




































Highlights in BioScience
ISSN:2682-4043
DOI:10.36462/H.BioSci.202501

Research Article

Open Access

1 Biology Department, Faculty of Mathematics

and Natural Sciences, University of Jember,

Jember, East Java, Indonesia.

* To whom correspondence should be
addressed: senjarini@unej.ac.id

Editor: Hatem Zayed, Department of Biomedical
Science, College of Health Sciences, Qatar
University, Doha, Qatar.

Reviewer(s):
Laila Dabab Nahas, Usher Institute, University of
Edinburgh, Scotland, United Kingdom.

Hala Abdelgaid, National Hepatology and Tropical
Medicine Research Institute (NHTMRI) Corniche El
Nil, Imbaba, Giza 12651, Egypt.

Received: December 20, 2024

Accepted: April 25, 2025

Published: May 5, 2025

Citation: Oktarianti R, Nurdianti F, Wathon S,
Senjarini K. In silico study of the interaction between
serotonin and D7 protein from the salivary gland of
Aedes aegypti. 2025 May 5;8:bs202501

Copyright: © 2025 Oktarianti R et al.. This is an
open access article distributed under the terms of
the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduc-
tion in any medium, provided the original author and
source are credited.
Data Availability Statement: All relevant data are
within the paper and supplementary materials.
Funding: This research is supported by the Na-
tional Research and Innovation Agency (BRIIN)
through the Research and Innovation Program for
Advanced Indonesia, Batch 3, under reference num-
ber 12/II.7/HK/2023
Competing interests: The authors declare that they
have no competing interests.

In silico study of the interaction between serotonin and D7 protein
from the salivary gland of Aedes aegypti

Rike Oktarianti1
><�, Faranisa Nurdianti1

>< , Syubbanul Wathon1
>< �, Kartika

Senjarini1 >< �

Abstract

Protein components of the salivary glands of disease vectors have been known
to facilitate the blood-feeding process in the host body. The main component of
the salivary glands of Aedes aegypti is the immunogenic D7 protein. During the
blood-feeding process, the D7 protein can bind to biogenic amine compounds, such
as serotonin, which is a neurotransmitter involved in platelet activation. This ability
indicates that the D7 protein can inhibit the platelet aggregation process. This study
aims to explore in silico the interaction between serotonin and the D7 protein from the
salivary glands of Ae. aegypti using a molecular docking approach. The methods used
in this study include the selection of the 3D structure of the D7 protein and serotonin
ligand, preparation of the 3D structure of the D7 protein, native ligands, and test
ligands, validation of the molecular docking method, and analysis and visualization of
the molecular docking results. The results of molecular docking between the D7
protein and the serotonin ligand showed a ∆G value for the interaction of −9.25
kcal/mol. The serotonin ligand binds to the active site of the D7 protein through several
amino acid residues, including GLU 158, ILE 175, ARG 176, TYR 178, TYR 248,
ASP 265, and GLU 268. These amino acid residues of the D7 protein bind to atoms
on the serotonin ligand through conventional hydrogen bonds, carbon hydrogen bonds,
π-σ bonds, π-π T-shaped bonds, and π-alkyl bonds. Based on the in silico data, it is
shown that the D7 protein from the salivary glands of Ae. aegypti can bind stably and
spontaneously to serotonin ligands. This indicates that the D7 protein has potential as a
platelet aggregation inhibitor agent for the development of drug discovery in the fields
of health and pharmacy.

Keywords: Aedes aegypti, D7 protein, molecular docking, serotonin, platelet aggregation

Introduction
The salivary glands of disease vector arthropods are known to be important organs for the success

of the blood-feeding process in the host body [1]. This is because the salivary glands contain various

bioactive components that can suppress the host’s immune response. In general, components in the

salivary glands of disease vectors act as vasodilator and immunomodulatory factors that can affect the

host’s hemostasis [2]. Several previous studies have identified components of the salivary glands of

disease vectors that affect the host’s immune response, including bioactive components in the salivary

glands of Aedes aegypti as a vector of dengue fever [3; 4].

The salivary glands of Ae. aegypti contain various types of bioactive components in the form of

protein molecules. The protein components of the salivary glands of Ae. aegypti generally include

apyrase, aegyptin, serpin, and the D7 Family [5]. The apyrase protein has the activity to hydrolyze

ATP into ADP and AMP, which can inhibit platelet activation [6]. Aegyptin is an allergen that can

bind to collagen and von Willebrand factor, reducing the formation of blood clots [7]. Serpin is a

protease inhibitor that inhibits the activity of serine proteases in various host hemostasis reactions [8].

The D7 protein is known to be the most abundant component in the salivary glands of Ae. aegypti

[9]. Ae. aegypti performs the blood-feeding process on the host’s body by inserting its proboscis into

the skin layer until it reaches the endothelium of the blood vessels. The host’s body responds to this

action by releasing biogenic amine compounds to stop the blood flow through the mechanism of

Highlights in BioScience Page 1 of 9 May 2025|Volume 8

https://doi.org/10.36462/H.BioSci.202501
https://creativecommons.org/licenses/by/4.0/
mailto:rike.fmipa@unej.ac.id
https://orcid.org/0000-0001-9402-7746
mailto:201810401060@mail.unej.ac.id
mailto:syubbanulwathon@unej.ac.id
https://orcid.org/0000-0003-2935-7786
mailto:senjarini@unej.ac.id
https://orcid.org/0000-0001-7041-1719
http://bioscience.highlightsin.org/


