How to cite:

Sofyan Hidayatulloh (2021). Docking Molecular Simulation of

Secondary Metabolic Compounds Annona Muricata as Anti-Cancer.

Journal Eduvest. 1(11): 1196-1202

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2775-3727

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Eduvest – Journal of Universal Studies

Volume 1 Number 11, November 2021

p- ISSN 2775-3735 e-ISSN 2775-3727

DOCKING MOLECULAR SIMULATION OF SECONDARY

METABOLIC COMPOUNDS

ANNONA MURICATA

AS ANTI-

CANCER

Sofyan Hidayatulloh

Muhammadiyah Malang University, Indonesia

E-mail: [email protected]

ARTICLE INFO ABSTRACT

Received:

October, 26

2021

Revised:

November, 16

2021

Approved:

November, 18

2021

This study aims to test and determine the affinity and

molecular mechanism of Annona muricata to COX-2

target protein, which can be used to test the potential of

Annona muricata as an anticancer drug using the

molecular docking in silico method (computer modeling).

By identifying and optimizing guide molecules in the drug

discovery process, this computational chemical technique

can be utilized to accelerate the selection of compounds

to be isolated and synthesized. The research use

descriptive quantitative as a research design and the

experimental factorial design as an approach. The results

of this study indicate that curcumin and its analogues

have potential to became anticancer, and can be used for

further drug development related to anticancer.

KEYWORDS

Cancer, Anti-Cancer, Annona Muricata

This work is licensed under a Creative Commons

Attribution-ShareAlike 4.0 International

INTRODUCTION

Cancer is a disease that does not distinguish between one social status and the

other and can attack anyone and arises due to abnormal growth of body tissue cells that

become cancer cells in their development which can spread to other parts of the body

which leads to death. Cancer arises from the transformation of normal cells into tumor

cells in a multistage process that generally progresses from precancerous lesions to

malignant tumors (Sirait and Sulistiowati, 2014)

Sofyan Hidayatulloh

Docking Molecular Simulation of Secondary Metabolic Compounds Annona Muricata

as Anti-Cancer 1197

Cancer is a major public health problem worldwide and the second leading cause

of death in the United States. In 2017, the American Cancer Society (ACS) estimated the

number of cancer cases was 1.68 million with 4,600 new cancer diagnoses per day and

600,920 cancer deaths (Siegel et al., 2017). Nationally, the prevalence of cancer in people

of all ages in Indonesia in 2013 was 1.4‰ or an estimated 347,792 people, while for East

Java Province it was 61,230 people (Rio and Suci, 2017).

COX-2 is an enzyme that plays a role in the metabolism of arachidonic acid in

cell membranes. Arachidonic acid metabolism is considered to play a very important role

in the occurrence of carcinogenesis. This metabolic pathway is associated with the

formation of prostanoids. Prostanoids belong to the subclass of eicosanoids which are

converted into prostaglandins, thromboxane and prostacyclin (Singh et al., 2010).

Cyclooxsigenase-2 (COX-2) is a key enzyme in the conversion of arachidonic acid to

prostaglandins which was first identified 20 years ago. Several studies have suggested the

involvement of prostanoids in the pathogenesis of cancer (Grösch et al., 2006, p. 2). In

vitro studies have shown that growth factors, tumor promoters and oncogenes induce

prostanoid synthesis (Singh et al., 2010). Other studies, through in vivo studies, have

shown that arachidonic acid metabolism via the cyclooxygenase pathway is increased in

some tumor events in humans and is thought to be mediated by the induction of the COX-

2 enzyme (Chen et al., 2018; Kern et al., 2006).

The most successful therapies for localized and non-metastatic cancers are

surgery and radiotherapy, but they are not effective if the cancer has spread throughout

the body. Cancer treatments (Chemotherapy, hormone, and biologic therapy) can reach

any organ in the body infected by cancer cells. through the bloodstream. Until now these

drugs are the best treatment of choice for metastatic cancer (Chabner and Roberts, 2005).

Chemotherapy treatment for cancer still has a drawback that besides destroying cancer

cells, it also affects normal cells with high growth rates, such as hair follicles, bone

marrow, and gastrointestinal tract cells, causing side effects typical of chemotherapy.

Because of this, it is necessary to find new treatments that selectively kill cancer cells

without affecting normal cells.

The Annona muricata tree, similar to other Annona species, including Annona

squamosa and Annona reticulata is widely used as traditional medicine for various

diseases, especially cancer and parasitic infections. Phytochemical tests state that

annonaceous acetogenins are the main constituents of Annona muricata

(Moghadamtousi, et al., 2015). In addition to annonaceous acetogenin, phytochemical

screening results from aqueous and ethanol extracts of soursop leaves contain secondary

metabolites such as alkaloids, saponins, terpenoids, flavonoids, coumarins, lactones,

anthraquinones, tannins, cardiac glycosides, phenols and phytosterols (Gavamukulya, et

al., 2014). Zetolite and Annonaceous acetogenin are the main ingredients in the soursop

tree, which have anti-cancer, anti-inflammatory, neuroprotective, anti-Alzheimer's and

anti-oxidant activities (Adri and Hersoelistyorini, 2013). Knowing the affinity and

molecular mechanism of Annona muricata to the COX-2 target protein, can be used to

test the potential of Annona muricata as an anticancer drug using the molecular docking

in silico method (Computer modeling). By identifying and optimizing guide molecules in

the drug discovery process, this computational chemical technique can be utilized to

accelerate the selection of compounds to be isolated and synthesized. As a result, it will

be known the ability of Annona muricata to suppress COX-2 receptor overexpression.

