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Article
Volume 12, Issue 6, 2022, 8385 - 8393
https://doi.org/10.33263/BRIAC126.83858393
In Silico Effects of Steviol on Depression, Inflammation
and Cancer Biomarkers
Kun Harismah 1, Farzaneh Fazeli 2, Issa Amini 3, Muhammad Da’i 4, Mahmoud Mirzaei 5,*
1 Department of Chemical Engineering, Faculty of Engineering, Universitas Muhammadiyah Surakarta, Surakarta,
Indonesia; kun.harismah@ums.ac.id (K.H.);
2 Department of Biology, Payame Noor University, Tehran, Iran; seacorales@yahoo.com (F.F.);
3 Department of Chemistry, Payame Noor University, Tehran, Iran; issaamini5548@gmail.com (I.A.);
4 Department of Pharmaceutical Biotechnology, Faculty of Pharmacy, Universitas Muhammadiyah Surakarta, Surakarta,
Indonesia; m.dai@ums.ac.id (M.D.);
5 Biosensor Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences,
Isfahan, Iran
* Correspondence: mdmirzaei@pharm.mui.ac.ir (M.M.);
Scopus Author ID 57226521283
Received: 25.10.2021; Revised: 23.11.2021; Accepted: 26.11.2021; Published: 11.12.2021
Abstract: Steviol (ST1), a known natural product, and methylated models (ST2-ST4) were investigated
in this in silico work to see their effects were examined on each of depression, inflammation, and cancer
biomarkers by participating in interactions with each of monoamine oxidase-A (MAO-A),
cyclooxygenase-2 (COX-2), methyltransferase (MTN) enzymes, respectably. The stabilized structures
of ST1-ST4 were achieved by performing optimization calculations. Subsequently, formations of
interacting ligand-target complexes were examined by molecular docking (MD) simulations. The
evaluated molecular orbital features showed a different tendency of ST1-ST4 models for contributing
to electron transfer processes. Accordingly, the interacting ligand-target complexes showed differential
interactions of each ligand towards each target, making ST1-ST4 as appropriate compounds for the
detection of targets. The methylated ST2-ST4 models worked even better than the original ST1 model
to affirm the benefit of steviol modification to achieve desired results. Meaningful interactions of ST1-
ST4 with the targets also showed the possible application of steviol for the medication of each of
depression, inflammation, and cancer cases.
Keywords: steviol; depression; inflammation; cancer; molecular docking; in silico.
© 2021 by the authors. This article is an open-access article distributed under the terms and conditions of the Creative
Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
1. Introduction
Natural products have always been used as complementary food materials in addition
to their long-time roles in traditional medicine [1–3]. In this regard, several attempts have been
dedicated to characterizing the features of such natural products besides developing their
synthesis procedures in laboratories [4–6]. Moreover, exploring new functions for such related
compounds has also been an important task regarding drug design, discovery, and
developments [7–9]. Indeed, so many available pharmaceutical compounds have been
generated from the already available natural products, which leads to further investigations for
increasing knowledge about the topic of applications of natural products in living systems [10–
12]. Steviol (Figure 1) is a known natural product isolated from the Stevia rebaudiana plant,
which could be used as a sweet compound instead of sugar [13–15]. Earlier works have
investigated the bioactivity of steviol, showing the importance of this compound for
applications in living systems [16–18]. Various types of research works have been performed

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on the issue of steviol to this time to show their features for employing in living systems [19].
Accordingly, the effects of steviol on depression, inflammation, and cancer biomarkers have
been investigated in this work based on employing the in silico approach for solving problems
in science and technology [20–22]. Indeed, combinations of mathematical algorithms and
theoretical approaches could help provide a useful tool for investigating the materials at the
smallest scales. The tool has been seen applicable for doing such issues by performing
computer-based works [23–25].
Biomarkers have dual importance in recognizing and managing diseases in living
systems [26–28]. In this regard, providing useful tools for detecting such biomarkers could also
help to medicate them, especially in the early stages of disease progression [29–31]. To achieve
such a purpose, examining the benefits of the natural product is an important issue regarding
the development of new pharmaceutical compounds [32–34]. In this work, monoamine
oxidase-A (MAO-A), cyclooxygenase-2 (COX-2), and methyltransferase (MTN) were
employed as biomarkers of depression, inflammation, and cancer to interact with the steviol
compound [35–37]. Indeed, complex formations of interacting ligand-target systems were
investigated for achieving the purpose of detection and medication of biomarkers with the
assistance of the ST compounds. In this regard, earlier works indicated that the investigation of
interacting substances could help recognize the effects of such complex formations on features
of each substance counterpart [38–40]. To this aim, computation on individual ST ligand
structures was performed first, and their interactions by performing molecular docking (MD)
simulations were examined next. Consequently, the effects of steviol ligands on each of MAO-
A, COX-2, and MTN targets were investigated to show the potency of this natural product for
further application in living systems. For more clarifying the purpose of this work, it could be
mentioned that the model of a natural product was investigated towards three biomarkers to
find possible interacting complex formations of the models. The in silico work was employed
to obtain the required information for discussing the current purpose.
Figure 1. Steviol (left) and its derivatives representation (right).
2. Materials and Methods
As described in Table 1, the geometers of four steviol models (ST1-ST4) were
optimized by employing the semi-empirical PM3 method as included in the Gaussian program
[41]. After doing this step, ligand structures were prepared for further analysis regarding their
specifications, in addition to examining their interactions with the specified targets. ZINDO
calculations were performed on the already optimized ST1-ST4 compounds to evaluate their
molecular orbital features, including HOMO and LUMO implying for the highest occupied and
the lowest unoccupied molecular orbitals, EG implying for energy gap, H and S implying for
chemical hardness and softness, and DM implying for dipole moment, which were all listed in
Table 1. Additionally, representations of HOMO-LUMO distribution patterns were visualized
for the optimized ligands, as shown in Figure 2. As a consequence, the ligands' specifications

