Article  
In Silico Study of the Potential of Belimbing Wuluh (Averrhoa bilimbi) for the  
Treatment of Type 2 Diabetes Mellitus  
Rachman Aziz Dwinuari1 , Sugeng Supriyanto1,a , Naelaz Zukhruf Wakhidatul Kiromah1  
Herniyatun1, Aditya Mahe Saputra1, Dimas Aji Pratama1  
,
1Pharmacy Department, Faculty of Health Sciences, Universitas Muhammadiyah Gombong, Gombong-  
5441, Indonesia  
Abstract  
Diabetes mellitus (DM) is a metabolic disorder characterized by prolonged elevated blood sugar levels  
due to impaired insulin secretion or function. Metformin is the first-line therapy for type 2 DM, but it has  
side effects such as nausea, vomiting, bloating, gastrointestinal disturbances, and hypoglycemia, which  
some patients may not tolerate. Averrhoa bilimbi has potential as an alternative therapy for type 2 DM.  
This study aims to identify secondary metabolites from bilimbi that have potential as antidiabetic agents  
by activating AMPK and PPAR-γ protein receptors through in silico studies. The study employed molecular  
docking methods between AMPK and PPAR-γ protein receptors with bilimbi test compounds and  
comparators. Test compounds were selected based on compliance with Lipinski's rule, pharmacokinetic  
predictions, and toxicity. Metformin and rosiglitazone were used as comparators. Screening results  
identified three bilimbi secondary metabolites: Anapheline, Elaeokanine C, and Tetradecylamine.  
Docking results showed binding energy values between AMPK protein receptor and Anapheline,  
Elaeokanine C, Tetradecylamine, and comparators were -8.28, -5.77, -7.04, and -6.12 kcal/mol,  
respectively. For the PPAR-γ receptor, the binding energy values were -7.28, -5.97, -5.54, and -9.42  
kcal/mol, respectively. Anapheline and Tetradecylamine demonstrated potential as antidiabetic agents  
with 75% and 25% amino acid residue similarity to the comparator compounds.  
Keywords: : Antidiabetic, Averrhoa bilimbi, in silico, binding energy, docking  
Graphical Abstract  
*
Corresponding author  
Email addresses: sugengsupriyanto@unimugo.ac.id  
DOI: https://doi.org/10.22437/chp.v8i2.36624  
Received August 10th 2024; Accepted December 08th 2024; Available online December 31st 2024  
Copyright © 2024 by Authors, Published by Chempublish Journal. This is an open access article under the CC BY License  
(https://creativecommons.org/licenses/by/4.0)  
51  
Chempublish Journal, 8(2) 2024, 51-64  
Introduction  
need for equipment and materials and increasing  
cost efficiency in research [12]. This approach is  
also useful for predicting interactions between  
two types of molecules, such as between  
proteins and ligands [13]. Proteins that can be  
used as therapeutic targets for type 2 diabetes  
mellitus to stimulate insulin and prevent  
resistance include Adenosine Monophosphate  
Activated Protein Kinase (AMPK) [14] and  
Diabetes Mellitus (DM) has become one of the  
top ten leading causes of death worldwide [1].  
According to data compiled by the International  
Diabetes Federation (IDF) compiled data in 2021,  
estimating that approximately 537 million adults  
(ages 20–79) are living with diabetes. This  
number is projected to increase to 643 million by  
2030 and 783 million by 2045[2]. Indonesia ranks  
sixth among countries with the highest number  
of diabetes patients in the world, with 20.4  
million cases [3].  
Peroxisome  
Proliferator-Activated  
Receptor-  
Gamma (PPAR-γ) [6].  
Based on the above description, bilimbi has the  
potential to be developed as an alternative  
therapy for individuals with diabetes mellitus  
Diabetes mellitus is a disorder in the body's  
metabolic process characterized by elevated  
blood sugar levels that persist for an extended  
period. This condition can be caused by  
impairments in insulin secretion, insulin action,  
or both. Diabetes mellitus is divided into two  
types: type 1 and type 2 diabetes mellitus. Type 1  
diabetes mellitus arises from an autoimmune  
reaction that attacks the pancreatic β cells,  
completely inhibiting insulin production [4]. Type  
2 diabetes occurs because the pancreas loses its  
ability to respond effectively to stimulate insulin  
production, known as insulin resistance [5].  
(DM).  
Research  
on  
the  
potential  
active  
compounds in bilimbi through in silico methods  
has not yet been conducted. Therefore, it is  
important to explore the potential of these  
secondary metabolites as  
a
solution for  
addressing type 2 diabetes mellitus.  