RESEARCH METHOD

The research used descriptive quantitative as a research design and the

Eduvest – Journal of Universal Studies

Volume 1 Number 10, October 2021

1198 http://eduvest.greenvest.co.id

experimental factorial design as an approach. The databases used to predict

pharmacokinetics are PubChem, Protein Data Bank, and SwissADME. The structure of

plant chemical compounds can be accessed on PubChem in 2D and 3D but the ones

chosen in this study are in 2D form because not all compounds can read 3D structures on

PubChem. Subsequent research materials are secondary metabolites obtained from the

dr.duke website and target proteins which can be obtained from the Protein Data Bank.

SMILES inputted into PubChem is a chemical notation designed by computer experts that

can be used to interpret a chemical structure accurately and specifically with the help of a

computer (Weininger, 1988). The results are Druglikeness and Boiled Egg data which

will be used to conclude secondary metabolite compounds with good bioavailability.

RESULT AND DISCUSSION

A. Experimental

The databases used to predict pharmacokinetics are PubChem, Protein Data Bank,

and SwissADME. The structure of plant chemical compounds can be accessed on

PubChem in 2D and 3D but the ones chosen in this study are in 2D form because not all

compounds can read 3D structures on PubChem. Subsequent research materials are

secondary metabolites obtained from the dr.duke website and target proteins which can be

obtained from the Protein Data Bank. SMILES inputted into PubChem is a chemical

notation designed by computer experts that can be used to interpret a chemical structure

accurately and specifically with the help of a computer (Weininger, 1988). The results are

Druglikeness and Boiled Egg data which will be used to conclude secondary metabolite

compounds with good bioavailability.

Of the 81 compounds from the Annona muricata tree, analysis was carried out

using the SwissADME webserver using the Boiled Egg method with a range according to

the white circle on TPSA (0-140); WLOGP (-2 – 6,8). According to (Mirza, 2019) the

Boiled Egg method is able to read two parameters at once, namely Human

Gastrointestinal Absorption (HIA) and Blood Brain Barrier (BBB). The egg plot area can

be easily read, where the yolk represents the physicochemical space for drugs capable of

absorbing BBB and the white space represents the physicochemical space for HIA

absorption.

Figure 1. Boiled Egg

Sofyan Hidayatulloh

Docking Molecular Simulation of Secondary Metabolic Compounds Annona Muricata

as Anti-Cancer 1199

For pharmacodynamic testing, it begins with determining the target protein, in

which the criteria is that the target protein to be used must be included in homo sapiens.

This is because the target protein to be studied is drug interactions in humans. The

selection of the target protein must be complete with ligands because the molecular

docking process can only be done with drugs that have ligands. The docking method is

said to be valid if the RMSD value is 2Å. In table 3, the table of the results of the docking

method validation shows a lot of RMSD 2Å this shows that the docking method is valid.

The final result of the docking process is the RMSD value, the number of clusters, the Ki

value and the binding energy that has been inputted in Table 4. The prediction of ligand

interaction activity with the target protein is in the form of binding energy.

The software used for pharmacodynamic profile prediction is Autodock PyRx 0.8

series for docking compounds or drugs, Discovery Studio to separate the contents of the

PDB file and also to visualize the final results in 3D which, for 2D visualization is done

using a webserver. protein.plus. Avogadro is one of the software which has a role in

optimizing the geometry for adjusting the atomic structure and bonds. In addition,

Avogadro also plays a role in stabilizing molecular geometries so that very small changes

in geometry do not change energy (Cahyono, 2019).

The results of the docking simulation carried out 100 times have 100 docking poses

that have their respective bond energies. The bond energy taken is the most negative

because it has a strong interaction. This is due to the presence of polar functional groups

on the ligands such as methyl (-CH3), hydroxyl (-OH) and amine (-NH3).

B. Docking Results Evaluation

The docking simulation in this study was carried out under flexible ligand

conditions. Flexible conditions were used to adjust the structure of the most stable ligand

to interact with the receptor. The stability parameter observed was Gibbs free energy

(∆G). The more negative the value of G indicates a good level of stability between the

ligand and the receptor, so that the bond formed will be stronger. The following table

shows the Gibbs free energy (∆G) of the docking simulation results.

