In Silico Analysis of the Phytochemical Compounds in Carica papaya

Seeds for Optimizing the Inhibitors of HMG-CoA Reductase

Hariyanti, Rizky Arcinthya Rachmania, Mutia Karinah, and Hadi Sunaryo

Faculty of Pharmacy and Science, Universitas Muhammadiyah Prof. DR. HAMKA

Keywords: Carica papaya seed, HMG-CoA reductase, Molecular docking, Anti-hypercholesterolemia.

Abstract: HMG-CoA Reductase, a key enzyme in the cholesterol biosynthesis, catalyzes the conversion of 3-

hydroxy-3-methyl-glutaryl coenzyme A (HMG-CoA) into mevalonate. Therefore, this enzyme is the target

of the cholesterol-lowering drugs known as statins. Carica papaya seed extract contains phytochemical

compounds that are thought to have a cholesterol-lowering effect. The present study was designed to

examine the ability of the secondary metabolites of Carica papaya seeds as an antagonist to HMG-CoA

reductase using in silico molecular docking. The docking analysis was carried out in PLANTS 1.2 software

in which the lowest ChemPLP score, i.e., free energy, was the molecular docking parameter. Seven ligands

were docked with HMG-CoA reductase receptor, three of which were benzyl glucosinolate, oleic acid, and

glucotropaeolin that had the best ChemPLP scores, namely -90.5491 kcal/mol, -81.7665kcal/mol, and -

85.1919 kcal/mol, respectively. Benzyl glucosinolate formed hydrogen bonds with the active site of the

targeted protein. As a conclusion, this compound can inhibit the enzyme HMG-CoA reductase, and it has

the potential for anti-hypercholesterolemia

1 INTRODUCTION

Hypercholesterolemia, excessively high levels of

plasma cholesterol, emerges as a strong risk factor

for cardiovascular disease (CVD) (Stapleton et al.,

2010). Cholesterol is an important component of the

cell membrane and is essential for the synthesis of

various important metabolites. HMG-CoA

Reductase (HMGCR), a key enzyme in the

cholesterol biosynthesis, catalyzes the conversion of

3-hydroxy-3-methylglutaryl coenzyme A (HMG-

CoA) into mevalonate. Human HMGCR consists of

polypeptide chains of 888 amino acids with three

functional portions: residues 1-339 span the

membrane of the endoplasmic reticulum eight times,

while residues 340-459 connect the membrane

portion to the catalytic portion (i.e., residues 460-

888), which resides in the cytoplasm. This enzyme is

anchored in the membrane of the endoplasmic

reticulum, which has seven transmembrane domains,

with the active site located in a long carboxyl-

terminal domain in the cytosol (Nakanishi et al.,

1988). The inhibition of this enzyme results in a

significant decrease in cholesterol levels (Goldstein

and Brown, 1990).

Carica papaya seeds contain some compounds

that are suspected to have a cholesterol-lowering

effect on the mechanism of inhibiting the enzyme

HMG-CoA reductase. The phytochemical

substances in Carica papaya seeds have been

reported to contain flavonoids, saponins, and tannins

(Olivera et al., 2007). These compounds can

decrease the HMG-CoA reductase activity and,

therefore, inhibit cholesterol synthesis (Siregar

2015; Afrose et al., 2010). The main components of

papaya seeds are fatty acids, crude protein, crude

fiber, papaya oil, carpaine, benzyl isothiocyanate,

benzyl glucosinolate, glucotropaeolin, benzyl

thiourea, hentriacontane, β-sitosterol, caricin, and

myrosin enzyme (Yogiraj et al., 2014). Also, there

are other compounds, such as alkaloids, steroids,

essential oils, oleic acid, and palmitic acid (Satriyasa

and Pangkahila, 2010). Oleic acid is part of the fatty

acids found in papaya seeds. Natali et al. (2007)

state that oleic acid has an inhibitory effect on

HMG-CoA reductase enzyme.

The mechanism of interaction between HMG-

CoA reductase enzyme and the compounds in

papaya seed (Figure 1) can be investigated using

molecular docking method. Molecular docking is

used to discover compounds for potentially potent

Hariyanti, ., Rachmania, R., Karinah, M. and Sunaryo, H.

In Silico Analysis of the Phytochemical Compounds in Carica papaya Seeds for Optimizing the Inhibitors of HMG-CoA Reductase.

DOI: 10.5220/0008240501230132

In Proceedings of the 1st Muhammadiyah International Conference on Health and Pharmaceutical Development (MICH-PhD 2018), pages 123-132

ISBN: 978-989-758-349-0

123

drugs in relatively short periods of time (Zukrullah

et al., 2012). Based on this background, this research

attempted to determine the mechanism of the

interaction of HMG-CoA reductase enzyme and the

ligands of the compounds in papaya seeds. The

enzyme was rolled with each ligand in PLANTS 1.2

software and then visualized to see the interaction

formed between the ligand and the receptor in

Molecular Molegro Viewer (MMV) software. Using

the Lipinski Rule of Five, the in silico analysis

determined any compounds that had oral

bioavailability.