Material and Methods  
Materials and Instrumentation  
The  
materials  
used  
include  
the  
three-  
dimensional structures of the proteins AMPK  
(code: 3aqv) and PPAR-γ (code: 2prg) obtained  
from the Protein Data Bank, secondary  
metabolite compounds of bilimbi acquired from  
the KNApSAcK database with the keyword  
Averrhoa bilimbi, and the control comparators  
metformin and rosiglitazone.  
Metformin is the first-line therapy for type 2  
diabetes mellitus that has been proven effective  
in controlling blood sugar levels [6]. However, it  
can cause side effects that should not be  
overlooked, such as nausea, vomiting, bloating,  
gastrointestinal disturbances, and hypoglycemia,  
which may be intolerable for some patients [7].  
Therefore, it is necessary to develop alternative  
treatments from herbal plants with minimal side  
effects [8].  
Methods  
Prediction  
Pharmacokinetics,  
Metabolites of Averrhoa bilimbi. The prediction  
was performed using the pkCSM  
of  
Lipinski's  
Rule of Five  
and  
Toxicity  
of Secondary  
Indonesia ranks second after Brazil in terms of  
biodiversity richness [9]. Indonesia has identified  
approximately 31,750 plant species, but only  
about 7,000 species are actually utilized as raw  
materials in the production of medicine [10]. One  
of the plants believed to have antidiabetic  
properties is the bilimbi plant (Averrhoa bilimbi)  
[11].  
website(http://structure.bioc.cam.ac.uk/pkcsm)  
by inputting the SMILES code obtained from the  
KNApSAcK database. The results of the  
prediction  
metabolite  
pharmacokinetic  
indicated  
compounds  
requirements  
that  
the  
secondary  
the  
(absorption,  
meet  
distribution, metabolism, and excretion), toxicity,  
In silico studies are a strategy used in the  
discovery of compounds with potential as drug  
candidates. The application of in silico methods  
offers several advantages, such as reducing the  
and Lipinski's rule.  
Protein Receptor Preparation. The protein receptor  
was downloaded from the Protein Data Bank  
52  
Chempublish Journal, 8(2) 2024, 51-64  
(http://www.rcsb.org).  
Preparation  
was  
secondary metabolites of belimbing wuluh  
(Averrhoa bilimbi) is the same as in the redocking  
method.  
performed using BIOVIA Discovery Studio  
Visualizer 2021 by removing water molecules and  
ligands.  
Study of Docking Interactions and Visualization. The  
application used to study interactions and  
Natural Ligand Preparation. The ligand was  
separated from water molecules and protein  
using BIOVIA Discovery Studio Visualizer 2021,  
followed by the addition of hydrogen atoms.  
visualizations  
is  
BIOVIA  
Discovery  
Studio  
Visualizer 2021. Observations of ligand-protein  
interactions can be examined through both 2D  
and 3D visualizations. This tool facilitates the  
analysis of interactions by clearly presenting  
various types of interactions, such as van der  
Redocking (Validation). Autodock is an application  
used for the redocking process. Redocking is  
performed by re-docking the original ligand with  
the receptor protein to obtain the grid box  
coordinates, which serve as a reference for  
docking comparison ligands and secondary  
metabolite ligands. The rigid receptor molecule  
was subjected to the Lamarckian genetic  
algorithm (LGA) during the redocking and  
docking stages in order to find the best  
conformers; a maximum of 100 conformations  
were specified for each ligand. The maximum  
number of energy evaluations was raised to  
2,500,000, the genetic generation limit was set at  
100,000, and the population size was set at 150.  
Other parameters were kept at the default values  
of AutoDock 4.2. The docking methodology was  
evaluated through redocking to enhance result  
accuracy. The conformation with the lowest  
binding energy from the densest cluster was  
selected as the best docking result and further  
analyzed for hydrogen bond interactions [15].  
The final results of the redocking are based on  
the conformation data with the lowest binding  
energy and an RMSD value of less than 2 Å.  
Waals  
hydrogen bonds, and others, directly within its  
interface, making the process more  
efficient and insightful.  
forces,  
hydrophobic  
interactions,  
Results and Discussions  
Searching for secondary metabolite compounds.  
The results of the search for secondary  
metabolite compounds from Averrhoa bilimbi in  
the Knapsack database, using the keyword  
Averrhoa bilimbi, revealed the presence of 22  
secondary metabolites (Table 1).  
