Phytomedicine Plus 1 (2021) 100002
Contents lists available at ScienceDirect
Phytomedicine Plus
journal homepage: www.elsevier.com/locate/phyplu
Nature’s therapy for COVID-19: Targeting the vital non-structural proteins
(NSP) from SARS-CoV-2 with phytochemicals from Indian medicinal plants
Pratap Kumar Parida a,1, Dipak Paul a,1, Debamitra Chakravorty b,1,∗
a Noor Enzymes Private Limited, 37-B, Darga Road, Kolkata 700 017, India
b Novel Techsciences (OPC) Private Limited, 37-B, Darga Road, 1st Floor, Kolkata 700 017, India
article info
Keywords:
SARS-CoV-2
India
Phytochemicals
Molecular dynamics simulation
Binding energy
Pathway enrichment
abstract
Background: Containing COVID-19 is still a global challenge. It has affected the "normal" world by targeting its
economy and health sector. The effect is shifting of focus of research from life threatening diseases like cancer.
Thus, we need to develop a medical solution at the earliest. The purpose of this present work was to understand
the efficacy of 22 rationally screened phytochemicals from Indian medicinal plants obtained from our previous
work, following drug-likeness properties, against 6 non-structural-proteins (NSP) from SARS-CoV-2.
Methods: 100 ns molecular dynamics simulations were performed, and relative binding free energies were com-
puted by MM/PBSA. Further, principal component analysis, dynamic cross correlation and hydrogen bond oc-
cupancy were analyzed to characterize protein–ligand interactions. Biological pathway enrichment analysis was
also carried out to elucidate the therapeutic targets of the phytochemicals in comparison to SARS-CoV-2.
Results: The potential binding modes and favourable molecular interaction profile of 9 phytochemicals, majorly
from Withania somnifera with lowest free binding energies, against the SARS-CoV-2 NSP targets were identified. It
was understood that phytochemicals and 2 repurposed drugs with steroidal moieties in their chemical structures
formed stable interactions with the NSPs. Additionally, human target pathway analysis for SARS-CoV-2 and
phytochemicals showed that cytokine mediated pathway and phosphorylation pathways were with the most
significant p-value.
Conclusions: To summarize this work, we suggest a global approach of targeting multiple proteins of SARS-CoV-2
with phytochemicals as a natural alternative therapy for COVID-19. We also suggest that these phytochemicals
need to be tested experimentally to confirm their efficacy.
Introduction
The pandemic COVID-19 (coronavirus disease 2019) has spread to
more than 203 countries and territories. Initial reports suggested that
20% cases are severe but its fast transmission rates has severely im-
pacted world economy and the health care sector causing global panic
(Guan et al., 2020; Huang et al., 2020; Wang et al., 2020). SARS-CoV-2
has been related to gastroenteritis, heart failure and respiratory com-
plications. It is also severe in immune compromised patients, in men-
independent of age and elderly patients with co-morbidities (Deng and
Peng, 2020; Guan et al., 2020; Huang et al., 2020; Jin et al., 2020a;
Islam et al., 2020). Given the severity of the infection, the proposed
therapies include targeting viral replication and blocking the virus at-
tachment to human cell receptors by its structural proteins (Wu et al.,
2020). Unfortunately, coronavirus vaccines are not yet on the market
and large-scale manufacturing capacity for these vaccines does not exist
yet (Amanat and Krammer, 2020). Only two candidate vaccines have
reached stage 2/3 clinical trials and one in phase 2 (COVID-19 vaccine
Abbreviations: ACPYPE, AnteChamberPYthon Parser interface; ATP, Adenosine tri phosphate; COVID-19, Corona Virus Disease 2019; DCCM, Dynamic Cross
Correlation Matrix; GO, Gene Ontology enrichment; H-bond, Hydrogen Bond; HIV, Human Immuno Deficiency Virus; JAK-STAT, The Janus kinase (JAK)-signal
transducer and activator of transcription (STAT); KEGG, Kyoto Encyclopedia of Genes and Genomes; MD, Molecular dynamics; MAPK, Mitogen-activated protein
kinase; MM/PBSA, Molecular Mechanics/Poisson–Boltzmann (Generalized Born) surface area; NSP, Non Structural Proteins; PCA, Principal Component Analysis;
PME, Particle-Mesh-Ewald summation; RdRp, RNA-dependent RNA polymerase; Rg, Radius of gyration (Rg); RMSD, Root mean square deviation; RMSF, Root mean
square fluctuations; RNA, Ribonucleic Acid; SARS-CoV, Severe acute respiratory syndrome/coronavirus; SARS-CoV-2, Severe acute respiratory syndrome coronavirus
2; vdW, van der Waal’s energy..
∗ Corresponding author.
E-mail address: dc@noveltechsciences.com (D. Chakravorty).
1 All the authors have contributed equally to the paper.
https://doi.org/10.1016/j.phyplu.2020.100002
2667-0313/© 2020 Novel Techsciences (OPC) Private Limited. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
P.K. Parida, D. Paul and D. Chakravorty
tracker, 11 June 2020). Considering economic impact on stock markets
in recent times, funding for vaccine production seems to be great invest-
ment soon (Amanat and Krammer, 2020). In this context development
of natural drug therapy can help to contain the spread of the virus.
Nature provides a vast library of chemicals yet to be explored as
drugs for treatment of various viral ailments (Denaro et al., 2020). Re-
search have been conducted in screening natural drugs against SARS-
CoV-2 targets (Jin et al., 2020b; Kumar et al., 2020; Luo et al., 2020;
Pang et al., 2020; Xu et al., 2020; Yang et al., 2020; Zhang et al.,
2020; Islam et al., 2020). In this context, we have recently presented
66 virtually screened phytochemicals from 55 Indian medicinal plants
against 8 SARS-CoV-2 targets (Parida et al., 2020a). We also presented
work related to molecular dynamics simulation of phytochemicalsfor the
spike glycoprotein and the main protease of SARS-CoV-2 (Parida et al.,
2020b). Interestingly from our previous work and the vast literature that
exist for drug development against SARS-CoV-2, it was realized other
proteins of SARS-CoV-2 must be targeted to avoid multi drug resistance
strains of the SARS-CoV-2 virus as it is prone to high rate of mutations
(Pachetti et al., 2020). In this context 6 non-structural proteins (NSPs) as
targets for drug development is being suggested. These NSPs may have
conserved roles within the viral lifecycle of SARS-CoV-2 (Littler et al.,
2020). The NSPs selected in this study were due to their indispensable
nature in the viral life cycle. Other than the main protease NSP5 (3CL-
pro) vital targets of SARS-CoV-2 considered in this work were NSP3,
NSP9, NSP10, NSP12, NSP15 and NSP16.
