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Journal of Biomolecular Structure and Dynamics
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Structure-based virtual screening, molecular
simulation and free energy calculations of
traditional Chinese medicine, ZINC database
revealed potent inhibitors of estrogen-receptor α
(ERα)
Muhammad Shahab, Maryam Zulfat & Guojun Zheng
To cite this article: Muhammad Shahab, Maryam Zulfat & Guojun Zheng (30 Oct 2023):
Structure-based virtual screening, molecular simulation and free energy calculations of
traditional Chinese medicine, ZINC database revealed potent inhibitors of estrogen-receptor α
(ERα), Journal of Biomolecular Structure and Dynamics, DOI: 10.1080/07391102.2023.2275174
To link to this article: https://doi.org/10.1080/07391102.2023.2275174
Published online: 30 Oct 2023.
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Structure-based virtual screening, molecular simulation and free energy
calculations of traditional Chinese medicine, ZINC database revealed potent
inhibitors of estrogen-receptor a (ERa)
Muhammad Shahab
a
, Maryam Zulfat
b
and Guojun Zheng
a
a
State Key Laboratories of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, China;
b
Department of
Biochemistry, Abdul Wali Khan University, Mardan, Pakistan
Communicated by Ramaswamy H. Sarma
ABSTRACT
Breast Cancer, a heterogeneous disease at the molecular level, is the most common cause of woman
mortality worldwide. We used molecular screening and simulation approaches to target nuclear recep-
tor protein-estrogen receptor alpha (Era) protein to design and develop of specific and compelling
drugs from traditional Chinese medicine (TCM), and ZINC database against pathophysiology of breast
cancer. Using virtual screening, only six hits TCM22717, TCM23524, TCM31953, while ZINC05632920,
ZINC05773243, and ZINC12780336 demonstrated better pharmacological potential than the 4-hydroxy-
tamoxifen (OHT) taken as control. Binding mode of each of the top hit revealed that these compounds
could block the main active site residues and block the function of Era protein. Moreover, molecular
simulation revealed that the identified compounds exhibit stable dynamics and may induce stronger
therapeutic effects in experimental setup. All the complexes reported tighter structural packing and
less flexible behaviour. We found that the average hydrogen bonds in the identified complexes
remained higher than the control drug. Finally, the total binding free energy demonstrated the best hits
among the all. The BF energy results revealed −30.4525 ± 3.3565 for the 4-hydroxytamoxifen (OHT)/Era
complex, for the TCM22717/Era −57.0597 ±3.4852 kcal/mol, for the TCM23524/Era complex the BF energy
was −56.9084 ± 3.3737 kcal/mol, for the TCM31953/Era the BF energy was −32.4191 ± 3.8864 kcal/mol while
for the ZINC05632920/Era complex −46.3182 ± 2.7380, ZINC05773243/Era complex −38.3690 ± 2.8240, and
ZINC12780336/Era complex the BF energy was calculated to be −35.8048 ± 4.1571 kcal/mol.
ARTICLE HISTORY
Received 15 July 2023
Accepted 7 September 2023
KEYWORDS
ERa; breast cancer; virtual
screening; molecular
docking; MD simulation
Introduction
Cancer is a group of various severe diseases that have the
potential to spread to different parts of the body and are
marked by uncontrolled cell growth. Breast cancer, a molecu-
larly diverse disease, is the leading global cause of death
among women (Nahmias Blank et al., 2022; Shen et al.,
2023). Multiple studies have shown evidence indicating that
genetic modification play a crucial role as an early and fre-
quent modulator in various processes that contribute to the
development of cancer (Musgrove & Sutherland, 2009).
Recent research has confirmed that alterations in DNA
methylation, a genetic modification, have a profound impact
on the potential for carcinogenesis, the rate of tumor devel-
opment, and the overall prognosis of different types of
malignant tumors in humans (Lupien et al., 2008; Yang &
Park, 2012). Breast cancer is a significant form of cancer that
primarily affects women. It accounted for approximately 25%
of diagnosed cancer cases in 2012 (Baskar et al., 2014). In
around 70% of breast cancer cases, there is a phenomenon
known as estrogen receptor (ER)-a activation. The nuclear
receptor protein family, which plays a crucial role in control-
ling and regulating the majority of estrogen-responsive
genes (ERGs), encompasses estrogen receptor-alpha (ESR1).
Any abnormality in the expression pattern of ESR1 is linked
with the progression of breast cancer. In this specific malig-
nancy, a poor prognosis is attributed to the methylation of
the ESR1 promoter (Sheng et al., 2017). This activation is
highly significant as it contributes significantly to the devel-
opment and progression of ER-a-positive breast cancer
(Basse & Arock, 2015). It serves not only as an essential indi-
cator for prognosis and clinical outcomes but also holds
promise as a target for potential therapeutic interventions in
breast cancer treatment (Boon et al., 2010). When estrogen
binds to the ER located on the nuclear membrane, it triggers
a series of signals that enhance the potential for cell division
in the breast epithelium. The degree of cancer-causing
effects resulting from this ligand binding is influenced by the
specific variations in the ESR1 gene (Blakely et al., 2023;
Boon et al., 2010). Estrogen binds to ER’s hormone-binding
domain, forming an intermediate complex. This complex
forms dimers and interacts with co-regulatory proteins to
form a final complex, which can have either activating or
inhibiting effects on ERG transcription (Chari, 2013). Co-
activators such as AIB1 or SRC3 facilitate the binding of
estrogen response elements on the ERG promoter to the
DNA binding domain of ER (Xin et al., 2016).
