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Molecular Simulation
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/gmos20
Structure-Based Virtual Screening, Molecular
Docking, Molecular Dynamics Simulation and
Pharmacokinetic modelling of Cyclooxygenase-2
(COX-2) inhibitor for the clinical treatment of
Colorectal Cancer
Manasi Yadav, Mohnad Abdalla, Maddala Madhavi, Ishita Chopra, Anushka
Bhrdwaj, Lovely Soni, Uzma Shaheen, Leena Prajapati, Megha Sharma,
Mayank Singh Sikarwar, Sarah Albogami, Tajamul Hussain, Anuraj
Nayarisseri & Sanjeev Kumar Singh
To cite this article: Manasi Yadav, Mohnad Abdalla, Maddala Madhavi, Ishita Chopra, Anushka
Bhrdwaj, Lovely Soni, Uzma Shaheen, Leena Prajapati, Megha Sharma, Mayank Singh Sikarwar,
Sarah Albogami, Tajamul Hussain, Anuraj Nayarisseri & Sanjeev Kumar Singh (2022): Structure-
Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation and Pharmacokinetic
modelling of Cyclooxygenase-2 (COX-2) inhibitor for the clinical treatment of Colorectal Cancer,
Molecular Simulation, DOI: 10.1080/08927022.2022.2068799
To link to this article: https://doi.org/10.1080/08927022.2022.2068799
Published online: 27 Apr 2022.
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Structure-Based Virtual Screening, Molecular Docking, Molecular Dynamics
Simulation and Pharmacokinetic modelling of Cyclooxygenase-2 (COX-2) inhibitor
for the clinical treatment of Colorectal Cancer
Manasi Yadav
a
†
, Mohnad Abdalla
b
†
, Maddala Madhavi
c
, Ishita Chopra
a,d
, Anushka Bhrdwaj
a,h
,
Lovely Soni
a
, Uzma Shaheen
a
, Leena Prajapati
a
, Megha Sharma
a
, Mayank Singh Sikarwar
a
,
Sarah Albogami
e
, Tajamul Hussain
f,g
,Anuraj Nayarisseri
a,d,f,h
and Sanjeev Kumar Singh
h
a
In silico Research Laboratory, Eminent Biosciences, Indore, Madhya Pradesh, India;
b
Key Laboratory of Chemical Biology (Ministry of Education),
Department of Pharmaceutics, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong Province, PR
People’s Republic of China;
c
Department of Zoology, Osmania University, Hyderabad, Telangana State, India;
d
Bioinformatics Research Laboratory,
LeGene Biosciences Pvt Ltd, Indore, Madhya Pradesh, India;
e
Department of Biotechnology, College of Science, Taif University, Taif, Saudi Arabia;
f
Research Chair for Biomedical Applications of Nanomaterials, Biochemistry Department, College of Science, King Saud University, Riyadh, Saudi
Arabia;
g
Center of Excellence in Biotechnology Research, College of Science, King Saud University, Riyadh, Saudi Arabia;
h
Computer Aided Drug
Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, India
ABSTRACT
Colorectal cancer is the third most frequent cause of cancer worldwide and is more prevalent in older
individuals of all ages. The recent statistics from World Health Organisation (WHO) on cancer
accounted for 1.93 million new cases of colorectal cancer alone, in 2020[1]. The exact cause of this
disease is still unknown; however, obesity, sedentary lifestyle, and changed food consumption habit
are thought to be driving forces. Earlier clinical studies have found non-steroidal anti-inflammatory
drugs (NSAIDs) to be potent in treating colorectal cancer by inhibiting cyclooxygenase enzyme, and
further research ascertained cyclooxygenase-2 gene (COX-2) inhibitors to be the most effective
chemotherapy treatment. The aim of this study is to find the most effective inhibitor with a superior
affinity against COX-2 protein and its pharmacological profile. The pre-established compound,
parecoxib (PubChem ID: 119828) was taken up for Structure-based Virtual Screening to identify a
novel compound (PubChem ID: 10151468) that has a strong binding affinity than the established
compound. Additionally, the comparative studies of both the screened and established compounds
were examined using the MD simulation approach to confirm structural stability. Our conclusion
suggests that the virtual screened compound (PubChem ID: 10151468) could be a potential
therapeutic for the treatment of colorectal cancer.
ARTICLE HISTORY
Received 5 June 2021
Accepted 13 April 2022
KEYWORDS
COX-2 inhibitors; Colorectal
cancer; Molecular Docking;
Molecular Dynamics
Simulation; Virtual Screening
1. Introduction
Colorectal cancer is the commonest malignancy accounting
for 7.9% of all annually diagnosed cancer and 8.7% of can-
cer-related deaths [2]. Colorectal cancer is also known as
colon cancer or rectal cancer, a major reason for the devel-
opment of a precancerous polyp in the colon and rectum.
This polyp may eventually develop into an adenoma with
high-grade dysplasia, which then becomes cancerous. It can
affect both men and women but is most common in older
individuals. The risk of colorectal cancer increases with
declining age. Though it may have an impact on the younger
population as a result of modern lifestyles, the actual expla-
nation of the increase has yet to be determined. Inflam-
mation in the colorectal region is thought to be caused by
a variety of factors. People with inflammatory diseases such
as bowel (disease) illnesses, Crohn’s disease [3], ulcerative
colitis [4], or hereditary syndromes such as Familial Adeno-
matous Polyposis (FAP) [5], are at an increased risk of color-
ectal cancer [6], besides to the genetic and environmental
changes [7]. Colorectal cancer is caused by a combination
of genetic and environmental factors [7].
The high degree of genetic variability of colorectal cancer
makes identifying the clinical effects of specific mutations
difficult [8]. Other cancers may be involved in these rare
mutations in colorectal cancer, according to some researchers,
which could lead to therapeutic and diagnostic intervention
and offer new avenues in tumor research. The genome’s stab-
ility is required for maintaining cellular integrity, and disrup-
tion in genetic stability could contribute to tumor progression
in the colon. Multiple genetic changes that affect cell matu-
ration and proliferation, such as chromosomal instability,
microsatellite instability, and aberrant DNA-methylation,
have been found, indicating the genetic role in cancer occur-
rence [9,10].
Chromosomal instability is a well-known type of genetic
instability that results in mutation. Even though most colorec-
tal cancers exhibit chromosomal instability, only a few genes
have been identified so far. Chromosomal instability leads to
© 2022 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Anuraj Nayarisseri anuraj@eminentbio.com; Sanjeev Kumar Singh skysanjeev@gmail.com
†
contributed equally to this work.
MOLECULAR SIMULATION
https://doi.org/10.1080/08927022.2022.2068799
the loss of wild alleles of suppressor genes such as APC, P53,
SMAD4, that prevent the occurrence of the malignant pheno-
type. The loss of wild alleles of suppressor genes such as APC,
P53, and SMAD4, which inhibit the establishment of the
malignant phenotype, is caused by chromosomal instability
[6,7]. COX-2 has also been linked to mutant RAS and the c-
Myb gene in research [8–10]. Environmental factors, genetic
and acquired somatic mutation in the colonic epithelium are
all examples of chromosomal instability-related molecular
processes. The microsatellite instability phenomenon (MSI)
is linked to the loss of DNA mismatch repair function [3].
Mutations in certain genes such as MLH1, MSH2 lead to the
development of Lynch syndrome. Lynch syndrome is caused
by mutations in certain genes, such as MLH1, MSH2 [11].
Even, most of these cancers occur within the proximal colon,
which over a period in numerous cases has come about in col-
orectal cancer.
There is strong data showing the usefulness of a COX-2
inhibitor in the search for a colorectal cancer inhibitor.
When mice with human colorectal cells (HCA-7) expressing
a high level of COX-2 protein were treated with an investiga-
tional selective COX-2 inhibitor, SC-58125, tumor develop-
ment was reduced by 85% to 90% [12]. The drug’s
inhibitory impact was resistant in HCA-7 cells lacking
COX-2 expression, showing a clear relationship between col-
orectal cancer and COX-2. There is evidence that SC-58125
is cytostatic In vivo, presumably through inhibition of pro-
gression of the cell cycle at the G (2)/M phase and that it
works by preventing the cell cycle from progressing to the
G (2)/M phase [13].
Arachidonic acid is a dietary derivative of linoleic acid that
is usually found in esterified form in the cell membrane.
During the arachidonic acid cascade, it is released by phospho-
lipase and transported to the cyclooxygenase enzyme
[Figure 1]. Cyclooxygenase is a bifunctional enzyme that cata-
lyzes cyclooxygenase and peroxidase activities consecutively to
form PGG2 and PGH2, the precursors of all prostaglandins
(PGs) and thromboxane A2 (TXA2), respectively [14]. Distinct
types of cells create different prostaglandins (PGD2, PGE2,
PGI2), indicating that there are many synthetase enzymes in
the cell that use PGH2 as a substrate. These PGs are the pri-
mary cause of inflammation, invasiveness, and apoptosis,
hence, inhibiting the COX enzyme could curtail metastatic
cell incision.COX-1 and COX-2 are the two types of COX
enzymes. COX-1 is found in almost all tissues and produces
prostaglandins that are involved in gastric mucosa regener-
ation and maintenance, renal blood flow regulation, and plate-
let aggregation [15–18]. COX-2 is an inducible enzyme that is
expressed in inflamed and neoplastic tissue induced by stimuli
such as epidermal growth factor (EGF), vascular endothelial
growth factor (VEGF), fibroblast growth factor (FGF), and
cytokines (tumor necrosis factor (TNF), interleukins) in infla-
med and neoplastic tissue [19,20]. There are a number of
additional theories about COX-2 regulation, however, its
mechanism of expression may vary from cell to cell.
NSAIDs like indomethacin, sulindac, and piroxicam have
been confirmed as COX inhibitors because they inhibit both
isoenzymes; however, literature studies suggest that COX-2
inhibition is more effective in colorectal cancer, so COX-2
selective inhibitors like celecoxib and rofecoxib have been
developed [21–24]. COX-2 has a larger active site than
COX-1 because it has a smaller valine molecule at position
523 instead of isoleucine, like in COX-1, which creates a
side pocket that may support a larger structure and is
thought to be the binding site for many selective inhibitors.
In contrast to COX-1 inhibition, which is immediate and
competitively reversible, COX-2 inhibition is selective and
time-dependent [25,26]. Using various pharmacological
tools, this study aims to identify the most effective COX-2
inhibitor that has a lower re-rank score and a broader spec-
trum of activity that are more effective than those currently
available on the market.
Figure 1. (Colour online) One of many plausible ways of COX-2 gene over expression, if down regulatory gene APC got mutated and its possible outcomes.
2M. YADAV ET AL.
2. Results& Discussion
2.1. Protein and ligand preparation
The protein worked with is prostaglandin G/H synthetase 2
(PDB ID: 5F1A)[27], displayed in ribbon form [Figure 2], con-
stitute polypeptide chains A/B of 553 residues. However, there is
no difference in function between Chain A and Chain B, as they
are the same protein within the asymmetric unit of the crystal
that’s just the way the protein packs to form a crystal, and
both have the same number of amino acids in Cox-2. Hence
the present investigation has selected chain A for Molecular
docking. The secondary structure alpha helices are represented
in red colour; beta-sheets in yellow; and turns and loops are in
green colour. 3D structure of COX-2 was visualized in Accelrys
Discovery Studio and observed that the numbers of groups in
prostaglandin G/H synthetase 2 are 1105 with a total of 17528
atoms and 17801 bonds, in which, 703 are backbone hydrogen
bonds. The protein has more helices, turns, and loops than beta-
sheets, indicating that it is more vulnerable to mutation and
flexibility. Because of the systematic production of hydrogen
bonds in parallel to the axis of the helix/ helices, which are gen-
erated between the amino and carbonyl groups of every fourth
peptide bond, helices are more stable than beta-sheetsin protein
structure. Proline is known as a ‘helix breaker’because it con-
tains no free hydrogen to contribute to helix stability. Because
there are 66 helices in red, 62 strands in green, and 114 turns
in green, it’s reasonable to assume that the protein includes sev-
eral domains for ligand molecule binding.