2 MATERIALS AND METHODS

2.1 Materials

The molecular docking program was run in LINUX

with UBUNTU 16 system (64 bit). The ligand

design and visualization were developed using

Windows 10 operating system. The software used in

this research included PLANTS 1.2

(http://www.tcd.unikonstanz.de/ research/plants.php)

for docking, YASARA 17.4.17 (http:

//www.yasara.org/viewdl.htm) for protein

preparation and visualization, Marvinsketch 17.9.0

(http://www.chemaxon.com/marvin/download-

user.html) for ligand preparation, and Molegro

Molecular Viewer 2.5 for visualization.

The 3-dimensional crystallographic structure of

HMG-CoA reductase was downloaded from the

Protein Data Bank at http://www.rcsb.org/pdb in

.pdb format (Purnomo, 2013). Previously, this

research had consulted scientific journals to

determine the receptor. The 3D structures of the

ligands included simvastatin acid and simvastatin, as

well as the phytochemical compounds from Carica

papaya seeds, i.e., carpaine, benzyl isothiocyanate,

benzyl glucosinolate, glucotropaeolin, benzyl

Figure 1: The ligand structures of the phytochemical compounds in Carica papaya seeds

Carpaine

Glucotropaeolin

Oleic Aci

β-Sitosterol

Benzyl Thiourea

Benzyl Isothiocyanate

Benzyl Glucosinolate

MICH-PhD 2018 - 1st Muhammadiyah International Conference on Health and Pharmaceutical Development

124

thiourea, β-sitosterol, and oleic acid. These

structures were designed in Marvin Sketch in .mol2

and .mrv formats

2.2 Methods

2.2.1 Protein Preparation

The protein macromolecule of HMG-CoA reductase

with the PDB code 1HW9 for homo sapiens

downloaded from the Protein Data Bank at

http://www.rcsb.org/ was inserted into YASARA

software for preparation. The protein

macromolecules were separated from solvents and

ligands or non-standard residues. The separation of

macromolecules from unnecessary molecules used

YASARA program (edit > delete > residue). The

elimination of water molecules (edit > delete >

water) and the addition of hydrogen to the structures

(edit > add > hydrogen to all) were also run in this

program. The results were stored with the protein

name and in .mol2 format.

2.2.2 Ligand Preparation

The ligand structures were downloaded from

www.pubchem.ncbi.nlm.nih.gov in 2D model. The

protonation was changed at pH 7.4 using the Marvin

Sketch (Calculation > Protonation > Major

Microspecies), the resulted data were then stored in

.mrv files. These files were opened, and a

conformational search in the same software was

stored in .mol2 format (Calculation > Conformation

> Conformer).

2.2.3 Validation of Molecular Docking

Methods

Before the virtual screening, validation was

performed to determine the values of the root mean

square distance (RMSD). It was run in the YASARA

program (Analyze > RMSD > Molecule) by entering

specific ligands and receptors in .mol2 format. A

protocol was accepted if the RMSD of the heavy

atom was smaller than 2.0 Å.

2.2.4 Molecular Docking with PLANTS 1.2

Software

The molecular docking was processed in PLANTS

program. This software can only run in Linux

operating system. All data and PLANTS

applications were moved from the desktop to the

root (sudo -s), and the terminal was opened in Linux

afterward, The command "cp

/home/desktop/PLANTS1.2 PLANTS" was typed in

and followed by the command "chmod u + x

PLANTS" to activate the PLANTS application. The

results of the ligand and receptor preparations were

stored in .mol2 and moved to the root with the

command "cp /home/desktop/*.mol2."

The next step was to find the binding site using

the command "./PLANTS - bind ref_ligand.mol2 5

protein.mol2". To examine whether the settings on

the PLANTS were correct, this research used the

command "kwrite plantsconfig", followed by

"./PLANTS --mode screen plantsconfig". When the

docking process was complete, the results were

displayed in the terminal by entering the command

"cd results /" and, then, "more bestranking.csv".

From the ten docking results, the one with smaller

conformation value was selected, and the result was

stored using the command "cp*_entry_

(conformation number)_conf_01.mol2/home/

desktop/".

2.2.5 Molecular Docking Result Analysis

And Visualization

The docking results were observed from the output

in a notepad format. The complex conformation of

the docking result was determined by choosing

conformation based on the CHEMPLP score, i.e.,

the lowest free energy. The docking results were

visualized using YASARA software to determine

whether the hydrogen bond distance was <3.5 Å.

2.2.6 Drug Scan Analysis

The drug scan was analyzed on a website

(http://chemicalize.com). The analysis involved

uploading the ligand file in .mol format. Then, the

results were downloaded in PDF format.

