Antidiabetic Activity of Daun Wungu (Graptophyllum pictum L. Griff) Extract via Inhibition Mechanism of TNF-α, IL-6, and IL-8: Molecular Docking and Dynamic Study

 

Listijani Suhargo1*, Dwi Winarni1, Fatimah1, Viol Dhea Kharisma2,

Arif Nur Muhammad Ansori3

1Department of Biology, Faculty of Science and Technology, Airlangga University, Surabaya, Indonesia.

2Department of Biology, Faculty of Mathematics and Natural Sciences,

Brawijaya University, Surabaya, Indonesia.

3Professor Nidom Foundation, Surabaya, Indonesia.

*Corresponding Author E-mail: lis.suhargo@gmail.com

 

ABSTRACT:

Diabetes is caused by many factors such as hyperglycemia conditions, it triggers type 1 and 2 diabetes. Hyperglycemia was triggered by the inhibition of glucose absorption of  cells and triggers an increase in ROS, insulin signaling can be disrupted due to high ROS levels, the cycle will repeat and then trigger fat accumulation, increase in proinflammatory cytokines, and exacerbate the disease. Previous studies have explained the benefits of Daun Wungu (Graptophyllum pictum L. Griff) to inhibit the activity of proinflammatory cytokines, but the molecular mechanism has not been identified. Prediction of the molecular mechanism of compound activity from Daun Wungu (Graptophyllum pictum L. Griff) with proinflammatory receptors was carried out using an bioinformatics approach, the methods used were preparation, drug-likeness prediction, docking, molecular interaction, and dynamic simulation. The results showed that quinoline compounds from Daun Wungu (Graptophyllum pictum L. Griff) had more negative binding energy with more stable chemical bonds and were predicted to inhibit the activity of proinflammatory cytokines consisting of TNF-, IL-6, and IL-8 proteins.

 

KEYWORDS: Antiinflamatory, bioinformatics, diabetes, Graptophyllum pictum, hyperglycemia.

 

 


INTRODUCTION: 

One of metabolic disorder was diabetes caused by hyperglycemia condition. There were 2 type of diabetes such as type 1 and 2. Type 1 diabetes was caused by pancreas damage, so it could not produce insulin, and type 2 by impairing in insulin signaling, so the activity of glucose transportation was inhibited and glucose couldn’t enter to the cell, so glucose was still in circulation and caused hyperglycemia. Hyperglycemia might cause damage in many organ system.  Insulin signaling could be impaired by increasing of Reactive Oxygen Species (ROS) in obesity condition. Obesity was characterized by excessive fat accumulation in abdomen, abdominal fat could be visceral fat and subcutaneous fat1.

 

 

Fat accumulated in adipose tissue, then adipose tissue released increased amounts of fatty acid and proinflammatory cytokines (TNF-α, IL-6 and IL-8) to circulation that were involved in impairing insulin signaling and caused insulin resistance2.

 

Proinflammatory cytokines binded to its receptor then induced JNK (c-Jun N-terminal kinase) activity to inhibit IRS1 (Insulin receptor substrat I) activity and then inhibit the activity of GLTU 4 (Glucose Transporter 4). Then it would impair insulin signaling3. Actually IRS1 would induce PI3K that induced Phosphorilation of Akt/PKB (Protein Kinase B) to induce the movement of GLTU 4 to cell membrane. GLTU4 would make glucose enter to the cell. If inflammatory factor increased, the effect was decreasing glucose in cells, but increasing glucose in circulation4.

 

The use of medicinal plants is widely used for the prevention of diabetes. On of medicinal plants was Daun Wungu (Graptophyllum pictum L. Griff)5. Daun Wungu ethanol extract contained some active compounds such as 6-Octadecenoic acid, 8-Octadecenoic acid, 9-Octadecenoic acid, Coumaran-5,6-Diol-3-One, 4H-1-Benzopyran-4-one, 1,2-Benzisothiazol-3-amine, Quinoline, 6-(4,5-diphenylimidazol-2-yl), As-Indacen-1(2H)-one, 2,4-Cyclohexadien-1-one, 1,4-Phthalazinedione, Benzo[h]quinoline, Thymol, Phytol, Phenol, and Galangin6,7. This reseach was aimed to know the effect of Daun Wungu (Graptophyllum pictum L. Griff) ethanol extract to prevent type 2 diabetes by inhibiting the activity of proinflammatory factor (TNF-α, IL- 6 and IL- 8).  If all of active compound could bind to TNF-α, IL- 6 and IL- 8, so proinflammatory factor would be inhibited to its receptor and the activity was inhibited.