2.2. Molecular Docking of established compounds
Molegro Virtual Docker [28] was used to perform molecular
docking of 15 pre-established inhibitors acquired from Pub-
Chem. The best-established chemical, parecoxib (PubChem
CID:119828), had the lowest re-rank score of −109.67 KJ/
mol, indicating stronger interaction energy between the ligand
and the protein as well as a higher affinity score for COX-2
protein (Table 1). Parecoxib has a molecular weight of 370.4
g/mol. The structure represented in [Figure 3] has one hydro-
gen bond donor (blue color) and four hydrogen bond accep-
tors (red color).
2.3. Virtual Screening Docking Results
Similarity searches against the PubChem database yielded 506
virtual screened compounds, with a compound having (Pub-
Chem CDI: 10151468) emerging as the best virtual docked
compound (Table 2). It has a lower re-rank score than the
established compound (−131.607 KJ/mol), indicating that it
has a stronger affinity for the target protein. The structure rep-
resented in [Figure 3] has a molecular weight of 445.446 g/mol,
one hydrogen bond donor (blue color), and seven hydrogen
bond acceptors (red color).
2.4. Molecular Dynamics Simulation
The RMSD and RMSF, which assess the intermolecular dis-
tance between the protein C
α
backbone and the ligand along
Figure 2. (Colour online) 3D structure of COX-2(PDBID: 5F1A) obtained from PDB database.
Table 1. Molecular docking studies of established inhibitors with COX-2.
Ligand
MolDock
Score
(KJ/mol)
Re-rank
Score
(KJ/mol) HBond
Molecular
weight
(g/mol)
Parecoxib −137.066 −109.67 −1.04124 370.422
Tilmacoxib −129.707 −106.733 −5.44013 338.397
meloxicam −131.199 −100.549 −3.62667 351.401
rofecoxib −119.296 −99.1017 −1.27403 314.356
(R)-Etodolac −123.638 −98.1057 −2.78925 287.354
Valdecoxib −127.582 −97.0832 −3.17877 314.359
Nabumetone −112.37 −94.9384 −4.0083 228.286
N-(2-Cyclohexyloxy-4-
nitrophenyl)
methanesulfonamide
−117.152 −94.8287 −5 314.357
DuP697(5-Bromo-2-(4-
fluorophenyl)−3-(4-
methylsulfonylphenyl)
thiophene)
−125.422 −94.7369 −1.11433 411.308
Piroxicam −120.575 −92.0715 −2.4746 331.346
MOLECULAR SIMULATION 3
the trajectory, were calculated for the established compound
and virtual screened compound with the lowest re-rank score
and a strong affinity with the protein. The RMSD values in
the protein-parecoxib combination fluctuated on a regular
basis, with a mean progressive increase. The protein RMSD
value increased from 1.2 Åto roughly 2.4 Åin the first 20
ns, then steady after 60 ns [Figure 4(i)]. Lower RMSD indi-
cates high stability of protein than the ligand (parecoxib). As
a result of a high degree of flexibility in the α-helix backbone
of the protein-parecoxib complex, the RMSF value was high
for terminal end protein residues, and the values were sub-
sequently stable with minimal fluctuations at the end of
the simulation period [Figure 4(ii)].
On the other side, the protein-lead compound (PubChem
CID:10151468) complex has been noted with RMSD values
ranging from 1.2 Åto 3.6 Å, implying that amino acid resi-
dues in the C backbone are more flexible and have less tor-
sional stress. The ligand RMSD value, on the other hand, was
substantially smaller and irregular throughout the trajectory,
and it could be separated into three uneven quarters. Due to
high flexibility in bond length and bond angle in the ligand
molecule, the RMSD value was in the range of 4.5 Åuntil 80
ns, and a steep dip at 80 ns stipulated the optimization of the
ligand structure; similarly, there was a sharp dip around 120
ns following consecutive spikes at around 125 ns and 140 ns,
stabilization at 9.5 Å(resultant of the aliphatic chain in the
lead compound), as shown in [Figure 5(i)]. Additionally, it
has been set at a lower RMSF value, making it more stable
than the protein-parecoxib complex, represented in [Figure
5(ii)]. Comparing simulation studies of established com-
pounds and the screened virtual compounds in complex
with protein reveals that the established compound shows
medium fluctuations that make the interaction with protein
relatively slow.
2.5. Protein–Ligand interaction
Figures 6,7are envisaged histogram maps, timeline rep-
resentation of PL contacts and 2D interaction to study the
protein–ligand interaction between the established com-
pound and the virtual screened compound. Herein, the
hydrogen bonds (which play a critical role in drug binding
because of their strong influence on drug specificity, metabo-
lization, and adsorption), hydrophobic interaction (pi-cation,
pi-pi, and other non-specific interactions involve binding of
hydrophobic amino acid of protein with an aromatic or ali-
phatic group of the ligand), ionic bonds or polar interaction
between two oppositely charged atoms (of two subtypes:
those mediated by protein backbone or its side-chain),
and water bridges (hydrogen-bonded protein–ligand inter-
action mediated by water molecules)were all taken into
account.
In Figure 6, the amino acid residues GLN 203, HIS 214,
LEU 391 interact with the protein mostly through hydro-
phobic interactions, while HIS 388 interacts with the protein
via hydrogen bonding [Figure 6 (A)], which shows the stron-
gest contact [Figure 6 (C)], where it is in strong H-bonding
with a substituted electronegative atom of the ligand. The resi-
dues ALA 199, PHE 200, ALA 202, HIS 207, TYR 385, TYR
387, HIS 388, LEU 391, VAL 444, VAL 447 connect via hydro-
phobic bonds, with HIS 388 interacting with the ligand’s aro-
matic ring in a 34 percent interaction. ASN 382 and HIS 386
form two strong ionic contacts between Parecoxib and protein
residue. Most of the protein –ligand interactions are water
bridges, whereas GLN 454 and THR 212 only interact through
producing water molecules. The individual amino acid residue
interaction is seen as a timeline representation of PL Contacts
in Figure 5(C), with the intensity of the color enumerating
interactions of the protein residue. The richer color indicates
Figure 3. (Colour online) Best established compound obtained from molecular docking and best virtual screened compound obtained from virtual screening.
Table 2. Virtual Screened compounds molecular docking results.
PubChem ID
MolDock
Score
(KJ/mol)
Re-rank
Score (KJ/mol) HBond
Molecular weight
(g/mol)
[01] 10151468 −160.325 −131.607 −9.60495 445.446
[01] 143005233 −162.446 −128.565 −6.6715 384.449
[04] 53054415 −151.941 −127.684 −4.86896 435.517
[00] 91810726 −158.019 −127.518 −7.66925 370.422
[03] 9979353 −155.107 −125.777 −7.38802 417.393
[03] 138110680 −166.884 −125.623 −3.9933 505.564
[02] 53054415 −148.819 −125.611 −0.86359 435.517
[00] 22643565 −157.243 −125.469 −2.68371 420.43
[02] 9979353 −157.243 −125.469 −2.68371 420.43
[02] 15870960 −154.97 −123.966 −2.29307 417.393
4M. YADAV ET AL.
that a residue, such as HIS 386, LEU 391, or HIS 386, is inter-
acting with more than one atom, and on average, there are four
contacts between protein-parecoxib complexes across the
period of trajectory lower than the protein-lead drug
interaction.
Whereas, Figure 7 has showed a high range of hydro-
phobic and hydrophilic interactions. THR 212, ASN 382,
HIS 388, and HIS 214 interacted more effectively with the
ligand molecule [Figure 7(A)]. The virtual screened com-
pound, on the other hand, has a higher hydrophobic percen-
tage, with the amino acid residues HIS 207, TRP 387, and
HIS 388 showing the most hydrophobic bonding with the
ligands aliphatic group and aromatic ring [Figure 7 (B)].
The color intensity of the timeline depiction of PL contacts
represents the degree of individual interaction between the
ligand and the amino acid residues: a more intense colour
suggests stronger binding. THR 212, ASN 382, and HIS
214 increased hydrogen bonding interaction with the ligand
by contributing side chains and backbones, [Figure 7(C)]. In
contrast to the protein-Parecoxib interaction, the ligand
developed an ionic contact with residues LYS 211 and
GLU 290. The ligand molecule was also water bridged with
most of the residues [Figure 7(A)]. Overall, the degree of
hydrophobicity for the virtually screened compound has
increased as the surface area has increased, although the
number of protein–ligand connections has gradually reduced
over time, with an average of five contacts.
2.6. Ligand property
Throughout the 150 ns trajectory, the ligand properties of the
best established and virtual screened compounds were com-
pared using the following parameters: Polar Surface Area
(PSA), Solvent Accessible Surface Area (SASA), Molecular
Surface Area (MolSA), Radius of Gyration (rGyr), and Root
Mean Square Deviation (RMSD). The Parecoxib RMSD
value trajectory ranges from 0.5–1.8 Å, with a mean of 0.6
Å. The rGyr is the radial distance from the centroid at which
the full mass of the ligand is supposed to be concentrated in
order to obtain the same moment of inertia; in this example,
it is 4.3 Å. With a probe radius of 1.4 Å, the molecular surface
is comparable to the van der Waals surface area. It remains
stable throughout the run, with little variation at 65 ns before
reaching equilibrium at 340 Å. The surface accessible by a
water molecule is the solvent surface area, the SASA value is
volatile, and the equilibrium is approximately 130 Å. PSA
can be accessed by polar or ionic molecules such as oxygen
or nitrogen atoms on the surface. Throughout the trajectory,
it’s essentially steady, with equilibrium of 150 Å[Figure 8],
which relates to the accessible surface area for amino acid
Figure 4. (Colour online) Molecular dynamics analysis of RMSD and RMSF. (I, II) Cyclooxygenase 2 complex with best established compound Parecoxib (PubChem
CID:1192828.
MOLECULAR SIMULATION 5
residues to bind. The rare sharp spike in the trajectory demon-
strates the ligand compound’sflexibility.
Figure 9 shows the ligand pathway for the virtual screened
molecule. The RMSD number is unpredictable, constant until
20ns after which it fluctuates, and it ranges from 1.5 Åto 3 Å
with a mean of 2.25 Å, which suggested the average value of
stability in terms of RMSD between a group of atoms (e.g.
backbone atoms of a protein). The lead compound’s gyration
radius is dynamic, with two means detected, one around 5 Å
and the other around 4 Å. Similarly, the MolSA values are
steady until 20ns, then fluctuate and peak erratically during
the simulation time, with an equilibrium observed at about
390 Å, which is higher than parecoxib-protein, demonstrating
the lead compound’s hydrophobicity. PSA values range spora-
dically over the trajectory period, with a mean of around 260
Å, which is higher than parecoxib, indicating that there is a
greater ionic interaction between the lead drug and the
amino acid residue in the protein [Figure 6]. The ligand’s4-
oxobutyl group could alter with little torsional constraint,
which could explain the abrupt configurational change
shown in all graphs; this supports the virtual screened com-
pound’sflexibility and strong binding to the target.