Oktarianti R et al., 2025 In silico study of the interaction between serotonin and D7 protein

platelet aggregation. During the blood-feeding process, Ae.
aegypti releases the D7 protein component from its salivary
glands into the host’s body. The D7 protein has a high affinity for
biogenic amine compounds, such as norepinephrine, histamine,
and serotonin [10]. Serotonin is a type of biogenic amine that
plays an important role in platelet activation [11]. When the D7
protein from the salivary glands of Ae. aegypti binds to serotonin,
the process of platelet aggregation around the host’s wound is in-
hibited, allowing the blood-feeding process to proceed smoothly.
This activity indicates that the D7 protein can inhibit the platelet
aggregation process in the host’s body. D7 has the ability to
block platelet aggregation, which makes it an excellent candidate
for an anti-platelet medication. However, its effects on biogenic
amines, including its interaction with serotonin from the salivary
glands of Ae. aegypti, are not well characterized. One branch
of biochemistry involves the study of proteinligand interactions,
which can be explored through in silico approaches that predict
molecular interactions using computer simulations. For instance,
molecular docking analysis enables the identification of specific
binding sites on target proteins for a test ligand [12].

Previous studies have primarily focused on the interaction
between the D7 protein and leukotriene A4, demonstrating the
formation of a stable and natural complex [13]. However, no
prior research has specifically examined the ability of D7 from
Ae. aegypti to bind serotonin, a crucial biogenic amine involved
in platelet aggregation. This study addresses this gap by utilizing
in silico molecular docking to explore the potential interaction
between D7 and serotonin. By doing so, we aim to provide
new insights into the functional role of D7 and its potential as a
foundation for developing novel anti-platelet aggregation agents.

Materials and Methods
Downloading 3D structure D7 protein and ligand

The amino acid sequence of the D7 protein from Ae. aegypti
was downloaded from the UniProt database with accession code
P18153 [14]. The three-dimensional (3D) structure of the D7
protein was obtained from the SWISS-MODEL database [15].
The resulting model was selected based on its best quality and
downloaded in .pdb file format [16]. The 3D model structure
of the D7 protein uses the D7 protein with the template PDB
id 3dye.1. The model structure from this template includes a
native ligand, which is l-norepinephrine. In this study, a test
ligand was used to observe its interaction with the D7 protein.
The test ligand selected was serotonin. The three-dimensional
structure of serotonin was obtained from the PubChem database
with accession code 5202. This serotonin entry originates from
human metabolism. The serotonin molecule was downloaded
from the PubChem database in .sdf file format [17].

Preparation and optimization of the 3D structure of D7 protein and
the native ligand l-norepinephrine

The preparation of the 3D structure of the D7 protein involves
removing the native ligand, non-functional ligands, and water

molecules. This preparation is carried out using AutoDock Tools
software [18]. The first step in preparing the D7 protein struc-
ture is the removal of all water molecules and non-functional
ligands. The D7 protein is then separated from its native ligand,
l-norepinephrine. The structure of the D7 protein is saved in
.pdb format, and the structure of l-norepinephrine is also saved
in .pdb format within the same folder. The structure of the D7
protein is then optimized by adding polar hydrogen atoms and
checking for missing atoms to ensure the integrity of the down-
loaded 3D structure. The D7 protein is subsequently charged
using Kollman charges, resulting in charge neutralization. The
optimized structure is saved in .pdbqt format [19].

Further optimization is performed on the 3D structure of
the l-norepinephrine ligand. The ligand is optimized by adding
Gasteiger charges, followed by a non-polar merge, with the ex-
pectation that only hydrogen atoms will be available to form
bonds with the target protein residues (D7 protein) [20]. The
native ligand is then prepared for docking by selecting rotation
points using the torsion tree menu. This process identifies and
assigns torsion points, improving the accuracy of ligand position-
ing predictions. The final optimized structure of the native ligand
is saved in .pdbqt format in the same folder as the optimized
D7 protein structure [19]. Preparation and optimization of the
native ligands 3D structure were also performed using AutoDock
tools software.

Validation of the molecular docking method
The next step in the molecular docking phase is to validate the

method through a re-docking process of the D7 protein with its
native ligand, l-norepinephrine, which has already been prepared
and optimized. The initial step of the re-docking process involves
determining the interaction site by defining the grid area using a
grid box. This stage is conducted using AutoDock tools software
[18].

The grid box settings include the number of points in each di-
mension (x, y, and z), the spacing in Å, and the center coordinates
of the grid box (x, y, and z). These settings are saved in the .gpf
(grid parameter file) format. The validation of the molecular
docking method between the D7 protein and its native ligand is
evaluated using the RMSD (root mean square deviation) value.
An RMSD value of less than 2 Å(< 2 Å) is considered acceptable
and indicates good reproducibility of the docking result [21].

Preparation of 3D structure of serotonin test ligand
The preparation and optimization of the test ligand, serotonin,

were carried out using Chem3D and AutoDock tools software
[19]. The 3D structure of serotonin was prepared through energy
minimization using the force field molecular mechanism (MM2)
method [22]. The resulting minimized structure was saved in
.pdb format. Subsequently, the test ligand structure was opti-
mized by adding hydrogen atoms, followed by a non-polar merge.
Gasteiger charges were then applied to the ligand structure. The
final step involved adjusting the torsional rotation points using
the same procedure applied to the native l-norepinephrine ligand.

Highlights in BioScience Page 2 of 9 May 2025|Volume 8

http://bioscience.highlightsin.org/


Oktarianti R et al., 2025 In silico study of the interaction between serotonin and D7 protein

The fully optimized structure of the serotonin ligand was saved
in .pdbqt format.