Name of compound

Number

clusters

Number

of poses

in the

largest

cluster

Predicted

Binding

Energy

(kcal/mol)

Predicted

Inhibition

constant

RETICULINE

-9,7

77,87 nM

SCYLLITOL

-9,85

75,94 nM

PROCYANIDIN

-9,89

52,97 nM

RETICULINE

100

-9,91

51,63 nM

GERANYL-CAPROATE

-9,8

32,09 nM

ARACIDONIC ACID

-5,20

154,37

Celecoxib

100

-10,11

28,73 nM

Table 1. The best secondary metabolite compound Annona muricate with the smallest

binding energy (∆G) and the largest inhibition constant

The results of the docking simulation carried out 100 times have 100 docking poses

that have their respective bond energies. The bond energy taken is the most negative

because it has a strong interaction. This is due to the presence of polar functional groups

in the ligands such as methyl (-CH3), hydroxyl (-OH) and amine (-NH3) in curcumin

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ligands and their analogues.

Secondary metabolite compounds in Annona muricata plant can inhibit arachidonic

acid. This is indicated by the value (∆G) of Annona muricata compound and its analogues

is smaller than that of arachidonic acid (-5.20 kcal/mol). These data indicate that the

Cyclooxyge receptornase-2 (COX-2) will be more stable to bind to compounds in

Annona muricata plant and its analogues than arachidonic acid. Arachidonic acid will

produce prostaglandins due to enzymatic reactions by the Cyclooxsigenase-2 (COX-2)

enzyme so that it can trigger cancer if there is an overexpression of the enzyme.

Celecoxib has a lower G value than Annona muricata compound. This indicates that

celecoxib can bind more stably to the Cyclooxsigenase-2 (COX-2) enzyme receptor

compared to the compound in Annona muricata.

The inhibition constant (Ki) is a value that describes the affinity between the

compound and its decomposition. The smaller the Ki value, the greater the affinity of the

ligand to the receptor (Arfi et al., 2020). From the table above, it can be seen that the

secondary metabolites of Annona muricata have a lower inhibition constant than

arachidonic acid. This shows that high affinity is found in the secondary metabolite

compound Annona muricata. By having a high affinity, there will be inhibition of the

Cyclooxsigenase-2 (COX-2) enzyme, so that prostanoid overproduction can be prevented

and will reduce proliferation and apoptosis in cancer cells (Arfi et al., 2020).

C. Hydrogen Bonds

Observations of hydrogen bonds and amino acids were carried out using the

protein.plus webserver. Chemical bonds other than hydrogen bonds can occur due to

flexible ligands interacting with receptors. Interactions can be in the form of non-covalent

or non-bonded interactions, which occur between the ligand and the receptor, which can

increase the affinity of the ligand to the receptor. The most common bonds are

electrostatic interactions and van der Waals bonds.

Table 2. Hydrogen and Hydrophobic Bonds

Name of

compound

Bond Type

Hydrogen

Hydrophobic

RETICULINE

Ser339A, Leu338A

Val509A, Gly512A, Leu338A,

Ala513A, Ser339A

SCYLLITOL

Ser339A

Val509A, Val335A, Ser339A

PROCYANIDIN

Ile503A, Phe504A,

Tyr341A, Gln178A,

Met508A

Val509A, Ser339A, Phe504A,

Gly512A

RETICULINE

GERANYL-

CAPROATE

Gln178A, Phe504A,

Tyr341A, Met508A

Phe504A, Gly512A, Val509A,

Ser339A

RETICULINE

Gln178A, Phe504A,

Tyr341A, Met508A

Ser339A, Gly512A, Phe504A,

Val509A

Table 2 shows hydrogen bonding and amino acid interactions. Table 2 illustrates

the hydrogen bonds and amino acids that interact between the Cyclooxsigenase-2 (COX-

2) enzyme. The docking simulation can also determine the hydrophobic interaction

between the ligand and the amino acid residue of the Cyclooxsigenase-2 (COX-2)

enzyme in the most stable conformation (small G value).

Sofyan Hidayatulloh

Docking Molecular Simulation of Secondary Metabolic Compounds Annona Muricata

as Anti-Cancer 1201

Figure 2. Visualization of Docking EO (Ethinyl Estradiol)

Figure 3. Visualization of Celecoxib Docking

Observation of residue contacts aims to determine interactions other than hydrogen

bonds that occur between the ligand and the target protein. The residue that makes contact

with the ligand has a non-binding interaction between the ligand and the target protein

which will increase the affinity and inhibitory activity of the Cyclooxsigenase-2 (COX-2)

enzyme. scyllitol has 1 of 2 amino acid residues similar to celecoxib, namely Ser 339A.

This shows that scyllitol compounds have potential as anticancer, which in celecoxib the

sulfonamide group in the hydrophilic pocket attached to the active site of COX-2 is able

to specifically inhibit the COX-2 enzyme.

CONCLUSION

Based on the results of this study, it can be concluded that reticuline compounds

have good stability, followed by geranyl-caproate which is seen from the value of binding

energy (∆G). Good secondary metabolite compounds of Annona muricata plant were

shown by reticuline with a value (∆G) of -9.91 kcal/mol. Furthermore, the observation of

hydrogen bonding and amino acid scyllitol showed the similarity of 1 of 2 residues

possessed by celecoxib, which previously had known anticancer activity. It can be

concluded that curcumin and its analogues have potential as anticancer, and can be used

for further drug development related to anticancer.

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