Table 1. Secondary Metabolites of Averrhoa  
bilimbi [16]  
No  
1
2
3
4
ID  
Secondary Metabolites  
Anapheline  
Codonopsine  
19519-53-0  
26989-20-8  
33023-03-9  
142741-31-9  
Elaeokanine C  
Afzelechin  
3-O-alpha-L-  
rhamnopyranoside  
Cucumerin A  
Pentadecanal  
Preparation of Reference and Test Ligands. The  
reference and test ligands were downloaded  
from the PubChem website by entering the  
SMILES code obtained from Knapsack. The  
resulting three-dimensional structures were then  
energy-optimized using Avogadro.  
5
6
7
8
613253-63-7  
2765-11-9  
629-54-9  
1160155-55-  
4
Palmitic acid amide  
14-Methyl-8-hexadecen-1-  
ol  
2-Ethyldodecanoic acid  
2-Hydroxyhexadecanoic  
acid  
7-Hexadecen-1-ol  
Diglycidyl resorcinol ether  
Dihydroceramide C2  
4-Hydroxy-8-sphingenine  
Ethyl 3-(N-butylacetamido)  
propionate  
9
10  
2874-75-1  
764-67-0  
Docking with Secondary Metabolites of Averrhoa  
bilimbi and Comparators. The use of AutoDock in  
linking secondary metabolite compounds with  
AMP-Kinase and PPAR-γ proteins involves setting  
11  
12  
13  
14  
15  
24546-19-8  
101-90-6  
2304-80-5  
3687-54-5  
52304-36-6  
a
grid  
box  
that  
is  
identical  
to  
the  
redocking/validation process. This approach  
ensures consistency in results during both  
docking and redocking, avoiding significant  
variations. The method for docking with  
16  
862472-69-3  
Enigmol  
53  
Chempublish Journal, 8(2) 2024, 51-64  
(MW) of less than 500 Daltons, a log P value of  
less than 5, fewer than 5 hydrogen bond donors  
(HBD), and fewer than 10 hydrogen bond  
acceptors (HBA) [17] (Table 2). A molecular  
weight exceeding 500 Da indicates that the  
compound may not penetrate cell membranes. A  
No  
17  
18  
19  
20  
21  
22  
ID  
Secondary Metabolites  
Isoavocadienofuran  
Linoleamide  
34227-09-3  
3999-01-7  
17352-32-8  
554-62-1  
2016-42-4  
129825-28-1  
Nonadecanal  
Phytosphingosine  
Tetradecylamine  
Xestoaminol C  
higher  
log  
P
value  
indicates  
increased  
hydrophobicity of the molecule; compounds with  
excessive hydrophobicity tend to exhibit higher  
toxicity. The number of hydrogen bond donors  
and acceptors describes the capacity for  
hydrogen bonding: a higher capacity requires  
more energy for absorption to occur [18].  
Screening using Lipinski's Rule. All obtained  
secondary metabolite compounds were then  
screened for drug likeness based on Lipinski’s  
Rule of Five. This rule states that a compound is  
considered drug-like if it has a molecular weight  
Table 2. Lipinski's Rule Screening of Secondary Metabolite Compounds  
No. Secondary Metabolites Lipinski Screening  
Description  
MW  
<500  
Log p  
<5  
HBA HBD  
<10  
<5  
1
Anapheline  
224.348  
1.6199  
3
2
Qualified  
2
Codonopsine  
Elaeokanine C  
267.325  
211.305  
0.8006  
1.2008  
0.6923  
1.9491  
5.2765  
4.953  
5
3
9
11  
1
1
1
1
2
1
4
3
4
3
3
1
1
1
4
1
2
2
1
6
8
0
1
1
1
2
1
0
3
4
0
3
0
1
0
4
1
2
Qualified  
3
Qualified  
4
Afzelechin 3-O-alpha-L-rhamnopyranoside 420.414  
Not qualified  
Not qualified  
Not qualified  
Qualified  
5
Cucumerin A  
552.532  
226.404  
255.446  
254.458  
228.376  
272.429  
240.431  
222.24  
6
Pentadecanal  
7
Palmitic acid amide  
14-Methyl-8-hexadecen-1-ol  
2-Ethyldodecanoic acid  
2-Hydroxyhexadecanoic acid  
7-Hexadecen-1-ol  
8
5.482  
Not qualified  
Qualified  
9
4.628  
10  
11  
12  
13  
14  
15  
16  
17  
18  
19  
20  
21  
22  
4.5231  
5.236  
Qualified  
Not qualified  
Qualified  
Diglycidyl resorcinol ether  
Dihydroceramide C2  
4-Hydroxy-8-sphingenine  
Ethyl 3-(N-butylacetamido) propionate  
Enigmol  
1.2418  
567.984 10.5672  
Not qualified  
Qualified  
315.498  
215.293  
301.515  
246.394  
279.468  
282.512  
317.514  
213.409  
229.408  
2.895  
1.5882  
4.1466  
5.6851  
5.2852  
6.8369  
3.119  
Qualified  
Qualified  
Isoavocadienofuran  
Linoleamide  
Not qualified  
Not qualified  
Not qualified  
Qualified  
Nonadecanal  
Phytosphingosine  
Tetradecylamine  
4.6462  
3.6154  
Qualified  
Xestoaminol C  
Qualified  
54  
Chempublish Journal, 8(2) 2024, 51-64  
Pharmacokinetics and toxicity prediction. The  
secondary metabolite compounds that pass the  
Lipinski's rule screening are those that meet all  
the requirements of Lipinski's rules. Out of 22  
secondary metabolite compounds screened, 13  
met the criteria. After screening for drug-  
including  
CYP1A2,  
CYP2C9,  
CYP2D6,  
and  
CYP3A4/5, which account for approximately 72%  
of all drug metabolism. Therefore, it is important  
to evaluate the potential of compounds to inhibit  
cytochrome P450, which in this study is  
represented by the CYP2D6 and CYP3A4  
isoforms.  