Molecular docking between D7 protein and serotonin test ligand
The process of molecular docking of D7 protein with the

serotonin test ligand is carried out using AutoDock tools soft-
ware. The first step of this process is to place the structure file
of D7 protein in .pdbqt format and the structure file of the sero-
tonin ligand into the same folder. This folder also contains the
programs AutoGrid4 and AutoDock4 [23].

The D7 protein was first set up as the macromolecule, and
the serotonin ligand configuration was used to determine and
create the gridbox. The gridbox used is based on the result of
coordinate adjustments from the validation or re-docking process
between D7 protein and the native ligand l-norepinephrine. The
coordinate adjustments of the gridbox on protein D7 with the
test ligand serotonin are then saved in .gpf format in the same
folder. The next step is to run the AutoGrid4 program using
the Command Prompt (CMD). This program is executed based
on the coordinate settings of the gridbox that have been created.
The next step is to run the AutoDock4 program, which treats the
protein as rigid. The AutoDock4 program is run with the same
command based on the previous AutoGrid data [24].

Analysis and visualization of molecular docking results between D7
protein and serotonin test ligand

The analysis of the molecular docking results between D7
protein and the serotonin test ligand is shown from several pa-
rameters such as the Gibbs free energy value (∆G), types of
chemical bonds, and amino acid residues involved in the interac-
tions formed. The results of the Gibbs free energy values were
compared with the validation results of the re-docking of D7
protein with the native ligand l-norepinephrine. This comparison
was made to observe the differences in Gibbs free energy in the
formation of interactions between D7 protein and the test ligand.

Table 1. Properly formatted D7 protein sequence of Ae. aegypti

>sp|P18153|ALL2_AEDAE 37 kDa salivary gland allergen

Aed a 2 OS=Aedes aegypti OX=7159 GN=D7 PE=1 SV=2

MKEDTLAAVIFSVVASTGPFDPEEMLFTFTRCMEDNLLEDGPNRLPMLAKW

KEWINEPVDSPATQCGFKCVLVRTGLYDPVAQKFDASVIQEGFKAYPSLG

EKSKVEAYANAVQQLPSTNNDCAAVFKAYDPVHKAHKDTSKNLFHGNKEL

TKGLYEKLGKDIROKKQSYFEECENKYYPAGSDKRQQLCKIROYTVLDDA

LFKEHTDCVMKGIRYITKNNELDAEEVKRDEMQVNKDTKALEKVLNDCKS

KEPSNAGEKSWHYXKCLVSSVKDDEKEAFDYREVKSQIYAFNLPKKQVYS

KPAVQSQVMEIDGKQCPQ

The results of the molecular docking were then visualized to
observe the conformation of the bond between the 3D structure
and the two-dimensional structure of the protein and test ligand.

The visualization results can include information on amino acid
residues and the types of bonds formed. Visualization and analy-
sis of the interaction between D7 protein and the serotonin test
ligand were performed using BIOVIA discovery studio software
[25].

Results and Discussions
3D structure of D7 protein from Ae. aegypti salivary gland

The amino acid sequence data of the D7 protein from the Ae.
aegypti salivary gland was obtained from the UniProt database
with accession number P18153. The protein is identified by the
gene name D7 and is known as the salivary gland allergen Aed
a 2, with a molecular weight of 37 kDa. The D7 protein with
accession number P18153 is a monomeric protein with a long
chain domain that has binding affinity for a ligand. The protein is
composed of 321 amino acid residues, as shown in Table 1. The
3D structure of the D7 protein from Ae. aegypti was obtained us-
ing homology modeling techniques through the SWISS-MODEL
protein database. Construction of the 3D protein structure was
carried out using amino acid sequence data of the D7 protein
from the salivary glands of Ae. aegypti. Based on the homology
modeling process, the best model selected used the template with
PDB id 3dye.1. The information on the 3D structure model of
the protein is known as the D7 protein crystal structure of the
AeD7–norepinephrine complex.

The 3D structure of the D7 protein contains a norepinephrine
molecule which will then be used as a native ligand. The 3D
structure of the D7 protein shows two domains of the polypeptide
chain with different functions. The N-terminal domain can bind
cysteinyl leukotriene molecules, while the C-terminal domain
can bind biogenic amine molecules [10]. Visualization of the
3D structure model of protein D7 was carried out using the X-
ray crystallography method with a resolution of 1.75 Å. The 3D
structure model of a protein is said to have good quality if it shows
a resolution of < 2.5 Å from the results of X-ray crystallography
[20]. The 3D structure model of the D7 Ae. aegypti protein can
be seen in Figure 1.

Figure 1. 3D structure of D7 protein (STML ID: 3dye.1), A: domain terminal-N,

B: domain terminal-C, C: native LNR ligand binding site

Highlights in BioScience Page 3 of 9 May 2025|Volume 8

http://bioscience.highlightsin.org/


Oktarianti R et al., 2025 In silico study of the interaction between serotonin and D7 protein

Assessment of the structural quality of the Ae. aegypti salivary
gland D7 protein model

Data from the results of homology modeling of D7 protein
were obtained from the UniProt website with the accession code
P18153. The sequence obtained was then copied to the SWISS-
MODEL website to build the model. The results of the model
build obtained the template PDB id 3dye.1. The results of ho-
mology modeling were obtained with crystallography results of
1.75 Å using the X-ray method. For crystallography, a smaller
value indicates better resolution [26]. Assessment of homology
modeling results is based on several parameters such as GMQE
value, QMEAN value, QMEANDisCo value, and sequence iden-
tity [27].