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and descriptions were evaluated to show their original features prior to their involvement in
interactions with the target. Next, 3D macromolecular structures of each of MAO-A (2z5y),
COX-2 (3ln1), and MTN (4x61) enzymes were obtained from the protein data bank [42] to be
designated for the target compounds of molecular docking (MD) simulations. For performing
accurate MD simulations, ligands and targets were submitted to the SwissDock web server [43].
Each of the interacting ST ligands and MAO-A, COX-2, and MTN targets were examined for
complex formations in a defined 40*40*40 grid box. As a result, values of ΔG for showing the
strength of ligand-target complexes and values of RMSD for showing structural variations of
ligands from beginning up to completing the MD simulation processes were evaluated in
addition to representation of surrounding amino acids of centralized ligand (Figure 3). To this
point, the effects of ST ligands on each of MAO-A, COX-2, and MTN targets, implying
depression, inflammation, and cancer biomarkers, were calculated for providing information to
achieve the goal of this work.
Table 1. Molecular descriptors for the optimized models.
R1
R2
HOMO eV
LUMO eV
EG eV
H eV
S eV-1
DM Debye
ST1
H
H
-9.14
0.59
9.73
4.86
0.20
7.79
ST2
Me
H
-9.09
0.62
9.71
4.85
0.21
7.68
ST3
H
Me
-9.04
0.63
9.67
4.83
0.21
7.57
ST4
Me
Me
-9.01
0.68
9.69
4.84
0.21
7.48
3. Results and Discussion
This work's main goal was to investigate the effects of steviol (Figure 1) on depression,
inflammation, and cancer biomarkers based on employing the in silico approach. To this aim,
four models of steviol, including ST1-ST4, were designed by methylation (Me) of hydroxyl
groups of the structure, as described in Table 1. As a result, ligands were obtained based on
optimization processes to reach the minimized energy structures. Afterward, their molecular
orbital features were evaluated. At this step, the results of Table 1 could show that the evaluated
descriptors of models detected effects of the methyl group addition by changes of values of
features compared to each other. Both HOMO and LUMO levels underwent changes in energy
levels, in which their energy differences also detected the effects of such changes of frontier
molecular orbitals. It is worth mentioning here that each of HOMO and LUMO levels could be
designated for possible levels of electron transfer processes, in which HOMO could imply for
that of electron-donating level, and LUMO could imply for that of electron-accepting level. In
this case, variations of such levels could change the electronic features of the models for
contributing to both internal and external electron transfer processes. Accordingly, values of
EG could help to know the distance of HOMO and LUMO levels for the internal electron
transferring process. The results indicated that St1-ST4 models showed different features
regarding the energy vales of HOMO and LUMO levels with a reduction of energy distance
between the two levels in the methylated group. This reduction could help to proceed internal
electron transferring process easier, in which values of chemical hardness and softness (H and
S) could affirm this issue. In the case of the contribution of a compound to reaction processes,
both H and S features could help predict the purpose. Smaller H and larger values of S could
be suitable for substance participation in the reaction processes.

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HOMO
LUMO
ST1
ST2
ST3
ST4
Figure 2. Visualized HOMO-LUMO distribution patterns and ESP surfaces.
Visualized HOMO-LUMO distribution patterns (Figure 2) also showed slight variations
of molecular orbital patterns among the models. As an important point for such HOMO-LUMO
patterns, they were localized at opposite sides of the molecule, making it suitable for interaction
with other substances. In this regard, notable dipole moment (DM) values could affirm such
typical HOMO-LUMO distribution patterns for ST1-ST4 ligand models. As a consequence,
stabilized structures of ST1-ST4 ligand models were obtained, and their frontier molecular
orbital features were analyzed for making a description of such ligand systems for involving in
interactions.
To examine the effects of each of ST1-ST4 ligands on each of depression, inflammation,
and cancer biomarkers, formations of interacting ligand-target complexes were investigated by
performing MD simulations. In this case, MAO-A, COX-2, and MTN enzymes were located at
the position of the target for involvement in interaction processes. To do this, the results of
energy strength of formations of such interactions were evaluated in addition to the
conformational location of centralized ligand among the surrounding amino acids. The obtained
results were represented in Figure 3 to show surrounding amino acids and energy strengths.
Moreover, conformational variations of ligands from the start point to the endpoint of MD
simulations were summarized in the term of RMSD, meaning the magnitude of such variation.

