Indonesian Journal of Applied Research
Vol 1 No 3 2020
THE POTENTIAL OF BUTTERFLY PEA FLOWER METHANOL
EXTRACT AS AN ANTIOXIDANT BY IN SILICO
Tiana Fitrilia1 .
Fakih Kurniawan1 .
Febryana Rahayu Kurniawati1.
Tirta Setiawan2 Food Technology.
Djuanda University Data Science.
Institute Technology Sumatera University Corresponding Author E-mail address: tiana.
fitrilia@unida.
Abstract: Butterfly pea flower (Clitoria ternatea L.
) is a flowering plant from the Fabecea family that can grow vines.
Butterfly pea flower are known to have chemical components that can act as antioxidants.
This study aims to predict the potential of active compounds from methanol extract of butterfly pea flower in inhibiting reactive oxygen species (ROS) based on bond affinity (OIG), the value of Root Mean Square Deviation (RMSD) and their The method used was a computational method with in silico technique.
The software used was Autodock Vina with visualization using the Biovia Discovery Studio Visualizer 2020.
The enzyme receptor was Nicotinamide Adenine Dinucleoide Phosphate (NADPH) Oxidase (NOX) obtained from Protein Data Bank and the test ligands were a chemical compound from methanol extract of butterfly pea flower.
The results of the in silico study showed that the NO had a innate ligand, namely the GTP ligand which has a OIG value of -7.
3 kcal/mol, an RMSD value of 1111 yI and the interaction with the receptor that involves the presence of hydrogen bonds.
Based on the results of the analysis of 11 test ligands, the chemical component of caffeine was predicted to have the most potential in inhibiting ROS compounds with a value of OIG -5.
RMSD value of 328 yI and had the same amino acid residue in hydrogen bonding, namely ASP118, and GLY15.
The test ligand had the ability to inhibit ROS compounds with a lower level of stability than the innate ligand.
Keywords: antioxidant.
butterfly pea flower.
in silico.
NADPH oxidase.
ROS
INTRODUCTION
Butterfly pea flower is a flower that has a distinctive color and produces green peas.
Butterfly pea flowers grew with the way vines in the yard of the house or in wild places.
According to Dalimartha .
, this flower has a name that varies depending on the region such as blue flowers, kelenit flowers and telang flowers for Sumatra, teleng flowers for Java.
Talang flowers for Sulawesi and bisi for Maluku.
Usually butterfly pea flowers are used as ornamental plants because of their attractive colors.
In addition, butterfly pea flower has also been used as a family medicinal plant for healing various diseases.
Butterfly pea flowers contain tannin compounds, saponins, triterpenoids, flavonoids, alkaloids, phenols, flavanol glycosides, anthraquinones, anthocyanins, 163 | P a g e Indonesian Journal of Applied Research Vol 1 No 3 2020 essential oils, proteins and carbohydrates which have health benefits (Al-Sanafi, 2.
One of the pharmacological effects of butterfly pea flower is as an antioxidant.
Antioxidants are compounds with small molecular weights that can ward off free radicals in the body so that cell damage can be inhibited (Winarsi, 2.
The human body can produce free radicals from the metabolic process and also produce endogenous antioxidants as a form of According to Werdhasari .
, free radicals are destructive if there is an oxidative stress condition, which is a condition where there is an imbalance between free radicals and antioxidants in the body.
Reactive oxygen compounds (ROS) are a form of oxygen-derived free radicals which, if produced in excess, can cause many diseases.
According to Panday et al.
, .
, many ROS in cells are produced by NADPH oxidase.
The potential methanol extract of butterfly pea flower in reducing ROS compounds was studied in silico.
In silico method is a computer simulation-based approach by docking molecules.
According to Jensen .
, molecular docking can align ligands which are small molecules and receptors in the form of proteins by paying attention to the properties of both.
Based on this background, it is necessary to conduct research using computational techniques to predict the tethering of active compounds of butterfly pea flowers to ROS.
METHODS
Preparation of Reseptor Stucture NADPH Oxidase (NOX) macromolecules was downloaded from the Protein Data Bank at http://w.
The molecular identity was 1E96.
Macromolecule data was downloaded in pdb format, with a ligand and water molecule bound form.
Protein macromolecules was separated from the solvent .
ater molecul.
and non-standard ligands or residues.