The GMQE (global model quality estimate) value parameter
is a value that describes the quality of the alignment between the
target and template. The range for the GMQE value means that
if it approaches one, it indicates a higher level of accuracy of
the protein model [28]. The value of GMQE is expressed with
a range of values between 0 and 1. The value of the D7 protein
model on the GMQE value parameter is 0.91, where the results
indicate that the accuracy of the model is good. Table S1 shows
the results of the model quality assessment on the D7 protein.

The QMEAN value is a composite score of a combined as-
sessment that can determine an estimate of the global absolute
quality (entire structure) and local (per amino acid residue) based
on a single model. The QMEAN score ranges from 0 to 1, where
a value of 1 means good [29]. This QMEAN value can be rep-
resented by the Z-score value. The Z-score value at the model
position (marked with a red asterisk) in the Z-value distribution.
This result is marked in Figure 2. The red asterisk indicates the
model’s Z-score is within the typical range for native proteins of
similar size, suggesting reasonable overall quality. Estimates of
the local quality of the model per amino acid residue can also
help in providing an explanation regarding the quality of the D7
protein to be used. This Z-score reflects the overall model quality
(QMEAN).

The sequence identity of the D7 protein model results has a
value of 95.70%. Sequence identity is a value that can indicate
the percentage of residues in the target protein sequence that are
identical to those in the template protein sequence [30]. The
value of the acceptable sequence identity starts at 30%, where the
higher value indicates the level of accuracy between the target
protein and the template protein.

The results based on the Ramachandran plot in Figure 3, the
protein has good structural quality if the amino acid residues are
mostly in the favored area rather than the outliers. Lighter shades
or white indicate residues with less favorable conformations. The
Φ sign is the phi dihedral angle and the Ψ sign is the psi dihedral
angle, where both represent the dihedral angles of the amino
acid residue backbone. The Ramachandran plot shows residues
primarily in the allowed regions. The results of the D7 protein
parameters in the Ramachandran plot show residues distributed
across the favored and allowed regions, indicating a good and

Figure 2. Model position (star) on Z Score of protein D7

stable structure. Protein D7 shown in Table 2 has a favored
area of 98.66% and an outlier area of 0.00%, which means the
structure of the model is very good. The plot uses contour lines
to indicate regions of probability density; 98.66% of residues
fall within the favored regions (darkest contours), and 0.00% are
in outlier regions Table 2, indicating very good stereochemical
quality.

Table 2. Ramachandran Plot Parameters of D7 protein

Parameter Model-1

MolProbity Score 0.65

Clash Score 0.41

Ramachandran Favoured 98.66%

Ramachandran Outliers 0.00%

Rotamer Outliers 0.00%

C-beta Deviations 0

Bad Bonds 0/2504

Bad Angles

A276 ASP, (A296 LEU-A297 PRO),

A163 ASP, A140 ASP, (A132 ASP-

A133 PRO), (A304 LYS-A205 PRO),

A264 HIS, A135 HIS, A207 HIS,

A138 HIS, A263 TRP

The MolProbity score of Model-1 is 0.65. The MolProbity
score is a combination of the log-weighted clash score, the per-
centage of unfavorable Ramachandran outliers, and the percent-
age of bad side chain rotamers. This score indicates a value that
is expected to describe the equivalent resolution of a compara-
ble experimental structure. A lower MolProbity score generally
indicates better quality. If the score is lower than typical for
structures at the template’s resolution (1.75 Å), then the model

Highlights in BioScience Page 4 of 9 May 2025|Volume 8

http://bioscience.highlightsin.org/


Oktarianti R et al., 2025 In silico study of the interaction between serotonin and D7 protein

Figure 3. Ramachandran Plot of Aedes aegypti salivary gland D7 protein

quality is considered very good. The resolution of the template
crystallography is 1.75 Å.

3D structure of native ligand l-norepinephrine and test ligand
serotonin

The 3D structure model of D7 protein with template PDB
id 3dye.1 has a native ligand in the form of l-norepinephrine.
The l-norepinephrine molecule is a type of biogenic amine which
is a component of monoamine neurotransmitters [31]. The l-
norepinephrine molecule is a non-polymer molecule with a molec-
ular weight of 169.178 g/mol. L-norepinephrine has a structure
containing an aromatic ring and a carbon chain with the chemical
formula C8H11NO3. The structure of l-norepinephrine consists
of 23 atoms, each of which is connected by chemical bonds and
has one aromatic ring [32]. The use of native ligands is impor-
tant for the validation stage of the molecular docking method as
well as for determining the orientation of the binding site for test
ligands in the molecular docking process [33].

The test ligand used in this study was the serotonin molecule,
which is one of the biogenic amines in the human bloodstream.
Serotonin in the human body plays a role in the platelet aggrega-
tion process through the activation mechanism between platelet
cells [34]. The structure of serotonin can interact with functional
groups to form hydrogen bonds and can also participate in aro-
matic interactions [35]. Serotonin (also known as enteramine)
has the chemical formula C10H12N2O with a molecular weight
of 176.21 g/mol. Serotonin is composed of 25 atoms connected
by chemical bonds and has an indole ring system [36]. The differ-
ences in the 2D and 3D structures of the l-norepinephrine ligand
and the serotonin test ligand can be seen in Table 3.

Molecular docking validation method
Validation of the molecular docking method was carried out

through the re-docking technique using the native ligand and
D7 protein as the target protein in this study. Before validating

Figure 4. Visualization of the overlapping conformation between the native

ligand l-norepinephrine from crystallography (green) and the conformation of

the native ligand l-norepinephrine from re-docking (yellow).

the molecular docking method, it is necessary to prepare and
optimize the structure of the D7 protein by separating it from the
native ligand l-norepinephrine and removing water molecules,
which are non-standard residues. The separation of the native
ligand from the target protein structure aims to provide a binding
pocket for the test ligand and the target protein. The removal of
water molecules from the target protein structure is carried out so
that they do not become an obstacle during the molecular docking
process. This step is important so that in the molecular docking
process, only the ligand structure interacts with the target protein
[37].

