Score Binding Free Energy
(ΔG bind) (Kcal/mol)
Interacting Residues Bond Length (Å)
Remdesivir
(GS-441524)
24.99 -8.6 Ala E32 3.5
Cordifolioside-A (1) 62.72 -9.6 Ala A32
Arg B38
Ala E32
2.3
2.0
3.6
Tinosinenoside-A (16) 60.40 -9.1 Ala B32
Leu B31
2.9
2.5
Rhumphioside-B (25) 57.87 -7.7 Ala D32 2.3
Rhumphioside-A (24) 55.87 -7.6 Leu D31 1.8
Cordifolide-C (6) 53.08 -8.5 Leu D31 1.9
Palmitoside-G (14) 52.41 -8.7 Leu A28 2.4

30. Future Persective
• Extraction and invitro analysis using standard protocol.
• Use of Cordifolioside A and Tinosinenoside A may boost immune system
which will be confirmed after measurement of Antibody level using
standard protocol.

31. Status of Research Paper
 Submitted for publication

32. Future Perspectives
• Extraction and Invitro analysis using standard protocol
• Use of Cordifolioside-A and Tinosinenoside-A may boost immune
system which will be confirmed after measuring the Antibody level
using standard protocol.

33. Suggestions
• We can start Preliminary Clinical Trials using combined dose of
Cordifolioside-A and Tinosinenoside-A.

34. Conclusion
 An efficient strategy integrating molecular docking, binding energy
calculation, and ADMET analysis and potential toxicity was developed
for screening spike protein RBD and envelope protein TMD inhibitors
from natural compounds.
 Two molecules Cordifolioside-A (1), and Tinosinenoside-A (16) were sort
out from Tinospora species as potent spike protein RBD and envelope
protein TMD inhibitors.
 This research provides the basis for further exploration of natural
compounds from Tinospora species in the intervention and prevention of
the COVID-19 related diseases for future clinical use.