NSP3 is the papain-like proteinase- reported to induce protective im-
munity by blocking host immune response by inhibiting the expression
of innate immunity genes (Fehr et al., 2016). Its conserved macrodomain
is involved in binding of ADP-ribose in most of the corona viruses
(Han et al., 2011; Cho et al., 2016; Lei et al., 2018) and bears 91.8%
sequence similarity with SARS-CoV NSP3 (Yoshimoto, 2020). Another
promising target used in this study was NSP9 (RNA-binding protein)
as it is thought to mediate viral replication. NSP9 from SARS-CoV-2
bears 99.1% sequence similarity with that from SARS-CoV (Yoshimoto,
2020; Sutton et al., 2004; Frieman et al., 2012).NSP10 was chosen
as another important drug target as it acts as a cofactor for the ac-
tivation of the replicative enzyme in SARS-CoV (Joseph et al., 2006;
Matthes et al., 2006; Bouvet et al., 2014).Its sequence similarity (99.3%)
with SARS-CoV makes it a promising drug target (Yoshimoto, 2020).
NSP12 or the RNA dependent RNA polymerase has been chosen as an-
other important drug development candidate as it is involved in vi-
ral replication (Neogi et al., 2020), however, but no specific inhibitors
have been validated until now. Therefore, in this work other than the
main catalytic pocket of NSP12, NSP12-NSP8 and NSP12-NSP7 inter-
faces were also chosen as potential targets. NSP8 synthesizes primer- up
to six nucleotides in length, for NSP12-RdRp RNA synthesis. Further,
the NSP7 acts as a cofactor to enhance the RdRps enzyme activity of
NSP12 (Wu et al., 2020). Another interesting SARS-CoV-2 target used
in this work was NSP15 with 97.7% sequence similarity with SARS-
CoV (Yoshimoto, 2020). This protein was reported to degrade polyuri-
dine sequences and viral dsRNA to prevent host recognition of the virus
to prevent the host immune sensing system from detecting the virus
(Hackbart et al., 2020; Decroly et al., 2012). It bears 99% sequence sim-
ilarity with SARS-CoV NSP16.
Briefly it can be said that the aim of the present work was to ex-
plore various NSPs from SARS-CoV-2 as promising targets for phyto-
chemicals as drug candidates. In this study, we present the analysis of
100 ns molecular dynamics trajectories for 6 NSPs from SARS-CoV-2
and their phytochemical complexes. The binding free energy and the
interaction dynamics of the phytochemicals were analysed by various
computational approaches. Further, the therapeutic efficacy of these
phytochemicals was also experimented by biological pathway enrich-
ment analysis. In summary it can be said that targeting these NSPs with
phytochemicals can serve to overcome the predicted multi-drug resis-
tance as SARS-CoV-2 which is fast evolving due to its propensity to high
mutations.
Phytomedicine Plus 1 (2021) 100002
Methods
Molecular dynamics simulations and trajectory analyses of the
phytochemicals against the vital NSPs of SARS-CoV-2
Phytochemicals were successfully screened from Indian medicinal
plants by molecular docking against 10 SARS-CoV-2 protein target bind-
ing sites in our previous work (Parida et al., 2020a). 22 phytochemi-
cals with good docking scores that followed drug-like properties against
SARS-CoV-2 NSP3, NSP9, NSP10, NSP12, NSP16 and NSP15 were anal-
ysed by 100 ns molecular dynamics (MD) simulations (Table 1). The
repurposed drugs that had the lowest binding score were also subjected
to MD simulations as a control group. All the simulations were carried
out with Gromacs 2020.2 software package (Lindahl et al., 2020) with
AMBER99SB-ILDN force field and TIP3P water molecules. The force
field parameters for the phytochemicals and the repurposed drugs were
generated by ACPYPE (AnteChamberPYthon Parser interface) (da Silva
andVranken, 2012). Charges were neutralized and system was mini-
mized with 5000 steps by steepest descent algorithm. The systems were
equilibrated by 1 ns position restraint simulations of 1000 kJ mol−1
nm−2 in the NVT and NPT ensembles. Equilibrated systems were used
to simulate a 100 ns with no restraint production run. Electrostatic in-
teractions were calculated with Particle-Mesh-Ewald summation (PME)
(Darden et al., 1993). Post-MD analyses were performed, this includes
root mean square deviation (RMSD), root mean square fluctuations
(RMSF), the radius of gyration (Rg) and hydrogen bond occupancy.
Molecular Mechanics - Poisson Boltzmann Surface Area (MM-PBSA)
was applied on snapshots obtained from MD trajectory to estimate the
binding free energy using the GROMACS tool g_mmpbsa (Baker et al.,
2001; Kumari et al., 2014). The Principal Component Analysis (PCA)
was performed using GROMACS v2020.2. Further, Dynamic Cross Cor-
relation Matrix (DCCM) analysis performed using the Bio3D package
(Grant et al., 2006).
High Performance Computing resources of Amazon EC2 G4 instances
were employed for carrying out computational work on Amazon Web
Services cloud computing platform (https://aws.amazon.com) with the
help of RONIN interface (https://ronin.cloud/) as an award granted by
the COVID-19 High Performance Computing (HPC) Consortium for car-
rying out this work (https://covid19-hpc-consortium.org/projects).
Biological pathway enrichment analysis of human protein targets identified
for the phytochemicals
SwissTargetPrediction server was employed to identify top 15 hu-
man protein targets of the phytochemicals (Gfeller et al., 2014). Redun-
dancy removal resulted into 93 target proteins for the phytochemicals.
Further, 172 protein targets for human coronavirus were obtained by
literature survey (Chakrabarty et al., 2020; Zhou et al., 2020). Enrichr
and REVIGO webserver were used for gene ontology (GO) enrichment
analysis (Supek et al., 2011; Kuleshov et al., 2016). The generated net-
works were visualized in Cytoscape v3.8 (Shannon et al., 2003). Enrichr
was also employed to calculate the p- values using the Fisher exact test
for pathway and database enrichment for Kyoto Encyclopedia of Genes
and Genomes (KEGG) pathways and VirusMint databaseichr. VirusMint
is database of interactions between viral and human proteins (Chatr-
aryamontri et al., 2009).
Results
Molecular dynamics simulation trajectory analysis of the NSP complexes
In this work, 6 NSP and 8 potential drug target sites docked with
22 phytochemicals and 10 repurposed drugs obtained from our pre-
vious work, were explored by 100 ns molecular dynamics simulations
(Parida et al., 2020a). Throughout this paper the NSP complexes with
the phytochemicals and the repurposed drugs have been abbreviated as
P.K. Parida, D. Paul and D. Chakravorty
Table 1
The phytochemicals and their tartgets from SARS-CoV-2.