The results of the molecular docking method validation pro-
cess can be seen from the RMSD value. The RMSD parameter
can show the results of the conformational alignment between the
native ligand pose resulting from the re-docking process and the
native ligand conformation from X-ray crystallography [21]. The
RMSD value is obtained by examining the overlap between the
conformation of the native ligand resulting from re-docking and
the native ligand in its original crystallographic conformation. A
smaller RMSD value indicates that the predicted binding pose
from re-docking closely matches the experimentally observed
binding pose. The validity of the RMSD value is indicated by a
value < 2 Å [38].

The RMSD value of the re-docking process between the
l-norepinephrine ligand and the D7 protein shows a value of
1.088 Å. These results indicate that the validation of the molecu-
lar docking method is accepted and the method can be used for
the molecular docking process between the D7 protein and the
serotonin ligand. The results of the alignment or overlapping
conformation between the crystallographic pose (green) and the
re-docked pose (yellow) of the native l-norepinephrine ligand can
be seen in Figure 4.

Molecular docking of protein D7 from Ae. aegypti and serotonin
test ligand

The molecular docking process is carried out using serotonin
ligands that have been prepared and optimized against the D7

Highlights in BioScience Page 5 of 9 May 2025|Volume 8

http://bioscience.highlightsin.org/


Oktarianti R et al., 2025 In silico study of the interaction between serotonin and D7 protein

Table 3. Differences in 2D and 3D structure of native ligands and test ligands

Ligand 2D 3D

[l]Native ligand (l-norepinephrine)

[l]Test ligand (serotonin)

protein from Ae. aegypti. The grid box coordinates obtained
from the re-docking stage with the native ligand are then used
as the basis for the grid box for the molecular docking process
between the D7 protein and the serotonin ligand. This is done so
that the test ligand can bind to the active site of the target protein
[39]. In the molecular docking process, the structure of the D7
protein is kept rigid during the docking process. The structure of
the serotonin ligand is treated as flexible. This is done so that the
test ligand can interact and bind in the most stable conformation
within the active site of the amino acid residues of the target
protein [40].

Analysis and visualization of molecular docking results of Ae.
aegypti D7 protein with serotonin ligand

A ∆G value > 0 indicates that the binding reaction between
the target protein and the test ligand cannot occur spontaneously.
Conversely, if the ∆G value < 0, it indicates that the binding reac-
tion between the target protein and the test ligand occurs sponta-
neously (a reaction that favors product formation). A ∆G value =
0 indicates that the reaction is at equilibrium. The more negative
the ∆G value, the stronger the binding affinity between the target
protein and the ligand [41]. In addition, the negative value of
∆G indicates that the interaction between the target protein and
the ligand binding process is thermodynamically favorable. This
means that the binding between the ligand and the active site of
a target protein occurs in a stable condition and spontaneously
[42]. The results of molecular docking between protein D7 and

serotonin ligand show a ∆G value of -9.25 kcal/mol. These re-
sults indicate that protein D7 can bind to serotonin ligands in a
stable and spontaneous manner. This can be correlated with the
mechanism during the blood feeding process, where protein D7
from the salivary glands of Ae. aegypti can inhibit platelet aggre-
gation by binding biogenic amines, including serotonin, thereby
inhibiting serotonin’s role in platelet aggregation and disrupting
the host’s homeostasis reaction [5].

The D7 protein from Ae. aegypti has been studied for its
potential role in modulating host hemostasis, primarily through
its ability to bind biogenic amines and eicosanoids, thereby fa-
cilitating blood feeding. However, direct experimental evidence
demonstrating its specific function as an inhibitor of platelet
aggregation is limited. In contrast, studies on the D7 protein
from Aedes albopictus, a related mosquito species, have provided
functional evidence of its role in inhibiting platelet aggregation.
For instance, AlboD7L1, a long-form D7 protein from Aedes
albopictus, has been shown to bind various ligands and inhibit
platelet aggregation in ex vivo experiments [10].

Visualization of molecular docking results was carried out
to determine the binding interaction mode between protein D7
and serotonin ligand. The target protein can bind to the test
ligand through several amino acid residues [20]. The interaction
between the target protein and the test ligand is indicated by the
formation of several types of chemical bonds, such as hydrogen
bonds, hydrophobic interactions, electrostatic interactions, and

Highlights in BioScience Page 6 of 9 May 2025|Volume 8

http://bioscience.highlightsin.org/


Oktarianti R et al., 2025 In silico study of the interaction between serotonin and D7 protein

Figure 5. Visualization of the interaction of amino acid residues of D7 protein

with serotonin ligand.

Van der Waals bonds [43]. The results of molecular docking
visualization between protein D7 and serotonin ligand can be
seen in Figure 5.

