Content uploaded by Arindam Maity
Author content
All content in this area was uploaded by Arindam Maity on Nov 07, 2020
Content may be subject to copyright.
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=tbsd20
Journal of Biomolecular Structure and Dynamics
ISSN: 0739-1102 (Print) 1538-0254 (Online) Journal homepage: https://www.tandfonline.com/loi/tbsd20
In silico analysis and identification of promising
hits against 2019 novel coronavirus 3C-like main
protease enzyme
Shilpa Chatterjee, Arindam Maity, Suchana Chowdhury, Md Ataul Islam, Ravi
K. Muttinini & Debanjan Sen
To cite this article: Shilpa Chatterjee, Arindam Maity, Suchana Chowdhury, Md Ataul Islam, Ravi
K. Muttinini & Debanjan Sen (2020): In silico analysis and identification of promising hits against
2019 novel coronavirus 3C-like main protease enzyme, Journal of Biomolecular Structure and
Dynamics, DOI: 10.1080/07391102.2020.1787228
To link to this article: https://doi.org/10.1080/07391102.2020.1787228
View supplementary material
Published online: 01 Jul 2020.
Submit your article to this journal
View related articles
View Crossmark data
In silico analysis and identification of promising hits against 2019 novel
coronavirus 3C-like main protease enzyme
Shilpa Chatterjee
a
, Arindam Maity
b
, Suchana Chowdhury
c
, Md Ataul Islam
d,e
, Ravi K. Muttinini
f
and
Debanjan Sen
c
a
Department of Biomedical Science, Chosun University, Gwangju, South Korea;
b
School of Pharmaceutical Technology, Adamas University,
Kolkata, India;
c
BCDA College of Pharmaceutical Technology, Hridaypur, Kolkata, India;
d
Division of Pharmacy and Optometry, School of
Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK;
e
School of Health Sciences, University
of Kwazulu-Natal, Durban, South Africa;
f
Immunocure Discovery solution Pvt. Ltd, IKP, Hyderabad, India
Communicated by Ramaswamy H. Sarma
ABSTRACT
The recent outbreak of the 2019 novel coronavirus disease (COVID-19) has been proved as a global
threat. No particular drug or vaccine has not yet been discovered which may act specifically against
severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and causes COVID-19. For this highly
infectious virus, 3CL-like main protease (3CL
pro
) plays a key role in the virus life cycle and can be con-
sidered as a pivotal drug target. Structure-based virtual screening of DrugBank database resulted in 20
hits against 3CL
pro
. Atomistic 100ns molecular dynamics of five top hits and binding energy calcula-
tion analyses were performed for main protease-hit complexes. Among the top five hits, Nafarelin and
Icatibant affirmed the binding energy (g_MMPBSA) of –712.94 kJ/mol and –851.74 kJ/mol, respectively.
Based on binding energy and stability of protein-ligand complex; the present work reports these two
drug-like hits against SARS-CoV-2 main protease.
ARTICLE HISTORY
Received 27 April 2020
Accepted 16 June 2020
KEYWORDS
COVID-19; cobicistat; drug
hits; icatibant; main
protease enzyme; nafarelin
Introduction
The coronaviruses (CoVs) belong to the Coronaviridae family,
a positive single-stranded largest RNA virus (Tyrrell & Bynoe,
1966). The CoVs possesses of four genera included
Alphacoronavirus, Betacoronavirus, Gammacoronavirus, and
Deltacoronavirus which are responsible for disease associated
with the event of a severe acute respiratory syndrome. In the
past two decades, Middle East respiratory syndrome corona-
virus (MERS-CoV) and severe acute respiratory syndrome cor-
onavirus (SARS-CoV), a type of b-coronavirus infected 1
million humans worldwide (Boopathi et al., 2020). The 2019
novel coronavirus is currently known as SARS-CoV-2 was
identified at Wuhan fish market in China in the year 2019
(Wu et al., 2020), which turned out to be an epidemic and
already an outbreak in almost in every country worldwide
(Hasan et al., 2020). The disease caused by the SARS-CoV is
known as COVID-19 and has infected more than 2.8 million
people around the world (www.worldometers.info/corona-
virus) with 20% death rates. World Health Organization
(WHO) declared this infection as a global threat and entire
world compelled to locked down. All over the world
researchers are working very hard to find suitable drugs or
vaccines for effective management of COVID-19 or to inhibit
its spreading. Li et al. (2020) reports the re-purposing drug
options for COVID-19, and suggested to use antiviral drugs
Favipiravir, Ribavirin, Remdesivir, Galidesivir (Li & De Clercq,
2020) for this purpose. A recent study reports, the genome
of coronavirus possesses a long RNA strand (Chen et al.,
2020) which acts as a messenger RNA when it infects the
host cell, regulate the synthesis of two crucial long polypro-
teins required for viral replication. These polyproteins are
associated with the events of viral replication or transcription
process to produce other viral structural proteins to form
virions and protease enzymes. The formed protease enzymes
are responsible for cutting polyproteins to produce necessary
functional proteins (Li et al., 2020). The 3CL
pro
is the main
protease (Islam et al., 2020) which is encoded by non-struc-
tural protein 5 (NSP5), involved in polyprotein cleavage,
immune regulation and important for viral replication. The
3CL
pro
in SARS-COV-2 has substrate recognition pockets use-
ful for binding with viral polyprotein including the activator
of transcription 2, signal transducer and the NF-jB essential
modulator signalling protein (Perlman & Netland, 2009;
Wang et al., 2016; Yang et al., 2005; Zhu et al., 2017).
Targeting 3CL
pro
is believed to act against the virus by
inhibiting viral maturation and refurbishing the natural
immune response (Lamarre et al., 2003). Designing or identi-
fication of small molecules has a close structural similarity
with the natural substrate of an enzyme, exploit superior
interaction with binding site residues and drug resistance-
related issues can be avoided (Deeks et al., 1997).
CONTACT Debanjan Sen debanjansen48@gmail.com Department of Pharmaceutical Technology, BCDA College of Pharmacy and Technology, 78 Jessore
Road, Hridaypur, Kolkata 700127, India.
Supplemental data for this article can be accessed online at https://doi.org/10.1080/07391102.2020.1787228.
ß2020 Informa UK Limited, trading as Taylor & Francis Group
JOURNAL OF BIOMOLECULAR STRUCTURE AND DYNAMICS
https://doi.org/10.1080/07391102.2020.1787228
Therefore, considering SARS-COV-2 main protease enzyme
as a drug target might be an effective approach to develop
novel therapeutic agents against this uncontrollable SARS-
COV-2 infection. Jin et al. (2020) reported the structure of
SARS-COV-2 main protease enzyme complexed with a pep-
tide-like inhibitor N3. The N3 ligand form covalent bond with
one of the catalytic amino residues CYS145 of the protease
enzyme. Residues like HIS41, PHE140, LEU141, ASN142,
HIS163, MET165, HIS172, GLN189, and GLN192 shows non-
covalent interaction with N3 ligand. These residues can be
considered as the crucial amino acid residues of 3CL
pro
receptor binding site (Das et al., 2020).
It is a well-known fact that drug discovery is a time
demanding and costlier process. Therefore, observing the
worst scenario lately it was resolved to screen the US Food
and Drug Administration (FDA) approved small molecule
database using computational methods (S. A. Khan et al.,
2020). Such an approach accelerates the drug repositioning
process against SARS-CoV-2 main protease enzyme. Drug
repurposing or repositioning (R. J. Khan et al., 2020)isa
drug development approach to find out and re-use of
already approved existing drugs for application in different
new diseases. The process is now becoming a universal strat-
egy mainly for a neglected/RARE disease (Pushpakom et al.,
2019). This strategy offers many advantages including, fewer
clinical trial steps which will minimize the cost and time
(Paul et al., 2010). The advantages of re-purposing the drug
discovery approach can also be used to explore potential
lead molecules to combat the COVID-19 pandemic. In the
current study, structure-based virtual screening of FDA-
approved drug database was carried out against the SARS-
COV-2 main protease. Best molecules obtained based on
binding affinity were further considered for molecular
dynamics (MD) simulation. Finally, the binding free energy
was estimated from the MD simulation trajectories through
molecular mechanics Poisson-Boltzmann surface area (MM-
PBSA) (Kumari et al. 2014). The credential of the work, sub-
stantiated by the finding of two most crucial drug molecules
effective for inhibition of SARS-COV-2 main protease.