∗ Phytochemicals/ Repurposed drugs
NSP 10 (PDB 6W4H)
27-Hydroxywithanolide B
Complexes
N10P1
Docking Score (Kcal/mol)
−8.32
Anaferine
N10P2
−6.22
# 2D structure
Baricitinib
N10-R3
−9.22
NSP12 D1 (RNA Binding Site) (PDB 6M71)
12-Deoxywithastramonolide
N12d1P1
Methylprednisolone
N12d1R2
−7.58
−6.54
NSP12 D2 (NSP12-NSP7 Interface) (PDB 6M71)
Withastramonolide
N12d2P1
Withanolide B
N12d2P2
−9.72
−9.2
12-Deoxywithastramonolide
N12d2P3
−9.05
Withanolide R
Withaferin A
N12d2P4
−8.94
N12d2P5
−7.9
Hydroxychloroquine
N12d2R6
−7.73
NSP12 D3 (NSP-NSP8 Interface) (PDB 6M71)
Withaferin A
N12d3P1
−9.94
Phytomedicine Plus 1 (2021) 100002
∗ Plant Source
Withania somnifera
Withania somnifera
Repurposed drug
Withania somnifera
Repurposed drug
Withania somnifera
Withania somnifera
Withania somnifera
Withania somnifera
Withania somnifera
Repurposed drug
Withania somnifera
(continued on next page)
P.K. Parida, D. Paul and D. Chakravorty
Table 1 (continued)
∗ Phytochemicals/ Repurposed drugs
Lopinavir
Complexes
N12d3R2
Docking Score (Kcal/mol)
−8.33
# 2D structure
NSP16 (PDB 6W4H)
Solvanol
(-)-Anaferine
Limonin
N16P1
−10.98
N16P2
−10.91
N16P3
−10.73
Phytomedicine Plus 1 (2021) 100002
∗ Plant Source
Repurposed Drug
Solanum nigrum
Withania somnifera
Nigella sativa
Chloroquine
N16R4
−10.11
NSP9 (PDB 6W4B)
27-Hydroxywithanolide B
12-Deoxywithastramonolide
Azadiradionolide
N9P1
N9P2
NPP3
−8.28
−8.19
−7.3
Baricitinib
NSP15 (PDB 6W01)
Somniferine
Vindolinine
Dexamethasone
N9R4
−6
N15P1
−7.21
N15P2
−6.87
N15R3
−7.08
Repurposed Drug
Withania somnifera
Withania somnifera
Azadirachta indica
Repurposed drug
Withania somnifera
Withania somnifera
Repurposed drug
(continued on next page)
P.K. Parida, D. Paul and D. Chakravorty
Table 1 (continued)
∗ Phytochemicals/ Repurposed drugs
NSP3 (PDB 6W02)
2,3-Dehydrosomnifericin
Complexes
N3P1
Docking Score (Kcal/mol)
−12.3
# 2D structure
Withanolide B
24,25-dihydrowithanolide D
N3P2
N3P3
−11.44
−10.24
Phytomedicine Plus 1 (2021) 100002
∗ Plant Source
Withania somnifera
Withania somnifera
Withania somnifera
27-Deoxy-14-hydroxywithaferin A
N3P4
−9.49
Withania somnifera
Baricitinib
N3R5
−10.38
Repurposed drug
∗ The phytochemicals (docking scores) and interacting residues within the binding pocket were identified from a previous work to possess drug likeness
properties (Parida et al., 2020a).
# The 2D images of the phytochemicals were obtained from PubChem Database (https://pubchem.ncbi.nlm.nih.gov/).
presented in Table 2. The binding sites have been illustrated in Fig. 1.
The binding site of NSP10 and NSP16 were the NSP10-NSP16 binding
interface (PDB ID: 6w4h). The binding site in NSP10 (known to stimulate
methyl transferase activity of NSP 16) comprised of residues Asn4293-
Tyr4349 between helix 1 and helix 3. In NSP 16 it involved residues
Lys 6836-Met6839 and Val6842- Asp6904 which lies in between he-
lix 3 and 𝛽-sheet A edge 𝛽-strand 4. The inhibitor binding site in NSP9
(involved in viral replication, PDB ID: 6w4b) was the dimerization in-
terface consisting of residue Arg100- Thr110 (helix 1). In NSP 12 (PDB
ID: 6m71) three sites were chosen: D1 which is the catalytic pocket; D2:
NSP12-NSP7 binding interface and D3: NSP12-NSP8 binding interface.
In case of NSP15 (PDB ID: 6w01), an uridylate specific endoribonucle-
ase, active site pocket with conserved residues His235, His250, Lys290,
Thr341, Tyr343, and Ser294 was the site of inhibitor docking. In NSP3
(PDB ID: 6w02) which is the ATP ribose phosphatase of SARS-CoV-2,
the adenosine-5-diphohoribose binding site was the inhibitor docked
site. The site was in between 𝛽-strand 3 of 𝛽-sheet B- helix 2 and strand
3 of 𝛽-sheet A and helix 7. The details of the virtual screening of these
8 target sites can be found in our previous work (Parida et al., 2020a).
Results of RMSD, RMSF and Rg were plotted and have been illus-
trated in Fig. 2. In the case of NSP10 (Fig. 2A), slightly lower average
RMSD value was obtained for N10P1 (0.49) and N10R3 (0.48) in com-
parison with NSP10 apo-protein (0.51 nm). The average RMSF fluctua-
tions of residues were slightly lower for N10P1 and N10R3 (~0.2 nm)
in comparison to the apo-protein (0.25 nm). It can also be observed
from Fig. 2A that N10P1 showed comparatively decreased fluctuation
(∆RMSF) in the residues interacting with the phytochemicals w.r.t. the
apo-protein.
For NSP12d1 (Fig. 2B) the average RMSD (0.3 nm), RMSF (0.16 nm)
and Rg (3 nm) were similar to that of the apo-protein. The ∆RMSF was
lower for the phytochemical and repurposed drug interacting residues.
For NSP12d2 (NSP12-NSP7 interface) the average RMSD (0.3 nm) were
similar with the apo-protein for N12d1P2, N12d1P3, N12d1P4 and
N12d1R6 compared to the slightly higher value obtained for N12d1P1
(0.36 nm). Similar trend in average RMSF and Rg were obtained N12d1
as evident from the ∆RMSF and Rg plots illustrated in Fig. 2C. In the
case of N12d3 binding (Fig. 2D), the average RMSD (0.27 nm), RMSF
(0.14 nm) of N12d3P1 was slightly lower than the apo-protein, the other
phytochemicals and the repurposed drug. The compactness of all the
complexes were similar with average Rg (0.3 nm). The compactness of
all the simulated structures were similar with average Rg of ~1.44 nm.