CONTACT Guojun Zheng zhenggj@mail.buct.edu.cn
� 2023 Informa UK Limited, trading as Taylor & Francis Group
JOURNAL OF BIOMOLECULAR STRUCTURE AND DYNAMICS
https://doi.org/10.1080/07391102.2023.2275174
Immunohistochemistry investigations have revealed that ele-
vated levels of ER contribute to increased sensitivity and
responsiveness of benign breast epithelial tissues to estrogen
(Elledge et al., 2000; Wong & Hussain, 2020). Moreover, a sig-
nificant proportion of individuals diagnosed with primary
breast cancer exhibit amplification of the ESR1 gene (Budwit-
Novotny et al., 1986). This particular subtype of breast cancer
patients can be effectively treated by combining optimized
hormone therapy with therapeutic medications specifically
targeting the ESR1 gene. Computational techniques includ-
ing molecular docking, molecular simulation, virtual screen-
ing, and MMGBSA calculations offer a superior platform for
investigating inhibitors targeting the ESR1 gene compared to
laboratory experiments (Aljuaid et al., 2022; Farajzadeh-
Dehkordi et al., 2023; Mirzadeh et al., 2022). Therefore, the
ongoing research focuses on identifying a potent inhibitor
for the ESR1 target in breast cancer using structure-based
virtual screening and molecular simulation.
Materials and methods
The design of overall mechanism and various tools employed
in this study, illustrating the design of lead compounds
through rational drug design Figure 1.
Target structure retrieval and preparation
The three-dimensional structure of ERa complexed with 4-
hydroxytamoxifen was obtained through X-ray diffraction
and retrieved from the Protein Data Bank (PDB) using the
PDB ID 3ERT. The structure is available in PDB format and
can be accessed at (https://www.rcsb.org/) (Nickel et al.,
2022; L. Wang et al., 2023; L. Wang, Wang et al., 2023). The
preparatory steps included the deletion of heteroatoms,
water molecules, and the cognate ligand. Furthermore,
charge ions and missing hydrogen atoms were added.
Subsequently, the resulting structure was subjected to an
energy minimization algorithm using the Swiss-PdbViewer
v.4.10 software program then the protein were saved in.pdb
format (Gutti et al., 2023; Liguori et al., 2020).
Validation of virtual screening protocol
To validate whether our approach can distinguish between
active and inactive compounds, a virtual screen experiment
was performed using actives (640 ERa inhibitors, i.e. binders)
as positive control and decoys (18500 compounds, i.e. non-
binders) as negative datasets obtained from the Database of
Useful Decoys: Enhanced (DUD-E) (https://dude.docking.org/
generate). All the dataset compounds were docked into the
binding site of ERa (PDB ID: 3ERT).
Natural products compound libraries for virtual
screening
The application of virtual screening methods has greatly rev-
olutionized the identification of compounds with targeted
bioactivity. By employing computer simulations, these meth-
ods enable the evaluation of large structure libraries against
a biological target, revolutionizing the traditional approach
to compound discovery (Duay et al., 2023). For the successful
implementation of structure-based virtual screening (SBVS)
methods, it is essential to have the 3D structures of both the
drug target (receptor) and the ligands present in the data-
base. These structural data play a crucial role in enabling the
screening process and facilitating the identification of poten-
tial drug candidates (M. R. Yadav et al., 2023). To identify
new potential inhibitors, we utilized a molecular docking
approach known as structure-based drug design to evaluate
the binding modes of drug target proteins and ligands. This
method helps predict enhanced and favorable interactions
between the target receptor and the drug. The study utilized
Figure 1. Flow diagram of the present study to develop potent inhibitors against cancer treatment.
2 M. SHAHAB ET AL.
drug libraries containing traditional Chinese medicines (TCM)
from (http://tcm.cmu.edu.tw/) and the ZINC database
(Abdusalam & Murugaiyah, 2020) were downloaded and pre-
processed for this study. This database was screened for
Lipinski’s rule (R5) and Hopkins rule and removes ligands of
violating these rules, and a reactive functional group using
FAF4drug online webserver (Boopathi et al., 2021). Molecular
screening offers a means to identify potential inhibitor lead
compounds. This screening method involves scanning the
TCM and ZINC databases, enabling the discovery of new lead
hits from thousands of compounds. These hits are assessed
based on their ‘drug-likeness’ properties, indicating their
potential suitability as drug candidates (Shahab et al., 2023).
The top three hits from each database were visually analyzed
using PyMOL and Discovery Studio Visualizer. Additionally,
molecular simulation was performed to further validate the
results (Bell et al., 2012; DeLano, 2002).
Interaction study of lead compound from TCM, and
ZINC dataset
The study of molecular docking examines the interactions
between two or more molecular structures, such as a drug
and an enzyme or receptor. There are two distinct issues
with molecular docking. The optimal number of configura-
tions that contain the empirically discovered binding modes
should be produced by the search method. To determine
the optimum binding configuration, these configurations
have been assessed using scoring methods (Bender et al.,
2021; McNutt et al., 2021). However, the structure typically
lacks information regarding bond orders, topologies, formal
atomic charges, and may contain misaligned terminal amide
groups or unassigned ionization states and tautomeric states.
To optimize the results, the target receptor was subjected to
3D protonation, followed by energy minimization using the
default parameters of Discovery Studio 2020 software. This
included assigning bond orders, adding hydrogens, creating
disulfide bonds, and completing missing side chains and
loops. The retrieved compounds were subsequently docked
with Estrogen Receptor a (ERa) to evaluate their drug-like
properties. For every hit, twenty conformations were gener-
ated, and the highest-ranked conformations from each data-
set were chosen for in-depth analysis. The docking analysis
was carefully scrutinized, with a focus on docking scores and
interactions between the protein and the hits. Additionally,
the outcomes of molecular docking were validated through
Molecular Dynamics Simulations (MDS).