likeness,  
pharmacokinetic  
prediction  
was  
conducted to determine the pharmacokinetic  
profile of each compound, including Absorption,  
Distribution, Metabolism, Excretion, and Toxicity.  
PkCSM is a website that is often used to predict  
the pharmacokinetic properties and toxicity of a  
substance that has the potential to become a  
new drug. Access to this web server is free  
The  
next  
pharmacokinetic  
parameter  
is  
excretion. The process of excreting a compound  
can be assessed by measuring the Total  
Clearance (CLTOT) constant. CLTOT represents a  
combination of hepatic clearance (metabolism in  
the liver and bile) and renal clearance (excretion  
via the kidneys). This is related to bioavailability  
and is crucial for determining the dosage  
required to achieve a steady-state concentration.  
The value of CLTOT can be used to predict the  
rate of excretion of the compound. An important  
toxicity parameter is LD50, which is defined as the  
dose of a test substance, determined through  
statistical calculation, that causes the death of  
50% of test animals when administered orally.  
(http://structure.bioc.cam.ac.uk/pkcsm).  
From  
the pharmacokinetic and toxicity screening of the  
13 compounds, only 3 secondary metabolite  
compounds met the requirements: Anapheline,  
Elaeokanine C, and Tetradecylamine (Table 3). The  
criterion for each parameter is that a compound  
is considered to have good absorption if the  
absorption value is >80%, and poor absorption if  
it is <30%. The intestine is the primary site for the  
absorption of orally administered drugs [19].  
Preparation of protein receptors and natural  
ligands.  
The next pharmacokinetic prediction parameter  
is distribution. Distribution refers to the process  
by which a drug enters the bloodstream. The  
After pharmacokinetic prediction screening is  
conducted on a compound, the next step is to  
prepare it for molecular docking. The docking  
process begins with validation, which involves  
separating the protein from its natural ligand and  
then re-docking the separated protein and  
ligand. The validation phase starts with the  
preparation of the protein. The BIOVIA Discovery  
Studio app is used for protein and ligand  
preparation because it provides a variety of  
advanced tools for molecular manipulation, such  
as protein structure cleaning, hydrogen addition,  
geometry optimization, and active pouch  
detection [21]. Protein preparation involves the  
removal of water molecules, natural ligands, and  
other complex compounds present [22], [23]. The  
prepared protein is then used for validation by  
performing redocking with its natural ligand. The  
natural ligand is first prepared by removing  
proteins and other complex molecules except for  
the ligand (Figure 1 and Figure 2).  
greater  
the  
extent  
of  
drug  
distribution  
throughout the body, the more rapidly the drug  
reaches its site of action and the quicker its  
effects are felt. The parameter used is VDss  
(volume of distribution at steady state). A  
compound is considered to have a low volume of  
distribution if the Log VDss value is < -0.15, and  
high if it is > 0.45. The volume of distribution  
(VDss) is the theoretical volume required for the  
total dose of a drug to be distributed evenly so  
that it achieves a concentration equal to that in  
plasma. A higher VD indicates that a larger  
proportion of the drug is distributed in tissues  
rather than in plasma [20].  
The distributed drug is then metabolized.  
Metabolism generally occurs in the liver with the  
assistance of cytochrome P450 enzymes.  