In the case of NSP16 (Fig. 2B) the lowest average RMSD was ob-
tained for N16P2 (0.28 nm) compared to the apo-protein (0.33 nm).
The average RMSF fluctuations of the phytochemicals were like that of
the apo-protein (~0.15 nm). It is to be noted here that the repurposed
drug (N16R4) did not form stable complex with NSP16 after 20 ns of the
MD simulation and thus was not analysed further. Lower fluctuations at
the inhibitor interacting residues were obtained for the phytochemicals
w.r.t. the apo-protein as shown by the ∆RMSF plot.
In the case of NSP15 (Fig. 2E), N15P1 showed comparable average
RMSD, RMSF and Rg with the apo-protein. N15P2 and N15R2 did not
form stable complex with NSP15 with remarkably high average RMSD
(>0.7 nm) and thus were not further analysed. N15P1 also showed de-
crease in fluctuations at the inhibitor binding site as evident from the
∆RMSF plot.
In the case of NSP9 (Fig. 2F), the phytochemicals showed compara-
ble RMSD with an average of ~0.3 nm, average RMSF (0.17 nm) and
average Rg (1.4 nm). The ∆RMSF plot showed lower fluctuations at the
inhibitor interacting residue. Interestingly a lower dip in ∆RMSF was
obtained for Phe76 for N9P2 and N9R4.
P.K. Parida, D. Paul and D. Chakravorty
Phytomedicine Plus 1 (2021) 100002
Table 2
Relative lowest free binding energy calculated by MM/PBSA for each NSP.
Inhibitor
N10P1
N10R3
NSP12D1
N12d1P1
van der Waal energy
(kJ/Mol)
NSP 10
−184.657
+/- 20.543
−177.662
+/−18.014
−124.475
+/- 21.162
Electrostatic energy
(kJ/Mol)
−5.743
+/- 4.589
−4.835
+/- 5.206
−5.636
+/- 4.267
N12d1R2
−96.233
+/- 13.862
−3.993
+/−2.454
NSP12D2
N12d2P4
−142.880
+/−23.178
−6.687
+/−10.524
N12d2R6
−167.847
+/−10.302
−11.017
+/−2.508
N12D3
N12d3P1
−169.341
+/−18.584
−2.053
+/−3.186
N12d3R2
−325.095
+/- 24.003
−5.013
+/−2.809
NSP16
N16P1
NSP9
N9P2
−152.891
+/−23.291
−125.731
+/−15.961
−1.058
+/−4.995
−2.591
+/- 2.944
N9R4
−119.176
+/−13.521
−1.765
+/−2.925
NSP15
N15P1
−128.354
+/−16.899
−6.799
+/- 8.384
NSP3
N3P1
−204.165
+/−16.108
−7.942
+/−3.650
N3P4
−183.720
+/- 23.432
−12.307
+/−4.755
Polar solvation
energy (kJ/Mol)
97.672
+/−19.470
75.330
+/−17.752
51.656
+/−16.145
30.061
+/- 8.921
72.704
+/−36.840
108.884
+/−10.887
65.330
+/−10.075
138.228
+/- 11.958
48.993
+/−10.961
37.451
+/−11.636
30.670
+/- 8.861
69.397
+/−21.569
107.923
+/−13.714
89.847
+/−18.679
SASA energy
(kJ/Mol)
−20.243
+/- 1.783
−17.317
+/−1.421
−13.259
+/−1.881
−10.827
+/−1.178
−16.064
+/- 2.008
−15.784
+/- 0.797
−17.814
+/- 1.169
−30.849
+/−1.728
−15.571
+/−1.899
−13.235
+/- 1.491
−12.280
+/- 1.230
−13.507
+/- 1.749
−21.812
+/- 0.982
−18.589
+/−1.892
Relative Binding
Free energy
(kJ/Mol)
−112.971
+/−16.527
−124.484
+/−25.220
−91.714
+/−16.660
−80.992
+/ 13.123
−92.926
+/−31.586
−85.765
+/- 13.760
−123.877
+/−15.553
−222.729
+/−21.472
−120.528
+/−23.072
−104.106
+/−12.125
−102.552
+/−15.112
−79.263
+/−15.485
−125.995
+/−13.066
−124.769
+/−16.441
For abbreviations please refer Table 1. The 500-step bootstrap +/- standard error have been presented.
# The top 3 contributing residues are arranged in descending order of the contribution to the binding energy.
# Residue
contribution Binding
Free energy
(kJ/Mol)
Leu4345, Ile4308,
Val4369
Tyr4329, Leu4345,
His4336
Phe440,
Phe415,
Leu437
Phe415,
Phe440,
Leu437
Lys798,
Arg553,
Tyr455
Trp800,
Glu811,
Trp617
Val398,
Val330,
Tyr273
Phe396,
Tyr273,
Val675
Tyr6950, Leu6898,
Phe6947
Leu113,
Phe76,
Leu104
Leu104,
Leu113,
Leu107
Glu340,
Glu234,
His235
Phe132,
Ile131,
Val49
Ile131,
Phe132,
Ala50
In the case of NSP3 (Fig. 2G) the average RMSD of N3P1 was com-
parable to the apo-protein. The other phytochemicals and the repur-
posed drugs showed slightly higher RMSD. The ∆RMSF plot revealed
that lowest fluctuations were obtained for residues 126–132. N3P4 and
N3R5 showed higher fluctuations at residues 38–50. The average Rg was
2.4 nm. It can be noted that the compactness of N3R4 started increasing
after 60 ns.