3 RESULTS AND DISCUSSION

The initial stage of the docking process was the

preparation of the protein structure where the

proteins were selected from the GDP site

purposively. The HMG-CoA reductase enzyme in

.pdb format was downloaded from the protein

database organized by the Research Collaboratory

for Structural Bioinformatics (RCSB) at

http://www.rcsb.org/. The protein chosen for the

HMG-CoA reductase was 1HW9 (PDB code). It is

an enzyme complex with simvastatin acid

(endogenous ligand). The protein structure

In Silico Analysis of the Phytochemical Compounds in Carica papaya Seeds for Optimizing the Inhibitors of HMG-CoA Reductase

125

downloaded from GDP generally still contains a

solvent (water). In this study, the protein structures

of the other residues were analyzed without the

endogenous ligand structure. In other words, the

protein structures depicted a protein without

endogenous ligand and other molecules such as

water and other single atoms; therefore, the docking

process only analyzed the interaction of test

compounds and proteins (Kitchen et al., 2004).

These water ligands and molecules had to be

removed from the protein macromolecules as they

might prolong the duration of the docking

simulation. The addition of the hydrogen atom in

question afterward aimed to bring up the existing

hydrogen atoms in the structure and create a three-

dimensional form that determined the interaction

with the ligand. The docking simulation of all

processes involved in the preparation of the protein

structure was run in the YASARA program.

The process of preparing the ligand structure

aimed to achieve optimal ligand conformation. The

conformation of drug molecules may depend on the

acidity (pH) and ionic composition of the medium in

which the drug is studied (Siswandono and

Soekardjo 1998). Since the drug worked on a

biological system, each ligand was subjected to

protonation to obtain a structure adjusted to the

blood pH, i.e., about 7.4. A total of nine ligands

were tested in this study. The nine ligands consisted

of one endogenous ligand (SIM) from the crystal

structure of HMG-CoA reductase (for redocking

process), one comparator ligand (simvastatin), and

seven test ligands of the compounds in papaya seeds,

namely carpaine, benzyl isothiocyanate, benzyl

glucosinolate, glucotropaeolin, benzyl thiourea, and

oleic acid. Using a conformational search in Marvin

Sketch, the optimization yielded as many as ten (10)

conformations that represented the positions of all

ligands against the pocket cavity. Then, the analysis

proceeded with the docking process to find out

which ligand conformation best represented the

ligand position against the pocket cavity, as

evidenced by the ChemPLP score analysis. Agistia

et al. (2013) state that the most appropriate

conformation can be identified from the output of

the molecular docking run in PLANTS 1.2, which is

the ChemPLP score. Therefore, the next step in this

research determined the ChemPLP score of the

ligand against the receptor resulted from the docking

process to find out the best ligand conformation.

The identification of appropriate docking

protocols is a key step to a valid docking pose

(Oniga et al., 2017). The validation of the docking

method in this study was conducted by redocking

the endogenous ligand in the protein group

downloaded from the Protein Data Bank. The

evaluation of the validation results relied on the

RMSD (Root Mean Square Deviation) of the pose

visualization (Moitessier et al., 2008). RMSD is a

measurement of two poses by comparing the

positions of atoms in experimental structures with

the ones in docked or predicted structures (Hawkins

et al., 2008) The RMSD values of successful

docking methods are <2.0 Å (Hevener et al., 2009;

Jain and Nicholls, 2008; Moitessier et al., 2008).

The RMSD of the validation results in this research

was 1.0138 Å (<2 Å), proving that 1HW9 could be

used for further analysis in this research. The closer

the RMSD to zero, the more similar the poses of

endogenous ligand and copy ligand. Small RMSD

suggests that the developed protocols are accepted,

and they can be further developed for virtual

screening in the discovery of new compounds

(Purnomo, 2011; 2013). Superposing the

endogenous ligand and the copy ligand, the

visualization validated that the atoms of the two-

molecule structures had similar positions and angles

(Figure 2). In other words, the conformation of the

endogenous ligand structure of GDP was similar to

the well-selected copy ligand in the docking process

(Adelina, 2014). With RMSD >2.0 Å, the

visualization shows two molecules with significantly

different angles and positions even though they have

equal number of atoms.

Docking is a simulation method to find out the

orientation between ligand and receptor. After the

redocking process with endogenous ligand, the

cartesian coordinates of the binding site were x=

4.0308, y= -9.4318, and z= -11.5016. Figure 3

shows the visualization of the binding site on the

receptor (1HW9). The binding site is an area where

protein binds to molecules and ions (i.e., ligands)

that will affect the conformation and function of the

Figure 2. The Visualization of the Superposition of

Endogenous Ligand and Copy Ligand in Molegro

Molecular Viewer Software. The endogenous ligand is

yellow; the copy ligand is blue.

MICH-PhD 2018 - 1st Muhammadiyah International Conference on Health and Pharmaceutical Development

126

protein. The binding site involves amino acid

residues that play an important role in binding with

ligands (Pratama et al., 2016). Based on the docking

scores listed in Table 1, the endogenous SIM

(simvastatin acid) ligand has the lowest ChemPLP

score, i.e., -101.899 kcal/mol, while the score of

simvastatin (comparator ligand) is -80.3996

kcal/mol. Simvastatin acid is the active metabolite of

simvastatin (Pubchem, 2017). The difference lies in

the structure. Mycek et al. (2001) mention that the

structure of simvastatin is a lactone that needs to be

hydrolyzed into active drugs. This hydrolysis

(simvastatin acid) adds OH-group and carboxylic

group to the structure. The OH-group influences the

amount of hydrogen bond interaction. Siswandono

and Soekardjo (1998) explain that hydrogen bond

interaction generally occurs in compounds that have

clusters of, for example, OH-, and NH-. Based on

the data (Table 2), the most prevalent hydrogen bond

interactions in the endogenous ligand (simvastatin

acid) involved the OH group, especially OH- in the

carboxylic group of simvastatin acid. The difference

in the number of OH-groups causes different scoring

results between simvastatin and simvastatin acid.