Cytochrome P450 (CYP) is a crucial detoxification  
enzyme in the body that oxidizes xenobiotics for  
excretion. Cytochrome P450 enzymes are  
responsible for the metabolism of many drugs,  
55  
Chempublish Journal, 8(2) 2024, 51-64  
Table 3. Pharmacokinetic and Toxicity Screening of Secondary Metabolite Compounds [25]  
No. Secondary Metabolites  
ADME (Absorption, Distribution, Metabolism, Excretion) Screening  
Description  
IAH  
(%  
Absorbed)  
VDss  
(log  
L/kg)  
CYP2D6  
CYP3A4  
CYP2D6  
CYP3A4  
TC (log  
ml/min/kg)  
LD50  
(mol/kg)  
substrate substrate inhibitor inhibitor  
1
2
Anapheline  
93.738  
0.908  
No  
No  
No  
No  
No  
No  
No  
No  
1.272  
0.824  
2.483  
2.396  
Qualified  
Codonopsine  
75.276  
85.76  
0.24  
0.545  
0.319  
-0.638  
-0.708  
0.072  
-0.466  
Not  
Qualified  
Qualified  
3
4
5
6
7
8
Elaeokanine C  
No  
No  
No  
No  
No  
No  
No  
Yes  
No  
No  
No  
No  
No  
No  
No  
No  
No  
No  
No  
No  
No  
1.003  
1.837  
1.701  
1.832  
0.267  
1.314  
2.03  
1.802  
1.611  
1.371  
2.13  
Palmitic acid amide  
90.399  
94.023  
90.023  
92.38  
Not  
Qualified  
Not  
2-Ethyldodecanoic acid  
2-Hydroxyhexadecanoic acid  
Diglycidyl resorcinol ether  
4-Hydroxy-8-sphingenine  
Qualified  
No  
Not  
Qualified  
Not  
Yes  
Yes  
Qualified  
90.898  
3.769  
Not  
Qualified  
9
Ethyl.3-(N-butylacetamido)  
propionate  
Phytosphingosine  
94.929  
94.24  
-0.155  
-0.307  
No  
No  
No  
No  
No  
No  
No  
0.832  
1.43  
2.2  
Not  
Qualified  
10  
Yes  
1.782  
Not  
Qualified  
Qualified  
11  
12  
Tetradecylamine  
89.411  
91.165  
0.933  
0.278  
No  
No  
Yes  
No  
No  
No  
No  
No  
1.338  
1.31  
2.375  
2.512  
Xestoaminol C  
Not  
Qualified  
13  
Enigmol  
92.061  
-0.074  
No  
No  
No  
No  
1.363  
3.723  
Not  
Qualified  
Description: IAH = Intestinal Absorbsi Human; TC = Total Clerance  
56  
Chempublish Journal, 8(2) 2024, 51-64  
a
B
c
Figure 1. Protein and Ligand Preparation (3AQV) (a) Protein before preparation; (b) Protein after  
preparation; (c) Natural ligand after preparation  
a
b
c
Figure 2. Protein and Ligand Preparation (2PRG) (a) Protein before preparation; (b) Protein after  
preparation; (c) Natural ligand after preparation  
a
b
Figure 3. Interaction of Protein and Natural Ligand (AMPK); (a) Three-Dimensional Form (b) Two-  
Dimensional Form  
a
b
Figure 4. Interaction of Protein and Natural Ligand (PPAR-γ); (a) Three-Dimensional Form (b) Two-  
Dimensional Form.  
a
b
d
c
Figure 5. Results of Preparation of Test and Reference Compounds: (a) Anapheline; (b) Elaeokanine C (c)  
Tetradecylamine (d) Metformin  
57  
Chempublish Journal, 8(2) 2024, 51-64  
Redocking (Validation).  
acid residues: SER 289, HIS 323, CYS 285, MET  
348, MET 364, ILE 341, LEU 330, and ILE 281  
(Figure 4). The types of bonds observed in the  
natural ligand AMPK included 3 hydrogen bonds  
and 9 hydrophobic bonds, totaling 12 bonds.  
Meanwhile, the natural ligand PPAR-γ exhibited 3  
hydrogen bonds and 4 hydrophobic bonds,  
totaling 7 bonds.  
AutoDock 4.2 is often used in in silico research  
due to its reliable and flexible ability to accurately  
and efficiently perform molecular docking using  
genetic algorithms and Monte Carlo simulations,  
support  
the  
analysis  
of  
ligand-receptor  
interactions, and predict binding affinity and  
molecular conformation with wide validation in  
various scientific studies [24]. The prepared  
protein and natural ligand were then validated by  
re-docking the ligand to the protein, returning it  
to its original position. The validation results  
showed that the ligand successfully returned to  
its initial position, as evidenced by Root Mean  
Square Deviation (RMSD) values of 0.48 Å and  
0.86 Å. Validation is considered successful if the  
RMSD value is less than 2 Å. An RMSD value close  
to 0 indicates that the ligand can be accurately  
returned to its original position (Table 4). Another  
parameter is the Grid Parameter File (GPF) from  
the validation, which contains information about  
the grid box size and grid box position. The grid  
box size describes the dimensions of the grid  
box, while the grid box position represents the  
Preparation of test and comparison compounds.  