2.7. Drug –Drug Comparison
The interaction energies of the two compounds, established
(PubChem CID: 1192828) and the virtual screened compound
(PubChem CID: 10151468), show that the virtual screened
compound has a higher affinity for the target protein. The vir-
tual screened compound’s re-rank score and MolDock score
are higher than the established compounds, and the virtual
screened compound’s external ligand interaction and
Protein–ligand interaction are both significantly higher than
the established compound [Table 3]. PLP (Piecewise Linear
Potential) and LJ12 (Leonard-Jones approximation) steric
energies for the virtual screened compound are −119.652 KJ/
mol and −27.786 KJ/mol, respectively, which are higher than
the established compound.
2.8. Pharmacophore studies
Pharmacophore mapping elucidates how a structurally hetero-
geneous ligand can bind to a common receptor site, which is
required for molecular recognition of a biological macromol-
ecule for the identification of novel drugs. The many aligned
Figure 5. (Colour online) Molecular dynamics analysis of RMSD and RMSF –(I, II): Cyclooxygenase 2 complex with the best virtual screened compound (PubChem CID:
10151468).
6M. YADAV ET AL.
Figure 6. (Colour online) Interaction diagram of Cyclooxygenase 2 complex with best-established compound Parecoxib (PubChem CID: 1192828) observed during the
molecular dynamic simulation. (A) Histogram plot highlighting the P-L contacts, (B) Interactions diagram from stabilised complex, & (C) 2D Timeline representations of
the interactions.
Figure 7. (Colour online) Interaction diagram of Cyclooxygenase 2 complex with best virtual screened compound (PubChem CID: 10151468) observed during the
molecular dynamic simulation. (A) Histogram plot highlighting the P-L contacts, (B) Interactions diagram from stabilized complex, & (C) 2D Timeline representations
of the interactions.
MOLECULAR SIMULATION 7
postures of molecules aid in determining the best mode of
interaction between the target protein and the chemical. The
purpose of the pharmacophore study was to obtain data and
compounds in.sdf format. As a result of using this mapping
approach to find targets and compound interactions in its
cavity, the fourth cavity of the receptor site now has a higher
binding affinity. The H-bond interaction between the virtual
screening chemical and the target protein is seen in Figure 10.
The green dotted lines signify hydrogen bond interaction with
distance (Å) such as Chain A: Thr 212 (10237) HE2 –O7
(17733) Lig, 2.69 Å; Chain A: Thr 212 (10240) HG1 –O7
(17733) Lig, 2.05 Å; Chain A: Asn 382 (10614) HD22 –O7
(17733) Lig, 2.18 Å. The virtual screened compound shows
more interaction than pre-established compounds.
Many nonbonding and nonspecific interactions are pre-
vented by the van der Waals connection of the COX-2 protein
residue with the ligand molecule illustrated in [Figure 11].
[Figure 11(a)] depicts the van der Waals interaction between
the established compound Parecoxib and amino acid residues.
Parecoxib’s propanamide chain and phenyl group are both
open to interactions. The outcome is consistent with the ligand
feature mentioned in [Figure 5]. HIS 388, HIS 214, THR 212,
TYR 385, HIS 386, HIS207, and GLN 203 form a polar inter-
action, with GLN 203, HIS 207, and HIS 386 being particularly
tightly bonded. The ligand is greasily attached to the residues
VAL 444, ALA 199, PHE 210, LEU 391 and TRP 387. In
contrast, because of the increased overall surface area, the
protein-virtual screen compound van der Waals interaction
is stronger (TSA). In the pentyl ring, phenyl ring, and butyl
chain, the methyl group at the 5’position is exposed for
sufficient bonding. More ligand exposure is shown by the ris-
ing intensity of blue shading. The main residues are noted as
HIS 386, HIS 214, THR 212, ASN 222, LYS 211, PHE 210,
GLN 203, HIS 207, and HIS 388. The residues ASN 382,
THR 212, and PHE 210 establish hydrogen bonds with the
ligand’s sulfonyl group, while ASN 222 interacts with nitrate
by contributing side-chain hydrogen atoms. With the 4-oxo-
butyl group, THR 212 serves as a backbone acceptor. The
ligand and the GLU 290 residue create an ionic connection.
The ligand molecule’s maximal surface is in touch with the sol-
vent [Figure 11(b)]. This finding matches the MD simulation
of protein–ligand interaction [Figure 6]. Because the virtual
screening chemical has a larger surface area and is hydro-
phobic, it is thought to bind more efficiently with the COX-2
protein.
The aromatic interaction of the inhibitor compound (Pub-
Chem CID: 10151468) with the protein is depicted in Figure 12.
The cavity (fourth) of the protein is colored in blue to rep-
resent the surface with more aromatic residues, while orange
represents less aromatic amino acids in contact with the
most effective virtual screening drug PubChem ID:
10151468. Despite the fact that aromatic rings are non-polar,
they have a significant impact on ligand binding and function.
The receptor is depicted as a blue meshwork with the B
chain ligand group in its cavity in Figure 13, which shows a
clearer contact between ligand and receptor (PubChem CID:
Figure 8. (Colour online) The ligand property trajectory of the (Cyclooxygenase 2 complex with best-established compound Parecoxib (PubChem CID: 1192828))
during the 150 ns simulation.
8M. YADAV ET AL.
10151468). Water bridging H-bond interactions, such as THR
206, ALA 202, HIS 207, and THR 206, are represented by green
dashed lines. This corresponds to the MD simulation result
[Figure 6(a)], which shows the hydrophobic interactions
(blue color) and water bridges of amino acid residues with
ligand molecules, with orange color indicating a less aromatic
surface.
2.9. Admet
Table 4 shows the projected ADMET value for the established
inhibitor (PubChem CID: 119828) and best virtual screening
molecule (addition, distribution, metabolism, excretion, and
toxicity) (PubChem CID: 10151468). The absorptive value of
an established compound is higher in every way than the vir-
tual screened compound (PubChem CID: 10151468); the
established compound’s BBB value is 0.8694, while the virtual
screened compound’s value is 0.8237. The activity potential of
the compounds is shown in the bioavailability radar for the
four best-docked values acquired from the SwissADME web
application [29][Figure 15]. The established compound’sP-
glycoprotein probability value is higher than the virtual screen-
ing compound’s, indicating that its lipophilicity is higher. Both
substances have similar subcellular localization. The virtual
screened compound (PubChem CID: 10151468) has a high
CYP inhibitor promiscuity and might be employed as a
CYP3A4 inducer inhibitor, although its likelihood of acting
as a substrate for CYP450 2C9 and CYP450 2D6 is lower
Figure 9. (Colour online) The ligand property trajectory of the (Cyclooxygenase 2 complex with best virtual screened compound (PubChem CID: 10151468) during the
150 ns simulation.
Table 3. Drug-Drug comparison study.
Established compoundPubchemID:119828 Virtual Screened Compound PubChem ID: 10151468
Energy overview: Descriptors MolDock Score (KJ/mol) Re-rank Score (KJ/mol) MolDock Score (KJ/mol) Re-rank Score (KJ/mol)
Total Energy −137.057 −109.672 −163.978 −134.514
External Ligand interactions −151.026 −130.797 −187.666 −157.929
Protein –Ligand interactions −151.026 −130.797 −187.666 −157.929
Hydrogen bonds −1.043 −0.826 −13.247 −10.492
Internal Ligand interactions 13.969 21.126 23.688 23.415
Torsional strain 7.55 7.082 8.32 7.804
Re-rank Score (KJ/mol) Re-rank Score (KJ/mol)
Steric (by LJ12-6) −27.083 −27.786
Torsional strain (sp2-sp2) 4.265 1.462
Steric (by LJ12-6) 8.674 11.506
MOLECULAR SIMULATION 9
than the established molecule. Both compounds have no
AMES toxicity, indicating that they are non-mutagenic. Pare-
coxib had a higher regression value for absorption and toxicity
(aqueous solubility and rat acute toxicity) than the virtual
screened chemical [Table 5]. This means that the novel com-
pound (PubChem CID 10151468) is less soluble, has higher
bioavailability, and has a lower LD50, indicating that it is
less dangerous. Table 6 summarizes the pharmacokinetics,
physicochemical properties, and drug-likeness property of
the four best-docked SwissADME web tool results. R program-
ming was used to create a graphical representation of a com-
parative study between the two best virtual screened
compounds and the two best-established substances
[Figure 14]. It demonstrates that the virtual screening chemical
(PubChem CID 10151468) is significantly less hazardous than
the established drug (PubChem CID 119828) and that its
Figure 10. (Colour online) Diagrammatic representation of 3D profile interactions of the most effective compound virtual screened docked compound (PubChem ID:
10151468) with targeted COX-2. [Ligand in Brown Colour in 3D, Amino acids in Cyan Colour, Hydrogen bond in Green Colour Dotted line, π-πin Black Colour Dotted
line, Protein in white colour].
Figure 11A. (Colour online) PubChem CID:119828, the most effective compound obtained from docking established compounds shows van der Waals interaction.
10 M. YADAV ET AL.
absorption and BBB values are comparable to the established
compound.
2.10. Boiled Egg Plot
The purpose of the BOILED Egg plot is to properly forecast
the gastrointestinal absorption (HIA) and blood–brain-
barrier (BBB) properties of substances that are important
in therapeutic research. For the same aim, the two best
pre-established inhibitors CID119828 and CID159271, as
well as the two best virtual screened inhibitors
CID10151468 and CID143005233, were chosen. On the
plot, the white region represents the physiochemical space
of molecules with the highest probability of passive gastroin-
testinal absorption (HIA), while the yellow region (also
known as the yolk region) represents the physiochemical
space of molecules with the highest probability of permeating
the brain–blood barrier (BBB). The outer grey area is for
compounds with lower gastrointestinal absorption (HIA)
and limited BBB penetration, and the white and yellow sec-
tions are not mutually exclusive. In our study, all of the com-
pounds: CID119828, CID159271, CID10151468, and
Figure 11B. (Colour online) PubChemID: 10151468, the most effective compound obtained from Virtual Screening shows Van der Waals interaction.
Figure 12. (Colour online) PubChem ID: 10151468, the most effective compound obtained from Virtual Screening shows aromatic interaction.
MOLECULAR SIMULATION 11
CID143005233 are in the white region [Figure 16], but the
absorption probability of the lead compound CID10151468
is comparatively lower, and it also has the lowest MLogP
(Moriguchi octanol–water partition coefficient), but its
XLogP3 value and topological surface area are higher than
the others. The compound of interest PubChem
CID10151468 has a low GI (gastrointestinal) absorption
because of its greater topological surface area, which influ-
ences the solubility of the compound and results in reduced
GI absorption, although it boosts compound bioavailability
[Table 7]. P-glycoprotein (P-gp), the active efflux transporter
of substances from the CNS or the gut, is also colour-coded,
with blue dots signifying P-gp substrate (PGP+) and red dots
showing P-gp non-substrate (PGP-). CID 159271 is a P-gp
substrate implying its preferable lipophilicity to other com-
pounds. [Table 6].
3. Methodology
3.1. Selection of inhibitor
Based on their efficacy in inhibiting COX-2, pre-existing COX-
2 inhibitory medications for colorectal cancer were chosen
from a literature review. A total of fifteen COX-2 inhibitors
were considered for this research. The table shows the inhibi-
tor PubChem CID, molecular weight in g/mol, H-bond donor
and acceptor, and log-Pvalue [Table 8]. The structure of each
compound was stored in 3D SDF format.