Molecular dynamics simulation (MDS)
After selecting the top scoring hits based on high docking
scores from the TCM and ZINC databases, molecular simula-
tions were conducted along with control drug. To validate
their efficacy, free energy calculations were performed using
the Amber22 package with the ff19SB force field (Love et al.,
2023). For the top scoring compounds at the active sites of
Era, the drug topologies were generated and processed
using antechamber and parmchk2 (J. Wang et al., 2001).
Topology and coordinates files were used to minimize each
complex in two stages: 1) the first round of minimization of
10000 steps and 2) the second round of minimization for
5000 steps was achieved. The complexes were constructed
and solved using Tleap, a preparatory program. Counterions
(Na þor Cl−) were introduced to neutralize each system.
Energy minimization was performed in two steps (steepest
descent and conjugate gradient) to reduce the energy of
each neutralized system. The minimized complexes were
then heated to 298 K over 20 ps. To keep the temperature
stable, the Langevin thermostat was activated (Shahab et al.,
2023) and Berendsen barostat was used to monitor the sys-
tem pressure. The covalent bonds were refined using the
AMBER22 SHAKE algorithm (Khan et al., 2021). The GPU ver-
sion (PMEMD.cuda) of AMBER22 was used to run MD simula-
tions on total seven complexes (Shahab et al., 2023). During
the production stage, a 100ns simulation was conducted for
each complex. The resulting trajectories were processed
using PTRAJ and CPPTRAJ (Roe & Cheatham, 2013; Salomon-
Ferrer et al., 2013).
Analysis of free energy calculation
The protein-ligand binding free energy was calculated using
the MM-PBSA (Molecular Mechanics/Poisson-Boltzmann
Surface Area) approach, utilizing the mmgbsa module in
three steps (Bhrdwaj et al., 2023; Deb et al., 2019; M. Yadav
et al., 2022).
1. Calculation of potential energy in a vacuum (DG_vacuum):
DGvacuum ¼DE MM þDG solvent þDG SA . . . :i
2. Prediction of polar solvation energy (DG_solvation_
polar):
DGsolvation polar ¼DG polar þDG cavitation
þDGdispersion
þDGrepulsion ...:ii
3. Estimation of non-polar solvation energy (DG_solvation_
nonpolar):
DGsolvationnonpolar ¼cSASA þb...iii
Finally, the total binding free energy (DGbind) is obtained
by summing up the contributions from each step:
DGbind ¼DG_vacuum þDG_solvation_polar þDG_solv-
ation_nonpolar
Where: DE_MM: Molecular Mechanics energy change
upon ligand binding, DG_solvent: Free energy change due to
solvation effects, DG_SA: Free energy change due to solute-
solvent interactions, DG_polar: Polar solvation energy
change, DG_cavitation: Energy change due to cavity forma-
tion in the solvent, DG_dispersion: Dispersion energy change,
DG_repulsion: Repulsion energy change, c: Surface tension
coefficient, SASA: Solvent-accessible surface area, and b:
Constant offset.
JOURNAL OF BIOMOLECULAR STRUCTURE AND DYNAMICS 3
Bioactivity probability and bioavailability analysis
To predict the bioactivity of the final hits, the SMILES (simpli-
fied molecular-input line entry system) canonical data was
inputted into the PASS (Prediction of Activity Spectra for
Substances) web server available at https://www.way2drug.
com/PASSOnline/predict.php. The server provides a
Probability of Activity (Pa) value, which represents the likeli-
hood of activity between the target protein and the pre-
dicted compound. It is important that the Pa value is higher
than the Probability of Inhibition (Pi) value. A Pa value
greater than 0.3 (medium confidence) indicates a computa-
tionally proven result, suggesting the need for further ana-
lysis (Probojati et al., 2022; Tamam et al., 2022). To assess the
permeability and solubility of the final compounds in the
body, we conducted bioavailability predictions. This involved
utilizing the SCFBio web server (http://www.scfbio-iitd.res.in/
software/drugdesign/lipinski.jsp) to evaluate the Lipinski rule-
based parameters, specifically R5, which includes molecular
weight, hydrogen bond donors, hydrogen bond acceptors,
log P (partition coefficient), and molar refractivity. The com-
pounds that comply with the Lipinski rule were further ana-
lyzed to determine their potential toxicity.
Pan assay interference
During the early stages of drug design, it is crucial to screen
for compounds that possess the desired pharmacokinetic
properties, such as ADMET. To ensure the selection of high-
quality compounds, an electronic filter known as Pan Assay
Interference Compounds (PAINS) is employed, which focuses
on identifying compounds of superior quality in databases
(Baell & Holloway, 2010). The PAINS filter is designed to scru-
tinize compounds that have a higher probability of interfer-
ing with assays, displaying chemical reactivity, being
frequently hit, and not being recognized by toxicophoric fil-
ters. To predict the ADMET characteristics of the compounds,
we utilized an online PAINS server (https://biosig.lab.uq.edu.
au/pkcsm/prediction). Those substances that passed the
PAINS filter and exhibited superior ADMET characteristics.