The separation of macromolecules from unnecessary molecules was carried out using the Biovia Discovery Studio Visualizer 2020.
The separation results was used for tethering, and the results was saved in *.
The NOX macromolecules that had been separated from the residue were optimized using Autodock Tools 1.
6 software.
The optimization includes: adding hydrogen atoms and setting the grid box parameter.
Results are saved in *.
pdbqt format.
Preparation of Ligands Structure The ligands used were GTP ligands as a comparison ligand and 11 active compounds of Buterfly Pea Flower methanol extract identified by Neda et al.
downloaded on the PubChem website .
ttps://pubchem.
gov/) in the form of a 2D structure with *.
The format of the ligands were converted into 3D to *pdb format using Marvin Sketch The ligands structure that has been made was optimized using AutoDock Tools 1.
6 by increasing the gaisteiger load and setting the number of active torsion.
These results were saved in pdbqt format.
All ligand compounds were filtered according to Lipinski's rules using online access http://w.
scbio-iitd.
in/software/utility/lipinskifilters.
jsp (Lipinski, 2.
Molecular Docking with Autodock Vina 164 | P a g e Indonesian Journal of Applied Research Vol 1 No 3 2020 The ligands and proteins that had been stored in the *.
pdbqt format were copied into the Vina folder.
Then type the vina configuration into notepad, saved as 'config_rigid.
Vina was run via the Commond Prompt and collected docking data in Vina folder.
Analysis The docking result files were analyzed for Lipinski filter results, bond energy .
G), root mean square deviation (RMSD) values and hydrogen bonding.
The value of iG.
RMSD and interaction between ligand and receptor obtained were comared with validation ligand and other journals to determine the potential of ligands as antioxidants.
RESULTS AND DISCUSSION
1 Identification of Receptor Structure The NO receptor structure downloaded from the Protein Data Bank contains natural ligands and H2O molecules.
These ligands and H2O molecules must be removed from the receptors because they can interfere with the docking process (Fikry, 2.
The optimization process is carried out by adding hydrogen atoms and arranging the grid box as the ligand mooring space.
According to Droe .
, hydrogen atoms are added to adjust the tethering atmosphere.
2 Identification of Ligand Structure The test ligands used were the results of GC-MS analysis of methanol extract of butterfly pea flower conducted by Neda et al.
, .
and the natural ligands found in the NOX receptor.
There were 11 types of chemical compounds that were tested, namely acetic acid, cyano-.
pyridine2-d, 6-methyl.
Hirsutene.
pyrimidine, 4-hydroxy-.
butane, 2-isothiocyanate.
bicyclo- .
hept3-ene, 3,7,7-trimethyl-.
cyclohexen, 1-methyl-4- .
-methylethylidem.
1,3-benzodioxole, 5- .
1-nitro-2-acetamido-1,2-dideoxy-d-monnitol.
and hexadecanoic acid.
The optimization of the ligands was carried out by adding a gaisteiger charge and setting the number of active torsion.
Adjusting the amount of active torsion can reduce the performance and time required to determine the active bonds (Morris et al.
, 2.
All ligands attached to the receptor must comply with Lipinski's rule (Table .
Table 1 LipinskiAos Rule of Ligand Ligand GTP ligand .
acetic acid, cyanopyridine-2-d, 6-methylHirsutene pyrimidine, 4-hydroxybutane, 2-isothiocyanate bicyclo.
hept-3-ene, 3,7,7-trimethylcyclohexen, 1-methyl-4-.
-methylethylidem.
1,3-benzodioxole, 5-.
-propeny.
165 | P a g e
LipinskiAos rule
-4,737250 0
-0,0155320 1
1,095620
4,414999
-0,341800 1
1,887700
-0,053101 5
3,532899
2,143800
82,351898
17,865801
30,638794
64,582985
25,774696
34,778992
77,145782
46,005985
46,465992
Indonesian Journal of Applied Research Vol 1 No 3 2020 1-nitro-2-acetamido-1,2-dideoxy-d-mannitol Caffeine hexadecanoic acid Information :
A : Relative atomic mass <500 Da B : Log P <5 C : Bond donors H <5 -3,157201 0,061900 5,552299 54,572292 49,100494 77,947777 D : Bond Acceptor <10 E : Molar refractifity 40 Ae 130 All test ligands comply with Lipinski's rules.