The molecular docking performed on the

compounds of papaya seed against HMG-CoA

reductase (Table 1) resulted in three (3) best

compounds whose ChemPLP scores were lower than

the comparator ligands (simvastatin). They were

benzyl glucosinolate (ChemPLP score= -90.5491

kcal/mol), glukotropeolin (-85.1919 kcal/mol), and

oleic acid (-81.7665 kcal/mol). The ChemPLP score

of simvastatin ligand was -80.3996 kcal/mol.

Schneider and Bohm (2000) mention that a smaller

docking score implies a more stable bond or, in

other words, a more potent compound. Serina (2013)

affirms this assertion with the energy linkage to

affinity, i.e., that the best ligand will have stable

(free energy) performance and a better affinity.

Affinity is a measure of the drug's ability to bind

receptors. It is highly dependent on the molecular

structure of the drug and the receptor (Siswandono

and Soekardjo 1998). The ChemPLP scores of the

ligands in Table 1 were compared with simvastatin

to determine which ligand had the best interaction

and affinity. The comparison results were validated

by further analysis based on the inter-molecular

bond interactions (ligand and receptor). The analysis

also included the interpretation of the bond

interaction between amino acid residues and ligands

in Molegro Molecular Viewer (MMV).

Glucosinolate is included in the glycoside class.

A glycoside is composed of two entities, namely the

sugar group (glycone) and the non-sugar group

(aglycone/genin). The sugar portion of a glycoside

may be associated with the aglycone in various

ways, and the most common one is through the

oxygen (O-glycosides) atoms. However, the atoms

Figure 3. (A) Zoom Out, (B) Zoom in; The Visualization

of the Binding Site on 1HW9 (Grid Box: Green Circle)

using MMV Software Based on the Redocking Results in

PLANTS 1.2 software

(

)

(

)

Table 1. The Molecular Docking Results of the

Comparator Ligands and Ligands in Papaya Seed against

HMG-CoA Reductase Using PLANTS 1.2 Software

Ligands

CHEMPLP

Scores

(Kcal/mol)

Simvastatin Acid

(endogenous ligand) -101.899

Simvastatin

(Comparative Ligand) -80.3770

Carpaine

-66.6044

β-Sitosterol

-73.2094

Benzylthiourea

-55.9158

Benzyl Isothiocyanate

-52.0111

Benzyl Glucosinolate

-90.5491

Oleic acid

-81.7665

Glucotropaeolin

-85.1919

In Silico Analysis of the Phytochemical Compounds in Carica papaya Seeds for Optimizing the Inhibitors of HMG-CoA Reductase

127

that connect them may be Carbon (C-glycosides),

Table 2. The visualization Results of the hydrogen bond interaction between the ligands (endogenous ligands, test

ligands, and comparator ligands) and the receptor (HMG-CoA reductase) using MMV software

Ligands

Bonded

Amino

Acid

Residues

Bond

distance

(Å)

Group on

Ligands

Bonded

Amino

Acid

Residues

Bond

distance

(Å)

Group on

Ligands

Simvastatin

Acid

Asp 767 2.75 OH- group

Glucotropaeolin Asp 767 2.97 Nitro

rou

Gln 770 3.30 OH-group

carboxylate

Gln 766 3.18

Sulfonic

group

Gln 770 3.18 OH-group

carboxylate

Ser 774 2.40 OH

group on

glucose

Glu 801 3.12 O- pada

carboxylate

Ser 774 3.24 OH

group on

lucose

Asp 690 3.14 OH-group

carbox

late

Gln 770 2.76 OH

group on

lucose

Asp 690 3.37 OH-group

carbox

late

Gln 770 2.60 OH

group on

lucose

Asn 771 3.03 Side chains

Gln 770 3.11 OH

group on

glucose

Arg 702 3.26 OH-group

carboxylate

Tyr 761 2.79 OH

group on

glucose

Simvastatin Tyr 761 2.85 Lactone

ring

Glu 801 2.62 OH

group on

lucose

Gln 770 2.77 Side chains

Glu 801 3.24 OH

group on

lucose

Gln 766 3.26 OH- group

lactone’s

rin

Asn 771 3.37 OH

group on

glucose

Benzyl

glucosinolate

Lys 691 2.15 OH

group

on glucose

Ala 769 3.27 O

glucose

rin

Asp 767 2.66 OH

group

on glucose

Oleic acid Arg 702 3.14 OH-

group

Gln 770 2.51 OH

group

on glucose

Asp 690 3.11 OH-

group

Gln 770 2.41 OH

group

lucose

Asp 690 2.89 OH-

rou

Glu 801 2.39 OH

group

lucose

Glu 801 2.46 OH-

rou

Ser 774 2.95 Nitro

group on

lucose

Glu 801 2.82 OH-

group

Tyr 761 2.32 Sulfonic

group

Tyr 761 2.77 Sulfonic

group

MICH-PhD 2018 - 1st Muhammadiyah International Conference on Health and Pharmaceutical Development

128

Nitrogen (N-glycosides), or sulfur atoms (S-

glycosides) (Sarker and Nahar, 2009). In the

glucosinolate compound, the sugar portion (glycone)

is connected to a sulfur atom (S-glycoside).