The test and reference compounds were  
prepared for docking by optimizing their  
molecular  
structures  
through  
energy  
minimization using Avogadro, an advanced  
cross-platform molecular editor and visualizer  
designed for computational chemistry, molecular  
modeling, bioinformatics, and related fields[26].  
Energy minimization aims to obtain molecules  
with a stable structure in three-dimensional  
form.  
The  
prepared  
test  
and  
reference  
compounds were then used for docking with the  
target protein (Figure 5).  
Docking with test and comparator compounds.  
ligand's  
coordinates  
within  
the  
three-  
The binding energy values are the result of the  
docking process between the test compounds  
and the reference compounds. Docking between  
the AMPK protein receptor with the test and  
reference compounds yielded binding energy  
values of -8.28 kcal/mol for Anapheline, -5.77  
kcal/mol for Elaeokanine C, -7.04 kcal/mol for  
Tetradecylamine, and -6.12 kcal/mol for the  
reference compound (metformin). Subsequently,  
docking between the PPAR-γ protein receptor  
with the test and reference compounds yielded  
binding energy values of -7.28 kcal/mol for  
Anapheline, -5.97 kcal/mol for Elaeokanine C, -  
5.54 kcal/mol for Tetradecylamine, and -9.42  
dimensional space, expressed as X, Y,  
Z
coordinates. The interactions between the  
natural ligand and the protein obtained from the  
validation process can also be visualized using  
the Biovia Discovery Studio software.  
Table 4. Results of the Redocking Process  
Parameters  
Grid Box Size (Å)  
Grid  
Position  
PPAR-γ  
40 x 40 x 40  
Box X : 59.415  
AMPK  
40 x 40 x 40  
X : -6.735  
Y : -5.607  
Z : 42.406  
0.375  
Y : 44.128  
Z : 7.029  
0.375  
kcal/mol  
for  
the  
reference  
compound  
Spacing  
(rosiglitazone). Binding energy provides insight  
into the stability of the interaction between a  
ligand and its receptor. The lower the binding  
energy value, the more stable the interaction and  
the higher the likelihood of the ligand interacting  
with the receptor [12].  
RMSD  
Binding energy  
0.86 Å  
-9,42  
kkal/mol  
0.48 Å  
-8,89  
kkal/mol  
Based on the visualization results, it was  
observed that the natural ligand AMPK interacted  
with 11 amino acid residues: SER 97, VAL 96, GLU  
94, VAL 30, MET 93, LYS 107, ALA 43, LEU 146, LYS  
45, ALA 156, and MET 164 (Figure 3). In contrast,  
the natural ligand PPAR-γ interacted with 8 amino  
Based on the binding energy values obtained in  
Table 5, the compounds that show potential as  
antidiabetic  
agents  
are  
Anapheline  
and  
Tetradecylamine. These compounds have lower  
58  
Chempublish Journal, 8(2) 2024, 51-64  
binding energy values compared to metformin,  
the reference compound, although they are not  
superior to rosiglitazone.  
ligand-protein interaction, resulting in a more  
stable bond between the ligand and the protein  
[12].  
The lower binding energies of Anapheline and  
Tetradecylamine relative to metformin in the  
context of AMPK receptor interaction. Lower  
binding energy often correlates with stronger  
Table 5. Binding energy values from the docking  
process  
Binding energy value  
Secondary  
(kcal/mol)  
Metabolites  
ligand-receptor  
affinity,  
suggesting  
these  
compounds could be more effective at activating  
AMPK pathways, which is relevant in glycemic  
control for type 2 diabetes management. This  
finding underscores a promising therapeutic  
potential of compounds in Averrhoa bilimbi, but  
we recognize that binding energy alone may not  
fully predict clinical efficacy. Thus, it is crucial to  
set specific binding energy thresholds that could  
more accurately reflect therapeutic viability in  
clinical settings.  
AMPK  
-8.28  
-5.77  
-7.04  
-6.12  
PPARγ  
-7.28  
-5.97  
-5.54  
-9.42  
Anapheline  
Elaeokanine C  
Tetradecylamine  
Comparator (positive  
control)  
Interaction and visualization studies.  