Materials and method
Protein preparation
The novel coronavirus main protease enzyme structure com-
plex with a peptide-like ligand (N3) having PDB ID: 6LU7
(resolution: 2.16 Å and r-value:0.235) and consisting of 312
amino acid residues, was retrieved from Protein Data Bank
(www.rcsb.org). The protein structure was prepared using
the Autodock Tools (Morris et al., 2009), a part of MGL Tools
(Forli et al., 2016) molecular visualization interface. The miss-
ing atoms were checked and repaired. A sufficient number
of polar hydrogen atoms and Gasteiger charges were added.
The co-crystalline water molecules and bound ligand N3
were removed. The prepared protein molecule was saved for
molecular docking study. Also, molecular visualization soft-
ware included UCSF chimera (www.cgl.ucsf.edu/chimera),
and VMD (Humphrey et al., 1996) were used for rendering
images. The binding site was determined by confining N3
with the coordinates of –9.231, 11.910 and 69.213 along
X-, Y-, and Z-axis, respectively.
FDA-approved database screening
Structure-based virtual screening of small molecular data-
bases is an important and effective tool to find out promis-
ing molecules for a specific target. A large number of
macromolecular crystal structure availability offers an excel-
lent opportunity to explore potent and safer molecules. To
find out potential anti-COVID compounds the FDA-approved
drug data set was screened through the LEA3D (Douguet
et al., 2005) online software tool. The FDA-approved drug
molecules are already tested and explored small molecular
dataset for their function and effects on humans. The dataset
is consisting of a total of 1930 drug molecules. All molecules
in the database are highly enriched with characteristics being
effective candidates as administered drug and optimize the
search for new therapeutic agents (Douguet, 2018). The
LEA3D is an online tool, used for de novo design and screen-
ing of in-house database or FDA-approved drug molecules.
The prepared protease protein molecule was imported in the
online LEA3D server. The binding site radius and the weight
in the final score were set to 10 Å and 1, respectively. In the
virtual screening option, the ‘FDA-approved drug data set’
was selected. After successful completion, a set of hit mole-
cules was retrieved. The server uses a genetic algorithm
based protein-ligand ANT system known as PLANTS docking
program associated with empirical scoring functions (Korb
et al., 2009) for molecular docking, reliable pose prediction
and search speed. The LEA3D program generates conformers
with RDKit (www.) and the maximum number of conformers
was set to 10. The scoring function of PLANTS can be calcu-
lated by employing the following functions (Islam & Pillay,
2020).
fPLANTS ¼fplp þfclash þftors þCsite (1)
where f
plp
is the piecewise linear potential to model steric
complementarity between protein and ligand atoms, f
clash
represents the heavy atom potential to prevent internal lig-
and clashes, the f
tors
is the torsional potential of ligand, C
site
denotes the distance-dependent quadratic potential in order
to calculate the reference point of the ligand.
The structural information (Isomeric SMILES) of co-crystal-
line ligand N3 (ID: PRD_002214) was retrieved from Protein
Data Bank. The structure was prepared by using LEA3D web
server tool (https://chemoinfo.ipmc.cnrs.fr/LEA3D/drawonline.
html). PLANTS docking exercise was conducted to dock the
prepared ligand N3 with SARS-CoV-2 main protease enzyme
(6lu7) in the same binding site. The Ligplotþ(Laskowski &
Swindells, 2011) and Discovery studio visualizer (www.
3dsbiovia.com) were used to explore the ligand-protein
interactions.
Molecular dynamics simulation
To explore the dynamic states of any protein-ligand complex
the all-atoms MD simulation is an important application.
2 S. CHATTERJEE ET AL.
Final proposed molecules complex with the protease were
considered for the all-atom MD simulation study. The study
was conducted in GPU (NVIDIA RTX 2070) accelerated
Gromacs 2019.4 software (Abraham et al., 2015; R. J. Khan
et al., 2020)(www.gromacs.org), running over Linuxmint
64-bit operating system supported by 9th generation Intel i7
9700k processor. Total 100 ns dynamics simulation was con-
ducted with a time step of 2 fs considering constant pressure
of 1 atm and constant temperature of 300K. The SwissParam
tool (Zoete et al., 2011)(www.swissparam.ch), online server
software, was used to generate ligand topology and param-
eter. For the protein molecule, the CHARMM36 (Huang &
Mackerell, 2013) all-atom force field was used. The TIP3P
water model was used for the solvation of the system fol-
lowed. In order to define the system size for simulation, 10 Å
distance from the surface of the center of the protein was
maintained. The system was neutralized by adding an appro-
priate amount of Na
þ
ions or Cl
–
ions. The steepest descent
algorithm of 25,000 steps was used to equilibrate and min-
imize the system. The cut off was set to 0.9 and 1.4 nm for
long-range interaction of van der Waals and electrostatic,
respectively. Parameters like root-mean-square deviation
(RMSD), root-mean-square fluctuation (RMSF), and radius of
gyration (Rg) were calculated from simulation trajectory.
Grace software (http://plasma-gate.weizmann.ac.il/Grace/)
was used as a plotting program.
Binding free energy calculation
The binding free energy (DG
bind
) (Wahedi et al., 2020) was
calculated by MM-PBSA approach through the g_mmpbsa
script program (Kumari et al., 2014) for the selected mole-
cules in order to investigate relative binding affinity toward
the protease protein. The MD simulation trajectory of last
10 ns was considered for the DG
bind
calculation. The detailed
procedure of DG
bind
is described below.
Equation (1) was used to calculate the DG
bind
.
DGbind ¼GComplexGprotein Gligand (1)
Here, the G
complex
represents the total free energy of pro-
tein and ligand complex. Individual binding energy of pro-
tein and ligand in the solvent can be defined as G
protein
and
G
ligand
, respectively. Separate binding energy of each compo-
nent such as complex (G), protein (G) and ligand (G) is calcu-
lated as per Equation (2).
G¼hEMMiTS þhGsolvationi(2)
The E
MM
describes the average molecular mechanics (MM)
potential energy in a vacuum. The temperature and entropy
are represented by the Tand S, respectively. The free energy
of solvation is denoted by G
solvation
.
Further, the E
MM
is calculated using Equation (3).
EMM ¼Ebonded þEnonbonded (3)
The term, E
bonded
represents the bonded interaction such as
bond length, bond angle and dihedral angle. On the other
hand, the E
nonbonded
explains the nonbonding interactions
including the electrostatic and van der Waals interactions.
The term G
solvation
in Equation (2) is the energy required
to transfer a solute from a vacuum to the solvent. This term
can be calculated by Equation (4)
Gsolvation ¼Gpolar þGnonpolar (4)
The electrostatic and nonelectrostatic contribution to the
solvation free energy is represented by the G
polar
and
G
nonpolar
, respectively.
Results
Analysis of novel coronavirus protease enzyme
structure complex
For a successful structure-based drug design, verifying the
quality of protein structure is a crucial step. Therefore after
collecting crystal structure of novel coronavirus protease
enzyme (PDB ID: 6LU7) from the RCSB Protein Data Bank, the
quality was checked with PROCHECK server (Laskowski et al.,
1993). The obtained Ramachandran plot (Figure S1 in supple-
mentary material) was analysed and it was found that 90.6%
residues are in the favourable region whereas 8.7% residues
lie in the additional allowed region. Only 0.4% of residues lie
in generously allowed regions and disallowed regions. The
above results unambiguously support the quality and reliabil-
ity of the protein molecule and considered for further study.
The crystal structure of protease enzyme which is complex
with covalently bound with a peptide-like ligand (N3), con-
sists of alanine, valine and leucine amino acid residue along
with non-amino acid organic moiety. The ligand N3 form
hydrogen bond with PHE140, GLY143, CYS145, HIS163,
HIS164, GLU166, GLN189 and THR190 amino acid residues
present in the binding pocket of this protease enzyme.
Among these amino acid residues, GLU166 form two hydro-
gen bonds with N3. The amino acid CYS145 forms a covalent
interaction with electrophilic carbon of the ligand (Figure 1).