Results and discussion
Cancer is a group of malignant diseases marked by unregu-
lated cell growth and the capacity to metastasize to different
regions of the body. Among them, Breast Cancer, a heteroge-
neous disease at the molecular level, is the most common
cause of woman mortality worldwide (Shahab et al., 2022;
2023). The binding of estrogen with its receptor on the nuclear
membrane produces signaling cascades that’s undergoes the
mitotic development of the breast cancer. The degree of car-
cinogenicity that develops due to binding of ligand determined
by the allelic polymorphism of the ESR1 gene. Different SNPs
in the ER gene have been associated with various clinical, char-
acteristics in both normal and malignant breast tissue
(Dahlgren, 2021). Therefore, the development of new and
potent therapeutics is very important for effective treatment. In
this study, we have used computational and simulation based
methods to design potential natural products inhibitors for the
breast cancer due to its essential role. This protein, Era, is a
viable drug target and can be used for the early-stage inhib-
ition of breast cancer. Therefore, the traditional Chinese medi-
cine (TCM), and ZINC database was search for potential small
molecules in three steps to design potential inhibitors to target
Era protein. For docking, the active site of the ERa protein was
identified using standard drugs used to treat breast cancer i.e.
Tamoxifen, and Arimidex, as a positive control group, while
progesterone and were utilized as a negative control group.
The six compounds retrieved from Chinese traditional natural
database and commercially available Zinc database TCM22717,
TCM23524, TCM31953, while ZINC05632920, ZINC05773243,
and ZINC12780336 demonstrated better pharmacological
potential than the 4-hydroxytamoxifen taken as control exhib-
ited the docking score of −10.68 kcal/mol, −9.71 kcal/mol,
−9.56 kcal/mol, −7.57 kcal/mol, −7.50 kcal/mol, and −7.45 kcal/
mol respectively. We are optimistic that these significant dis-
coveries may represent a powerful ERa inhibition. Therefore,
these findings have significant medications for future diagnos-
tic and therapeutic approaches for this particular breast cancer.
We highly recommend the researchers wield the compounds
which we have selected in the aforementioned research.
Interaction study of the final hits through docking
Traditional Chinese Medicine (TCM) offers an excellent selec-
tion of medications for treating various diseases. A database
consisting of around 55,000 compounds from traditional
Chinese herbs underwent filtration based on Lipinski’s rule of
five and Hopkins rule. This filtering process identified 32,000
compounds that potentially adhere to these criteria, while
the remaining compounds were excluded. During the initial
screening, approximately 295 compounds were found to
have docking scores exceeding −6.0 kcal/mol. In the subse-
quent round, which employed the Induced-Fit docking (IFD)
method, three specific compounds emerged. While its
pharmacological potential has been previously documented,
its antiviral properties have recently been identified for the
first time. Although TCM has been reported to inhibit the
Mac-1 domain of NSP3 from SARS-CoV-2, this study provides
further insights into its interactions and binding patterns.
The docking analysis and binding energy of the final hits are
shown in Table 1 revealed that TCM22717 forms four hydro-
gen bonds and three hydrophilic interactions with specific
amino acids, including Glu 353, Arg 196, Thr 347, Arg 394,
and Met 421. These interactions contribute to a docking
score of −12.24kcal/mol, indicating strong affinity. For
TCM23524, the interacting residues are Leu 387, Glu 353, Asp
351, Thr 347, and Leu 346. Lastly, for TCM31953, the interact-
ing residues are Asp 351 and Thr 347. Notably, the binding
pattern of TCM22717 blocks more residues of the active site
compared to the control drug, suggesting potent inhibition.
The specific amino acids involved in the interactions for
TCM23524 and TCM31953 are Leu 387, Glu 353, Asp 351, Thr
347, and Leu 346. The binding mechanisms of these com-
pounds within the active site of the ERa protein involve mul-
tiple hydrogen bonds. These findings highlight the potential
4 M. SHAHAB ET AL.
of TCM22717, TCM23524, and TCM31953 as promising anti-
viral compounds and contribute to our understanding of
their mechanisms of action Figure 2.
In Figure 3, the binding mechanisms of the final three
selected hits from the ZINC database within the active site of the
ERa protein are as follows: (A) ZINC-05632920 binds to the ERa
binding pocket and is involved in three hydrogen bond interac-
tions. The interacting residues are Thr 347 (hydrophilic) and Leu
525 (hydrophobic). (B) ZINC-05773243 binds to the ERa binding
pocket and is involved in seven hydrogen bond interactions. The
interacting residues are Arg-394 (hydrophilic) and Leu 349
(hydrophobic). (C) ZINC-12780336 binds to the ERa binding
pocket and is involved in three hydrogen bond interactions. The
interacting residues are Leu 387 (hydrophobic), Arg 394 (hydro-
philic), Thr 347 (hydrophilic), and Leu 346 (hydrophobic). (D)
Represent the active site pockets where the drugs are binds.
RMSD analysis
To determine binding stability using a molecular simulations
approach is necessary for small molecules to have a stronger
pharmacological potential. It also affects the extent to which
the interaction between the protein and ligand exists. For
the molecular optimisation of the current drugs for improved
therapeutic potentials, binding stability is also crucial.
Processing the simulation trajectories and computing the
RMSD as a function of time allowed us to determine
the binding stability. Here we investigated and evaluated the
stability of all optimized hits along with the control (4-
hydroxytamoxifen). Based on the results, all six complexes
from both datasets exhibit consistent behavior with minimal
fluctuations. The averaged RMSD for each system is calcu-
lated between 1 and 4 Å and for control was initially 2.5 Å.
Upon reaching 15 ns of simulation, the RMSD graph exhib-
ited a reduction to 2.5, indicating a decrease in structural
deviation. Subsequently, at approximately 40 ns, the system
attained a state of stability. This stability persisted for the
remaining time of the simulation period of time. As can be
observed, the TCM22717 complex exhibits notable stability,
after 60 ns, the system temporarily demonstrated a slight
fluctuation. The system then attained an equilibrium and
entered the production stage. For TCM23524, the RMSD
demonstrates that the system exhibits relatively stable
Table 1. The finalized lead hit compounds along with positive and negative control’ binding interaction, docking score, and interaction details.