Figure 13. (Colour online) PubChem ID: 10151468, the most effective compound obtained from Virtual Screening shows ligand-receptor interaction.
Figure 14. (Colour online) Comparative ADMET studies of BBB, HIA, AMES Toxicity and LD50 of the established compound against virtual screened compounds.
12 M. YADAV ET AL.
3.2. Protein and ligand preparation
The structural coordinates of Human Cyclooxygenase-2 with
bound co-crystallized molecules (PDB ID: 5F1A) [27] were
obtained from the protein data bank (http://www.rcsb.org/)
using resolution (2.38 A°) and R value-free parameters
(0.218). The Protein Preparation Wizard module in Schrö-
dinger, 2020, pre-processes the downloaded 3D structure
coordinates with a default set of parameters such as assigning
bond orders, zero-order bonds to metal atoms, filling missing
hydrogen’s, side chains, and loops [44–48]. At a pH of 7.0,
several states of tautomerization and protonation were antici-
pated in favor of the ligand. Finally, the protein is optimized
and minimized in the presence of the force field OPLS3e,
which has an RMSD value of 0.30 Å(1-3) [49–54]. All of
the known inhibitors were downloaded from the PubChem
database, followed by ligand preparation using the LigPrep
module in Schrödinger, 2020, with appropriate parameters
for optimization, ring conformation, 2D to 3D conversion,
and resolving protomers, tautomers, and ionization states
at pH 7.0, including partial atomic charges, with the
OPLS3e force field [55–59].
3.3. Molecular Docking
Molegro Virtual Docker (MVD) was used for molecular dock-
ing analysis, it integrates high scoring functions Piece-wise
Linear Potential (PLP), heuristic search function, and Mol-
Dock scoring function [60–67]. To achieve the greatest volume
cavity for docking of the prepared ligand, the pre-existing
ligand was methodically removed from the complex structure
of COX-2 protein structure and cavities were recognized (by
cavity prediction algorithm). The 4th cavity, with a volume
of 355.328, was utilized as a molecular docking active site.
The docking technique parameters were chosen as needed:
50 maximum population size, 1,500 maximum iteration, and
0.3 grid resolution [68–74]. Hydrogen bond optimization
and energy reduction are part of the post-docking process,
which includes setting MolDock Simplex Evolution to
Figure 15. (Colour online) Bioavaliblity radar related to physicochemical properties of best 4 compounds from established docked result and virtual screened result.
The pink area represents the optimal range for each of these properties (LIPO: lipophilicity by XLOGP3 value ranging −0.7 to +0.5, SIZE: molecular weight between
150–500 g/mol, POLAR: Topological Polar Surface Area (TPSA) from 20 to 130 Å2, INSOLU: solubility with logS not higher than 6, INSATU: saturation fraction of carbons
with sp3 hybridization not less than 0.25 and FLEX: flexibility of not more than 9 rotatable bonds).
MOLECULAR SIMULATION 13
maximum steps 300 and a neighbor distance factor of 1.00
[75–79]. After docking, the molecule creates a stable complex.
Finally, the Nelder Mead Simplex Minimization algorithm was
used to minimize the complex energy of the ligand–receptor
interaction (using a non-grid force field and H-bond
directionality).
3.4. Virtual Screening
The compounds with the highest affinity (best-established
compound) toward the target protein were identified using
molecular docking [80–84]. This compound was chosen for
virtual screening primarily because of its greater negative re-
rank score, which indicates a strong binding affinity. After
then, a similarity search was done against NCBI’s PubChem
compound database to find a better compound than the one
that had already been discovered [85–88]. As a component
rule of Lipinski’s rule of five, the similarity threshold was set
at 95%. A list of candidate compounds was obtained and
docked with the target protein COX-2 to identify a drug
with a better affinity for the target than the best-known
molecule.
3.5. Molecular Dynamics Simulation
MDS is a widely used technique for analysing the spatial
conformation of atoms in a molecule. The best-established
chemical is investigated using these models, which are
based on Newtonian dynamic equations [89–95]. In this sec-
tion, we computed MD simulation studies on the virtual
screened compounds and established compounds in complex
with targeted protein. MD simulations were done by using
Desmond (Desmond, Schrödinger, 2015) with Optimized
Potentials for Liquid Simulations (OPLS) 2005 force field.
Initially, the prepared structures were imported in Desmond
system builder and solvated with the TIP3P water model in a
cubic box. Then, overlapping water molecules were removed
and the system neutralized again using the proper NaCl
counter ions with the fixed salt concentration of 0.15 M
based on the total charge of the system. 10 Å was set between
the box wall and protein–ligand complex to avoid the direct
interaction with its own periodic image. Later, the system
was subjected to energy minimization by applying a hybrid
method of the steepest descent with a maximum of 5000
steps or until a gradient threshold of 25 kcal/mol/Å was
reached. This process was followed by Limited-memory
Broyden–Fletcher–Goldfarb–Shanno (LBFGS) algorithms,
till a convergence threshold of 1 kcal/mol/Å was touched.
The minimized systems were equilibrated by applying the
default protocol of NVT and NPT available in Desmond.
Further, both systems were taken into MDS for 150 ns
with default relaxation protocol followed by periodic bound-
ary condition with a number of atoms, pressure, and temp-
erature (NPT) ensemble, where temperature Nose–Hoover
and isotropic scaling were utilized to adjust the temperature
at 310 K and atmospheric pressure at 1.013 atm. Root means
Square Fluctuations (RMSF) and Root Means Square Devi-
ation (RMSD) were used to examine the thermodynamic
stability of WT (wild type) and mutant (MT) E proteins
Table 4. ADMET classification from admetSAR.
Model Result
CID
10151468
CID
119828
Absorption
Blood-Brain-Barrier BBB 0.8237 0.8694
Human Intestinal
Absorption
HIA 0.9901 1.0000
Caco-2 Permeability Caco2- 0.6367 0.6440
P-glycoprotein Substrate Non-substrate 0.7759 0.8144
P-glycoprotein inhibitors Non-inhibitor 0.6982 0.7995
Non-inhibitor 0.6232 0.8537
Renal Organic Cation
Transporter
Non-inhibitor 0.7468 0.8889
Distribution
Subcellular localization Mitochondria 0.6056 0.6175
Metabolism
CYP450 2C9 Substrate Non-substrate 0.6053 0.7020
CYP450 2D6 substrate Non-substrate 0.8148 0.9115
CYP450 3A4 substrate Non-substrate 0.5184 0.6159
CYP450 1A2 inhibitor Non-inhibitor 0.6970 0.8459
CYP450 2C9 inhibitor Inhibitor 0.6537 0.8010
CYP450 2D6 inhibitor Non-inhibitor 0.8079 0.8356
CYP450 2C19 inhibitor Inhibitor 0.5892 0.6608
CYP450 3A4 inhibitor Inhibitor 0.7708 0.5165
CYP inhibitor Promiscuity High CYP inhibitory
promiscuity
0.9338 0.8722
Excretion
Toxicity
Human Ether-a-o-go-
Related gene
Weak inhibitor 0.9506 0.9874
Non-inhibitor 0.7856 0.9083
AMES Toxicity Non-AMES toxic 0.5714 0.7504
Carcinogens Carcinogens 0.6510 0.5086
Fish Toxicity High FHMT 0.9405 0.9024
Tetrahymena Pyriformis
Toxicity
High TPT 0.9503 0.8747
Honey Bee Toxicity Low HBT 0.7107 0.7703
Biodegradation Not readily
biodegradable
0.8683 0.9899
Active Oral Toxicity III 0.5767 0.5981
Carcinogenicity (Three-
class)
Non-required 0.5209 0.6188
Table 5. ADMET predictive regression profile.
Model Unit
CID 10151468
values
CID 119828
values
Absorption
Aqueous solubility LogS −3.5369 −3.3855
Caco-2 permeability LogPapp,
cm/s
0.4226 0.6423
Toxicity
Rat Acute Toxicity LD50, mol/kg 2.5166 2.2665
Fish Toxicity pLC50, mg/L 1.4900 1.8352
Tetrahymena Pyriformis
Toxicity
pIGC50, ug/L 0.5944 0.4567
Table 6. ADME profiling for 4 compounds from established docked result and virtual screened result.
Compound Name TPSA (A
2
) Water solublity Log S(ESOL) BBB permetant P-gp substrate CYP isoform interact Ghose
CID 119828 97.65 −4.28 No No Yes expect CYP1A2 and CYP2D6 inhibitor Yes
CID 159271 94.57 −4.15 No Yes Yes except CYP2D6 inhibitor Yes
CID 10151468 152.70 −4.46 No No Yes except CYP2D6 inhibiotr Yes
CID 143005233 97.65 −4.48 No No Yes expect CYP1A2 and CYP2D6 inhibitor Yes
14 M. YADAV ET AL.
(RMSD). For better results, all of the simulations were run
three times. The simulations were done at a rate of about
1000 frames per second. The molecule’s conformational
profile gives critical information on its thermodynamical
characteristics, structural, and functional aspects, simulating
dynamic processes in biological systems. The simulation
employs an iterative approach that considers many inter
and intramolecular interactions, necessitating the usage of
supercomputers for optimization [96–99]. Other prominent
molecular dynamics software that could be utilized for mol-
ecular dynamics simulation are AMBER, GROMACS, GRO-
MAS, NAMD, CHARMM, and OPLS [100–112].
3.6. Drug –Drug comparative study
A docking folder and an unnamed folder with clustered data of
the binding score and re-rank score, respectively, were gener-
ated as a result of docking existing drugs and virtual screened
compounds using Molegro Virtual Docker. The results were
then imported into the MVD program as PDB files and
adjusted to remove ligands, cavities, and constraints, leaving
simply the protein structure [113–129]. The compounds
with the finest poses were then exported. On the basis of an
excel sheet, affinities, re-rank score, hydrogen bond
interaction, and MolDock score were compared between
these two compounds [130–138].
3.7. ADMET. Analysis
The AdmetSAR (http://lmmd.ecust.edu.cn/admetsar1)isan
open web resource that offers a user-friendly interface for eval-
uating a compound’s chemical and biological profile [139–
145]. Absorption, digestion, metabolism, excretion, and tox-
icity level are all factors that have a role in the creation and dis-
covery of new medications. It consists of five quantitative
regression models and 22 qualitative classifications, resulting
in a highly predictive result. AdmetSAR was used to investigate
the biological activity and toxicity of both established as well as
virtual screening compounds [146–151].
3.8. Boiled Egg Plot
The boiled egg (Brain or Intestinal EstimateD permeation
method) plot is an accurate predicting model for small mol-
ecule lipophilicity and polarity [152–162]. It features a statisti-
cal plot that predicts two important passive predictions:
gastrointestinal absorption and brain penetration, which is
an essential pharmacokinetics parameter for the discovery of
Figure 16. (Colour online) Predictive model brain or intestinal estimated permeation method (BOILED-Egg plot).
Table 7. best 4 compounds from established docked result and virtual screened docked results.