Trajectory analyses for relative free energy of binding and hydrogen bond
occupancy of the NSP complexes
The relative binding free energy were calculated for each complex
of NSP using MM/PBSA. The standard errors were calculated by 500
steps of bootstrap analysis. It was observed that the relative binding
free energies obtained for the complexes were in agreement with the
RMSD, RMSF and Rg of their respective trajectories (Fig. 2A). The com-
puted lowest relative free energy of binding among all the complexes
with respect to each NSP has been presented in Table 2. Interestingly
repurposed drugs were computed to possess high relative free energy
of binding with NSP3, NSP15 and NSP16. These complexes were thus
not analysed further. Among all the complexes the phytochemical with
the lowest relative binding energy (< ~ −120 kJ/Mol) was obtained for
N12d3P1, N3P1, N3P4 and N16P1. For the repurposed drug lowest rel-
ative binding free energy was obtained for N10R3 (−120 kJ/Mol) and
N12d3R2 (~ −223 kJ/Mol). Interestingly among the NSP12 complexes
the D3 site (NSP12-NSP8 interface) showed better binding with phyto-
chemicals and the repurposed drugs than the D1 (active site) and D2
(NSP12-NSP7 interface) sites. This reveals that NSP12-NSP8 interface is
a good target site for drug targeting. NSP10-NSP16 interface and NSP3
also showed good binding energies with the phytochemicals. Whereas
NSP15 showed the highest relative binding energies (worst performer
in binding to phytochemicals and repurposed drugs) compared to the
rest of the NSP analysed through this work.
P.K. Parida, D. Paul and D. Chakravorty
Phytomedicine Plus 1 (2021) 100002
Fig. 1. The cartoon representation of the NSP from SARS-CoV-2 showing the binding sites of the inhibitors. A) NSP10, B) NSP16, C) NSP9, D) NSP12 D1, E) NSP12
D2, F) NSP12 D3, G) NSP15, H) NSP3. The amino acid residues are represented by one letter code.
Additionally, in order to understand the contribution of each residue
of the NSPs towards the total relative binding free energy, decomposi-
tion of the energy for each residue was computed (Supplementary Table
S1). The top 3 residues with the lowest relative free energy of binding
have been presented in Table 2. Most of the residues in all the NSP com-
plexes with lowest binding free energy were observed to be non-polar
and aromatic.
Further, hydrogen bond (H-bond) occupancy was calculated over
100 ns trajectory for the complexes (Supplementary Table S2). Inter-
estingly the residues with low relative free energy of binding were also
with highest H-bond occupancy. In the case of NSP10 highest H-bond
occupancy for N10P1 was obtained for Ile4308-CD atom (31.43%). In
N10R3, the atom-His4336-CA was with 24% occupancy. In the case of
NSP12d1, occupancy of 21.4% was obtained for Leu437-N in N12d1P1
and Phe249 in N12d1R2 (23%). In the case of N12d2, Ala550-N and
Trp800-CD1 were with 30% and 53% occupancies for N12d2P4 and
N12d2R6 respectively. In the case of N12d3P1 and N12d3R2, Tyr273-
CB and Phe326-CB were with 31% and 57% occupancies, respectively.
From relative free energy and H-bond analysis of NSP12, it was real-
ized that phytochemicals and repurposed drugs do not bind with the
catalytic site residues S759-D761 for N12d1 and the NSP7 interact-
ing interface (NSP12d2). However, it was realized that NSP12-NSP8
interface is a better target site for these phytochemicals and repur-
posed drugs as these complexes had comparatively the lowest rela-
tive free energy of binding. The residues involved in binding to NSP8
were also observed to interact with the phytochemicals and the repur-
posed drugs. For NSP16P1, Tyr6950-CZ was with 33.2% occupancy.
The residues which are involved in high H-bond occupancy (Supple-
mentary Table S2) are near the NSP16-NSP10 interface. In the case of
N9P2 and N9R4, Ala108-CA (23%) and Leu104-O (39%) were with the
highest H-bond occupancies, respectively. These residues are present at
the NSP9 dimerization interface (PDB ID: 6W4B). In the case of NSP15,
His235-CB atom was with 31% H-bond occupancy. Thus, the active site
residues were involved in H-bond with the phytochemicals. Finally, in
the case of NSP3, N3P1 and N3P4, Leu126-CB (31%) and Ile131-CG2
(40%) were with the highest H-bond occupancies. These residues inter-
P.K. Parida, D. Paul and D. Chakravorty
Phytomedicine Plus 1 (2021) 100002
Fig. 2. Comparative trajectory analysis of the 8 NSP binding sites with the phytochemicals and the repurposed drugs along with their apo-proteins. For abbreviations
please refer main text and Table 1. A) NSP10, B) NSP12d1, C) NSP12d2, D NSP12d3, D) NSP16, E) NSP15, F) NSP9, G) NSP3. Purple brackets in ∆RMSF plots represent
residue atom numbers interacting with the phytochemicals and the repurposed drugs (refer Fig. 1). The atom numbering follows respective PDB structures.
act with Adenosine-5-Diphosphoribose in the crystal structure of NSP3
(6W02).
Principal component and dynamic cross correlation matrix analysis of the
NSP-phytochemical and repurposed drug complexes
To extract the structural variations in detail for the complexes with
the lowest relative free energy of binding (Table 2), principal com-
ponent analysis (PCA) on the C𝛼 atom was performed and compared
to their respective apo-proteins. The results of PCA corroborates with
the RMSD, RMSF, Rg and relative binding free energy obtained for the
phytochemicals and the repurposed drugs. The first two eigen vectors
captured around ~50% of the motions (Fig. 3). In the case of NSP10
much larger conformational space was explored by N10P1 and N10R3
(Fig. 3A). Their corresponding residue fluctuation plots showed over-
all lower RMSF for N10P1 and N10R3 compared to the apo-protein.
In the case of NSP9 comparable conformational space were explored
by the apo-protein and the complexes (Fig. 3B). Residue fluctuation
were lower for N9P2 compared to the apo-protein. However, increase in
fluctuations were observed for N9R4 in comparison to the apo-protein.
This indicates that binding of the phytochemical was more stabilizing
than the repurposed drug. In the case of NSP15, N15P1 explored more
conformational space (Fig. 3C). Residue fluctuations were reduced in
comparison to the apo-protein, showing creation of a stable environ-
ment upon ligand binding. In the case of N12d1 (Fig. 3D), more con-
formational space was explored by N12d1R2 in PC1. Comparable con-
formational space was explored by the apo-protein and the N12d1P1.
Residue fluctuations were similar for N12d1P1 and N12d1R2 compared
to the apo-protein (residues 500–800). Residue fluctuations were also
similar in the case of N12d2 (residues 300–400) and N12d3 (200–
500) at the ligand binding site in comparison to the apo-protein. In the
case of NSP16 (Fig. 3E), larger conformational space was explored by
N16P1 compared to the apo-protein. Overall decrease in residue fluc-
tuations were observed for N16P1. In the case of NSP3 similar con-
formational space was explored by N3P1 and N3P4 compared to the
apo protein (Fig. 3F). Slight decrease in residue fluctuations at the
C-terminal was observed for N3P1. Residue fluctuations increased for
N3P4 compared to N3P1 and apo-protein. Therefore, PCA showed over-
all reduction in residue fluctuations upon ligand binding for all the NSP
complexes.