Table 4. Amino acid residue D7 protein that binds to the serotonin ligand

Amino acid residue D7 protein Serotonin ligand atom Chemical bond

Glutamic Acid 158 (GLU 158)

H (Hydrogen)
Conventional

hydrogen bond

Tyrosine 178 (TYR 178)

Tyrosine 248 (TYR 248)

Aspartic Acid 265 (ASP 265)

Glutamic Acid 268 (GLU 268)

Arginine 176 (ARG 176) C (Carbon)
Carbon hydrogen

bond

Isoleucine 175 (ILE 175) C-H (Carbon Hydrogen) π-sigma bond

Tyrosine 178 (TYR 178) Pi-Orbitals π-π T-shaped bond

Arginine 176 (ARG 176) Pi-Orbitals π-Alkyl bond

Table 4 shows the various types of chemical bonds formed
between amino acid residues of the D7 protein that interact with
atoms on the serotonin ligand. The chemical bonds formed are
interactions that occur at the active site of the D7 protein binding
to the serotonin test ligand. Amino acid residues on the active site
of the D7 protein that bind and interact with the serotonin ligand
include GLU 158, ILE 175, ARG 176, TYR 178, TYR 248, ASP
265, and GLU 268. The chemical bonds formed in the interaction
between the D7 protein and the serotonin ligand include conven-
tional hydrogen bonds, carbon-hydrogen bonds, π-sigma bonds,
π-π T-shaped bonds, and π-alkyl bonds. Conventional hydrogen

bonds are formed involving several amino acid residues: GLU
158, TYR 178, TYR 248, ASP 265, and GLU 268. The carbon-
hydrogen bond formed involves one amino acid residue, namely
ARG 176. The π-sigma bond formed only involves one amino
acid residue, namely ILE 175. The π-π T-shaped bond formed
also involves one amino acid residue, namely TYR 178. The
π-alkyl bond also involves one amino acid residue, namely ARG
176.

Hydrogen bonds are formed between hydrogen atoms and
electronegative atoms. This hydrogen bond formation is related
to binding energy (∆G). The formation of hydrogen bonds can
release energy due to covalent interactions, resulting in a negative
change in enthalpy (∆H). A negative change in enthalpy can
occur when a protein and ligand bind. This result can lead to a
negative ∆G value, meaning the binding occurs spontaneously or
is stable [44]. The interaction between the serotonin ligand and
protein D7 involved in hydrogen bonding consists of conventional
hydrogen bonds. In conventional hydrogen bonds, hydrogen is
shared between electronegative atoms acting as donors (e.g., O-H,
N-H on the ligand or protein) and acceptors (e.g., O, N on the
protein or ligand) [45].

Hydrogen bonds significantly influence the stability of the
D7 protein-ligand complex because both the protein and ligand
contain potential hydrogen bond donors (like N-H and O-H)
and acceptors. These structures act as donors or acceptors in
hydrogen bonds. The next type is the π-sigma bond, which is
an interaction involving the π-system of an aromatic ring and a
sigma bond (like C-H) [46]. The π-sigma bond occurs between
the amino acid ILE 175 and the ligand. The next bond is a π-π
T-shaped bond, which is an electron interaction between two
aromatic groups but in a T shape [47]. In this geometry, the edge
of one aromatic ring points towards the face of the other aromatic
ring. The last interaction is a type of π-alkyl bond, which is an
interaction between electrons from the aromatic group and the
electron group from the alkyl group [47].

Conclusions
The molecular docking results show that there is an interac-

tion between the D7 protein from the salivary gland of Ae. aegypti
(accession number P18153) and the serotonin test ligand (acces-
sion number 5202). The interaction between the D7 protein and
the serotonin test ligand shows stability and spontaneity based on
the ∆G value of -9.25 kcal/mol. Based on the ∆G parameter, this
indicates that the D7 protein and the serotonin ligand can bind
spontaneously and stably. The amino acid residues of protein D7
that interact with the serotonin ligand atoms include GLU 158,
ILE 175, ARG 176, TYR 178, TYR 248, ASP 265, and GLU
268. Thus, the D7 protein from the salivary gland of Ae. aegypti
has potential as a new agent for platelet aggregation inhibition
for drug discovery and development in the fields of health and
pharmacy.

Supplementary
Table S1: D7 protein model quality assessment parameters.

Highlights in BioScience Page 7 of 9 May 2025|Volume 8

http://bioscience.highlightsin.org/


Oktarianti R et al., 2025 In silico study of the interaction between serotonin and D7 protein

Reference
1. Wathon S, Mutiah F, Oktarianti R, Senjarini K. Purifikasi

protein imunogenik 31 dan 56 kDa dari kelenjar saliva
Aedes aegypti. Jurnal Bioteknologi dan Biosains Indone-
sia. 2020;7(1):59-71.

2. Wathon S, Oktarianti R, Senjarini K. Kelenjar saliva Aedes
aegypti harapan baru pengembangan vaksin demam berdarah
dengue. 2016.

3. Guerrero D, Cantaert T, Missé D. Aedes mosquito salivary
components and their effect on the immune response to ar-
boviruses. Frontiers in Cellular and Infection Microbiology.
2020;10:407.

4. Barros MS, Lara PG, Fonseca MT, Moretti EH, Filgueiras
LR, Martins JO, et al. Aedes aegypti saliva impairs M1-
associated proinflammatory phenotype without promoting or
affecting M2 polarization of murine macrophages. Parasites
& Vectors. 2019;12:1-15.

5. Gavor E, Choong YK, Liu Y, Pompon J, Ooi EE, Mok YK,
et al. Identification of Aedes aegypti salivary gland proteins
interacting with human immune receptor proteins. PLoS
Neglected Tropical Diseases. 2022;16(9):e0010743.

6. Chowdhury A, Modahl CM, Missé D, Kini RM, Pompon
J. High resolution proteomics of Aedes aegypti salivary
glands infected with either dengue, Zika or chikungunya
viruses identify new virus specific and broad antiviral factors.
Scientific Reports. 2021;11(1):23696.