Glucosinolate is a secondary metabolite of almost all

Brassicales families (including Brassicaceae,

Capparidaceae, and Caricaceae) (James et al., 1996).

One example of glucosinolate found in the

Caricaceae family discussed in this study is

glucosinolate, derived from the benzyl glucosinolate

(

Figure 4) in papaya seed. Investigating the benzyl

glucosinolate content in various tissues, Najamura et

al. (2007) find the highest benzyl glucosinolate

content in papaya seeds.

The receptor’s interaction with ligands formed

after the docking process was visualized using

Molegro Molecular Viewer software. The breaking

lines described the hydrogen bonds that occurred

between the residues and the groups on the ligands.

The observation of residual interactions (amino

acids) aimed to identify any ligand-receptor

interactions. The hydrogen bonding is an interaction

that can stabilize the ligand bond and the receptor

bond. Another ligand-receptor interaction that can

improve the stability of the conformation is the

electrostatic interaction and van der Walls

interactions.

Table 2 shows the residual ratio of the two best

ligands to simvastatin (comparator ligand) after the

docking process. Simvastatin bonded to Tir 761, Gli

770, and Gli 766 (Figure 5a). The number of the

hydrogen bonds formed in simvastatin tended to be

lower than those of oleic acid, glucotropaeolin, and

benzyl glucosinolate. The hydrogen bond distances

formed in this research were greater than 3.0 Å

(close to 3.5 Å), but not one of them exceeded 3.5 Å.

Therefore, the hydrogen bond distance of

simvastatin still qualifies for an energetically

significant hydrogen bond interaction, i.e., not

exceeding 3.5 Å (Marcou and Rognan, 2007). In the

interaction between oleic acid ligands and HMG-

CoA reductase receptors, there were five hydrogen

bond interactions (Figure 5d and Table 2). The

hydrogen bonds occurred in the amino acid residue

Arg 702 (1 hydrogen bond interaction), Asp 690 (2

hydrogen bond interactions), and Glu 801 (2

hydrogen bond interactions). Two of the five

hydrogen bonds in oleic acid had the hydrogen bond

distance of greater than 3.0 Å (<3.5 Å), which meets

the requirement of significant hydrogen bond

(Marcou and Rognan, 2007). When compared with

simvastatin as a comparator ligand in this research

(Table 2), oleic acid had a greater number of

hydrogen bond interactions. This finding is in line

with Natali et al. (2007) who report that oleic acid

exhibits an inhibition activity toward the enzyme

HMG-CoA reductase.

Figure 4. The Structure of Benzyl Glucosinolate

(Chemicalize, 2018)

(A)

(B)

(C)

(D)

Figure 5: The Visualization of The Residual Contact

of Ligand and HMG-CoA Reductase Receptor using

Molegro Molecular Viewer Software: (A) Simvastatin,

(B) Oleic acid, (C) Glucotropaeolin, (D) Benzyl

glucosinolate.

In Silico Analysis of the Phytochemical Compounds in Carica papaya Seeds for Optimizing the Inhibitors of HMG-CoA Reductase

129

There were twelve hydrogen bond interactions in

the glucotropaeolin ligand (Figure 5c and Table 2).

Most hydrogen bond interactions were formed due

to a large number of electronegative atoms in the

glucotropaeolin molecule; hence, the tendency to

form hydrogen bonds. However, according to

Lipinski (2003), the number of hydrogen bonds in

the drug should not be more than ten. Otherwise,

drugs will have difficulty in passing through the

intestinal walls into the blood.

Pine et al. (1988) mention that the shorter the

bond distance, the stronger the bond. The

interactions in glucotropaeolin included six (6)

hydrogen bonds whose distances were more than 3.0

Å, i.e., in Ala 769 (3.27 Å), Asn 771 (3.37 Å), Glu

801 (3.24 Å), Gln 770 (3.11 Å), Ser 774 (3.24 Å),

and Gln 766 (3.18 Å). According to Marcou and

Rognan (2007), hydrogen interactions can occur

when two atoms are within 3.5 Å to each other. The

hydrogen bond distance in glucotropaeolin was

smaller than 3.5 Å, which satisfies the conditions for

hydrogen bonding.

Glucotropaeolin was one of the three (3) best

ligands in papaya seeds with a lower ChemPLP

score than simvastatin (Table 1). However, after

further analysis through visualization, each of its

hydrogen bond distances satisfied the conditions

hydrogen bonding (i.e., <3.5 Å) (Marcou and

Rognan, 2007). However, it is thought to be less

potent in penetrating the intestinal membrane

because it does not meet the requirement proposed

by Lipinski (2003), i.e., the number of hydrogen

bonds should not be more than ten. Considering the

number of hydrogen bond interactions,

glucotropaeolin ligand could not be categorized as

the best ligand.