The ligand-binding site was used to explore the FDA-
approved drug molecules and identifying high-affinity com-
pounds for this binding site using a structure-based drug
design approach. In the purpose of molecular docking, the
PLANTS, an open-source tool was used (Korb et al., 2009).
Validating docking protocol is a crucial step before per-
forming molecular docking based virtual screening and to
determine threshold parameters (Taha et al., 2011). PLANTS
program based docking of ligand N3 yielded 10 docking
poses. All the poses were found to be present inside the
binding pocket. RMSD value less than 2 Å after superimpos-
ing co-crystalline ligand upon the pose of dock ligand infer
the validity of docking protocol (Huang & Zou, 2006). In
order to validate the docking protocol, best docking pose of
ligand N3 were superimposed upon co-crystal structure of
ligand N3 (superimposed co-crystal structure given in supple-
mentary material) and the RMSD was found to be 0.746 Å
along with PLANTS score –115.467. Based upon the RMSD
value of 0.746 Å and PLANT score, docking pose of ligand
N3 was selected. The interactions between protein and
selected docked pose of ligand N3 were analysed.
JOURNAL OF BIOMOLECULAR STRUCTURE AND DYNAMICS 3
Virtual screening
FDA-approved drugs data set was screened by LEA3D server-
based program. For each molecule, the LEA3D program gen-
erates 10 conformers. Based on high negative PLANT score,
the best conformer was selected. After successful completion
of screening, the molecules were considered for further anal-
yses upon the a) threshold PLANTS score (–115.467), b) inter-
action with crucial amino acids (Umesh et al., 2020), and c)
reasonable orientation of the molecules inside the pockets.
In agreement with threshold PLANTS score, crucial amino
acid interaction, reasonable orientation, a total of 20 mole-
cules (Table 1) were considered. The PLANTS score of
selected molecules was found to be in the range of –142
to –128.
Among these 20 compounds, top five compounds were
Triptorelin, Nafarelin, Icatibant, Cobicistat and Histrelin
(Figure 2) exhibiting binding score in terms of PLANTS score
–142.900, –130.820, –130.710, –128.740, –128.710,
respectively.
Besides binding score, analysis of amino acid residue
interaction depicts, Triptorelin shows hydrogen bonds
(H-bond) with THR45, SER6, TYR54, ASN142, GLY143, GLU166
and GLN189 amino acid residues along with superior hydro-
phobic interactions. Nafarelin form H-bond with THR26,
ASN42, GLY43, SER46, PRO168, GLN189 amino acid residues.
Icatibant exhibits H-bond interactions with PHE140, ASN142,
GLU166, LEU167, PRO168, and GLN189. Cobicistat bind with
the main protease enzyme through one H-bond with GLY143
and by strong hydrophobic interactions with amino acid resi-
dues (residue id 25–27, 41, 46, 49, 140–142, 145, 164–167,
187–189). The fifth ranking compound Histrelin exhibits
H-bond interaction with ASN42, THR45, SER 46, GLY143, CYS
145, and ASP187. Other than Cobicistat all four ligands form
an average of 6 H-bonds with 2019-main protease enzyme.
Further analysis of protein-ligand interactions (supplementary
material Figure S2) shows Triptorelin form p-sulfur interaction
with CYS145 and MET49 residues. A p-p-stacking interaction
was found with HIS41, LEU50 residues. Nafarelin exhibits
p-alkyl interaction with MET49, HIS163, and MET165.
Icatibant form p-sulfur interaction with HIS41, MET165. p-sul-
fur interaction was also found in Cobicistat-main protease
enzyme complex and involved amino acid residues are
MET49, 165, GLU166. T-shaped p-pinteraction was observed
with HIS41 residues; however, LEU27, MET49 and CYS145
form p-alkyl interaction. The fifth ligand Histrelin exhibit
p-sulfur interaction with MET165 and p-pstacking with
HIS41, LEU141, HIS163, HIS172. Pharmacological properties of
shorted molecules indicate Triptorelin, Nafarelin and
Icatibant associated with the event of human hormone func-
tion with acceptable pharmacokinetic properties. Icatibant
acts as a bradykinin B2 receptor antagonist (Riedl et al.,
2018). Third molecule Cobicistat along with other antiviral
drugs as a combination is in phase II clinical trial as an anti
2019nCoVD management (Rosa & Santos, 2020), exhibit its
Figure 1. Interaction of ligand N3 with main protease enzyme.
4 S. CHATTERJEE ET AL.
Table 1. List of FDA-approved drugs exhibiting high negative binding with 2019 novel coronavirus protease enzyme.
S. no Name Pharmacological property Score (%) PLANTS score
1 Triptorelin Antineoplastic, synthetic analog of gonadotropin releasing hormone
palliative treatment of advanced prostate cancer. Stimulates the
release of luteinizing hormone
95.27 –142.900
2 Nafarelin Treatment of central precocious puberty (CPP) gonadotropin-releasing
hormone agonist
87.2 –130.820
3 Icatibant Hereditary angioedema bradykinin B2 receptor antagonist 87.14 –130.710
4 Cobicistat Inhibiting cytochrome P450 3A isoforms. Treatment of HIV-1 infection 85.83 –128.740
5 Histrelin Gonadotropin releasing hormone (gnrh) agonist 85.81 –128.710
6 Goserelin Plant prostate cancer (therapeutic) antineoplastic. Gonadotropin
releasing hormone (gnrh) agonist
84.45 –126.670
7 Angiotensin II A vasoconstrictor to increase blood pressure in adults 83.44 –125.160
8 Sincalide Various diagnostic imaging, choleretic 82.99 –124.480
9 Ceruletide Various spasmogenic effect, diagnostic agents 82.82 –124.230
10 Saralasin Cardiovascular antihypertensive angiotensin ii inhibitor 82.58 –123.870
11 Carfilzomib Antineoplastic, treatment of multiple myeloma 81.39 –122.080
12 Etelcalcetide Treatment of secondary hyperparathyroidism (hpt). Calcium-sensing
receptor (casr) agonist
81.04 –121.560
13 Venetoclax Antineoplastic agent. Bcl-2 (b-cell lymphoma 2) inhibitor 80.40 –120.600
14 Colfosceril palmitate Lung surfactants 79.69 –119.530
15 Latanoprost Antiglaucoma, antihypertensive agents 79.29 –119.530
16 Ritonavir Anti-infectives , antiviral, anti-HIV protease inhibitors 77.93 –116.900
17 Abarelix Antineoplastic, anti-testosterone agents gonadotropin-releasing
hormone (gnrh) antagonist
77.60 –116.400
18 Latanoprostene Antiglaucoma, antihypertensive agents , prostaglandin analog 77.35 –116.020
19 Fluphenazine Antipsychotic 77.33 –116.000
20 Amphotericin B Fungistatic or fungicidal 77.06 –115.590
21 Ligand N3 Co-crystalline ligand 76.46 –115.467
Figure 2. Binding site interactions of each ligand with 2019 novel coronavirus main protease enzyme (a. Triptorelin, b. Nafarelin, c. Icatibant, d. Cobicistat,
e. Histrelin).
JOURNAL OF BIOMOLECULAR STRUCTURE AND DYNAMICS 5
pharmacological action by inhibiting cytochrome P450 3A
isoforms and enhance anti-HIV drugs bioavailability.
Triptorelin (DrugBank id: DB06825) potentiate luteinizing
hormone-releasing activity and follicle-stimulating hormone-
releasing activity 13-folds and 21-folds, respectively. It is gen-
erally administered through oral and intravenous route (M€
uller
et al., 1997). Chemical structure of Nafarelin (DrugBank id:
DB00666) have 3-(2-naphthyl)-D-alanine substitution which
acts as a potent synthetic agonist of gonadotropin-releasing
hormone generally used as a nasal drop (Garner, 1994).
Synthetic peptidomimetic drug Icatibant having 10 amino acid
residues exhibits bradykinin B2 receptor agonistic activity
(Bachelard et al., 2018) and is generally used for the treatment
of hereditary angioedema with superior bioavailability profile.
Cobicistat is generally used in combination with other anti-
retroviral agents for effective management of HIV-1 infection.
During clinical trials, adverse effect like jaundice was reported
for this molecule. The fifth molecule Histrelin (DrugBank id:
DB06788) acts as a gonadotropin-releasing hormone agonist
(Klein et al., 2020) generally used as an implant. Based on the
above pharmacological analysis we have considered these top
five molecules for molecular dynamics simulation analysis in
order to examine stability with ligand-SARS-COV-2 main prote-
ase enzyme complex.