Molecule Molecular weight (g/mol) TPSA XLogP3 MLogP GI absorption BBB
CID 119828 370.42 97.65 3.27 1.75 High No
CID 159271 338.40 94.57 3.27 1.72 High No
CID 10151468 445.45 152.70 3.36 0.87 Low No
CID 143005233 384.45 97.65 3.57 1.98 High No
MOLECULAR SIMULATION 15
a new chemical. Also includes other features like molecular
weight, topological polar surface area (TPSA), membrane par-
tition coefficient (MLogP), GI, and BBB[163–176]. The boiled
egg plot is a cartesian plan in the shape of an eclipse with three
regions: yellow (yolk area), white (white region), and grey
(grey region) [177–186]. As a result, if the compound of inter-
est is in the yolk region, the likelihood of a Blood–Brain Barrier
increases, and if it is in the white region, the likelihood of good
intestine absorption increases. If the compound of interest is in
the grey zone, the chances of it being non-absorptive and non-
penetrative increases. Following that, two best compounds
from the established compound and two best compounds
from the virtual screened compound were retrieved for the
Boiled-egg plot based on a lower re-rank score [187–193].
4. Conclusion
As the incidence of colorectal cancer has expanded, so has
the scope of therapeutic development. COX-2 selective
inhibitors are readily discovered for colorectal cancer treat-
ment, ranging from NSAIDs to target-specific medicines.
This research aims to show the efficacy of a new inhibitor
(PubChem CID: 10151468) discovered through virtual
screening. This new chemical has a greater affinity for the
COX-2 protein receptor, which is the target. Parecoxib was
judged to be the best pre-established medicine among the
15 pre-established pharmaceuticals discovered through litera-
ture searches. Virtual screening and docking studies were
performed using this compound to find the most effective
compound PubChem CID: 10151468. Comparative investi-
gations of the virtual screened chemical PubChem CID
10151468 demonstrate that it has a higher affinity score
and binding capacity for the COX-2 receptor. The com-
pound’s pharmacophore mapping (PubChem CID:
10151468) reveals optimum interaction with the receptor
protein, confirming the results of molecular dynamics simu-
lations. Furthermore, the ADMET study reveals that the
chemical (PubChem CID: 10151468) is non-toxic and non-
carcinogenic and that its gastrointestinal absorption and
BBB likelihood are quite similar to existing agents. This vir-
tual screened chemical (PubChem CID: 10151468) has a
higher bioavailability and lower toxicity. The in-vitro study
is also necessary to determine its pharmacokinetic and
pharmacodynamic features, therapeutic utility in the treat-
ment of colorectal cancer, and overall efficacy in comparison
to other medications.
Author contributions
†MY contributed equally to this work with MA. MY, MA and
was involved in Molecular docking, Molecular Dynamics
Simulation, Writing –review & editing. MM, IC, AB, LS,
US, LP, MS, MSS and SA were contributed to Inhibitors collec-
tion, Data curation, Formal analysis, Validation, and Visual-
ization. IC, LS, AB, LP and AN were also involved in
Molecular Docking, ADMET analysis, R Programming analy-
sis, and Writing –review & editing. SA, TH, AN, and SKS were
contributed to the investigation, supervision, writing –review
& editing.
Ethics approval and consent to participate
Not applicable.
Human and animal rights
No animals/humans were used in the studies that are the basis
of this research.
Availability of data and materials
Not applicable.
Consent for publication
Not applicable.
Acknowledgments
1. We thank Taif University Researchers Supporting Project Num-
ber (TURSP-2020/202), Taif University, Taif, Saudi Arabia
2. The authors are grateful to the Deanship of Scientific
Research, King Saud University for funding through Vice
Deanship of Scientific Research Chairs.
Table 8. List of inhibitors for COX-2.
S. No. INHIBITORS PubChem CDI
Molecular weight
(g/mol) HBD HBA LogPvalue Ref.
1 Celecoxib 2662 381.4 1 7 3.4 [30]
2 Rofecoxib (Vioxx) 5090 314.4 0 4 2.3 [31]
3 Parecoxib 119828 370.4 1 5 3.3 [32]
4 Etoricoxib 123619 358.8 0 4 3.3 [33]
5 Valdecoxib 119607 314.4 1 5 2.6 [34]
6 Meloxicam 54677470 351.4 2 7 3 [35]
7 Piroxicam 54676228 331.3 2 6 3.1 [36]
8 Nabumetone 4409 228.29 0 2 3.1 [37]
9 (R) -Etodolac 667528 287.35 2 3 2.8 [38]
10 Nimesulide 4495 308.31 1 6 2.6 [39]
11 Diclofenac 3033 296.1 2 3 4.4 [40]
12 DuP697(5-Bromo-2-(4-fluorophenyl)−3-(4-methylsulfonylphenyl) thiophene) 3177 411.3 0 4 5.3 [41]
13 N-(2-Cyclohexyloxy-4-nitrophenyl) methanesulfonamide 4553 314.36 1 6 2.9 [41]
14 Ibuprofen 3672 206.28 1 2 3.5 [42]
15 Tilmacoxib 159271 338.4 1 6 3.3 [43]
16 M. YADAV ET AL.
3. SKS thank Alagappa University, Department of Biotech-
nology (DBT), New Delhi (No. BT/PR8138/BID/7/458/
2013, dated 23rd May, 2013), DST-PURSE 2nd Phase Pro-
gramme Order No. SR/PURSE Phase 2/38 (G dated
21.02.2017 and FIST (SR/FST/LSI –667/2016), MHRD
RUSA 1.0 and RUSA 2.0 for providing the financial assist-
ance. UP gratefully acknowledge Indian Council of Medical
Research (ISRM/11/(19)/2017, dated: 09.08.2018).
4. SKS thankfully acknowledges the Tamil Nadu State Council
for Higher Education (TANSCHE) for the research grant
(Au/S.o. (P&D): TANSCHE Projects: 117/2021).
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
Manasi Yadav http://orcid.org/0000-0003-2999-3467
Mohnad Abdalla http://orcid.org/0000-0002-1682-5547
Maddala Madhavi http://orcid.org/0000-0003-4803-1765
Ishita Chopra http://orcid.org/0000-0002-6215-6380
Anushka Bhrdwaj http://orcid.org/0000-0002-8219-3285
Lovely Soni http://orcid.org/0000-0002-6733-094X
Uzma Shaheen http://orcid.org/0000-0003-1228-7857
Leena Prajapati http://orcid.org/0000-0003-3946-3275
Megha Sharma http://orcid.org/0000-0003-1740-2041
Mayank Singh Sikarwar http://orcid.org/0000-0001-7275-6200
Sarah Albogami http://orcid.org/0000-0003-0774-5550
Tajamul Hussain http://orcid.org/0000-0001-7601-1417
Anuraj Nayarisseri http://orcid.org/0000-0003-2567-9630
Sanjeev Kumar Singh http://orcid.org/0000-0003-4153-6437
References
[1] https://www.who.int/news-room/fact-sheets/detail/cancer.
[2] https://seer.cancer.gov/statfacts/html/colorect.html.
[3] Bassotti G, Antonelli E, et al. Gastrointestinal motility disorders in
inflammatory bowel diseases. World J Gastroenterol. 2014;20:37–
44.
[4] Janout V, Kollárová H, et al. Epidemiology of colorectal cancer.
Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub.
2001;145:5–10.
[5] Jackson-Thompson J, Ahmed F, et al. Descriptive epidemiology of
colorectal cancer in the United States, 1998–2001. Cancer.
2006;107:1103–1111.
[6] Davies RJ, Miller R, et al. Colorectal cancer screening: prospects
for molecular stool analysis. Nature reviews. Cancer. 2005;5:199–
209.
[7] Haggar FA, Boushey RP, et al. Colorectal cancer epidemiology:
incidence, mortality, survival, and risk factors. Clin Colon Rectal
Surg. 2009;22:191–197.
[8] Siegel RL, Miller KD, et al. Colorectal cancer statistics. CA Cancer
J Clin. 2020;70:145–164.
[9] Munteanu I, Mastalier B, et al. Genetics of colorectal cancer. J Med
Life. 2014;7:507–511.
[10] Mastalier B, Simion S, et al. Surgical treatment results in rectal can-
cer-experience of last 10 years. Journal of Medicine and Life. 2011;
IV:68–78. ISSN 1844-3117.
[11] Pino MS, Chung DC, et al. The chromosomal instability pathway
in colon cancer. Gastroenterology. 2010;138:2059–2072.
[12] Yan H, Yuan W, et al. Allelic Variation in Human Gene
Expression. Science. 2002;16:1143.
[13] Kinzler KW, Vogelstein B, et al. Colorectal tumors. In: Vogelstein
B, Kinzler KW, editor. The genetic basis of human cancer. 2nd ed.
New York: McGraw-Hill; 2002. p. 583–612.
[14] Araki Y, Okamura S, et al. Regulation of cyclooxygenase-2
expression by the Wnt and ras pathways. Cancer research.
2003;63:728–734.
[15] Gilhooly E, Gilhooly E, et al. The association between a mutated
ras gene and cyclooxygenase-2 expression in human breast cancer
cell lines. International Journal of Oncology. 1999;15:267–337.
[16] Ramsay RG, Thompson MA, et al. Myb expression is higher in
malignant human colonic carcinoma and premalignant adenoma-
tous polyps than in normal mucosa. Cell Growth Differ.
1992;3:723–723.
[17] Hampel H, Frankel WL, et al. Screening for the Lynch syndrome
(hereditary nonpolyposis colorectal cancer. N Engl J Med.
2005;352:1851–1860.
[18] Sheng H, Shao J, et al. Inhibition of human colon cancer cell
growth by selective inhibition of cyclooxygenase-2. J Clin Invest.
1997;99:2254–2259.
[19] Williams CS, Sheng H, et al. A cyclooxygenase-2 inhibitor (SC-
58125) blocks growth of established human colon cancer xeno-
grafts. Neoplasia. 2001;3:428–436.
[20] Brown JR D, et al. COX-2: A Molecular Target for Colorectal
Cancer Prevention. J Clin Oncol. 2005;23:2840–2855.
[21] Stoehlmacher J, Lenz HJ. Cyclooxygenase-2 inhibitors in colorec-
tal cancer. Seminars in oncology. 2003;30(3 Suppl 6):10–16.
[22] Dannenberg AJ, Altorki NK, et al. Cyclo-oxygenase 2: A pharma-
cological target for the prevention of cancer. Lancet Oncol.
2001;2:544–551.
[23] Tsujii M, Kawano S, et al. Cyclooxygenase-2 expression in human
colon cancer cells increases metastatic potential. Proc Natl Acad
Sci U S A. 1997;94:3336–3340.
[24] Dohadwala M, Luo J, et al. Non-small cell lung cancer cyclooxy-
genase-2-dependent invasion is mediated by CD44. J Biol Chem.
2001;276:20809–20812.
[25] Sharma S, Stolina M, et al. Tumor cyclooxygenase 2-dependent
suppression of dendritic cell function. Clinical cancer research.
Clin Cancer Res. 2003;9:961–968.
[26] Pai R, Soreghan B, et al. Prostaglandin E2 transactivates EGF recep-
tor: a novel mechanism for promoting colon cancer growthand gas-
trointestinal hypertrophy. Nature medicine. 2002;8:289–293.
[27] Lucido MJ, Orlando BJ, et al. Crystal Structure of Aspirin-
Acetylated Human Cyclooxygenase-2: Insight into the
Formation of Products with Reversed Stereochemistry.
Biochemistry. 2016;55:1226–1238.
[28] Khandelwal R, Chauhan AP, et al. Structure-based virtual screen-
ing for the identification of high-affinity small molecule towards
STAT3 for the clinical treatment of osteosarcoma. Curr Top
Med Chem. 2018;18:2511–2526.
[29] Daina A, Michielin O, et al. SwissADME: a free web tool to evalu-
ate pharmacokinetics, drug-likeness and medicinal chemistry
friendliness of small molecules. Sci Rep. 2017;7:1–13.