Further, dynamic cross-correlation matrix was computed by using
the coordinates of C𝛼 atoms from the trajectories. Dynamic cross corre-
lation plots of the NSP complexes reveal that anti-correlation (indicative
of ligand binding) slightly increased and correlation (indicative of stabil-
ity upon ligand binding) increased at the binding sites on binding of phy-
tochemicals and repurposed drugs in comparison to the apo-proteins. In
case of NSP10 complex (Fig. 4A), increase in correlation was more for
N10P1 than N10R3.In case of NSP 16 (Fig. 4B), N16P1 showed increased
anti-correlation and correlation in comparison to the apo-protein. In the
case of NSP15 (Fig. 4C), overall increase in correlated motion was ob-
served. In case of NSP12- N12d1, N12d2 and N12d3 there was overall
decrease in anti-correlated motions. In the case of NSP9 (Fig. 4D) slight
increase in anti-correlation was observed on ligand binding. In the case
of NSP3 (Fig. 4F), both anti-correlation and correlation increased on
ligand binding compared to the apo-protein. Conclusively it can be in-
ferred that ligand binding created a more stable environment in all the
NSPs with respect to the apo-proteins.
Biological pathway enrichment analysis of human protein targets identified
for the phytochemicals
To perform pathway enrichment analysis for the 22 phytochem-
icals, SwissTarget Database was used to retrieve the top 15 human
protein targets for each phytochemical. The SARS-CoV-2 human pro-
tein targets were retrieved from literature (Chakrabarty et al., 2020;
Zhou et al., 2020). Enrichr and REVIGO analysis (Supek et al., 2011;
Kuleshov et al., 2016) of Gene Ontology enrichment of biological pro-
cesses for phytochemicals and SARS-CoV-2 with respect to p-value cal-
culated with Fisher’s exact test has been presented in Fig. 5A-B re-
spectively. Among the top 10 GO biological processes cytokine me-
P.K. Parida, D. Paul and D. Chakravorty
Phytomedicine Plus 1 (2021) 100002
Fig. 3. Comparative PCA of the 8 NSP binding sites with the phytochemicals and the repurposed drugs along with their apo-proteins (OP). The phytochemicals with
the most negative free energy of binding have been illustrated here. The projection of PC1 on PC2. The continuous color spectrum from blue to whit to red represents
simulation time. The initial timescale represented by blue, intermediate by white and final by red. The RMSF of residue contribution to PCA. (black: PC1, blue: PC2).
RES COBTRIB: Residue contribution to PCA (RMSF). For abbreviations please refer main text and Table 1. A) NSP10, B) NSP9, C) NSP15, D) NSP12d1, NSP12d2 and
NSP12d3, E) NSP16, F) NSP3.
diated pathway and phosphorylation were with the most significant
p-value for the SARS-CoV-2 and phytochemicals interacting with hu-
man protein targets, respectively. Among the top 100 enriched GO
terms- the biological processes common to both SARS-CoV-2 and phy-
tochemicals were cytokine-mediated signaling pathway (GO:0019221),
cellular response to cytokine stimulus (GO:0071345), positive regula-
tion of macromolecule metabolic process (GO:0010604), interleukin-
12-mediated signaling pathway (GO:0035722), cellular response to
interleukin-12 (GO:0071349), MAPK cascade (GO:0000165), response
to organic cyclic compound (GO:0014070), negative regulation of phos-
phoprotein phosphatase activity (GO:0032515) and JAK-STAT cascade
(GO:0007259).
Interestingly, Enrichr analysis of KEGG pathway revealed that In-
fluenza A and Epstein-Barr virus infection pathways were enriched for
SARS-CoV-2 and the neuroactive ligand-receptor interactions were en-
riched for the phytochemicals. Again through VirusMint database en-
richment analysis it was observed that the human protein targets of both
SARS-CoV-2 and the phytochemicals interacted with viral proteins from
Human Immunodeficiency Virus I with the most significant p- value cal-
culated with Fisher exact test (Fig. 5C-D).
Discussions
COVID-19 pandemic causative agent SARS-CoV-2 is an RNA virus
which codes for 16 non structural proteins (NSPs) from its ORF1a/b
(da Silva et al., 2020). Among the 16 NSPs, 6 NSP have been well char-
acterized (NSP3, NSP9, NSP10, NSP12, NSP15 and NSP16) to play piv-
otal role in viral replication (Astuti and Ysrafil, 2020). Therefore, these
NSPs qualify as potential drug targets of SARS-CoV-2. It is believed
that combination of drugs that can simultaneously target these NSPs
will have higher possibility in combating COVID-19 infection. In our
previous work we identified 18 different steroidal lactones from Witha-
nia somnifera and one each phytochemical from Solanum nigrum, Nigella
sativa and Azadirachta indica, by virtual screening from 55 Indian medic-
inal plants, can be used to target these NSPs (Parida et al., 2020a). In
the present work binding potential of (22 phytochemicals and 10 re-
P.K. Parida, D. Paul and D. Chakravorty
Phytomedicine Plus 1 (2021) 100002
Fig. 4. C) DCCM plots for 8 NSP binding sites with the phytochemicals and the repurposed drugs along with their apo-proteins (OP). The positive value represents
the positively correlated motions (cyan), while negative values represent the anti-correlated motions (pink). For abbreviations please refer main text and Table 1. A)
NSP10, B) NSP16, C) NSP15, D) NSP12d1, NSP12d2 and NSP12d3, E) NSP9, F) NSP3.
purposed drugs docked to 6 NSPs from SARS-CoV-2) were evaluated by
employing 100 ns molecular dynamics simulation analysis. The results
revealed the phytochemicals with the lowest binding energy against the
6 NSP targets were majorly from Withania somnifera. These phytochem-
icals thus have the potential for being developed into a concoction of
natural drugs which can be used as prophylactic therapy for containing
COVID-19 infections. Interesting observations were made while explor-
ing the dynamic binding of the phytochemicals and repurposed drugs
by RMSD, RMSF, Rg, PCA, DCCM, relative free energy of binding and
hydrogen bond occupancy analyses. RMSD and RMSF were analysed as
they reflect dynamic stability of the trajectory, necessary for obtaining
good binding affinities (Dubey et al., 2013). Rg analysis reflects protein
compactness which generally increase if binding with ligand forms a sta-
ble environment. High hydrogen bond occupancy is another parametric
indicator of strong binding affinity (Khan et al., 2020). PCA and DCCM
reflect overall conformational variance and atomic motions correlation
upon ligand binding respectively (Hünenberger et al., 1995; Islam et al.,
2019 ).