Molecular dynamics study
All atom molecular dynamics simulation was conducted for
top five hits complexed with SARS-COV-2 main protease.
Dynamic properties of each complex were compared with
the dynamic behaviour of co-crystalline ligand N3 and ligand
free protein. From 100 ns MD simulation trajectory, the RMSD
was calculated and plotted against time (Figure 3).
The average RMSD of the N3, Triptorelin, Nafarelin,
Icatibant and Cobicistat–protein systems was found to be
2, 9.2, ~2.3, 1.4, 2.07 Å, respectively. The ligand free
protein or apo protein exhibits average RMSD value of
2.15 Å. A sharp increase in RMSD (10 Å) was observed
with Triptorelin–main protease enzyme system after 10 ns of
simulation. However, the Triptorelin system exhibited incon-
siderable RMSD fluctuation (4 Å) throughout the simula-
tion. Histrelin system after 2 ns of simulation shows sudden
increase in RMSD (8 Å) and similar pattern (RMSD >3Å)
like Triptorelin–protein system. Notably large RMSD of the
atomic positions’behaviour were observed for the Triptorelin
and Histrelin–protein system, respectively. Therefore, the
simulation was not continued further for the Histrelin system
(average RMSD 8.45 Å up to 30 ns). The Icatibant–protein
system exhibits the lowest average RMSD 1.4 Å and the
RMSD was found consistent throughout the 100 ns simula-
tion time. Nafarelin (average RMSD 2.3 Å) at 31st ns to
38th ns exhibits rise in RMSD (0.017 Å higher than average
RMSD). At 50th ns to 52th ns time span and 56th–58th ns
time span of simulation this system exhibited 0.013 to
0.01 Å higher RMSD than average RMSD value. After 87th
ns to 100 ns the average RMSD value was found to be 1.5 Å.
The RMSD value of Cobicistat–protein system after 95 ns was
found to be 1.45 Å.
Figure 3. Protein backbone RMSD of each ligand-protein complex and ligand free protein.
6 S. CHATTERJEE ET AL.
In order to understand the deviation of each protein,
amino acid residues with respect to the reference position,
the RMSF plot was constructed from 100 ns atomistic
molecular dynamics trajectory which is shown in Figure 4.
The average value of RMSF for N3, Triptorelin, Nafarelin,
Icatibant and Cobicistat–protein complexes were found to be
1.3, 1.5, 1.3, 0.92, 1.4 Å, respectively. The average
RMSF value for ligand free protein was found to be 1.42 Å.
In Nafarelin–protein system, residues in between residue
ID: 210 and residue ID: 290 show slightly high RMSF (1.48 Å).
Residues number 222, 223, and 224 shows average RMSF 2.6
Å in Cobicistat–protein system. Comparatively higher fluctu-
ation can be observed with terminal residues (6.5 Å) of each
system including apo protein. HIS41 and CYS145 residues in
apo-protein exhibits RMSF 0.081 and 0.073 Å, respectively. The
residue HIS41 shows RMSF of 0.12, 0.068, 0.065 Å for N3,
Nafarelin, Icatibant–protein systems, respectively whereas in
Cobicistat–protein system HIS41 exhibits RMSF 0.095 Å. The
CYS145 residue exhibits the RMSF of 0.11, 0.053, 0.048, 0.081 Å
for N3, Nafarelin, Icatibant and Cobicistat–protein systems,
respectively. Icatibant system shows lowest average RMSF
0.92 Å. The radius of gyration (Rg) was calculated from
100 ns trajectory to analyse the compactness and rigidity of
the protein-ligand system (Figure 5).
Cobicistat–protein system after 26.4 ns exhibit little devi-
ation (23.24 to 22.75 Å) and for the rest of the simulation
time it shows average Rg value of 22.36 Å. Ligand free
SARS-COV-2 main protease enzyme exhibit average Rg of
22.6 Å. The average Rg value of N3, Triptorelin, Nafarelin,
Icatibant and Cobicistat–protein systems were found to be
22.5, 22.7, 22.4, 22.42, 23.1 Å, respectively. For a stable com-
plex, it is essential, that the ligand and protein should
maintain acceptable distance throughout the simulation. The
distance between protein and ligand during the simulation
was calculated and it is shown in Figure 6.
The average distance between protein and ligand during the
simulation was calculated from 100ns molecular dynamics tra-
jectory. Average distance 1.7, 1.8, 1.6, 1.6, 2.0 Å was
found for N3, Triptorelin, Nafarelin, Icatibant and Cobicistat
complexed with main protease enzyme, respectively. In order to
study the surface area that is accessible to solvent for each sys-
tem, a solvent accessible surface area (SASA) plot was con-
structed from the trajectory both for protein. (Figure 7).
The SASA information will be helpful to analyse, whether
the ligand retained inside the shallow binding pocket or it
expels out from the binding cavity. The ligand free protein
exhibits average SASA value of 1685.2 Å
2
. The average SASA
values 1540, 1515, 1545, 1555, 1545 (Å
2
) were found
for N3, Triptorelin, Nafarelin, Icatibant and Cobicistat protein
complex, respectively. The average SASA values for ligand N3,
Triptorelin, Nafarelin, Icatibant and Cobicistat was found to be
110, 1900, 161, 155, 110 (Å
2
), respectively. Analysis of the main
protease-ligand complexes revealed most of the compounds
form H-bond with the binding pocket amino acid residues. In
order to analyse the H-bond interaction property during the
100 ns period of simulation, H-bond interaction plot was con-
structed and it is depicted in Figure 8.
Average 5.5, 2.5, 9.5, 7 and 1.25 numbers of
H-bond was found for ligand N3, Triptorelin, Nafarelin,
Icatibant and Cobicistat complexed with main protease
enzyme, respectively. Percentage frequency of hydrogen
bonds (%HB) calculated from the 100 ns and last 25 ns of
molecular dynamics trajectory was calculated and it is shown
in Figure 9.
Figure 4. RMSF of protein-ligand complexes.
JOURNAL OF BIOMOLECULAR STRUCTURE AND DYNAMICS 7
From the plot it clearly observed that Nafarelin shows
maximum frequency of hydrogen bonding interaction with
main protease enzyme. Icatibant exhibits comparatively less
frequency of hydrogen bond formation during last 25 ns
of simulation.
High negative binding energy often infers the superiority
of protein-ligand complex (Gallicchio et al., 2010). The DG
bind
was calculated for each of five final molecules including N3
towards the main protease enzyme through MM-GBSA
approach. It was observed that the DG
bind
of N3 found to be
–599.15 kJ/mol. Moreover, the DG
bind
of Nafarelin, Icatibant
and Cobicistat was found to be –712.94, –851.75, –252.65 kJ/
mol, respectively. A number of parameters calculated from
the MD simulation are given in Table 2.
Discussion
Protease enzymes play a crucial role in peptide bond
hydrolysis. The functionally crucial cysteine and histidine
Figure 5. Radius of gyration (Rg) vs. time.
Figure 6. Distance calculated from protein-ligand complex.
8 S. CHATTERJEE ET AL.
residues (HIS41-CYS145 dyad) (Ul Qamar et al., 2020) are pre-
sent in the catalytic site of this enzyme. PLANTS docking was
performed for co-crystalline ligand N3. The RMSD value of
0.746 Å between co-crystal N3 and best dock pose of N3
clearly infer the validity of the docking protocol used for this
study. In agreement with this, it can be contemplated that
using the same protocol, docking of the other molecules
with this protein and considering the same binding pocket,
can furnish positive docked conformations. Hence, the same
docking protocol was applied for molecular docking based
virtual screening.
The co-crystalline ligand N3 forms hydrogen bond inter-
action with SARS-CoV-2–main protease enzyme. Beside
hydrogen bonds, it exhibits hydrophobic interactions. The
analysis revealed that the co-crystalline ligand forms a cova-
lent interaction as well as H-bond interaction with CYS145
amino acid residue and hydrophobic interaction with
HIS41 residue.
Figure 7. Solvent accessible surface area vs. time (ns) plot.
Figure 8. H-bond interactions exhibited by each ligand when complexed with this main protease enzyme.