[30] Hawkey C. COX-2 inhibitors. The Lancet. 1999;353:307–314.
[31] Kawamori T, Rao CV, et al. Chemopreventive activity of celecoxib,
a specific cyclooxygenase-2 inhibitor, against colon carcinogenesis.
Cancer res. 1998;58:409–412.
[32] Evans JF. Rofecoxib (Vioxx), a Specific Cyclooxygenase-2
Inhibitor, Is Chemopreventive in a Mouse Model of Colon
Cancer. Am J Clin Oncol. 2003;26:S62–S65.
[33] Wei X. Parecoxib: an Enhancer of Radiation Therapy
for Colorectal Cancer. Asian Pac J Cancer Prev. 2015;16:627–633.
[34] Sanyal SN, Jain S, et al. Activation of Mitochondrial Apoptosis and
Regulation of Ceramide Signalling by COX-2 Inhibitors in Colon
Cancer. Transl Med. 2015;5:159.
[35] Kurumbail RG, Stevens AM, et al. Structural basis for selective
inhibition of cyclooxygenase-2 by anti-inflammatory agents.
Nature. 1996;384:644–648.
[36] Goldman A. Meloxicam inhibits the growth of colorectal cancer
cells. Carcinogenesis. 1998;19:2195–2199.
[37] Roy HK, Karolski WJ, et al. Distal bowel selectivity in the chemo-
prevention of experimental colon carcinogenesis by the non-ster-
oidal anti-inflammatory drug nabumetone. Int J Cancer.
2001;92:609–615.
MOLECULAR SIMULATION 17
[38] Takuya I, Mitsuyuki M, et al. R-etodolac induces E-cadherin and
suppresses colitis-related mouse colon tumorigenesis. Oncol Rep.
2010;24:1487–1492.
[39] Kohno H, Suzuki R, et al. Suppression of colitis-related mouse
colon carcinogenesis by a COX-2 inhibitor and PPAR ligands.
BMC Cancer. 2005;5:5–46.
[40] Hawkey C. COX-1 and COX-2 inhibitors. Best Pract Res Clin
Gastroenterol. 2001;15:801–820.
[41] Marnett LJ, Kalgutkar AS, et al. Cyclooxygenase 2 inhibitors: dis-
covery, selectivity and the future. Trends Pharmacol Sci.
1999;20:465–469.
[42] Janssen A, Maier TJ, et al. Evidence of COX-2 independent induc-
tion of apoptosis and cell cycle block in human colon carcinoma
cells after S- or R-ibuprofen treatment. Eur J Pharmacol.
2006;540:24–33.
[43] Kobayashi H, Uetake H, et al. JTE-522, a selective COX-2 inhibi-
tor, inhibits growth of pulmonary metastases of colorectal cancer
in rats. BMC cancer. 2005;5:26.
[44] Sharma K, Ali PK, et al. Structure-based virtual screening for the
identification of high affinity compounds as potent VEGFR2
inhibitors for the treatment of renal cell carcinoma. Curr Top
Med Chem. 2018;18:2174–2185.
[45] Sahila MM, Babitha PP, et al. Molecular docking based screening
of GABA (A) receptor inhibitors from plant derivatives.
Bioinformation. 2015;11:280–289.
[46] Vuree S, Dunna NR, et al. Pharmacogenomics of drug resistance in
Breast Cancer Resistance Protein (BCRP) and its mutated variants.
J Pharm Res. 2013;6:791–798.
[47] Monteiro AFM, Viana JDO, et al. Computational studies applied
to flavonoids against alzheimer’s and parkinson’s diseases. Oxid
Med Cell Longev. 2018;30:7912765.
[48] Bandaru S, Gangadharan Sumithnath T, et al. Helix-Coil transition
signatures B-Raf V600E mutation and virtual screening for inhibi-
tors directed against mutant B-Raf. Curr Drug Metab.
2017;18:527–534.
[49] Kelotra A, Gokhale SM, et al. Alkyloxy carbonyl modified hexa-
peptides as a high affinity compounds for Wnt5A protein in the
treatment of psoriasis. Bioinformation. 2014;10:743–749.
[50] Basak SC, Nayarisseri A, et al. Editorial (Thematic Issue: chemoin-
formatics models for pharmaceutical design, part 1). Curr Pharm
Des. 2016;22:5041–5042.
[51] Basak SC, Nayarisseri A, et al. Editorial (Thematic Issue:
Chemoinformatics models for pharmaceutical design, part 2).
Curr Pharm Des. 2016;22:5177–5178.
[52] Prajapati L, Khandelwal R, et al. Computer-aided Structure predic-
tion of Bluetongue Virus coat protein VP2 assisted by Optimized
Potential for Liquid Simulations (OPLS). Curr Top Med Chem.
2020;20:1720–1732.
[53] Nayarisseri A, Khandelwal R, et al. Shape-based machine learning
models for the potential novel COVID-19 protease inhibitors
assisted by molecular dynamics simulation. Curr Top Med
Chem. 2020;20:2146–2167.
[54] Nayarisseri A. Most Promising Compounds for Treating COVID-
19 and Recent Trends in Antimicrobial & Antifungal Agents. Curr
Top Med Chem. 2020;20:2119–2125.
[55] Pochetti G, Mitro N, et al. Structural insight into peroxisome
proliferator-activated receptor γbinding of two ureidofibrate-like
enantiomers by molecular dynamics, cofactor interaction analysis,
and site-directed mutagenesis. J Med Chem. 2010;53:4354–4366.
[56] Soares Rodrigues GC, dos Santos M, et al. Quantitative Structure–
Activity Relationship Modeling and Docking of Monoterpenes
with Insecticidal Activity Against Reticulitermes chinensis
Snyder and Drosophila melanogaster. J Agric Food Chem.
2020;68:4687–4698.
[57] Wang Y, Wang LF, et al. Molecular mechanism of inhibitor bind-
ings to bromodomain-containing protein 9 explored based on
molecular dynamics simulations and calculations of binding free
energies. SAR QSAR Environ Res. 2020;31:149–170.
[58] Wang LF, Wang Y, et al. Revealing binding selectivity of inhibitors
toward bromodomain-containing proteins 2 and 4 using multiple
short molecular dynamics simulations and free energy analyses.
SAR QSAR Environ Res. 2020;31:373–398.
[59] Brugnoni M, Scotti A, et al. Swelling of a responsive network
within different constraints in multi-thermosensitive microgels.
Macromolecules. 2018;51:2662–2671.
[60] Montanari R, Saccoccia F, et al. Crystal structure of the peroxi-
some proliferator-activated receptor γ(PPARγ) ligand binding
domain complexed with a novel partial agonist: a new region of
the hydrophobic pocket could be exploited for drug design. J
Med Chem. 2008;51:7768–7776.
[61] Wang J, Qian Y, et al. Atomic Force Microscopy and Molecular
Dynamics Simulations for Study of Lignin Solution Self-
Assembly Mechanisms in Organic–Aqueous Solvent Mixtures.
ChemSusChem. 2020;13:4420–4427.
[62] Liguori N, Croce R, et al. Molecular dynamics simulations in
photosynthesis. Photosynth Res. 2020;144:273–295.
[63] Kuzmanic A, Bowman GR, et al. Investigating cryptic binding sites
by molecular dynamics simulations. Acc Chem Res.
2020;53:654–661.
[64] Klesse G, Rao S, et al. Induced polarization in molecular dynamics
simulations of the 5-HT3 receptor channel. J. Am. Chem. Soc.
2020;142:9415–9427.
[65] Shiau AK, Barstad D, et al. The structural basis of estrogen recep-
tor/coactivator recognition and the antagonism of this interaction
by tamoxifen. Cell. 1998;95:927–937.
[66] Berman H, Henrick K, et al. The worldwide Protein Data Bank
(wwPDB): ensuring a single, uniform archive of PDB data.
Nucleic Acids Res. 2007;35:D301–D303.
[67] Natchimuthu V, Bandaru S, et al. Design, synthesis and compu-
tational evaluation of a novel intermediate salt of N-cyclohexyl-
N-(cyclohexylcarbamoyl)-4-(trifluoromethyl) benzamide as
potential potassium channel blocker in epileptic paroxysmal sei-
zures. Comput Biol Chem. 2016;64:64–73.
[68] Bandaru S, Alvala M, et al. Identification of small molecule as a
high affinity β2 agonist promiscuously targeting wild and mutated
(Thr164Ile) β2 adrenergic receptor in the treatment of bronchial
asthma. Curr Pharm Des. 2016;22:5221–5233.
[69] Majhi M, Ali MA, et al. An In silico investigation of potential
EGFR inhibitors for the clinical treatment of colorectal cancer.
Curr Top Med Chem. 2018;18:2355–2366.
[70] Sinha K, Majhi M, et al. Computer-aided drug designing for the
identification of high-affinity small molecule targeting cd20 for
the clinical treatment of chronic lymphocytic leukemia (CLL).
Curr Top Med Chem. 2018;18:2527–2542.
[71] Chandrakar B, Jain A, et al. Molecular modeling of Acetyl-CoA
carboxylase (ACC) from Jatropha curcas and virtual screening
for identification of inhibitors. J Pharm Res. 2013;6:913–918.
[72] Nayarisseri A, Moghni SM, et al. In silico investigations on HSP90
and its inhibition for the therapeutic prevention of breast cancer. J
Pharm Res. 2013;7:150–156.
[73] Udhwani T, Mukherjee S. Design of PD-L1 inhibitors for lung can-
cer. Bioinformation. 2019;15:139–150.
[74] Shukla P, Khandelwal R, et al. Virtual screening of IL-6 inhibitors
for idiopathic arthritis. Bioinformation. 2019;15:121–130.
[75] Nayarisseri A, Hood EA, et al. Advancement in microbial chemin-
formatics. Curr Top Med Chem. 2018;18:2459–2461.
[76] Jain D, Udhwani T, et al. Design of novel JAK3 Inhibitors towards
Rheumatoid Arthritis using molecular docking analysis.
Bioinformation. 2019;15:68–78.
[77] Nayarisseri A, Singh SK, et al. Functional inhibition of VEGF and
EGFR suppressors in cancer treatment. Curr Top Med Chem.
2019;19:178–179.
[78] Gokhale P, Chauhan APS, et al. FLT3 inhibitor design using mol-
ecular docking based virtual screening for acute myeloid leukemia.
Bioinformation. 2019;15:104–115.
[79] Ali MA, Vuree S, et al. Identification of high-affinity small mol-
ecules targeting gamma secretase for the treatment of
Alzheimer’s disease. Curr Top Med Chem. 2019;19:1173–1187.
[80] Patidar K, Panwar U, et al. An in silico approach to identify high
affinity small molecule targeting m-TOR inhibitors for the clinical
18 M. YADAV ET AL.
treatment of breast cancer. Asian Pac J Cancer Prev.
2019;20:1229–1241.
[81] Pandey N, Yadav M, et al. Cross evaluation of different classes of
alpha-adrenergic receptor antagonists to identify overlapping
pharmacophoric requirements. J Pharm Res. 2013;6:173–178.
[82] Marunnan SM, Pulikkal BP, et al. Development of MLR and SVM
aided QSAR models to identify common SAR of GABA uptake
herbal inhibitors used in the treatment of Schizophrenia. Curr
Neuropharmacol. 2017;15:1085–1092.
[83] Sweta J, Khandelwal R, et al. Identification of high-affinity small
molecule targeting IDH2 for the clinical treatment of acute
myeloid leukemia. Asian Pac J Cancer Prev. 2019;20:2287.