Compound Docking score kcal/mol
Interaction details
Ligand Receptor Interaction Distance E(kcal/mol)
TCM database
TCM-22717 −10.68 C
O
O
O
10
30
30
30
SD
OE1
OE2
NH2
MET
GLU
GLU
ARG
421
353
353
394
H-donor
H-donor
H-donor
H-acceptor
4.16
2.94
3.08
2.96
−0.5
−0.1
−1.7
−3.2
TCM-23524 −9.71 O
O
N
N
5-ring
19
15
22
22
SD
OG1
OD1
OD2
CB
MET
THR
ASP
ASP
ASP
343
347
351
351
351
H-donor
H-acceptor
Ionic
Ionic
Pi-H
3.28
2.91
3.04
3.66
3.58
−1.2
−0.5
−4.2
−1.4
−0.5
TCM-31953 −9.56 N
N
N
N
N
O
O
N
N
N
24
26
26
26
27
32
10
24
26
26
OE2
OE1
OE2
O
O
OD1
OG1
OE2
OE1
OE2
GLU GLU
GLU
LEU
LEU
ASP
THR
GLU
GLU
GLU
353
353
353
387
387
351
347
353
353
353
H-donor
H-donor
H-donor
H-donor
H-donor
H-donor
H-acceptor
Ionic
Ionic
Ionic
2.94
3.24
2.90
3.13
3.22
2.78
2.67
2.94
3.24
2.94
−9.3
−3.6
−8.1
−3.2
−1.0
−6.7
−2.7
−4.3
−3.0
−4.2
ZINC database
ZINC05632920 −7.57 C16
N5
6-ring
20
21
SD
OG1
CB
MET
THR
ALA
343
347
350
H-donor
H-acceptor
Pi-H
3.58
3.28
4.25
−0.8
−1.0
−0.9
ZINC05773243 −7.50 N1
N2
O1
O2
6-ring
18
19
14
21
O
SD
NH2
OGI
CD1
LEU
MET
ARG
THR
ILE
346
343
394
347
424
H-donor
H-donor
H-acceptor
H-acceptor
Pi-H
3.05
4.41
2.95
3.24
3.85
−1.7
−1.5
−4.8
−0.1
−0.6
ZINC12780336 −7.45 C4
C9
O2
4
15
7
OE2
SD
NH2
GLU
MET
ARG
353
421
394
H-donor
H-donor
H-acceptor
3.19
3.34
2.88
−1.6
−1.0
−4.9
Positive Control
Tamoxifen −5.80 N3
5-ring
3 OG1
CD2
THR
LEU
347
351
H-acceptor
pi-H
3.53
3.79
−0.8
−1.0
Arimidex −7.15 C14
C17
6-ring
6-ring
14
17
SD
SD
CB
CD1
MET
MET
ILE
LEU
343
421
387
424
H-donor
H-acceptor
Pi-H
Pi-H
3.58
4.11
4.68
3.94
−0.3
−0.3
−0.3
−0.4
Negative Control
Progestron −5.57 C17
O2
17
2
SD
CB
MET
HIS
343
524
H-donor
H-acceptor
3.99
3.21
−0.3
−0.5
JOURNAL OF BIOMOLECULAR STRUCTURE AND DYNAMICS 5
Figure 3. The binding mechanism of the of the final three selected hits from ZINC dataset inside the binding site of the ERa protein (A) binding of ZINC-05632920
with the binding pocket of ERa involved three hydrogen bond. (B) Binding of ZINC-05773243 with the binding pocket of ERa involved seven hydrogen bond. (C)
Binding of ZINC-12780336 with the binding pocket of ERa involved three hydrogen bond. (D) The ERa protein molecular cartoon representation representing the
active site pocket.
Figure 2. The binding mechanism of the of the final three selected hits from TCM dataset inside the binding site of the ERa protein (A) binding of TCM-22717
with the binding pocket of ERa involved three hydrogen bond. (B) Binding of TCM-23524 with the binding pocket of ERa involved seven hydrogen bond. (C)
Binding of TCM-31953 with the binding pocket of ERa involved three hydrogen bond. (D) The ERa protein molecular cartoon representation representing the active
site pocket.
6 M. SHAHAB ET AL.
behaviour throughout the simulation period, with just slight
deviations from the mean position being recorded at 14 and
20 ns, after which the system stabilizes and no appreciable
fluctuation from the mean was noticed. A small rise in the
RMSD curve was seen for the TCM31953 complex at around
41 ns, followed by a slight reduction in the RMSD curve at
50 ns. The system initially exhibits stable behaviour. After
that the system equilibrates with an average RMSD value of
1.7 Å as shown in Figure 4. A small increase in the
ZINC05632920 complex RMSD analysis was visible at 15 ns,
after which it remained steady for the duration of the simu-
lation. Throughout the simulation time, the ZINC05773243
complex displayed a consistent pattern with very modest
increases and declines, with an average RMSD of 2.0 Å. From
the beginning to 65 ns, the RMSD graph for ZINC12780336
displayed a relatively stable curve with an average RMSD
value of 2.0 Å, however between 65 ns and 77 ns, the curve
jumps abruptly and approaches 4.0 Å, then falls to 2.0. The
compounds’ backbone RMSD was marginally lower than the
control’s, indicating stable binding, which was further con-
firmed by RMSF and MM-GBSA analysis.