JOURNAL OF BIOMOLECULAR STRUCTURE AND DYNAMICS 9
Results obtained by computational screening (Table S1,
given in supplementary material), Ritonavir a classical HIV-1
protease inhibitor (EC
50
25 nM), currently under anti-COVID
19 clinical trial (Cao et al., 2020) having PLANTS score and %
score—116.40, 77.93 respectively, formed H-bond with
GLN189 (Figure S2 in supplementary material). However, it
shows a strong hydrophobic interaction. The 4th ranked
drug Cobicistat, a cytochrome P450-3A isoform inhibitory
agent which facilitates the anti-retroviral properties of
Atazanavir or Darunavir (Crutchley et al., 2016) exhibiting sat-
isfactory PLANTS score and % score –128.740 and 85.83,
respectively and form H-bond with GLY143 amino acid resi-
due of protein molecules. Rest of the three molecules among
top five molecules namely Triptorelin (rank 1), Nafarelin (rank
2) are either synthetic analogues of gonadotropin-releasing
hormone or gonadotropin-releasing hormone agonists exhib-
iting superior binding with nCoV-main protease enzyme.
Triptorelin form two H-bonds with SER46 exhibiting distance
2.65 and 3.07 Å, respectively. Both N3 ligand and Triptorelin
show identical H-bond interaction with GLY43, GLU166,
GLN189 and identical hydrophobic interaction with THR25,
LEU141, MET165, ARG188 and PRO168. However, Triptorelin
depicts H-bond interaction with ASN142 but these residues
interact with ligand N3 through hydrophobic interaction.
Besides, residue like PHE140, HIS163, and HIS164 show H-
bond with ligand N3 but with Triptore in it forms hydropho-
bic interaction. The above findings provide suitable informa-
tion about Triptorelin that it exhibits binding energy of –142
with the main protease enzyme. However, Nafarelin forms
three H-bonds with ASN142 depicting the length of 2.81,
2.87 and 3.22 Å. A 2.89 Å H-bond was found in between
Nafarelin and GLN189 amino acid residue. GLN189 amino
acid residue also forms 2.89 Å H-bond with co-crystalline lig-
and N3. HIS41 and CYS145 can be considered as crucial resi-
dues for this enzyme function. Ligand N3 shows
hydrophobic interaction with HIS41, similarly Nafarelin
exhibit identical interaction with this residue. Like N3-
CYS145 H-bond interaction, Nafarelin does not show inter-
action with CYS145. However, identical hydrophobic inter-
action with THR 25, LEU141, MET165, ARG188, GLN192
amino acid residues was found. Few residues like PHE140,
HIS163, and THR190 form a hydrogen bond with co-crystal-
line ligand N3, but interact with Nafarelin through hydropho-
bic interaction. Similarly THR26, ASN142, PRO168 form H-
Figure 9. Percentage frequency of hydrogen bonds calculated from molecular dynamics trajectory for each protein-ligand system.
Table 2. Results of molecular dynamics simulation.
Name
Average
RMSD
protein-
ligand (Å)
Average
RMSF (Å)
Average
number of
H-bond
protein-
ligand
Frequency
of hydrogen
bond
(0–100 ns)
%
Frequency
of hydrogen
bond
(75–100 ns)
%
Average
distance
protein-
ligand (Å)
Average Rg-
protein (Å)
Average
SASA
protein (Å
2
)
Average
SASA
ligand (Å
2
)
Binding
energy
(kJ/mol)
6LU7-LF 2.15 1.42 ND ND –22.6 1685.2 ––
N3 2 1.3 5.5 237 228 1.7 22.5 1540 110 –599.15
Triptorelin 9.2 1.5 2.5 139 68 1.8 22.7 1515 190 ND
Nafarelin 2.3 1.3 9.5 284 253 1.6 22.4 1545 161 –712.94
Icatibant 1.4 0.92 7 205 100 1.6 22.4 1555 155 –851.74
Cobicistat 2.07 1.4 1.25 11.5 19 2.0 23.1 1545 110 –252.65
Histrelin
a
8.45 ND ND ND ND ND ND ND ND ND
a
Simulation continues up to 30 ns.
LF ¼ligand free, ND ¼not done, Rg ¼radius of gyration.
10 S. CHATTERJEE ET AL.
bond with Nafarelin; validated by 100 ns molecular dynamics
trajectory and those residues showing hydrophobic inter-
action with co-crystalline ligand N3. ASN142 of the main pro-
tease enzyme forms three H-bonds (length 2.81, 2.87, 3.22 Å
respectively) with this ligand. In addition, Nafarelin exhibits a
mean distance of 0.5 Å from CYS145 residue (Figure S3 in
supplementary material). The above facts infer Nafarelin
exhibit superior binding interaction with the catalytic domain
of 2019 main protease enzyme and exhibiting one identical
H-bond interaction and number of identical hydrophobic
interactions followed by either H-bond or hydrophobic inter-
action with binding site residues. Icatibant shows identical H-
bond interaction with PHE140, GLU 166, GLN 189 and hydro-
phobic intersection THR 24-26, LEU141, ARG188. Residues
like ASN142, PRO168 show H-bond interaction with Icatibant
but showing interacting with N3 ligand through hydrophobic
interaction. Similarly, residues like HIS164 form H-bond with
co-crystalline ligand but exhibiting hydrophobic interaction
with Icatibant. Though there is no H-bond and hydrophobic
interaction with CYS145 and HIS41 was observed, Icatibant
shows p-sulfur interaction with HIS 41, based on this fact this
molecule was considered for further study. Cobicistat exhibits
one identical H-bond interaction and identical hydrophobic
interaction with THR25-26, HIS41, ASN142, MET165, ARG188,
residues of main protease enzyme. On the other hand,
Histrelin forms H-bond with CYS145 like co-crystalline ligand
as well as exhibits identical hydrophobic interactions with
THR 24-26, HIS 41 residues of main protease enzyme.
Residues like PHE140; HIS143 shows hydrophobic interaction
with Histrelin but forms H-bond with a co-crystalline ligand.
Considering its binding interaction with CYS145 and HIS41,
this molecule was considered for molecular dynamics study.
Atomistic 100 ns MD simulation of ligand free protein and
each protein-ligand complexes were conducted. Various
parameters like RMSD, RMSF, Rg, distance of protein and lig-
and, SASA, number of hydrogen bonds, etc. were calculated
from the trajectory of MD simulation.
The RMSD parameter calculated through molecular
dynamics simulation provides brief insight into the structural
conformations of proteins. Overall information about the sta-
bility of protein backbone when bound with a ligand or
small molecule can be analysed by RMSD parameter. Lower
value of RMSD throughout the molecular dynamics simula-
tion suggests the high stability of protein ligand system and
higher RMSD value infer comparatively low stability of the
system. Changes in RMSD towards larger ranges might indi-
cate that the protein-ligand system during dynamics simula-
tion, probably undergoes remarkable or some shorts of
conformational changes. However, RMSD values that
undergo lower ranges are ideally acceptable for protein sys-
tems (Kufareva & Abagyan, 2012).
The average RMSD parameter is less than 3 Å which is
perfectly acceptable for globular protein (Kufareva &
Abagyan, 2012). RMSD plot of protein-ligand complex
depicts, Triptorelin bound protein backbone exhibits incon-
siderable divination of atomistic root mean square position
throughout the simulation time, which suggests probably
due to the presence of high degree of rotatable bond or
structural flexibility this molecule is unable to attain stability
inside the binding pocket, which is assumed to be shallow.
Cobicistat-protein system up to 86 ns exhibit average RMSD
of 2.2 Å and 86th ns to 95 ns it depicts RMSD of 1.35 Å.
After 95 ns, average RMSD for this system was found to be
1.45 Å. Total increase in RMSD from 86th to 95th time
span to 95th to 100th ns was 0.1 Å. For Nafarelin–protein
system exhibit 0.15 Å higher average RMSD than apo protein
RMSD value; however, after 87th ns the average RMSD value
is less than (0.65 Å) apo protein average RMSD value. In
agreement with the above observation, it can be concluded
this variation in RMSD found in Nafarelin and
Cobicistat–protein system does not cause significant struc-
tural changes and achieves equilibration state during
the simulation.