This shows that the ligand can interact with the NOX receptor.
3 Bond Affinity (OIG) and RMSD Value The ligand and receptor bond affinities were expressed in the form of Gibbs free energy (OIG).
Gibbs free energy shows the energy required for the ligand to bind to the receptor at the binding site (Karim, 2.
A small OIG value indicates that the conformation is more stable.
Apart from OIG, an analysis was also carried out on the RMSD value.
This value determines the success rate in predicting the mode of bonding.
Ligand Table 2.
Bond Affinity and RMSD Value of Ligand Bond Affinity (OIG) .
cal/mo.
GTP ligand .
acetic acid, cyanopyridine-2-d, 6-methylHirsutene pyrimidine, 4-hydroxybutane, 2-isothiocyanate bicyclo.
hept-3-ene, 3,7,7-trimethylcyclohexen, 1-methyl-4-.
-methylethylidem.
1,3-benzodioxole, 5-.
-propeny.
1-nitro-2-acetamido-1,2-dideoxy-d-mannitol Caffeine hexadecanoic acid
-7,3
-4,0
-4,4
-6,2
-3,9
-3,7
-5,0
-5,8
-5,7
-5,2
-5,4
-6,2
RMSD
I) 3,111 1,637 1,131 9,988 7,423 2,486 0,918 1,617 0,868 5,210
1,328
6,156
Based on the Table 2, the OIG value of the GTP ligand was greater than that of the test ligand.
This shows that the GTP ligand has a better level of stability in binding to the receptor.
According to Setiawan .
, the difference in OIG between the test ligand and the validation ligand is due to differences in the characteristics of the interactions in the bonds.
The RMSD value for the test ligand ranged from 0.
868 yI to 13.
296 yI, while for the GTP ligand was 3.
111 yI.
The RMSD value received has a limit of O 2.
5 yI (Jain & Nicholls, 2.
The RMSD value O 2.
5 yI indicates that the compound is a competitive inhibitor.
Phrueksanan et al.
, .
reported butterfly pea flower extracts could protect canine erythrocytes from hemolysis and oxidative damage induced by 2,20azobis-2-methyl-propanimidamide dihydrochloride (AAPH).
Interaction of Ligand and Receptors 166 | P a g e Indonesian Journal of Applied Research Vol 1 No 3 2020 The interaction of ligands and receptors can be determined by observing the interaction of amino acid residues.
GTP ligands had hydrogen bonds with amino acid residues ALA159.
LYS116.
CYS18.
VAL18.
GLY15.
GLY12.
PRO34.
TYR32.
THR35.
ALA13.
THR17.
GLY30.
ASP118.
Meanwhile, the test ligands that had the same binding to amino acid residues as the GTP ligand were caffeine compounds, namely ASP118 and GLY15.
Caffeine is predicted to inhibit ROS because it has an RMSD value of 1.
328 yI, where the RMSD value was <2.
5 yI with a OIG value of -5.
kcal/mol, and has the same residual residues in hydrogen bonds with validation GTP ligands.
Based on research by Sukoha et al.
, caffeine in Robusta coffee beans in Lampung had an antioxidant activity of 21.
41 ppm using the DPPH method.
In another study results show that there is an effective protection of caffeine against oxidative degredation of adenine (Viera et al.
, 2.
The ligand and receptor interactions can be seen in Figure 1.
Figure 1 Interaction of Ligand and Reseptor.
GTP-NOX.
Caffeine-NOX
CONCLUSION
Based on the analysis of antioxidant with in silico methode, the methanol extract of butterfly pea flower had a iG value greater between -3.
6 kcal/mol to -6.
2 kcal/mol compared to the GTP ligand as the comparative ligand of -7.
3 kcal/mol.
So that the test ligand of methanol extract of the butterfly pea flower compound can act as an antioxidant in inhibiting ROS with predicted activity is to be lower than the GTP ligand.
The active compound in butterfly pea flower, which was predicted to have the most potential to inhibit ROS, was caffeine.
ACKNOWLEDGMENT
167 | P a g e Indonesian Journal of Applied Research Vol 1 No 3 2020 Thanks to Djuanda University Bogor for financing this research activity and to all those who have supported and facilitated during the research activities.
REFERENCES