It was reported that in NSP3 (ATP ribose phosphatase of SARS-CoV-
2), targeting the Adenosine-5-Diphosphoribose substrate binding site
may result in enzyme inhibition (Wu et al., 2020). This work showed by
MD simulation analysis of NSP3 complexes, 24,25-dihydrowithanolide
D and 27-Deoxy-14-hydroxywithaferin A formed more stable complex
among the 4 phytochemicals and one repurposed drug (Baricitinib)
analysed. Phe132, Ile131 in its active site pocket which forms hydro-
gen bond (<~3 Å distance) with the Adenosine-5-Diphosphoribose sub-
strate, were with the lowest relative free energy of binding. Lowest
RMSF fluctuations were also obtained for residues 126–132. Leu126 and
Ile131 were also with the highest H-bond occupancies. Leu126 is also in-
volved in non-bonded contacts with Adenosine-5-Diphosphoribose C4′
and C5′ atoms at a distance of ~3 Å. Both PCA and DCCM analysis re-
vealed that ligand binding created a more stable environment.
It was also reported that inhibition of dimerization of NSP9 (impor-
tant in viral replication) can lead to therapeutic benefits by affecting
its RNA binding and SARS-CoV viral proliferation (Sutton et al., 2004;
Frieman et al., 2012). It was reported that 97% sequence identity is
shared by NSP9 from SARS-CoV and SARS-CoV-2 (Littler et al., 2020).
Interestingly, analysis of NSP9 complexes revealed that the phytochem-
ical 12-Deoxywithastramonolide and the repurposed drug Baricitinib
were with the lowest relative free energy of binding. RMSD, RMSF, Rg
were also stable for the complexes. PCA showed that the phytochemi-
cal complex has lower residue fluctuations compared to the repurposed
drug. DCCM also showed slight increase in anti-correlation was observed
on ligand binding. Interestingly a lower dip in ∆RMSF was obtained
for Phe76. As Phe76 of one chain in NSP9 is an important residue in-
volved in inter-protein contact with Pro7 of another chain (PDB ID:
6w4b). Thus, its interaction with the inhibitors may affect protein dimer-
ization. Further, interaction with the interface residues, Ala108 and
Leu104 with the high H-bond occupancies and Leu113, Phe76 and
Leu104 high affinity binding reflected by their binding free energy can
affect dimerization with these compounds are primed with NSP9. Leu
104 is also part of the conserved protein-binding motif Gly100-Gly105
(Littler et al., 2020).
It was also reported that targeting the NSP10-NSP16 binding in-
terface may result in attenuation of SARS-CoV-2 infection. NSP 10 is
known to stimulate methyl transferase activity of NSP 16 in SARS-CoV
(Bouvet et al., 2014). Therefore, similar role is expected in SARS-CoV-
2 due to its shared sequence homology. It can be postulated that in-
hibiting the complex formation of NSP10-NSP16 will aid in contain-
ing the SARS-CoV-2 infection. 27-Hydroxywithanolide B from Withania
somnifera and Solvanol from Solanum nigrum were with the lowest rel-
ative free binding energies with NSP10 and NSP16 respectively. The
repurposed drug Baricitinib also showed low relative free binding en-
ergy with NSP10. The RMSD, RMSF, Rg, PCA and DCCM analysis also
corroborated with the relative free binding energy results reflecting on
the stability of the compounds over 100 ns trajectories. The residues,
with low relative binding free energy and involved with H-bond with
the compounds, lie at the NSP10-NSP16 binding interface and are also
involved in zinc coordination (His4336). Zinc coordination was reported
to stimulate NSP16 activity in SARS-CoV (Bouvet et al., 2014). Similarly,
residues in NSP16 at the NSP10-NSP16 interface were observed to form
H-bonds with the compounds and have low relative free energy of bind-
P.K. Parida, D. Paul and D. Chakravorty
Phytomedicine Plus 1 (2021) 100002
Fig. 5. Enrichr analysis of biological processes enriched by the human proteins targets of A) phytochemicals and the B) SARS-CoV-2. The processes are colored
based on the dispensability score obtained from REVIGO- higher scores are represented by darker shades of red (Supek et al., 2011).C) Top 10 KEGG pathway and
VirusMint enrichment for phytochemicals. D) Top 10 KEGG and VirusMint enrichment for SARS-CoV-2. The bars in panel C and D represents the p-values computed
using the Fisher’s exact test. The longer and lighter colored bars reflects that the term is more significant.
ing. Thus, phytochemicals interacting with these residues may interfere
with the NSP10-NSP16 complex formation.
Further, targeting three sites of NSP12- RNA-dependent RNA poly-
merase (RdRp) involved in viral replication, revealed interesting results.
The three sites targeted were the catalytic pocket, the NSP7-NSP12 and
NSP8-NSP12 interfaces. The NSP12-NSP8 interface was realized to be
the best binding site by lowest free energy obtained for Withaferin A
and Lopinavir. Docking of Nsp8 to NSP12 was reported to be impor-
tant in synthesis of the primer required for Nsp12-RdRp RNA synthesis
(Wu et al., 2020). All the other trajectory analyses could be positively
correlated to the computed relative free energy of binding for the NSP12
complexes.
MD simulation results of NSP15 (uridylate specific endoribonucle-
ase) (Kim et al., 2020), showed that somniferine was with the lowest
free energy of binding. The other trajectory analyses agreed with the ob-
tained relative free energy of binding. The active site residues (His235)
were involved in H-bond with the phytochemical and positively con-
tributed to lowering the relative free energy of binding. However, NSP15
showed the highest relative binding energies compared to the rest of the
NSP analysed through this work. Thus, it can be assumed to be a poor
SARS-CoV-2 target in binding to these phytochemicals and repurposed
drugs compared to the other NSPs.
Interestingly, through trajectory analysis for H-bond occupancies,
lower occupancy of hydrogen bonds was observed in all the trajecto-
ries (~ <40%). This reveals that van der Waal’s interactions were more
pronounced in maintaining residue-residue contacts in the NSP com-
plexes rather than electrostatic interactions. This is also reflected by the
much lower values of van der Waal’s (vdW) energy than the electrostatic
energy, contributing to the overall relative free binding energy. There-
fore, it is seen that hydrophobic interactions are dominant in all com-
plexes. High negative value of vdW energy represents greater hydropho-
bic interaction between the NSP complexes. This can be attributed to
the presence of high number of non-polar residues at the binding sites
of the 6 NSPs. Further, due to the presence of ergostane framework of
steroidal lactone ring structures in the phytochemicals from Withania
somnifera, that formed stable complexes with the NSPs, can also be re-
lated to lower hydrogen bond occupancy but higher prevalence of hy-
drophobic interactions. Interestingly, the repurposed drugs with good
binding affinity also resembles the phytochemicals in having steroidal
rings in their structures and a greater number of hydrogen bond accep-
tors than donors.