The RMSF parameter explores the individual fraction of
the protein structure that is fluctuating from its mean struc-
ture. Higher degrees of RMSF values infer the protein struc-
ture will attain greater flexibility. On the other hand,
marginal flexibility of the protein-ligand system results in
lower RMSF values (Bhowmick et al., 2020). Each ligand-pro-
tein system other than Triptorelin–protein system exhibits
average RMSF value less than 1.42 Å, which is apo protein
RMSF value. Throughout the 100 ns simulation, the low RMSF
value was found for both HIS41 and CYS145 residues of
Nafarelin and Icatibant systems which indicate these two
ligands exhibit interaction with HIS41 and CYS145 residues.
The terminal residues exhibits highest fluctuation and can be
clearly observed in Figure 8. These residues are located in
the loop region of this protein. The RMSF of the terminal res-
idues of this protein reported by Muralidharan et al., 2020
exhibit almost identical RMSF profile.
The Rg parameter is defined as the mass-weighted root
mean square distance of a set of atoms from their typical
center of mass (Lobanov et al., 2008). Rg parameters provide
information about overall dimensions and quantify changes
of protein structure. Large variation in Rg value indicates
that protein-ligand system was inconsistent throughout the
simulation. Uniform variation of Rg value infers the consist-
ent stability of the protein during simulation (Bhowmick
et al., 2020). From the Rg value (Å) of each frame versus
time (ns) plot (Figure 9), it can be observed that the ligand
free protein was rigid and consistent with an average Rg
value of 22.6 Å. Nafarelin and Icatibant systems oscillated at
22.4 Å, which is 0.1 Å less than the Rg value of co-crystal
bound ligand N3. Moreover, these systems exhibit 0.2 Å less
Rg than the apo protein Rg value and exhibit a similar Rg
profile. The Rg of Triptorelin deviates little bit higher (0.1 Å)
than the ligand free protein. Cobicistat system exhibits 0.5 Å
higher Rg value than apo protein and 0.6 Å higher than co-
crystalline ligand N3. In argument with above observation, it
can be stated Nafarelin and Icatibant system exhibits accept-
able Rg profile. Binding of these two hits with SARS-COV-2
remained stable.
Nafarelin and Icatibant system exhibits alike SASA profile
with ligand N3 and apo-protein. This indicates, these two
ligands exhibiting superior contacts with this enzyme. After
analysing the trajectory it was our assumption and
JOURNAL OF BIOMOLECULAR STRUCTURE AND DYNAMICS 11
interpretation, declination pattern exhibited by the protein
when complexed with ligands probably due to expelling of
water from binding pockets which certainly occupied by the
ligands. This fact can be supported by SASA plot calculated
for the ligands (supplementary material Figure S4) which
depicted all the ligands follows uniformity throughout the
simulation time.
Surface area up to 110 Å
2
and 110 Å
2
depicted by
Cobicistat and ligand N3 suggests that these molecules are
less exposed to solvent. Nafarelin and Icatibant higher (51
Å
2
and 45 Å
2
, respectively) solvent-exposed surface area
than co-crystalline ligand N3 which decreased after 65 ns of
simulation. On the other hand, Triptorelin after 64 ns shows
a slight increase in SASA value. SASA analysis inferred that
Cobicistat, Nafarelin and Icatibant have the properties of
forming a stable complex with this main protease enzyme.
Mean distance of protein and ligand was calculated, which
depicts clearly Nafarelin and Icatibant show 1.7 Å distance
from the whole protein throughout the simulation. H-bond
plot calculated from dynamics trajectory depicts Cobicistat
form comparatively fewer H-bond, whereas Nafarelin and
Icatibant form a higher degree of H-bond interaction with
SARS-COV-2 main protease enzyme in comparison with co-
crystalline ligand and Triptorelin. The percentage frequency
of hydrogen bonds were calculated from 100 ns and last
25 ns (75–100 ns) trajectory indicates Nafarelin exhibits 284%
(100 ns) and 253% (last 25 ns) frequency in hydrogen bond
formation with main protease enzyme. Comparing with co-
crystalline ligand hydrogen bond formation pattern, Nafarelin
forms 47% and 25% more hydrogen bonds with main prote-
ase enzyme. Icatibant exhibits 32% and 196% less ability to
from hydrogen bond with SARS-CoV-2-main protease
enzyme when compared with co-crystalline ligands fre-
quency of hydrogen bonding profile. Binding energy calcu-
lated from last 10 ns of molecular dynamics trajectory
exhibits Nafarelin (–712.94 kJ/mol) and Icatibant (–851.74 kJ/
mol) exhibits superior binding energy in comparison with co-
crystalline ligand (–599.15 kJ/mol) and Cobicistat (–252.65 kJ/
mol), further supports there superior binding properties with
main protease enzyme.
The above results predicted from protein-ligand complex
generated by PLANTS docking server-based virtual screening
and molecular dynamics analysis highly infer that Nafarelin
and Icatibant can be considered as a reference point to re-pur-
posing existing drugs for efficient management of COVID-19.
Considering molecular dynamics is a statistical dynamics pro-
cess, we performed another 100 ns molecular dynamics simu-
lation for Nafarelin-protease enzyme complex (Figure 10)
using same force field only, supports the reproducibility of the
results, which strongly infer, the highest degree of protein-lig-
and complex stability can be observed with Nafarelin-main
protease enzyme complex.
Moreover, from pharmaceutical point of view, Nafarelin is
used as a nasal drop and attain bioavailability very quickly
with minimal unwanted effects. The RMSD, RMSF, Rg, SASA
and binding energy values unveils that Icatibant shows
acceptable stability with SARS-COV-2 main protease enzyme.
These hits can be a better re-purposing option; however,
there should be proper biological evaluations to be con-
ducted to validate its efficacy of the predicted molecules
against this global threat in terms of 2019-novel corona-
virus infection.
Figure 10. Repetition of molecular dynamics simulation for Nafarelin–protein (main protease enzyme) complex.
12 S. CHATTERJEE ET AL.
Conclusion
New anti-COVID-19 drug poses a worldwide challenge, drug
repositioning may be a quicker and safer approach to rely on
and find new therapy against this dangerous life-threatening
infection. Well-defined structure of 2019 novel coronavirus
main protease enzyme is an excellent reference point to iden-
tify new therapeutics, from already examined and approved
drugs for human use. Results obtained through computational
approach can be considered, as computational screening of
small molecules library and molecular dynamics simulation
techniques are the well-established and validated tools for the
drug discovery process. We performed 100 ns atomistic MD
simulations for top five protein-hit complexes that is
Triptorelin, Nafarelin, Icatibant, Cobicistat, Histrelin-main pro-
tease enzyme (PDB ID: 6LU7) of SARS-CoV-2, with an aim to
establish the binding potential of this FDA approved mole-
cules. Analysis of the results portrays higher stability of the
Nafarelin and Icatibant-enzyme systems and their established
pharmaceutical profiles unveils these hits may show a possible
promising application in anti-COVID-19 therapy. Nafarelin and
Icatibant exhibiting promising binding with SARS-CoV-2 main
protease enzyme can open a new window towards the discov-
ery of effective therapy against COVID-19.
Disclosure statement
Authors declare that there are no conflicts of interest.
ORCID
Md Ataul Islam http://orcid.org/0000-0001-6286-6262
Debanjan Sen http://orcid.org/0000-0001-7600-6781
Data availability statement
Data and materials are available upon request to the correspond-
ing author.
References
Abraham, M. J., Murtola, T., Schulz, R., P
all, S., Smith, J. C., Hess, B., &
Lindah, E. (2015). Gromacs: High performance molecular simulations
through multi-level parallelism from laptops to supercomputers.