Once proven by visualization, the number of

hydrogen bond interaction between benzyl

glucosinolate and amino acid residues at the receptor

(Figure 6) was eight. This number was greater than

the hydrogen bond interaction in simvastatin.

Additionally, it conforms with the qualification set

in Lipinski (2003), i.e., not exceeding 10. The amino

acid residues that interacted with benzyl

glucosinolate were Lys 691 (with a hydrogen bond

distance of 2.15 Å), Asp 767 (2.66), Gln 770 (2.51 Å

and 2.41 Å), Glu 801 (2.39 Å), Ser 774 (2.95 Å),

and Tyr 761 (2.32 Å and 2.77 Å). The average

length of the hydrogen bond on benzyl glucosinolate

ligand was less than 3.0 Å, which is in line with the

conditions for hydrogen bonding mentioned in

Marcou and Rognan (2007). The average length of

the hydrogen bond distance formed on benzyl

glucosinolate was shorter than the comparator

ligands (simvastatin) and the other two best ligands

in papaya seeds (glucotropaeolin and oleic acid)

(Table 2). The shorter the hydrogen bond distance,

the stronger the bond (Pine et al., 1988).

Drug-likeness is a qualitative concept used to

describe the similarity of a compound as a drug

candidate, such as the complex balance of various

molecular properties and structural features that

determine whether a particular molecule is similar to

a known drug. These molecular properties are

primarily hydrophobicity, electronic distribution,

hydrogen bond characteristics, molecular size and

flexibility, and other pharmacophore properties

affecting the behavior of molecules in living

organisms, including bioavailability, delivery

properties, affinity for proteins, reactivity, toxicity,

and other metabolic stability (Leeson, 2016; Mishra

et al., 2017). The Rule of Five (Ro5) or the

Lipinski’s Rule of Five is a set of in silico guidelines

applied to drug discovery to prioritize compounds

with a high probability of increased absorption

(Doak et al., 2014). This rule can be used to

determine the pharmacokinetics of a compound as a

drug candidate (Benet et al., 2016). For drug-

likeliness evaluation, it discusses four simple

physicochemical parameters (namely, molecular

weight ≤ 500, log P ≤ 5, hydrogen bond donor ≤ 5,

hydrogen bond acceptor ≤ 10) associated with 90%

of orally active drugs that have passed clinical status

of phase II (Lipinski, 2004; 2016).

Based on the prediction results (Table 3) run in

www.chemicalize.com using the Lipinski’s Rule of

Five, benzyl glucosinolate was within the threshold

of the partition coefficient (log P= 2.19; <5). The log

P values of benzyl glucosinolate and glucotropaeolin

were lower than the endogenous compounds and

ligands, but they still met the Lipinski’s rule (log P

<5). The log P values of benzyl glucosinolate and

glucotropaeolin indicated a solubility coefficient in

Table 3. The Prediction Results Based on the Lipinski’s

Rule of Five (Chemicalize, 2018)

Ligands

Prediction using the Lipinski’s

Rule of Five

(g/mol

)

Log

bond

Dono

bond

cce

Simvastatin 418.57 4.46 1 3

Benzyl

Glucosinolate

408.42 2.19 4 9

Oleic aci

282.47 6.78 1 2

Glucotro

aeolin 409.42 2.19 5 9

Simvastatin aci

436.59 3.9 3 6

MICH-PhD 2018 - 1st Muhammadiyah International Conference on Health and Pharmaceutical Development

130

fat or water within the range of -0.4 and 5. The

molecular weights of benzyl glucosinolate, oleic

acid, and glucotropaeolin were 408.42 g/mol, 282.47

g/mol, and 409.42 g/mol (<500 g/mol), and they

were lower than the BM of the comparator ligand.

The hydrogen bond donors in benzyl

glucosinolate and oleic acid were, respectively, 4

and 1 (<5). Meanwhile, glucotropaeolin had 5

hydrogen bond donors, which did not meet the

Lipinski’s rule. The number of the hydrogen bond

donors in benzyl glucosinolate was higher than the

comparator ligand, but it still met the standards set

by Lipinski (1997). Benzyl glucosinolate and

glucotropaeolin had 9 hydrogen bond acceptors

(close to 10), while oleic acid had two (2). However,

the Lipinski’s rule states that the hydrogen bond

acceptor should not exceed 10. As a conclusion, the

hydrogen bond donors and acceptors in benzyl

glucosinolate, oleic acid, and glucotropaeolin are

compliant with the Lipinski’s rule.