RMSF analysis
To understand the stability of the active site residues in ERa
upon the interacting with ligands, the RMSF (Root Mean
Square Fluctuation) analysis was performed on individual
amino acids. This analysis was conducted over the entire
100 ns MD (Molecular Dynamics) trajectories, as depicted in
Figure 5. The RMSF values were computed for the control
compound as well as six different compounds, three sourced
from TCM (Traditional Chinese Medicine) and three from
ZINC. When comparing the RMSF results of compound
TCM22717 with the control compound, it was observed that
the residues in the ranges 50-90 and 126-180 exhibited
smaller fluctuations, approximately 12.0 Å. Smaller fluctua-
tions in these regions indicate a more stable complex
between TCM22717 and ERa. On the other hand, the fluctua-
tions of the remaining residues were comparable to those
observed with the reference compound. Despite some
regions where the control compound exhibits fewer fluctua-
tions, considering binding energies, docking scores, RMSD,
and RMSF values, TCM-22717 appears to be a superior inhibi-
tor to the control (Figure 5A). TCM23524 demonstrates
reduced fluctuations (10.0 Å for residues 48-95, 15.0 Å for res-
idues 105-128, and 12.0 Å for residues 180-220) in specific
regions, while other regions exhibit local fluctuations com-
pared to the control. In certain regions, the control com-
pound also exhibits local fluctuations compared to the
selected compounds. Considering additional analysis parame-
ters, TCM23524 emerges as a more promising candidate
compared to the control compound (Figure 5B), despite the
control compound displaying stability in certain regions. In
the case of the TCM31953 complex (Figure 5C), there are
minor fluctuations observed for residues 1-170 (around 10.0
to 12.0 Å), while residues 175-200 exhibit a peak at 15.0 Å fol-
lowed by a decrease to 9.0 Å. Regarding the ZINC05632920
complex (Figure 5D), the RMSF analysis reveals significant
fluctuations in residues 30, 80-100, 160, and 190. However,
when considering other analysis parameters, this compound
remains comparable to or even superior to the control com-
pound. The ZINC05773243 complex appears stable in most
regions and fluctuates slightly in some regions Figure 5E.
The RMSf plot for ZINC12780336 showed that slight fluctua-
tions were observed for residues 60,165 and 190 Figure 5F.
Considering other analysis parameters, all these compounds
can be considered better candidates than the control.
Figure 4. Dynamic stability assessment of the top hits (a) show the RMSD results for the 4-hydroxytamoxifen(control) vs TCM22717 (b) show the RMSD results for
the 4-hydroxytamoxifen (control) vs TCM23524, (c) show the RMSD results for the 4-hydroxytamoxifen(control) vs TCM31953 while (d) show the RMSD results for
the 4-hydroxytamoxifen(control) vs ZINC05632920 (e) show the RMSD value for hydroxytamoxifen (control) vs ZINC05773243(f) show the RMSD results for the
4-hydroxytamoxifen(control) vs ZINC12780336.
JOURNAL OF BIOMOLECULAR STRUCTURE AND DYNAMICS 7
Hydrogen bond analysis
The strength of the interacting molecules can be seen by
analyzing hydrogen bonds, with criteria including a Distance
Criterion of approximately 3.5 Å−3.7Å, an Angle Criterion of
around 150−180, and a Donor-Hydrogen-Acceptor Angle
Criterion of 90−180. As a result, we examined all the
hydrogen bonds in each trajectory to see how the hydrogen
bonding pattern varied during the simulation. According to
the time-dependent hydrogen bonding analysis, the final hits
had the best and strongest hydrogen-bonding networks with
the target protein. To better understand how a medicine
interacts with a receptor, it is crucial to comprehend the
hydrogen bonding environment in molecular complexes. We
observed that the average number of hydrogen bonds in the
complexes of six hits (TCM22717, TCM23524, TCM31953,
ZINC05632920, ZINC05773243, and ZINC12780336) remained
higher than that in the control drug. For instance,
TCM22717, TCM23524, and ZINC05773243 reported the high-
est number of hydrogen bonds meeting the specific donor-
acceptor distances and angles within the defined criteria.
These bonds adhered to the Distance Criterion of
3.5 Å−3.7 Å, the Angle Criterion of 150−180, and the
Donor-Hydrogen-Acceptor Angle Criterion of 90−180,
between the ligand and receptor during the simulations. The
results shown in Figure 6 demonstrate that our identified
hits may strongly inhibit the function of the Era protein com-
pared to the control drug in the pharmacological drug assay.
RoG analysis
In order to examine the dynamics of each complex and
understand the binding and unbinding events that took
place during the simulation, we assessed the structural com-
pactness of the complexes in a dynamic environment. This
was done by calculating the radius of gyration (Rg) as a func-
tion of time. For the TCM22717 complex, initially, higher Rg
values were observed for a brief period between 2-8 ns.
Subsequently, the Rg value decreased and then increased
again for a shorter period between 32-41 ns. Following this,
the Rg value decreased once more and maintained a rela-
tively consistent pattern until the end of the 100 ns simula-
tion. TCM23524 complex showed an increase in the Rg curve
from the beginning up to 19 ns with an average RoG value
of 18.8–19.1 Å and then decreased to 18.8 Å and continued
to follow the same pattern until 100ns. The average Rg value
for TCM-31953 was calculated to be 18.8 Å. The TCM31953
RoG curve showed a slight increase from the beginning up
to 16 ns and then followed a uniform pattern up to 100 ns
without any significant increase or decrease in the Rg value.
ZINC05632920 showed a stable pattern from onset to 45ns,
and a slight increase in RoG value was observed between
45–52 ns and then decreased back to continue the same pat-
tern until 100 ns. The ZINC05773243 complex showed a simi-
lar pattern up to 100ns with slight increases in two regions
at 5 ns and 20 ns. ZINC12780336 showed little perturbation
in Rg value but considering other analysis parameters like
binding energy, docking score, RMSD, and RMSF values, this
compound appears to be a better inhibitor Figure 7(A–F).