7. McCracken M, Christofferson R, Grasperge B, Calvo E,
Chisenhall D, Mores C. Aedes aegypti salivary protein ae-
gyptin co-inoculation modulates dengue virus infection in
the vertebrate host. Virology. 2014;468:133-9.

8. Gulley MM, Zhang X, Michel K. The roles of serpins in
mosquito immunology and physiology. Journal of insect
physiology. 2013;59(2):138-47.

9. Alvarenga PH, Andersen JF. An overview of D7 protein
structure and physiological roles in blood-feeding nemato-
cera. Biology. 2022;12(1):39.

10. Martin-Martin I, Smith LB, Chagas AC, Sá-Nunes A, Shri-
vastava G, Valenzuela-Leon PC, et al. Aedes albopictus D7
salivary protein prevents host hemostasis and inflammation.
Biomolecules. 2020;10(10):1372.

11. Michael ZM. Obat Penginduksi Pendarahan. Program Studi
Apoteker, Fakultas Farmasi Universitas Padjadjaran. 2017.

12. Prasetiawati R, Suherman M, Permana B, Rahmawati R.
Molecular docking study of anthocyanidin compounds
against Epidermal Growth Factor Receptor (EGFR) as anti-
lung cancer. Indonesian Journal of Pharmaceutical Science
and Technology. 2021;8(1):8-20.

13. Wathon S, Oktarianti R, Senjarini K. Molecular Docking of
Interaction between D7 Protein from the Salivary Gland of
Aedes aegypti and Leukotriene A4 for Developing Throm-
bolytic Agent. In: BIO Web of Conferences. vol. 101. EDP
Sciences; 2024. p. 04002.

14. Consortium U. UniProt: a worldwide hub of protein knowl-
edge. Nucleic acids research. 2019;47(D1):D506-15.

15. Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G,
Gumienny R, et al. SWISS-MODEL: homology modelling
of protein structures and complexes. Nucleic acids research.
2018;46(W1):W296-303.

16. Bienert S, Waterhouse A, De Beer TA, Tauriello G,
Studer G, Bordoli L, et al. The SWISS-MODEL Repos-
itorynew features and functionality. Nucleic acids research.
2017;45(D1):D313-9.

17. Wang Y, Bryant SH, Cheng T, Wang J, Gindulyte A, Shoe-
maker BA, et al. Pubchem bioassay: 2017 update. Nucleic
acids research. 2017;45(D1):D955-63.

18. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK,
Goodsell DS, et al. AutoDock4 and AutoDockTools4: Auto-
mated docking with selective receptor flexibility. Journal of
computational chemistry. 2009;30(16):2785-91.

19. Huey R, Morris GM, Forli S, et al. Using AutoDock 4
and AutoDock vina with AutoDockTools: a tutorial. The
Scripps Research Institute Molecular Graphics Laboratory.
2012;10550(92037):1000.

20. Sari IW, Junaidin J, Pratiwi D. Studi Molecular Dock-
ing Senyawa Flavonoid Herba Kumis Kucing (Orthosiphon
Stamineus B.) Pada Reseptor A-Glukosidase Sebagai Antidi-
abetes Tipe 2. Jurnal Farmagazine. 2020;7(2):54-60.

21. Sari IW, Junaidin J, Pratiwi D. Studi Molecular Dock-
ing Senyawa Flavonoid Herba Kumis Kucing (Orthosiphon
Stamineus B.) Pada Reseptor A-Glukosidase Sebagai Antidi-
abetes Tipe 2. Jurnal Farmagazine. 2020;7(2):54-60.

22. Allinger NL. Conformational analysis. 130. MM2. A hydro-
carbon force field utilizing V1 and V2 torsional terms. Jour-
nal of the American Chemical Society. 1977;99(25):8127-34.

23. Trott O, Olson AJ. AutoDock Vina: improving the speed and
accuracy of docking with a new scoring function, efficient
optimization, and multithreading. Journal of computational
chemistry. 2010;31(2):455-61.

24. Endriyatno NC, Walid M. Studi In Silico Kandungan
Senyawa Daun Srikaya (Annona squamosa L.) Terhadap Pro-
tein Dihydrofolate Reductase Pada Mycobacterium tubercu-
losis. Pharmacon: Jurnal Farmasi Indonesia. 2022;19(1):87-
98.

Highlights in BioScience Page 8 of 9 May 2025|Volume 8

http://bioscience.highlightsin.org/


Oktarianti R et al., 2025 In silico study of the interaction between serotonin and D7 protein

25. Baroroh U, Biotek M, Muscifa ZS, Destiarani W, Rohmatul-
lah FG, Yusuf M. Molecular interaction analysis and visu-
alization of protein-ligand docking using Biovia Discovery
Studio Visualizer. Indonesian Journal of Computational Bi-
ology (IJCB). 2023;2(1):22-30.

26. Wijaya H, Hasanah F. Prediction of Three-dimensional
Structure From Food Allergen Protein Through Homology
Method Using Swiss-model Program. Biopropal Industry.
2016;7(2):83-94.

27. Ahmed MZS. Homology Modeling and Structural Analysis
of of the Flavanone 3-Hydroxylase (F3H) and Flavonoid
3-hydroxylase (F3H) Genes from Ginkgo Biloba (L.).

28. Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G,
Schmidt T, et al. SWISS-MODEL: modelling protein ter-
tiary and quaternary structure using evolutionary information.
Nucleic acids research. 2014;42(W1):W252-8.

29. Komari N, Hadi S, Suhartono E. Protein Modeling with Ho-
mology Modeling using SWISS-MODEL: Protein Modeling
with Homology Modeling using SWISS-MODEL. Journal
of Mathematics and Science Network. 2020;2(2):65-70.