This rule also states that a molecular weight of

more than 500 Da cannot diffuse through the cell

membrane by passive diffusion. The higher the log

P, the more hydrophobic the molecule. Molecules

that have too hydrophobic properties tend to have

high levels of toxicity because they will stay longer

in the lipid bilayer and spread more widely in the

body; therefore, the selectivity of the bond to the

target enzyme decreases. Too hydrophilic properties

(negative log P) are also not good because the

molecule cannot pass through the membrane lipid

bilayer. Based on the number of hydrogen bond

donor and acceptor, a higher hydrogen bonding

capacity represents larger energy required for the

absorption process to occur. In general, the

Lipinski’s rules describe the solubility of certain

compounds to penetrate cell membranes by passive

diffusion (Lipinski et al., 1997). As a conclusion,

benzyl glucosinolate does not violate any of the

Lipinski's rules, and, thereby, it can be developed as

anti-hypercholesterolemia drug candidates for oral

preparations.

4 CONCLUSIONS

The results of the ligand-receptor interaction of the

compounds in papaya seed (Carica papaya L.)

against HMG-CoA reductase receptor categorized

benzyl glucosinolate as the best compound because

it had a ChemPLP score of -90.5491 kcal/mol and

eight hydrogen bond interactions. This compound

has the potential as an anti-hypercholesterolemia

drug.

ACKNOWLEDGMENTS

Authors would like to gratefully appreciate the

Indonesian Ministry of Research, Technology, and

Higher Education for their support during the

research.

REFERENCES

Adelina R. 2014. Uji Molecular Docking Annomuricin E

dan Muricapentocin pada Aktivitas Antiproliferasi

(Molecular Docking Studies of Annomuricin E and

Muricapentocin on Antiproliferation Activity ). Jurnal

Ilmu Kefarmasian Indonesia. 12(1): 32–36.

Afrose S, Hossain S, Salma U, Miah AG, Tsujii H. 2010.

Dietary Karaya Saponin and Rhodobacter capsulatus

Exert Hypercolestolemics Effect by Suppression of

Hepatic Cholesterol Synthesis and Promotion of Bile

Acid Synthesis in Laying Hens. Research Article. p. 1-

Agistia DD, Purnomo H, Tegar M, Nugroho AE. 2013.

Interaksi Senyawa aktif dari (Aegle marmelos) Correa.

Sebagai Anti Inflamasi dengan Reseptor Cox-1 dan

Cox2. Journal of Traditional Medicine. 18(2):80-87.

Benet L Z, Hosey CM, Ursu O, Oprea TI. 2016. BDDCS,

the Rule of 5 and drugability. Advanced Drug Delivery

Reviews. 101: 89–98.

Chemicalize. 2018. Calcutaion Structure of Simvastatin,

Benzyl Glucosinolate, oleic acid, Glucotropaeolin and

Simvastatin acid. www.chemicalize.com. Diakses 2

Agustus 2018.

Doak B, Over B, Giordanetto F, Kihlberg J. 2014. Oral

Druggable Space beyond the Rule of 5: Insights from

Drugs and Clinical Candidates. Chemistry & Biology.

21(9): 1115–1142.

Goldstein, J.L., Brown, M.S. 1990 Regulation of the

mevalonate pathway. Nature, 343, 425-430.

Hawkins P, Warren G, Skillman A, Nicholls A. 2008.

How to do an Evaluation: Pitfalls and Traps. Journal

of Computer-Aidid Molecular Design. 22(3): 179–190.

Jain A, Nicholls A. 2008. Recommendations for

Evaluation of Computational Methods. Journal of

Computer-Aidid Molecular Design. 22: 133–139.

James ER, Kenneth GK, Robert AP, Kenneth JS. 1996.

Molecules Morphology and Dahlgren’s Expanded

Order Capparales. Jurnal Systematic Botany.

21(3):289.

Kitchen DB, Decornez H, Furr JR, Bajorah J. 2004.

Docking and Scoring in Virtual Screening for Drug

Discovery: Methods and Applications. Journal of

Nature Reviews Drug Discovery. 3(11): 935-949.

Leeson P. 2016. Molecular Inflation, Attrition and the

Rule of Five. Advanced Drug Delivery Reviews. 101:

22–23.

Lipinski CA. 2003. Physicochemical Properties and The

Discovery Of Orally Active Drugs: Technical And

In Silico Analysis of the Phytochemical Compounds in Carica papaya Seeds for Optimizing the Inhibitors of HMG-CoA Reductase

131

People Issues. Journal of Molecular Informatics:

Confronting Complexity. Hlm. 1-20.

Lipinski C. 2004. Lead-and Drug-like Compounds: The

Rule-of-Five Revolution. Drug Discovery Today:

Technologies. 1(4): 337–341.

Lipinski C. 2016. Rule of Five in 2015 and Beyond:

Target and Ligand Structural Limitations, Ligand

Chemistry Structure and Drug Discovery Project

Decisions. Advanced Drug Delivery Reviews. 101: 34–

41.

Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. 1997.

Experimental and Computational Approaches to

Estimate Solubility and permeability in drug discovery

And Development Settings. Journal of Advanced Drug

Delivery Reviews. 23(1-3):3-25.

Marcou G, Rognan D. 2007. Optimizing Fragment and

Scaffold Docking by Use of Molecular Interaction

Fingerprints. Journal of Chemical Information and

Modelling. 47(1):195-207.