Analysis of free-energy landscape
In molecular dynamics simulations, the free-energy land-
scape (FEL), together with the Gibbs free energy, is a repre-
sentation of possible protein conformations. Two variables
that determine conformational variability and reflect dis-
tinctive system characteristics are represented by FEL. The
free-energy landscape (FEL) was investigated against the
radius of gyration (RoG) and the root-mean square
Figure 5. Residual flexibility assessment of the top hits. (a) show the RMSF results for the 4-hydroxytamoxifen(control) vs TCM22717 (b) show the RMSF results for
the 4-hydroxytamoxifen (control) vs TCM23524, (c) show the RMSF results for the 4-hydroxytamoxifen(control) vs TCM31953 while (d) show the RMSF results for
the 4-hydroxytamoxifen(control) vs ZINC05632920 (e) show the RMSF value for hydroxytamoxifen (control) vs ZINC05773243 (f) show the RMSF results for the 4-
hydroxytamoxifen(control) vs ZINC12780336.
8 M. SHAHAB ET AL.
deviation (RMSD) as the two reaction coordinates in order
to depict the energy minima landscape of lead complexes.
It has been employed to describe conformational changes
associated to protein folding and unfolding processes.
Despite the fact that the free energy landscape of the pro-
tein can be rough and high dimensional (HaSSGaWPG,
1991). It usually can point out the predominate lower
energy regions and can show the routes used by proteins
to change their shape as they unfold. To learn more about
the conformational behaviour of the Era protein, the Gibbs
free energy landscapes (FELs) were analysed using the first
two EVs. Seven FEL contour maps in all (containing control
and lead impacts) have been studied; considerable overlap
between these FELs shows excellent agreement for the
described configurations. The FEL figure demonstrated the
transition from the control/Era, final hits state, and
unfolded state along a minimum free energy pathway com-
posed of many stable intermediates. Figure 8(A–F) displays
the FELs of control/Era and the final hits. The lower energy
conformational states are shown by the deeper purple in
the graphs. Only two global minimums contained within a
double local basin are seen in Era/control. The conform-
ational motion of ZINC05632920/Era, TCM22717/Era,
TCM31953/Era, and ZINC12780336/Era, on the other hand,
established distinct motions with noticeable modifications
and proceeded to several stable global minima. The 3D
graphs show that the overall complexes formed a multiple
funnel, which is conclusive evidence that the complexes
have several local energy minima and are hence stable fold-
ing systems.
Figure 7. Radius of gyration analysis for the control and top six hits identified through molecular search.
Figure 6. Hydrogen bonding analysis for the control and top six hits identified through molecular search.
JOURNAL OF BIOMOLECULAR STRUCTURE AND DYNAMICS 9
Molecular mechanics - Generalized born surface area
(MM-GBSA) calculation
MM-GBSA calculation method is primarily used for rescoring
the docked pose of ligand. These poses are taken as inputs
for the energy minimization for the protein-ligand com-
plexes. It generates different ligand orientations by using
various docking software which further employs it for start-
ing ligand-receptor coordinates for MD Simulation analysis.
The best binding free energy is obtained and the most reli-
able ligand Review Version orientation is identified.
Moreover, it also analyses the contribution of each receptor
residue interacting with the ligand and evaluates the individ-
ual energy terms of the binding free energy. therefore, by
using the MMGBSA method, it is possible to develop an in-
depth analysis paradigm for receptor-ligand interactions and
eventually design new active ligands. The MMGBSA method
was employed to assess the molecular docking process for
both wild and mutant types of the target protein. This evalu-
ation aided in ranking the most effective inhibitors for the
target protein. The MMGBSA values, represented by DG bind,
ranged from −30.4525 ± 3.3565 to −57.0597 ± 3.4852, as
shown in Table 2. These results were correlated with the
docking score to identify drug-like potent inhibitors. A higher
DG bind value indicates a stronger binding affinity between
the ligand and the receptor. Further, their individual energy
contribution was calculated which shows the residues Gly 40,
Leu 42, Leu 200, LYS 225, Glu 353, Arg 196, Thr 347, Leu
Table 2. Free energy analysis of the final hits and control drug.
No Compound ID vdW EEL ESURF EGB TOTAL (kcal/mol)
1 TCM22717 −60.2022 −3.6682 −7.9254 14.7361 −57.0597 ± 3.4852
2 TCM23524 −57.7227 −0.9380 −6.9757 8.7279 −56.9084 ± 3.3737
3 TCM31953 −48.8288 231.9621 −209.5666 −5.9858 −32.4191 ± 3.8864
4 ZINC05632920 −45.4340 0.6209 −5.7163 4.2112 −46.3182 ± 2.7380
5 ZINC05773243 −41.2409 0.0610 −5.2850 8.0959 −38.3690 ± 2.8240
6 ZINC12780336 −42.7117 −29.8767 −5.9334 47.7171 −35.8048 ± 4.1571
7 Control drug (4-hydroxytamoxifen) −29.3866 50.0872 −5.0529 −46.1002 −30.4525 ± 3.3565
Figure 8. (A–G) Free-energy landscape diagrams of the control/era, and final selected hits/era.
10 M. SHAHAB ET AL.
387, and Asp 351. The total energy contribution of these res-
idues showed correlation of −0.072, −0.48, −0.44, and
−0.034 respectively. The outcomes obtained through
MMGBSA analysis were particularly useful in understanding
the binding mode of the ligand with the receptor, facilitating
the design of potent inhibitors against Era.