The interaction results between Anapheline and  
the AMPK protein receptor revealed four  
hydrogen bonds and one hydrophobic bond,  
with the amino acid residues involved in these  
interactions being GLU 100, ASP 103, SER 165,  
ASP 166, and MET 164. In contrast, the interaction  
between Tetradecylamine and the AMPK protein  
receptor resulted in two hydrogen bonds and  
fourteen hydrophobic bonds, with the amino  
acid residues involved being ASP 100, GLU 100,  
VAL 30, ALA 43, ALA 156, LEU 146, MET 164, LYS  
45, MET 163, and LEU 146 (Table 6). Anapheline  
shares 75% of its amino acid residues with the  
Our study has not yet been tested in laboratory  
or clinical settings on humans. However, we  
found research indicating that the results from in  
silico methods correlate strongly with those  
obtained from in vivo or in vitro methods. The  
flavan-3-ol compound tested using in silico  
analysis yielded a binding energy value of -10.24  
kcal/mol, which is lower than the positive control,  
quercetin, at -8.95 kcal/mol. Furthermore, when  
tested  
in  
vitro  
using  
p-nitrophenyl-α-D-  
glucopyranoside as a substrate, the flavan-3-ol  
compound demonstrated the ability to inhibit the  
reference  
compound  
metformin,  
while  
Tetradecylamine shares only 25%. The similarity in  
amino acid residues with the reference  
compound suggests that the test compounds  
may inhibit the activity of the target protein and  
potentially exhibit similar activity to the reference  
compound [27].  
activity  
of  
the  
enzyme  
α-glucosidase  
in  
hydrolyzing p-nitrophenyl-α-D-glucopyranoside  
into p-nitrophenol, with inhibitory activity even  
superior to that of the standard quercetin [29].  
Supporting  
this  
approach,  
Setyani  
[30]  
successfully isolated flavonoid compounds from  
yellow root with structures similar to rutin and  
quercetin. Although the binding affinity of these  
compounds was lower than that of the natural  
ligand, in vivo tests revealed significant  
antidiabetic activity.  
Based on the docking results presented in Table  
5, it was found that the number of bonds  
between the ligand and the protein does not  
affect the binding energy (ΔG). However, the  
magnitude of the inhibition constant significantly  
influences the binding energy (ΔG). The lower the  
binding energy and inhibition constant values,  
the higher the ligand's affinity, as the non-  
covalent interactions between the compound  
and the receptor become more stable and  
stronger. A lower (negative) binding energy value  
indicates that less energy is required for the  
59  
Chempublish Journal, 8(2) 2024, 51-64  
Table 6. Analysis of docking results between protein receptors and test and reference compounds.  
Compound/  
Ligand  
Inhibition  
Constant  
(µM)  
Binding  
Energy  
(kcal/  
mol)  
Amino Acid Residue  
2D Interaction  
AMPK  
303.49  
-8.89  
SER 97, VAL 96, GLU 94,  
VAL 30, MET 93, LYS 107,  
ALA 43, LEU 146, LYS 45,  
ALA 156, MET 164  
3 Hydrogen Bonding  
9 Hydrphobic Bond  
PPAR- γ  
151.91  
32.43  
-9.42  
-6.12  
SER 289, HIS 323, CYS  
285, MET 348, MET 364,  
ILE 341 LEU 330, ILE 281  
3 Hydrogen Bonding  
4 Hydrphobic Bond  
Metformin  
ASP 103, GLU 100, ASP  
166, LEU 22  
2 Hydrogen Bonding and  
Elactrostatistical bonding  
1 Elactrostatistical bonding  
2 Hydrogen Bonding  
Anapheline with  
AMPK protein  
0.845  
-8.28  
-7.28  
GLU 100, ASP 103, SER  
165, ASP 166, MET 164  
4 Hydrogen Bonding  
1 Hydrphobic Bond  
Anapheline with  
PPAR- γ protein  
4.6  
TYR 327, TYR 473, CYS  
285, ARG 288, LEU 453,  
ILE326, LEU 330, PHE  
282, HIS 449  
3 Hydrogen Bonding  
6 Hydrphobic Bond  
60  
Chempublish Journal, 8(2) 2024, 51-64  
Compound/  
Ligand  
Inhibition  
Constant  
(µM)  
Binding  
Energy  
(kcal/  
mol)  
Amino Acid Residue  
2D Interaction  
Elaeokanine  
C
59.43  
-5.77  
VAL 96, VAL 30, ALA 43,  
ALA 156, LEU 22, MET  
164, ILE 77, LEU 146  
with  
AMPK  
protein  
1 Hydrogen Bonding  
8 Hydrphobic Bond  
Elaeokanine  
with PPAR-  
protein  
C
γ
42.33  
6.88  
-5.97  
-7.04  
TYR 327, TYR 473, HIS  
323, PHE 282, CYS 285,  
LEU 469, ILE 326, PHE  
363, HIS 449  
4 Hydrogen Bonding  
8 Hydrphobic Bond  
Tetradecylamine  
ASP 100, GLU 100, VAL  
30, ALA 43, ALA 156, LEU  
146, MET 164, LYS 45,  
MET 163, LEU 146  
with  
AMPK  
protein  
2 Hydrogen Bonding  
14 Hydrphobic Bond  
Tetradecylamine  
86.87  
-5.54  
CYS 285, SER 289, LEU  
453, LEU 469, LEU 330,  
MET 364, PHE 282, HIS  
323, PHE 363, HIS 449,  
TYR 473  
with PPAR-  
protein  
γ
3 Hydrogen Bonding  
15 Hydrphobic Bond  
The analysis showed that the bound amino acids  
were similar to those in the natural ligand,  
suggesting that the similarity in bound amino  
acids may influence the biological activity.  