Mishra SS, Sharma CS, Singh HP, Pandiya H. 2017. In

Silico Pharmacokinetic and Toxicity Study of Some

Selected Antidepressant Drugs. Chemistry Research

Journal. 2(1): 42–45.

Moitessier N, Englebienne P, Lee D, Lawandi J, Corbeil

C. 2008. Towards the Development of Universal, Fast

and Highly Accurate Docking Scoring Methods: a

Long Way to Go. British Journal of Pharmacology.

153(1): 7–26.

Mycek MJ, Harvey RA, Champe PC. 2001. Farmakologi

Ulasan Bergambar Edisi II Cetakan I. Widya Medika.

Jakarta. Hlm 216.

Nakamura Y, Yoshimoto M, Murata Y, Shimoishi Y, Asai

Y, Park EY. 2007. Papaya Seed Represents A Rich

Source of Biologically Active Isothiocyanate. Journal

of Agricultural and Food Chemistry 55(11): 4407-

4413.

Nakanishi M, Goldstein JL, Brown MS. 1988. Multivalent

Control of 3-hidroxy-3-methylglutaryl Coenzyime A

Reductase. Mevalonate Derived Product Inhibits

Translations of mRNA and Accelerates Degradation

of Enzyme. Journal of Biological Chemistry. 263(18):

8929-37.

Natali F, Siculella L, Salvati S, Gnoni GV. 2007. Oleic

Acid is A Potent Inhibitor of Fatty Acid and

Cholesterol Synthesis in C6 Gliomca Cells. Journal of

Lipid Resolution. 48(9):1966-1975.

Olivera T, Ricardi KFS, Almeida MR, Costa MR, Nagem

TJ. 2007. Hypolipidemic Effect of Flavonoids and

Cholesterolamine in Rats Tania. Latin American

Journal of Pharmacy. 26(3): 407-410.

Oniga SD, Pacureanu L, Stoica CI, Palage MD, Crăciun

A, Rusu LR, Araniciu C. 2017. COX inhibition profile

and molecular docking studies of some 2-

(Trimethoxyphenyl)-Thiazoles. Molecules. 22(9): 1–

15.

Pine SH, Hendrickson JB, Cram DJ, Hammond GS. 1988.

Kimia Organik 2 Jilid I, II. Terjemah: Roehyati J,

Sasanti WP. Institut Teknologi Bandung. Bandung. p.

93, 899.

Pratama MFY, Abidi SR, Firdaus KA, Aulia AF, Santoso

B. 2016. Kajian Docking Molekular Pada Binding Site

Pocket Dari Plavopiridol Dalam Menghambat

Glikogenfosforilase Menggunakan Pyrx-Autodock

Vina. Jurnal of The Fourth University Research

Coloquium. Hlm. 154-158.

PubChem. 2017. Benzyl Glucosinolate, Benzyl Thiourea.

https://www.ncbi.nlm.nih.gov. Accessed on August

19, 2017.

Purnomo H. 2011. Kimia Komputasi Molecular Docking

PLANTS. Yogyakarta: Pustaka Pelajar.

Purnomo H. 2013. Kimia Komputasi untuk Farmasi dan

Ilmu Terkait. Pustaka Pelajar. Yogyakarta. p. 17-80.

Sarker SD, Nahar L. 2009. Kimia Untuk Mahasiswa

Farmasi. Yogyakarta: Pustaka Pelajar. p. 15, 39, 41,

42, 44.

Satriyasa BK, Pangkahila W, 2010. Fraksi Heksan dan

Fraksi Metanol Ekstrak Biji Pepaya Muda

Menghambat Spermatogonia Mencit (Mus Musculus)

Jantan. Jurnal Veteriner. Denpasar-Bali. 11(1): 37-39.

Schneider G, Bohm J. 2000. Virtual Screening for

Bioactive Molecules. WILEY-VHC. Weinheim: 1-

308.

Serina JJC. 2013. Enzymatic Inhibitory Activity of

Hydroxycinnamates (HCs) In Silico Studies.

Universidade de madeira. Portugal. Master

Thesis.:112.

Siregar. 2015. The Effect Eugenia polyantha Extract on

LDL Cholesterol. Review. Universitas Lampung. 4(5):

85-92.

Siswandono, Soekardjo. 1998. Prinsip-prinsip Rancangan

Obat. Surabaya: Airlangga University Press. p. 65, 82,

91, 94.

Stapleton PA, Goodwill AG, James ME, Brock RW,

Frisbee JC. 2010. Hypercholesterolemia and

microvascular dysfunction: interventional strategies.

Journal of Inflammation. 7:54

Yogiraj V, Pradeep KG, Chetan SC, Anju G, Bhupendra

V. 2014. Carica papaya L: An Overview.

International Journal of Herbal Medicine. 2(5): 01-08.

Zukrullah M, Aswad M, Subehan. 2012. Kajian Beberapa

Senyawa Antiinflamasi Docking Terhadap

Siklooksigenase-2 Secara in Silico

. Majalah Farmasi

dan Farmakologi. 16(1): 37-44.

MICH-PhD 2018 - 1st Muhammadiyah International Conference on Health and Pharmaceutical Development

132