30. Kanduc D. Homology, similarity, and identity in pep-
tide epitope immunodefinition. Journal of Peptide Science.
2012;18(8):487-94.

31. Wu F, Liang T, Xiao W, Wang T. Norepinephrine in goal-
directed fluid therapy during general anesthesia in elderly
patients undergoing spinal operation: determining effective
infusion rate to enhance postoperative functions. Current
Genomics. 2021;22(8):620-9.

32. Angles R, Arenas-Salinas M, García R, Ingram B. An opti-
mized relational database for querying structural patterns in
proteins. Database. 2024;2024:baad093.

33. Aziz A, Andrianto D, Safithri M. Penambatan Molekuler
Senyawa Bioaktif Daun Wungu (Graptophyllum Pictum (L)
Griff) sebagai Inhibitor Tirosinase. Indonesian Journal of
Pharmaceutical Science and Technology. 2022;9(2):96-107.

34. Marcinkowska M, Kubacka M, Zagorska A, Jaromin A,
Fajkis-Zajaczkowska N, Kolaczkowski M. Exploring the
antiplatelet activity of serotonin 5-HT2A receptor antago-
nists bearing 6-fluorobenzo [d] isoxazol-3-yl) propyl) motif–
as potential therapeutic agents in the prevention of car-
diovascular diseases. Biomedicine & Pharmacotherapy.
2022;145:112424.

35. Yang D, Gouaux E. Illumination of serotonin transporter
mechanism and role of the allosteric site. Science Advances.
2021;7(49):eabl3857.

36. Kim S, Chen J, Cheng T, Gindulyte A, He J, He S,
et al. PubChem 2023 update. Nucleic acids research.
2023;51(D1):D1373-80.

37. Attique SA, Hassan M, Usman M, Atif RM, Mahboob S,
Al-Ghanim KA, et al. A molecular docking approach to eval-
uate the pharmacological properties of natural and synthetic
treatment candidates for use against hypertension. Interna-
tional journal of environmental research and public health.
2019;16(6):923.

38. Ferencz L, Muntean DL. Identification of new superwarfarin-
type rodenticides by structural similarity. The docking of lig-
ands on the vitamin K epoxide reductase enzymes active site.
Acta Universitatis Sapientiae, Agriculture and Environment.
2015;7(1):108-22.

39. Morris GM, Goodsell DS, Pique ME, Huey R, Hart WE, Hal-
liday S, et al.. AutoDock Version 4.2: Updated for version
4.2.6. Automated Docking of Flexible Ligands to Flexible
Receptors; 2014. The Scripps Research Institute, La Jolla,
CA, USA.

40. Khan T, Lawrence AJ, Azad I, Raza S, Khan AR. Molecu-
lar Docking Simulation with Special Reference to Flexible
Docking Approach. JSM Chemistry. 2018;6(1):1-5.

41. Jiménez JS, Benítez MJ. Gibbs Free Energy and Enthalpy–
Entropy Compensation in Protein–Ligand Interactions. Bio-
physica. 2024;4(2):298-309.

42. Choma CT. Characterizing Binding Interactions by ITC;
2006. Document No. 10111020706.

43. Forouzesh N, Mishra N. An Effective MM/GBSA Protocol
for Absolute Binding Free Energy Calculations: A Case
Study on SARS-CoV-2 Spike Protein and the Human ACE2
Receptor. Molecules. 2021;26(8):2383.

44. Du X, Li Y, Xia YL, Ai SM, Liang J, Sang P, et al. In-
sights into Protein–Ligand Interactions: Mechanisms, Mod-
els, and Methods. International Journal of Molecular Sci-
ences. 2016;17(2):144.

45. Mishra KK, Borish K, Singh G, Panwaria P, Metya S, Mad-
husudhan MS, et al. Observation of an Unusually Large IR
Red-Shift in an Unconventional S–H· · ·S Hydrogen-Bond.
Journal of Physical Chemistry Letters. 2021;12(4):1228-35.

46. Zierkiewicz W, Michalczyk M, Scheiner S. Noncovalent
Bonds through Sigma and Pi-Hole Located on the Same
Molecule. Guiding Principles and Comparisons. Molecules.
2021;26(6):1740.

47. Alencar WLM, da Silva Arouche T, Neto AFG, de Castro Ra-
malho T, de Carvalho Júnior RN, de Jesus Chaves Neto AM.
Interactions of Co, Cu, and Non-metal Phthalocyanines with
External Structures of SARS-CoV-2 Using Docking and
Molecular Dynamics. Scientific Reports. 2022;12(1):3316.

Highlights in BioScience Page 9 of 9 May 2025|Volume 8

http://bioscience.highlightsin.org/

	Abstract
	Introduction
	Materials and Methods
	Downloading 3D structure D7 protein and ligand
	Preparation and optimization of the 3D structure of D7 protein and the native ligand l-norepinephrine
	Validation of the molecular docking method
	Preparation of 3D structure of serotonin test ligand
	Molecular docking between D7 protein and serotonin test ligand
	Analysis and visualization of molecular docking results between D7 protein and serotonin test ligand

	Results and Discussions
	3D structure of D7 protein from Ae. aegypti salivary gland
	Assessment of the structural quality of the Ae. aegypti salivary gland D7 protein model
	3D structure of native ligand l-norepinephrine and test ligand serotonin
	Molecular docking validation method
	Molecular docking of protein D7 from Ae. aegypti and serotonin test ligand
	Analysis and visualization of molecular docking results of Ae. aegypti D7 protein with serotonin ligand

	Conclusions
	Supplementary