SoftwareX,1-2,19–25. https://doi.org/10.1016/j.softx.2015.06.001
Bachelard, H., Charest-Morin, X., & Marceau, F. (2018). D-arg0-bradykinin-
Arg-Arg, a latent vasoactive bradykinin B2 receptor agonist metabolic-
ally activated by carboxypeptidases. Frontiers in Pharmacology,9(Mar),
273. https://doi.org/10.3389/fphar.2018.00273
Bhowmick, S., AlFaris, N. A., ALTamimi, J. Z., ALOthman, Z. A., Aldayel,
T. S., Wabaidur, S. M., & Islam, M. A. (2020). Screening and analysis of
bioactive food compounds for modulating the CDK2 protein for cell
cycle arrest: Multi-cheminformatics approaches for anticancer thera-
peutics. Journal of Molecular Structure,1216, 128316. https://doi.org/
10.1016/j.molstruc.2020.128316
Boopathi, S., Poma, A. B., & Kolandaivel, P. (2020). Novel 2019 corona-
virus structure, mechanism of action, antiviral drug promises and rule
out against its treatment. Journal of Biomolecular Structure and
Dynamics,1–10. https://doi.org/10.1080/07391102.2020.1758788
Cao, B., Wang, Y., Wen, D., Liu, W., Wang, J., Fan, G., Ruan, L., Song, B., Cai,
Y., Wei, M., Li, X., Xia, J., Chen, N., Xiang, J., Yu, T., Bai, T., Xie, X., Zhang,
L., Li, C., …Wang, C. (2020). A trial of lopinavir-ritonavir in adults
hospitalized with severe covid-19. The New England Journal of
Medicine, 382(19), 1787–1799. https://doi.org/10.1056/NEJMoa2001282
Chen L., Liu, H. G., Liu, W., Liu, J., Liu, K., Shang, J., Deng, Y., & Wei, S.
(2020). Analysis of clinical features of 29 patients with 2019 novel cor-
onavirus pneumonia. Zhonghua Jie He Hu Xi Za Zhi, 43. https://doi.
org/10.3760/CMA.J.ISSN.1001-0939.2020.0005
Chen, Y., Liu, Q., & Guo, D. (2020). Emerging coronaviruses: Genome
structure, replication, and pathogenesis. Journal of Medical Virology,
92(4), 418–423. https://doi.org/10.1002/jmv.25681
Crutchley, R. D., Guduru, R. C., & Cheng, A. M. (2016). Evaluating the role
of atazanavir/cobicistat and darunavir/cobicistat fixed-dose combina-
tions for the treatment of HIV-1 infection. HIV/AIDS (Auckland, N.Z.),8,
47–65. https://doi.org/10.2147/HIV.S99063
Das, S., Sarmah, S., Lyndem, S., & Singha Roy, A. (2020). An investigation
into the identification of potential inhibitors of SARS-CoV-2 main pro-
tease using molecular docking study. Journal of Biomolecular Structure
and Dynamics,1–11. https://doi.org/10.1080/07391102.2020.1763201
Deeks, S. G., Smith, M., Holodniy, M., & Kahn, J. O. (1997). HIV-1 protease
inhibitors. JAMA,277(2), 145–153. https://doi.org/10.1001/jama.1997.
03540260059037
Douguet, D. (2018). Data sets representative of the structures and experi-
mental properties of FDA-approved drugs. ACS Medicinal Chemistry
Letters,9(3), 204–209. https://doi.org/10.1021/acsmedchemlett.
7b00462
Douguet, D., Munier-Lehmann, H., Labesse, G., & Pochet, S. (2005).
LEA3D: A computer-aided ligand design for structure-based drug
design. Journal of Medicinal Chemistry,48(7), 2457–2468. https://doi.
org/10.1021/jm0492296
Forli, S., Huey, R., Pique, M. E., Sanner, M. F., Goodsell, D. S., & Olson,
A. J. (2016). Computational protein-ligand docking and virtual drug
screening with the AutoDock suite. Nature Protocols,11(5), 905–919.
https://doi.org/10.1038/nprot.2016.051
Gallicchio, E., Lapelosa, M., & Levy, R. M. (2010). The binding energy dis-
tribution analysis method (BEDAM) for the estimation of protein-lig-
and binding affinities. Journal of Chemical Theory and Computation,
6(9), 2961–2977. https://doi.org/10.1021/ct1002913
Garner, C. (1994). Uses of GnRH agonists. Journal of Obstetric,
Gynecologic, and Neonatal Nursing,23(7), 563–570. https://doi.org/10.
1111/j.1552-6909.1994.tb01922.x
Hasan, A., Paray, B. A., Hussain, A., Qadir, F. A., Attar, F., Aziz, F. M.,
Sharifi, M., Derakhshankhah, H., Rasti, B., Mehrabi, M., Shahpasand, K.,
Saboury, A. A., & Falahati, M. (2020). A review on the cleavage pri-
ming of the spike protein on coronavirus by angiotensin-converting
enzyme-2 and furin. Journal of Biomolecular Structure and Dynamics,
1–9. https://doi.org/10.1080/07391102.2020.1754293
Huang, J., & Mackerell, A. D. (2013). CHARMM36 all-atom additive protein
force field: Validation based on comparison to NMR data. Journal of
Computational Chemistry,34(25), 2135–2145. https://doi.org/10.1002/
jcc.23354
Huang, S.-Y., & Zou, X. (2006). Efficient molecular docking of NMR struc-
tures: Application to HIV-1 protease. Protein Science,16(1), 43–51.
https://doi.org/10.1110/ps.062501507
Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: Visual molecular
dynamics. Journal of Molecular Graphics,14(1), 33–38. https://doi.org/10.
1016/0263-7855(96)00018-5 https://doi.org/10.1016/0263-7855(96)00018-
5 https://www.worldometers.info/coronavirus/dated 25.04.2020.
Islam, R., Parves, M. R., Paul, A. S., Uddin, N., Rahman, M. S., Mamun,
A. A., Hossain, M. N., Ali, M. A., & Halim, M. A. (2020). A molecular
modeling approach to identify effective antiviral phytochemicals
against the main protease of SARS-CoV-2. Journal of Biomolecular
Structure & Dynamics,1–12. https://doi.org/10.1080/07391102.2020.
1761883
Islam, M. A., & Pillay, T. S. (2020). Identification of promising anti-DNA
gyrase antibacterial compounds using de novo design, molecular
docking and molecular dynamics studies. Journal of Biomolecular
Structure & Dynamics,38(6), 1798–1809. https://doi.org/10.1080/
07391102.2019.1617785
Jin, Z., Du, X., Xu, Y., Deng, Y., Liu, M., Zhao, Y., Zhang, B., Li, X., Zhang,
L., Peng, C., Duan, Y., Yu, J., Wang, L., Yang, K., Liu, F., Jiang, R., Yang,
X., You, T., Liu, X., …Yang, H. (2020). Structure of Mpro from COVID-
JOURNAL OF BIOMOLECULAR STRUCTURE AND DYNAMICS 13
19 virus and discovery of its inhibitors. Nature,582(7811), 289–293.
https://doi.org/10.1038/s41586-020-2223-y
Khan, R. J., Jha, R. K., Amera, G. M., Jain, M., Singh, E., Pathak, A., Singh,
R. P., Muthukumaran, J., & Singh, A. K. (2020). Targeting SARS-CoV-2:
A systematic drug repurposing approach to identify promising inhibi-
tors against 3C-like proteinase and 2’-O-ribosemethyltransferase.
Journal of Biomolecular Structure & Dynamics,1–14. https://doi.org/10.
1080/07391102.2020.1753577
Khan, S. A., Zia, K., Ashraf, S., Uddin, R., & Ul-Haq, Z. (2020). Identification
of chymotrypsin-like protease inhibitors of SARS-CoV-2 via integrated
computational approach. Journal of Biomolecular Structure and
Dynamics,1–10. https://doi.org/10.1080/07391102.2020.1751298
Klein, K. O., Soliman, A. M., Bonafede, M., Nelson, J. K., & Grubb, E.
(2020). Treatment patterns, health resource utilization and costs
among central precocious puberty patients treated with leuprolide or
histrelin: An examination of the commercial and Medicaid popula-
tions. Journal of Medical Economics,23(4), 407–414. https://doi.org/10.