Additionally, enrichment of human biological pathway analysis was
performed for the phytochemicals in comparison to the SARS-CoV-2 hu-
man protein targets. Interestingly the results revealed that pathways re-
lated to immunity, RNA virus mediated disease pathways and apopto-
sis were observed for both SARS-CoV-2 and the phytochemicals. This
strongly implies that the action of phytochemicals in these pathways
will be therapeutic in case of controlling COVID-19 infections. For ex-
P.K. Parida, D. Paul and D. Chakravorty
ample, biological process enrichment analysis revealed that COVID-19
targets the cytokine mediated pathway. This corroborates with previ-
ous report of COVID-19 association with “cytokine storms” including
interleukin-6 (IL-6), interleukin 12 (IL-12), tumor necrosis factor al-
pha (TNF𝛼) and chemokines- causing damage to the host (Hirano and
Murakami, 2020). The biological process of phosphorylation associated
with the phytochemicals corroborates with research where phytochemi-
cals were reported to be protein modulators by potentiating signal trans-
duction cascades (Frigo et al., 2002). Again, the MAPK cascade was a
common target for both SARS-CoV-2 and phytochemical associated hu-
man protein targets. The MAPK pathway is expressed in response to
external stress such as viral infection and results in activation of p38
MAPK. This pathway also takes part in cell death and was reported to
be activated by SARS-CoV (Mizutani, 2010). Phytochemicals have been
previously reported to modulate MAPK signaling pathway mediated
apoptosis (Kaur et al., 2006). Further inhibition of anus-kinase/Signal
transducer and activator of transcription (JAK/STAT) cascade by drugs
like Baricitinib and Ruxolitinib has been reported to control SARS-CoV-
2 infection (Magro, 2020). Thus, phytochemicals acting on this pathway
can result in containing the SARS-CoV-2 infection. Furthermore, KEGG
pathways enriched were Influenza A andneuroactive ligand-receptor in-
teractions for SARS-CoV-2 targets and the phytochemical targets, respec-
tively. Association of SARS-CoV-2 protein targets with Influenza virus
disease pathway was expected as both lead to severe respiratory diseases
(van den Brand et al., 2014) reflecting the analysis accuracy. Interest-
ingly, neuroactive ligand-receptor interactions are responsible to trans-
mit signals for cell growth and survival. This pathway was reported to in-
teract with Ebola virus transcription (Yu et al., 2018). Therefore, phyto-
chemicals interacting with this pathway can be strongly assumed to con-
trol SARS-CoV-2 infections. Further, enrichment of VirusMint database
resulted in human proteins that are targeted by Human Immunodefi-
ciency Virus I for both the identified SARS-CoV-2 and phytochemicals
associated human protein targets. "Cloaked similarity" was reported for
SARS-CoV and HIV-I infections (Kilger and Levanon, 2003). Also there
have been sporadic reports of phytochemicals in the control of HIV-I in-
fections (Kashiwada et al., 2005). Further, anti‐HIV drugs have been pro-
posed to be effective against SARS‐CoV‐2 (Martinez, 2020). This reflects
that these phytochemicals can act as antivirals in not only controlling
SARS-CoV-2 but with potential in acting against RNA viruses. Another
interesting observation was negative regulation of phosphoprotein phos-
phatase activity being enriched for both SARS-CoV-2 and phytochemi-
cals. Viruses use phosphoprotein phosphatase activity especially Phos-
phoprotein Phosphatase 2A to specifically weaken key survival path-
ways of their hosts (Guergnon et al., al.,2011). Thus, involvement of
this biological process with SARS-CoV-2 and phytochemicals indicates
that Phosphatase 2A can be a potential drug target for controlling SARS-
CoV-2 infections. A novel observation about phytochemical-SARS-CoV-
2–human systems was that the human proteins targeted were enriched
in the regulation of metabolic processes. Metabolic processes like gly-
colysis have been reported to be targeted by viruses (DurmuşTekir et al.,
2012). Therefore, metabolic pathways should be studied further to un-
derstand SARS-CoV-2 infection process in order to comprehend the ef-
fects of the phytochemicals in controlling the SARS-CoV-2 infection.
Conclusions
The ongoing SARS-CoV-2 pandemic makes us painfully realize that
treating the infection is still a far-fetched goal. Therefore, any contri-
bution in this direction may lead to development of a treatment regime
for controlling the infection at an early stage. In this work stability of
phytochemicals and repurposed drug complexes with 6 NSPs from SARS-
CoV-2 were analysed by 100 ns MD simulations. Overall, the simulation
run confirms the stability of interaction of phytochemicals from With-
ania somnifera with the 8 NSPs binding sites (three sites belonging to
NSP12. Further, the important residues interacting with the phytochem-
icals showed that phytochemicals and repurposed drugs with steroidal
Phytomedicine Plus 1 (2021) 100002
moieties in their chemical structures formed stable interactions with the
NSPs. Again, Human protein pathway analysis revealed that pathways
related to immunity, RNA virus mediated disease pathways and apop-
tosis were enriched for both SARS-CoV-2 and the phytochemicals. Thus
it can be said that the multi-potency of these phytochemicals can be
used to regulate COVID-19 infection by targeting these pathways along
with the NSPs. Conclusively this work warrants further study from the
perspective of experimental validation to test the efficacy of these phy-
tochemicals and the repurposed drug for the prevention and treatment
of COVID-19.
Declaration of Competing Interest
No potential conflict of interest was reported by the authors.
CRediT authorship contribution statement
Pratap Kumar Parida: Conceptualization, Methodology, Software,
Formal analysis. Dipak Paul: Data curation, Writing - original draft.
Debamitra Chakravorty: Conceptualization, Visualization, Investiga-
tion, Supervision, Validation, Writing - review & editing.
Acknowledgements
This work used resources services, and support provided via
the COVID-19 HPC Consortium (https://covid19-hpc-consortium.org/),
which is a unique private-public effort to bring together government,
industry, and academic leaders who are volunteering free compute time
and resources in support of COVID-19 research. We would also like to
acknowledge Noor Enzymes Private Limited for providing us with the
human resources needed to successfully complete this work.
Author contributions
All data were generated in-house, and no paper mill was used. All
authors agree to be accountable for all aspects of work ensuring integrity
and accuracy.
Supplementary materials
Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.phyplu.2020.100002.
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