Similarly, Renganathan [28] conducted an in vivo  
study and found that the antihyperglycemic  
activity of certain compounds was comparable to  
that of acarbose. In silico analysis identified two  
active compounds, hexadecanoic acid and (Z)-  
octadec-9-enoic acid, with binding affinities of  
−1.313 and −1.266 kcal/mol, respectively. While  
these compounds were not directly compared  
with the natural ligand or acarbose, the similarity  
of the bound amino acids to those in the binding  
pocket supported the hypothesis that similar  
bound amino acids contribute to similar  
biological activities [31]. These findings highlight  
the complementary nature of in silico, in vitro,  
and in vivo analyses in evaluating the  
pharmacological potential of active compounds  
and  
suggest  
that  
the  
structure-activity  
61  
Chempublish Journal, 8(2) 2024, 51-64  
relationship plays a crucial role in the biological  
efficacy of these compounds.  
https://diabetesatlas.org/ (accessed Oct. 31,  
2024).  
[3] Kemenkes RI, “Pedoman Nasional Pelayanan  
Kedokteran Tata Laksana Diabetes Melitus  
Tipe 2 Dewasa,” vol. 21, no. 1, pp. 1–9, 2020.  
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[6] American Diabetes Association, “Standards  
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[7] A. P. M. N. Panamuan, E. K. Untari, and S.  
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Conclusions  
Based on the molecular docking analysis,  
secondary metabolites from Averrhoa bilimbi,  
specifically Anapheline and Tetradecylamine,  
show potential as antidiabetic agents. This is  
indicated by their lower binding energy values of  
-8.28 kcal/mol and -7.04 kcal/mol, respectively,  
compared to metformin, which was used as the  
reference compound for the AMPK protein  
receptor. However, these compounds are not as  
effective as rosiglitazone, which served as the  
reference compound for the PPAR-γ protein  
receptor. Additionally, both compounds share  
75% and 25% amino acid residue similarity with  
the reference compounds.  
Acknowledgement  
We extend our gratitude to the Ministry of  
Research, Technology, and Higher Education  
(Kemenristekdikti) for providing research funding  
through the Student Creativity Program (PKM) in  
2024, and to Muhammadiyah University of  
Gombong.  
[8] C. Lankatillake, T. Huynh, and D. A. Dias,  
“Understanding glycaemic control and  
current  
approaches  
for  
screening  
antidiabetic natural products from evidence-  
based medicinal plants,” Plant Methods, vol.  
15, no. 1, p. 105, 2019, doi: 10.1186/s13007-  
019-0487-8.  
Author Contibutions  
Conceptualization, R.A.D.; Methodology, D.A.P.;  
Software, S.S.; Validation, S.S. and N.Z.W.K.;  
Investigation, R.A.D..; Resources, A.M.S.; Data  
[9] G. Alexander, “Biodiversity in Indonesia,”  
Biodiversity  
Warriors,  
2020.  
https://biodiversitywarriors.kehati.or.id/arti  
kel/biodiversity-in-indonesia/ (accessed Jan.  
14, 2024).  
Curation, D.A.P.; Writing  
Preparation, R.A.D.; Writing – Review & Editing,  
S.S. and N.Z.W.K.; Visualization, R.A.D.;  
–
Original Draft  
[10] A. Retnowati, Rugayah, J. S. Rahajoe, and D.  
Supervision, S.S and H.; Project Administration,  
Arifiani,  
Status  
Keanekaragaman  
Hayati  
A.M.S and H.  
Indonesiaꢀ: Kekayaan Jenis Tumbuhan dan  
Jamur Indonesia. 2019.  
Conflict of Interest  
There are no significant conflicts  
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