1080/13696998.2019.1697700
Korb, O., St€
utzle, T., & Exner, T. E. (2009). Empirical scoring functions for
advanced protein-ligand docking with plants. Journal of Chemical
Information and Modeling,49(1), 84–96. https://doi.org/10.1021/
ci800298z
Kufareva, I., & Abagyan, R. (2012). Methods of protein structure compari-
son. Methods in Molecular Biology (Clifton, N.J.),857, 231–257. https://
doi.org/10.1007/978-1-61779-588-6_10
Kumari, R., Kumar, R., & Lynn, A, Open Source Drug Discovery
Consortium. (2014). g_mmpbsa-A GROMACS tool for high-throughput
MM-PBSA calculations. Journal of Chemical Information and Modeling,
54(7), 1951–1962. https://doi.org/10.1021/ci500020m
Lamarre, D., Anderson, P. C., Bailey, M., Beaulieu, P., Bolger, G., Bonneau,
P., B€
os, M., Cameron, D. R., Cartier, M., Cordingley, M. G., Faucher, A.-
M., Goudreau, N., Kawai, S. H., Kukolj, G., Lagac
e, L., LaPlante, S. R.,
Narjes, H., Poupart, M.-A., Rancourt, J., …Llin
as-Brunet, M. (2003). An
NS3 protease inhibitor with antiviral effects in humans infected with
hepatitis C virus. Nature,426(6963), 186–189. https://doi.org/10.1038/
nature02099
Laskowski, R. A., MacArthur, M. W., Moss, D. S., & Thornton, J. M. (1993).
PROCHECK: A program to check the stereochemical quality of protein
structures. Journal of Applied Crystallography,26(2), 283–291. https://
doi.org/10.1107/S0021889892009944
Laskowski, R. A., & Swindells, M. B. (2011). LigPlotþ: Multiple ligand-pro-
tein interaction diagrams for drug discovery. Journal of Chemical
Information and Modeling,51(10), 2778–2786. https://doi.org/10.1021/
ci200227u
Lobanov, M. Y., Bogatyreva, N. S., & Galzitskaya, O. V. (2008). Radius of
gyration as an indicator of protein structure compactness. Molecular
Biology,42(4), 623–628. https://doi.org/10.1134/S0026893308040195
Li, G., & De Clercq, E. (2020). Therapeutic options for the 2019 novel cor-
onavirus (2019-nCoV). Nature Reviews Drug Discovery,19(3), 149–150.
https://doi.org/10.1038/d41573-020-00016-0
Morris, G. M., Ruth, H., Lindstrom, W., Sanner, M. F., Belew, R. K.,
Goodsell, D. S., & Olson, A. J. (2009). Software news and updates
AutoDock4 and AutoDockTools4: Automated docking with selective
receptor flexibility. Journal of Computational Chemistry,30(16),
2785–2791. https://doi.org/10.1002/jcc.21256
M€
uller, F. O., Terblanch
e, J., Schall, R., Van Zyl Smit, R., Tucker, T., Marais,
K., Groenewoud, G., Porchet, H. C., Weiner, M., & Hawarden, D. (1997).
Pharmacokinetics of triptorelin after intravenous bolus administration
in healthy males and in males with renal or hepatic insufficiency.
British Journal of Clinical Pharmacology,44(4), 335–341. https://doi.
org/10.1046/j.1365-2125.1997.t01-1-00592.x
Muralidharan, N., Sakthivel, R., Velmurugan, D., & Gromiha, M. M. (2020).
Computational studies of drug repurposing and synergism of lopina-
vir, oseltamivir and ritonavir binding with SARS-CoV-2 protease
against COVID-19. Journal of Biomolecular Structure and Dynamics,
1–6. https://doi.org/10.1080/07391102.2020.1752802
Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H.,
Lindborg, S. R., & Schacht, A. L. (2010). How to improve R&D product-
ivity: the pharmaceutical industry’s grand challenge. Nature Reviews
Drug Discovery,9(3), 203–214. https://doi.org/10.1038/nrd3078
Perlman, S., & Netland, J. (2009). Coronaviruses post-SARS: Update on
replication and pathogenesis. Nature Reviews Microbiology,7(6),
439–450. https://doi.org/10.1038/nrmicro2147
Pushpakom, S., Iorio, F., Eyers, P. A., Escott, K. J., Hopper, S., Wells, A.,
Doig, A., Guilliams, T., Latimer, J., McNamee, C., Norris, A., Sanseau, P.,
Cavalla, D., & Pirmohamed, M. (2019). Drug repurposing: Progress,
challenges and recommendations. Nature Reviews Drug Discovery,
18(1), 41–58. https://doi.org/10.1038/nrd.2018.168
Riedl, M. A., Banerji, A., Manning, M. E., Burrell, E., Joshi, N., Patel, D.,
MacHnig, T., Tai, M. H., & Watson, D. J. (2018). Treatment patterns and
healthcare resource utilization among patients with hereditary
angioedema in the United States. Orphanet Journal of Rare Diseases,
13(1), 180. https://doi.org/10.1186/s13023-018-0922-3
Rosa, S. G. V., & Santos, W. C. (2020). Clinical trials on drug repositioning
for COVID-19 treatment. Revista Panamericana de Salud P
ublica,44,1.
https://doi.org/10.26633/RPSP.2020.40
Taha, M. O., Habash, M., Al-Hadidi, Z., Al-Bakri, A., Younis, K., & Sisan, S.
(2011). Docking-based comparative intermolecular contacts analysis
as new 3-D QSAR concept for validating docking studies and in silico
screening: NMT and GP inhibitors as case studies. Journal of Chemical
Information and Modeling,51(3), 647–669. https://doi.org/10.1021/
ci100368t
Tyrrell, D. A., & Bynoe, M. L. (1966). Cultivation of viruses from a high
proportion of patients with colds. Lancet (London, England),1(7428),
76–77. https://doi.org/10.1016/S0140-6736(66)92364-6 https://doi.org/
10.1016/S0140-6736(66)92364-6
Ul Qamar, M. T., Alqahtani, S. M., Alamri, M. A., & Chen, L.-L. (2020).
Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discov-
ery from medicinal plants. Journal of Pharmaceutical Analysis.https://
doi.org/10.1016/j.jpha.2020.03.009
Umesh Kundu, D., Selvaraj, C., Singh, S. K., & Dubey, V. K. (2020).
Identification of new anti-nCoV drug chemical compounds from
Indian spices exploiting SARS-CoV-2 main protease as target. Journal
of Biomolecular Structure & Dynamics,1–9. https://doi.org/10.1080/
07391102.2020.1763202
Wahedi, H. M., Ahmad, S., & Abbasi, S. W. (2020). Stilbene-based natural
compounds as promising drug candidates against COVID-19. Journal
of Biomolecular Structure and Dynamics,1–10. https://doi.org/10.1080/
07391102.2020.1762743
Wang, D., Fang, L., Shi, Y., Zhang, H., Gao, L., Peng, G., Chen, H., Li, K., &
Xiao, S. (2016). Porcine epidemic diarrhea virus 3C-like protease regu-
lates its interferon antagonism by cleaving NEMO. Journal of Virology,
90(4), 2090–2101. https://doi.org/10.1128/JVI.02514-15
Wu, F., Zhao, S., Yu, B., Chen, Y. M., Wang, W., Song, Z. G., Hu, Y., Tao,
Z. W., Tian, J. H., Pei, Y. Y., Yuan, M. L., Zhang, Y. L., Dai, F. H., Liu, Y.,
Wang, Q. M., Zheng, J. J., Xu, L., Holmes, E. C., & Zhang, Y. Z. (2020).
A new coronavirus associated with human respiratory disease in
China. Nature,579(7798), 265–269. https://doi.org/10.1038/s41586-
020-2008-3
Yang, H., Xie, W., Xue, X., Yang, K., Ma, J., Liang, W., Zhao, Q., Zhou, Z.,
Pei, D., Ziebuhr, J., Hilgenfeld, R., Kwok, Y. Y., Wong, L., Gao, G., Chen,
S., Chen, Z., Ma, D., Bartlam, M., & Rao, Z. (2005). Design of wide-spec-
trum inhibitors targeting coronavirus main proteases. PLoS Biology,
3(10), e324. https://doi.org/10.1371/journal.pbio.0030324
Zhu, X., Wang, D., Zhou, J., Pan, T., Chen, J., Yang, Y., Lv, M., Ye, X., Peng,
G., Fang, L., & Xiao, S. (2017). Porcine deltacoronavirus nsp5 antago-
nizes type I interferon signaling by cleaving STAT2. Virology,500(10),
91–95. https://doi.org/10.1128/jvi.00003-17
Zoete, V., Cuendet, M. A., Grosdidier, A., & Michielin, O. (2011).
SwissParam: A fast force field generation tool for small organic mole-
cules. Journal of Computational Chemistry,32(11), 2359–2368. https://
doi.org/10.1002/jcc.21816
14 S. CHATTERJEE ET AL.