[84] Nayarisseri A. Prospects of utilizing computational techniques for
the treatment of human diseases. Curr Top Med Chem.
2019;19:1071–1074.
[85] Schrodinger, LLC, NY, USA, 2009.
[86] LigPrep, Schrodinger LLC, Ney York, NY.
[87] Prime, Schrodinger, LLC, Ney York, NY.
[88] Protein Preparation Wizard, Schrodinger, LLC, Ney York, NY.
[89] Qikprop, Schrodinger, LLC, Ney York, NY.
[90] Shelley JC, Cholleti A, et al. Epik: a software program for pK a pre-
diction and protonation state generation for drug-like molecules. J
Comput Aided Mol Des. 2007;21:681–691.
[91] Baby K, Maity S, et al. Targeting SARS-CoV-2 main protease: A
computational drug repurposing study. Arch Med Res.
2020;52:38–47.
[92] Gahlawat A, Kumar N, et al. Structure-based virtual screening to
discover potential lead molecules for the SARS-CoV-2 main pro-
tease. J Chem Inf Model. 2020;60:5781–5793.
[93] Xu X, Mao L, et al. AC0010, an irreversible EGFR inhibitor selec-
tively targeting mutated EGFR and overcoming T790M-induced
resistance in animal models and lung cancer patients. Mol
Cancer Ther. 2016;15:2586–2597.
[94] Sharda S, Khandelwal R, et al. A Computer-Aided Drug Designing
for Pharmacological Inhibition of Mutant ALK for the Treatment
of Non-small Cell Lung Cancer. Curr Top Med Chem.
2019;19:1129–1144.
[95] Limaye A, Sweta J, et al. In silico insights on gd2: a potential target
for pediatric neuroblastoma. Curr Top Med Chem. 2019;19:2766–
2781.
[96] Nayarisseri A, Yadav M, et al. Editorial (Thematic Issue:
Mechanistics in drug design-experimental molecular biology vs.
molecular modeling). Curr Top Med Chem. 2015;15:3–4.
[97] Kleandrova VV, Scotti MT, et al. Cell-based multi-target QSAR
model for design of virtual versatile inhibitors of liver cancer cell
lines. SAR QSAR Environ Res. 2020;31:815–836.
[98] Nayarisseri A. Experimental and computational approaches to
improve binding affinity in chemical biology and drug discovery.
Curr Top Med Chem. 2020;20:1651–1660.
[99] Kaushik AC, Kumar S, et al. Structure based virtual screening
studies to identify novel potential compounds for GPR142 and
their relative dynamic analysis for study of type 2 diabetes. Front
Chem. 2018;6:23.
[100] Toledo Warshaviak D, Golan G, et al. Structure-based virtual
screening approach for discovery of covalently bound ligands. J
Chem Inf Model. 2014;54:1941–1950.
[101] Lyne PD, et al. Structure-based virtual screening: an overview.
Drug Discov Today. 2002;7:1047–1055.
[102] Dighe SN, Deora GS, et al. Discovery and structure–activity
relationships of a highly selective butyrylcholinesterase inhibitor
by structure-based virtual screening. J Med Chem. 2016;59:7683–
7689.
[103] Schrödinger L. (2011). QikProp: Rapid ADME predictions of drug
candidates.
[104] Lionta E, Spyrou G, et al. Structure-based virtual screening for
drug discovery: principles, applications and recent advances.
Curr Top Med Chem. 2014;14:1923–1938.
[105] Vidler LR, Filippakopoulos P, et al. Discovery of novel small-mol-
ecule inhibitors of BRD4 using structure-based virtual screening. J
Med Chem. 2013;56:8073–8088.
[106] Pitt WR, Calmiano MD, et al. Structure-based virtual screening for
novel ligands. Methods Mol Biol. 2013;1008:501–519.
[107] Choudhary S, Malik YS, et al. Identification of SARS-CoV-2 Cell
Entry Inhibitors by Drug Repurposing Using in silico Structure-
Based Virtual Screening Approach. Front Immunol. 2020;10:1664.
[108] Adhikary R, Khandelwal R, et al. Structural Insights into the
Molecular Design of ROS1 Inhibitor for the Treatment of Non-
Small Cell Lung Cancer (NSCLC). Curr Comput Aided Drug
Des. 2021;17:387–401.
[109] Aher A, Udhwani T, et al. In silico insights on IL-6: A potential tar-
get for multicentric castleman disease. Curr Comput Aided Drug
Des. 2020;16:641–653.
[110] Qureshi S, Khandelwal R, et al. A Multi-Target Drug Designing for
BTK, MMP9, Proteasome And TAK1 for the clinical treatment of
Mantle Cell Lymphoma. Curr Top Med Chem. 2021;21:790–818.
[111] Yadav M, Khandelwal R, et al. Identification of Potent VEGF
Inhibitors for the Clinical Treatment of Glioblastoma, A
Virtual Screening Approach. Asian Pac J Cancer Prev.
2019;20:2681–2692.
[112] Nayarisseri A, Khandelwal R, et al. Artificial Intelligence, Big data
and Machine Learning approaches in Precision Medicine & Drug
Discovery. Curr Drug Targets. 2021;22:631–655.
[113] Mendonça-Junior FJB, Scotti MT, et al. Natural Bioactive Products
with Antioxidant Properties Useful in Neurodegenerative
Diseases. Oxid Med Cell Longev. 2019;9:7151780.
[114] Celik I, Erol M, et al. Evaluation of activity of some 2, 5-disubsti-
tuted benzoxazole derivatives against acetylcholinesterase, butyryl-
cholinesterase and tyrosinase: ADME prediction, DFT and
comparative molecular docking studies. Polycycl Aromat
Compd. 2020:1–12.
[115] Pawar VS, Lokwani DK, et al. Design, docking study and ADME
prediction of Isatin derivatives as anti-HIV agents. Med Chem
Res. 2011;20:370–380.
[116] Dincel ED, Gürsoy E, et al. Antioxidant activity of novel imidazo
[2, 1-b] thiazole derivatives: Design, synthesis, biological evalu-
ation, molecular docking study and in silico ADME prediction.
Bioorg Chem. 2020;103:104220.
[117] Kumar S, Saini V, et al. Design, synthesis, DFT, docking studies
and ADME prediction of some new coumarinyl linked pyrazo-
lylthiazoles: Potential standalone or adjuvant antimicrobial agents.
PloS one. 2018;13:e0196016.
[118] Erol M, Celik I, et al. Synthesis, molecular docking and ADME
prediction of some new benzimidazole carboxamidines derivatives
as antimicrobial agents. Med Chem Res. 2020;29:2028–2038.
[119] Kashid AM, Dube PN, et al. Synthesis, biological screening and
ADME prediction of benzylindole derivatives as novel anti-HIV-
1, anti-fungal and anti-bacterial agents. Med Chem Res.
2013;22:4633–4640.
[120] Kalin TN, Kilic D, et al. Synthesis, molecular modeling studies,
ADME prediction of arachidonic acid carbamate derivatives, and
evaluation of their acetylcholinesterase activity. Drug Dev Res.
2020;81:232–241.
[121] Kumar A, Rathi E, et al. E-pharmacophore modelling, virtual
screening, molecular dynamics simulations and in-silico ADME
analysis for identification of potential E6 inhibitors against cervical
cancer. J Mol Struct. 2019;1189:299–306.
[122] Bhatt JD, Chudasama CJ, et al. Pyrazole clubbed triazolo [1, 5-a]
pyrimidine hybrids as an anti-tubercular agents: Synthesis, in
vitro screening and molecular docking study. Bioorg Med Chem.
2015;23:7711–7716.
[123] Dofe VS, Sarkate AP, et al. Novel O-Alkylated Chromones as
Antimicrobial Agents: Ultrasound Mediated Synthesis,
Molecular Docking and ADME Prediction. J Heterocycl Chem.
2017;54:2678–2685.
[124] Malik R, Mehta P, et al. Pharmacophore modeling, 3D-QSAR, and
in silico ADME prediction of N-pyridyl and pyrimidine benza-
mides as potent antiepileptic agents. J Recept Signal Transduct
Res. 2017;37:259–266.
[125] Hage-Melim LIDS, Federico LB, et al. Virtual screening,
ADME/Tox predictions and the drug repurposing concept for
MOLECULAR SIMULATION 19
future use of old drugs against the COVID-19. Life Sci.
2020;256:117963.
[126] Upadhyay S, Tripathi AC, et al. 2-pyrazoline derivatives in neuro-
pharmacology: Synthesis, ADME prediction, molecular docking
and in vivo biological evaluation. EXCLI J. 2017;16:628–649.
[127] Pandey RK, Narula A, et al. Exploring dual inhibitory role of feb-
rifugine analogues against Plasmodium utilizing structure-based
virtual screening and molecular dynamics simulation. J Biomol
Struct Dyn. 2017;35:791–804.
[128] Sadhasivam A, Vetrivel U, et al. Identification of potential drugs
targeting L, L-diaminopimelate aminotransferase of Chlamydia
trachomatis: An integrative pharmacoinformatics approach. J
Cell Biochem. 2019;120:2271–2288.
[129] Zakerali T, Shahbazi S, et al. Rational druggability investigation
toward selection of lead molecules: impact of the commonly
used spices on inflammatory diseases. Assay Drug Dev Technol.
2018;16:397–407.
[130] Suma KB, Kumari A, et al. Structure based pharmacophore mod-
elling approach for the design of azaindole derivatives as DprE1
inhibitors for tuberculosis. J Mol Graph Model. 2020;101:107718.
[131] Sukumar N, Pask JE, et al. Classical and enriched finite element
formulations for Bloch-periodic boundary conditions. Int J
Numer Methods Eng. 2009;77:1121–1138.
[132] Rohini K, Ramanathan K, et al. Multi-dimensional screening strat-
egy for drug repurposing with statistical framework—a new road to
influenza drug discovery. Cell Biochem Biophys. 2019;77:319–333.
[133] Halgren TA, Murphy RB, et al. Glide: a new approach for rapid,
accurate docking and scoring. 2. Enrichment factors in database
screening. J Med Chem. 2004;47:1750–1759.
[134] Sastry GM, Adzhigirey M, et al. Protein and ligand preparation:
parameters, protocols, and influence on virtual screening enrich-
ments. J Comput Aided Mol Des. 2013;27:221–234.
[135] Lenselink EB, Beuming T, et al. Selecting an optimal number of
binding site waters to improve virtual screening enrichments
against the adenosine A2A receptor. J Chem Inf Model.
2014;54:1737–1746.
[136] Verma P, Tiwari M, et al. In silico high-throughput virtual screen-
ing and molecular dynamics simulation study to identify inhibitor
for AdeABC efflux pump of Acinetobacter baumannii. J Biomol
Struct Dyn. 2018;36:1182–1194.
[137] Sherman W, Beard HS, et al. Use of an induced fit receptor struc-
ture in virtual screening. Chem Biol Drug Des. 2006;67:83–84.
[138] Bhachoo J, Beuming T, et al. Investigating protein–peptide inter-
actions using the Schrödinger computational suite. Methods Mol
Biol. 2017;1561:235–254.
[139] Deb PK, Chandrasekaran B, et al. Molecular modeling approaches
for the discovery of adenosine A2B receptor antagonists: current
status and future perspectives. Drug Discov Today.
2019;24:1854–1864.