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Consequently, the models were summarized through quantitative and qualitative descriptions
to achieve the goal of this work to see the effects of ST ligands on the specified targets. As a
benefit of performing in silico works, details of interactions could be achieved very well for the
investigated systems.
MAO-A
COX-2
MTN
ST1
ΔG = –6.32
RMSD = 33.88
ΔG = –7.19
RMSD = 21.44
ΔG = –7.21
RMSD = 18.21
ST2
ΔG = –7.15
RMSD = 25.75
ΔG = –7.35
RMSD = 18.48
ΔG = –7.31
RMSD = 21.24
ST3
ΔG = –6.34
RMSD = 27.17
ΔG = –7.36
RMSD = 25.54
ΔG = –7.64
RMSD = 21.77
ST4
ΔG = –6.86
RMSD = 27.66
ΔG = –7.61
RMSD = 27.79
ΔG = –7.32
RMSD = 22.35
Figure 3. Interacting ligand-target complexes.

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Comparing the obtained results of Figure 3 could show quick achievements regarding
the importance of the initial hypothesis about proposing methylated steviol models. The
strengths of those interacting ST2-ST4 ligands with the targets were more significant than the
original ST1 ligand. Moreover, the strength of each ligand towards targets was different,
showing a differential diagnosis of ligand for a specified target. In this regard, two main points
could be concluded; first, ST2-ST4 ligands could work better than ST1, and second, a
differential diagnosis could be occurred by employing a ligand towards different targets. As
mentioned before, two purposes of detection and medication could be explored for the ligands
through formations of interacting ligand-target complex systems, as could be seen by the
differential diagnosis of ST1-ST4 towards MAO-A, COX-2, and MTN targets. More analysis
of the obtained complex models could lead to some achievements regarding the importance of
∆G and RMSD values for the characterization of the models. In addition, the surrounding amino
acids and types of interactions were other important achievements of this work. On a quick
note, steviol could be considered a possible ligand for participating in interactions with the
specified targets. However, careful analysis of the models could distinguish steviol compounds
with specificity towards each target. By the obtained results, ST2 could be considered for
selective interaction with MAO-A, ST4 could be considered for selective interaction with
COX-2, and ST3 could be considered for selective interaction with MTN targets regarding their
calculated effects on depression, inflammation, and cancer biomarkers. This achievement
shows the importance of structural modifications for obtaining desired ligands, in which each
ligand could work its role in the living systems. Indeed, lead optimization is an important
procedure of drug design techniques. Steviol was considered a lead compound to be optimized
for participating in differential interaction with MAO-A, COX-2, and MTN targets. Comparing
with other related works could show that the investigated steviol could be considered an
appropriate candidate for the complementary medication of such depression, inflammation, and
cancer cases [44–46].
4. Conclusions
Effects of steviol compounds on each of depression, inflammation, and cancer
biomarkers were investigated in this work by examining formations of interacting methylated
ligands of steviol towards each of MAO-A, COX-2, and MTN enzyme targets. In this regard,
optimized models of ligands were obtained, and their features were evaluated in singular forms
and interactions with the target models. The achievements of this work can be summarized as
follows. First, methylated steviol compounds (ST2-ST4) showed geometrical stability in
addition to the original one (ST1), and their molecular features indicated differences in their
contribution to electron transfer processes. Second, both internal and external contributions of
ST1-ST4 ligands to electron transferring processes detected the effects of methylation. Third,
each ligand showed different stability towards the specified target, making them appropriate
structures for diagnosing biomarkers. Fourth, the methylated ST2-ST4 ligands worked better
than the original ST1 ligand in all cases f interactions showing the importance of lead
optimization to obtain new steviol compounds with specificity towards the enzyme target. And
as a final note, the proposed ST1-ST4 ligands could work for detection purposes by their
different strengths of interacting complex formations. They could also work for medication
purposes by their meaningful contribution to interactions with the target enzymes.

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Funding
This research received grant no. (299093) from the research council of the Isfahan University
of Medical Sciences.
Acknowledgments
Mahmoud Mirzaei acknowledges the support of this work by the research council of the Isfahan
University of Medical Sciences.
Conflicts of Interest
The authors declare no conflict of interest.
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