Table 3. Bioactivity, and Bioavailability, analysis based on Lipinski’s rule.
Criteria based on R5
Compound IDs Weight (g/mol) LogP (<5) H-Donor (<5) H-Acc(<10) Molar Refractivity (40-130)
TCM-22717 436.59 3.70 3 5 66.74
TCM- 23524 466.62 3.27 4 4 66.74
TCM- 31953 446.44 −3.75 2 2 82.62
ZINC05632920 308.39 3.64 0 4 98.15
ZINC05773243 299.33 0.61 4 3 70.62
ZINC12780336 311.43 −2.12 2 3 69.5
Table 4. 2D structures, TCM & ZINC database ID, IUPAC names and PAINS analysis.
Compound ID Pains filter 2D Structure IUPAC name
TCM-22717 Pass Acetic acid 1-[2-(3-cyclopentyloxy-4-hydroxy-
phentyl)-ethyl]-3,10-dihydroxy-decyl ester
TCM- 23524 Pass 5-(1-5[5-Butyl-furan-2-yl)-ethyl]-2-hydroxy-
phenoxymethyl)-cyclohexyl-3-isopropyl-
3H-pyrrolium
TCM- 31953 Pass 2-Amino-6-[4-(1,3-dicarboxy-propylcarbamoyl)-
phenylamono]-methyl-4-oxo-3,4,5,6,7,8-
hexahydro-pterdin-1-ium
ZINC05632920 Pass 1-[5-methyl-2-phenyl-3-(1,2,4-triazol-1-yl)
hex-1-enyl]-1,2,4-triazole
ZINC05773243 Pass [1-(2,4-dihydroxyphenyl)-3-phenyl-
propylidene]amino urea
ZINC12780336 Pass 2-(benzyl-methyl-amino)-N-[(3S)-3-
methyl-1,1-dioxo-thiolan-3-yl]acetamide
JOURNAL OF BIOMOLECULAR STRUCTURE AND DYNAMICS 11
Bioactivity and bioavailability prediction of the final
hits
To check the drug likeness properties of the final retrieved
we deposited these compound to an online server; (PASS
web server https://www.way2drug.com/PASSOnline/predict.
php), and SCFBio web server (http://www.scfbio-iitd.res.in/
software/drugdesign/lipinski.jsp) to check the bioactivity and
bioavailability. According to bioactivity analysis all the six
complexes found to have a good bioactivity utilising the
PASS online server. The Predictions made using Lipinski ser-
ver indicated that the final hits complied with Lipinski’s rules,
which are molecular weight (MW) <500 Daltons, lipophilicity
(LogP) <5, hydrogen donor bond (HBD) <10, and molar
refracticity (MR) 40–130 (Wahyuni et al., 2022). These com-
pounds are predicted no toxicity, have good absorptivity and
can trigger biological responses when interacting with target
proteins Table 3.
The pan assay interference compounds (PAINS) filter
assay
PAINS electronically filters quality compounds in the data-
base targeting substances that could potentially disrupt
assays due to their higher chemical reactivity, leading to a
higher incidence of false positive hits (Baell & Holloway,
2010). All the six compounds from both database were
passed from the electronic filter as well as their ADMET prop-
erties were studied using online pain server (Maruca et al.,
2019). During drug design, it is always recommended to sub-
ject compounds to multiple filtrations for desirable pharma-
cokinetic features, such as ADMET characteristics. The
compounds that passed the filter with appropriate drug like
properties are shown in Table 4.
Conclusion
The primary objective of this study was to perform a virtual
screening of substances sourced from the TCM and ZINC
databases. Additionally, molecular docking and molecular
dynamics simulations were carried out for chosen com-
pounds and a reference ligand (4-hydroxytamoxifen) to
assess their binding interactions with the ERa protein. Six
natural compounds, namely TCM22717, TCM23524,
TCM31953, ZINC05632920, ZINC05773243, and
ZINC12780336, exhibited significant interactions with the
active site of the ERa protein. Their binding affinities ranged
from −6.2 to −9 kcal/mol. Then the Molecular docking, and
MD simulation results govern that these compounds appear
to form extremely persistent hydrogen bonds with the com-
mon residues of Gly 40, Leu 42, Leu 200, Lys 225, Glu 353,
Arg 196, Thr 347, Leu 387, and Asp 351. Similarly, PCA, FEL,
and MMPBSA analysis suggests that all complexes exhibit a
more stable complex for their inhibitory activity as compared
to the reference complex. Furthermore, according to bio-
activity analysis, PAINS Filter Assay analysis all these com-
plexes found to have a good bioactivity utilising the PASS
online server. These findings strongly indicate that these
compounds hold potential for drug development targeting
the ERa protein, potentially serving as potent inhibitors for
cancer treatment. To further validate their candidacy as
inhibitors for the ERa protein, additional in-vitro and in-vivo
clinical testing is warranted.
Author’s contributions
Conceptualization, Muhammad Shahab; Formal analysis,
Muhammad Shahab; Methodology, Muhammad Shahab,
Maryam Zulfat; Funding acquisition, Zheng Guojun;
Investigation, Muhammad Shahab, Methodology, Validation,
Muhammad Shahab; Software, Maryam Zulfat; Supervision,
Zheng Guojun; Writing—original draft, Writing—review &
editing, Muhammad Shahab, Zheng Guojun.
Disclosure statement
The authors declare that they have no conflicts of interest.
Funding
This research was funded by the National Key R&D Program of China
No. 2021YFC2102900, No. 2021YFC2101503 and Beijing Natural Science
Foundation No. L212001.
Data availability statement
All data generated or analyzed during this study are included in the
article.
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