[140] Vanajothi R, Hemamalini V, et al. Ligand-based pharmacophore
mapping and virtual screening for identification of potential dis-
coidin domain receptor 1 inhibitors. J Biomol Struct Dyn.
2020;38:2800–2808.
[141] Politi A, Durdagi S, et al. Development of accurate binding affinity
predictions of novel renin inhibitors through molecular docking
studies. J Mol Graph Model. 2010;29:425–435.
[142] Liu K, Kokubo H, et al. Exploring the stability of ligand binding
modes to proteins by molecular dynamics simulations: a cross-
docking study. J Chem Inf Model. 2017;57:2514–2522.
[143] Munnaluri R, Sivan SK, et al. Molecular docking and MM/GBSA
integrated protocol for designing small molecule inhibitors against
HIV-1 gp41. Med Chem Res. 2014;24:829–841.
[144] Lyne PD, Lamb ML, et al. Accurate prediction of the relative
potencies of members of a series of kinase inhibitors using molecu-
lar docking and MM-GBSA scoring. J Med Chem. 2006;49:4805–
4808.
[145] Peddi SR, Sivan SK, et al. Molecular dynamics and MM/GBSA-
integrated protocol probing the correlation between biological
activities and binding free energies of HIV-1 TAR RNA inhibitors.
J Biomol Struct Dyn. 2018;36:486–503.
[146] Cheng F, Li W, et al. admetSAR: a comprehensive source and free
tool for evaluating chemical ADMET properties. J. Chem. Inf.
Model. 2012;52:3099–3105.
[147] Shen M, Zhou S, et al. Discovery and optimization of triazine
derivatives as ROCK1 inhibitors: molecular docking, molecular
dynamics simulations and free energy calculations. Mol Biosyst.
2013;9:361–374.
[148] Bathini R, Sivan SK, et al. Molecular docking, MM/GBSA and 3D-
QSAR studies on EGFR inhibitors. J. Chem. Sci. 2016;128:1163–
1173.
[149] Pandey RK, Kumbhar BV, et al. Structure-based virtual screening,
molecular docking, ADMET and molecular simulations to develop
benzoxaborole analogs as potential inhibitor against Leishmania
donovani trypanothione reductase. J Recept Signal Transduct
Res. 2017;37:60–70.
[150] Lagarias P, Barkan K, et al. Insights to the binding of a selective
adenosine A3 receptor antagonist using molecular dynamics simu-
lations, MM-PBSA and MM-GBSA free energy calculations, and
mutagenesis. J Chem Inf Model. 2019;59:5183–5197.
[151] Tang X, Wang Z, et al. Importance of protein flexibility on
molecular recognition: modeling binding mechanisms of amino-
pyrazine inhibitors to Nek2. Phys Chem Chem Phys.
2018;20:5591–5605.
[152] Negron C, Pearlman DA, et al. Predicting mutations deleterious to
function in beta-lactamase TEM1 using MM-GBSA. Plos one.
2019;14:e0214015.
[153] Rawat R, Kant K, et al. HeroMDAnalysis: an automagical tool for
GROMACS-based molecular dynamics simulation analysis.
Future Med Chem. 2021;13:447–456.
[154] Salas-Burgos A, Iserovich P, et al. Predicting the three-dimensional
structure of the human facilitative glucose transporter glut1 by a
novel evolutionary homology strategy: insights on the molecular
mechanism of substrate migration, and binding sites for glucose
and inhibitory molecules. Biophys J. 2004;87:2990–2999.
[155] Shukla R, Chetri PB, et al. Identification of novel natural inhibitors
of Opisthorchis felineus cytochrome P450 using structure-based
screening and molecular dynamics simulation. J Biomol Struct
Dyn. 2018;36:3541–3556.
[156] Chen YW, Wang Y, Leung YC, et al. Parameterization of Large
Ligands for Gromacs Molecular Dynamics Simulation with
LigParGen. Methods Mol Biol. 2021;2199:277–288.
[157] Thirumal KD, Mendonca E, et al. A computational model to pre-
dict the structural and functional consequences of missense
mutations in O6-methylguanine DNA methyltransferase. Adv
Protein Chem Struct Biol. 2019;115:351–369.
[158] Shukla R, Shukla H, et al. Structural insights into natural com-
pounds as inhibitors of Fasciola gigantica thioredoxin glutathione
reductase. J Cell Biochem. 2018;119:3067–3080.
[159] Kubarenko A, Frank M, et al. Structure–function relationships of
Toll-like receptor domains through homology modelling and mol-
ecular dynamics. Biochem Soc Trans. 2007;35:1515–1518.
[160] Zhao X, Jin H, et al. Numerical study of H2, CH4, CO, O2 and
CO2 diffusion in water near the critical point with molecular
dynamics simulation. Comput Math Appl. 2021;81:759–771.
[161] Liu H, Xiang S, et al. The Structural and Dynamical Properties of
the Hydration of SNase Based on a Molecular Dynamics
Simulation. Molecules. 2021;26:5403.
[162] Cardoso R, Valente R, et al. Analysis of kojic acid derivatives as
competitive inhibitors of tyrosinase: a molecular modeling
approach. Molecules. 2021;26:2875.
[163] Shah M, Anwar MA, et al. A structural insight into the negative
effects of opioids in analgesia by modulating the TLR4 signaling:
An in silico approach. Sci Rep. 2016;6:1–15.
[164] Tian J, Wang P, et al. Enhanced thermostability of methyl para-
thion hydrolase from Ochrobactrum sp. M231 by rational engin-
eering of a glycine to proline mutation. FEBS J. 2010;277:4901–
4908.
[165] Joshi T, Sharma P, et al. In silico screening of anti-inflammatory
compounds from Lichen by targeting cyclooxygenase-2. J
Biomol Struct Dyn. 2020;38:3544–3562.
20 M. YADAV ET AL.
[166] Torktaz I, NajafiA, et al. Molecular dynamics simulation (MDS)
analysis of Vibrio cholerae ToxT virulence factor complexed
with docked potential inhibitors. Bioinformation. 2018;14:101–
105.
[167] Raftopoulou S, Nicolaides NC, et al. Structural Study of the DNA:
Clock/Bmal1 Complex Provides Insights for the Role of Cortisol,
hGR, and HPA Axis in Stress Management and Sleep Disorders.
Adv Exp Med Biol. 2020;1195:59–71.
[168] Muthuvel SK, Elumalai E, et al. Molecular docking and dynamics
studies of 4-anilino quinazolines for epidermal growth factor
receptor tyrosine kinase to find potent inhibitor. J Recept Signal
Transduct Res. 2018;38:475–483.
[169] Gajendrarao P, Krishnamoorthy N, et al. Molecular modeling
study on orphan human protein CYP4A22 for identification of
potential ligand binding site. J Mol Graph Model. 2010;28:524–
532.
[170] Tanwar H, Kumar DT, et al. Bioinformatics classification of
mutations in patients with Mucopolysaccharidosis IIIA. Metab
Brain Dis. 2019;34:1577–1594.
[171] Kumari R, Kumar R, et al. g_mmpbsa–a GROMACS tool for high-
throughput MM-PBSA calculations. J Chem Inf Model.
2014;54:1951–1962.
[172] Paissoni C, Spiliotopoulos D, et al. GMXPBSA 2.0: A GROMACS
tool to perform MM/PBSA and computational alanine scanning.
Comput Phys Commun. 2014;185:2920–2929.
[173] Wang E, Sun H, et al. End-point binding free energy calculation
with MM/PBSA and MM/GBSA: strategies and applications in
drug design. Chem Rev. 2019;119:9478–9508.
[174] Kumar A, Srivastava G, et al. Docking, molecular dynamics, bind-
ing energy-MM-PBSA studies of naphthofuran derivatives to
identify potential dual inhibitors against BACE-1 and GSK-3β.J
Biomol Struct Dyn. 2019;37:275–290.
[175] Botelho FD, Santos D, et al. Ligand-based virtual screening, mol-
ecular docking, molecular dynamics, and MM-PBSA calculations
towards the identification of potential novel ricin inhibitors.
Toxins. 2020;12:746.
[176] Elkarhat Z, Charoute H, et al. Potential inhibitors of SARS-cov-2
RNA dependent RNA polymerase protein: molecular docking,
molecular dynamics simulations and MM-PBSA analyses. J
Biomol Struct Dyn. 2020;2:1–14.
[177] Martins LC, Torres PHM, et al. Investigation of the binding mode
of a novel cruzain inhibitor by docking, molecular dynamics, ab
initio and MM/PBSA calculations. J Comput Aided Mol Des.
2018;32:591–605.
[178] Kumari R, Lynn A, et al. Application of MM/PBSA in the predic-
tion of relative binding free energy: Re-scoring of docking hit-list. J
Nat Sci Biol Med. 2011;2:92–92.
[179] Schreiber JB. Issues and recommendations for exploratory factor
analysis and principal component analysis. Res Social Adm
Pharm. 2021;17:1004–1011.
[180] Chen J, Wang J, et al. Molecular mechanism and energy basis of
conformational diversity of antibody SPE7 revealed by molecular
dynamics simulation and principal component analysis. Sci Rep.
2016;6:1–12.
[181] Sittel F, Filk T, et al. Principal component analysis on a torus:
Theory and application to protein dynamics. J Chem Phys.
2017;147:244101.
[182] Riccardi L, Nguyen PH, et al. Free-energy landscape of RNA hair-
pins constructed via dihedral angle principal component analysis. J
Phys Chem B. 2009;113:16660–16668.
[183] Buslaev P, Mustafin K, et al. Principal component analysis high-
lights the influence of temperature, curvature and cholesterol on
conformational dynamics of lipids. Biochim Biophys Acta
Biomembr. 2020;1862:183253.
[184] Nguyen PH. Complexity of free energy landscapes of peptides
revealed by nonlinear principal component analysis. Proteins.
2006;65:898–913.
[185] Yamamoto N. Hot spot of structural ambivalence in prion protein
revealed by secondary structure principal component analysis. J
Phys Chem B. 2014;118:9826–9833.
[186] Michielssens S, van Erp TS, et al. Molecular dynamics in principal
component space. J Phys Chem B. 2012;116:8350–8354.
[187] Grasso G, Deriu MA, et al. Conformational fluctuations of the
AXH monomer of Ataxin-1. Proteins. 2016;84:52–59.
[188] Verma S, Singh A, et al. Natural polyphenolic inhibitors against
the antiapoptotic BCL-2. J Recept Signal Transduct Res.
2017;37:391–400.
[189] Mouritsen OG, Khandelia H, et al. Molecular mechanism of the
allosteric enhancement of the umami taste sensation. FEBS J.
2012;279:3112–3120.
[190] Swain SS, Paidesetty SK, et al. Molecular docking and simulation
study for synthesis of alternative dapsone derivative as a newer
antileprosy drug in multidrug therapy. J Cell Biochem.
2018;119:9838–9852.
[191] Villa A, Stock G, et al. What NMR relaxation can tell us
about the internal motion of an RNA hairpin: a molecular dynamics
simulation study. J Chem Theory Comput. 2006;2:1228–1236.
[192] Ng HW, Laughton CA, et al. Molecular dynamics simulations of
the adenosine A2a receptor: structural stability, sampling, and
convergence. J Chem Inf Model. 2013;53:1168–1178.
[193] Mukherjee S, Abdalla M, et al. Structure-Based Virtual Screening,
Molecular Docking, and Molecular Dynamics Simulation of VEGF
inhibitors for the clinical treatment of Ovarian Cancer. J Mol
Model. 2022;28:1–21.
MOLECULAR SIMULATION 21