 

MATERIAL AND METHODS:

Ligand-protein preparation:

The 3D structure of chemical compounds from Daun Wungu (Graptophyllum pictum L. Griff) in this study consisted of 6-Octadecenoic acid, 8-Octadecenoic acid, 9-Octadecenoic acid, Coumaran-5,6-Diol-3-One, 4H-1-Benzopyran-4-one, 1,2-Benzisothiazole-3-amine, Quinoline, 6-(4,5-diphenylimidazole-2-yl), As-Indacen-1(2H)-one, 2,4-Cyclohexadien-1-one, 1,4-Phthalazinedione, Benzo[h]quinoline, Thymol, Phytol, Phenol, and Galangin were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/). PubChem includes a specific database whose role is to store all information about natural, synthetic, and substance8,9. This study used TNF-α, IL-6, and IL-8 as targets and obtained from the Protein Databank (https://www.rcsb.org/). PyMol 2.5 version software is used for the sterilization process of 3D protein samples, then the OpenBabel plug in PyRx 0.9.9 version software is used for ligand file format conversion and increased flexibility through energy minimization10,11.

 

Druglikeness prediction:

Druglikeness is a qualitative test that is used to determine the level of similarity of natural ingredients compounds with the performance of drug molecules through several specific parameters12,13. Druglikeness in this study was carried out through the SwissADME server (http://www.swissadme.ch/), this prediction aims to determine the general physicochemical characteristics and performance of query compounds as drug-like molecules based on various test parameters such as Lipinski, Ghose, Veber, Egan, Muegge, and Bioavailability14,15. In addition, the druglikeness analysis is also used for this purpose, which aims to determine the activity and mode of action of query compounds when interacting with target cells16,17.

 

Molecular docking analysis:

The simulation of ligand binding to the target was carried out using molecular docking software on the VinaWizard plugin in PyRx 0.9.9 version. The simulation aims to determine the level of binding ability of a ligand to the protein domain based on the binding affinity value of the ligand-protein stable complex and generate negative energy18. The ligands in this study were all compounds from Daun Wungu and then TNF-α, IL-6, and IL-8 proteins acted as targets. The docked 3D structure is displayed with cartoons, surfaces, and sticks structures using PyMol 2.5 version software19.

 

Ligand-protein interactions:

The molecular complex as a result of the docking analysis identified the positions and types of chemical bond interactions formed through the Discovery Studio 2016 version software. Weak bonds formed in ligand and protein complexes can affect specific biological activities, the types of chemical bonds formed consist of hydrogen, hydrophobic, pi, and electrostatic. So the more interactions that are formed, it is possible for a ligand to affect the activity of the target protein20.

 

Molecular dynamic simulation:

The stability of the ligand based on the root mean square fluctuation (RMSF) of amino acids in the binding domain was identified through molecular dynamic simulation analysis with the CABS-flex 2.0 version server (http://biocomp.chem.uw.edu.pl/CABSflex2). This prediction refers to the parameters of protein restrants, rigidity, C-alpha, side-chain, number of cycles, trajectory, temperature range, and RNG seed. Ligands are stable if they have an RMSF value of not more than 421,22.

 

RESULT AND DISCUSSION:

Chemical compounds from Daun Wungu (Graptophyllum pictum L. Griff) consisting of 6-Octadecenoic acid (CID 5282754) 8-Octadecenoic acid (CID 5282758), 9-Octadecenoic acid (CID 637517) Coumaran-5,6-Diol-3-One (CID 313095), 4H -1-Benzopyran-4-one (CID 10286), 1,2-Benzisothiazol-3-amine (CID 89966), Quinoline, 6-(4,5-diphenylimidazole-2-yl) (CID 631489), As-Indacen-1(2H)-one (CID 611673), 2,4-Cyclohexadien-1-one (CID 141125), 1,4-Phthalazinedione (CID 283593), Benzo[h]quinoline (CID 9191), Thymol (CID 6989 ), Phytol (CID 5280435), Phenol (CID 996), and Galangin (CID 5281616) were obtained from the PubChem database with information, CID numbers and 3D structures. The 3D structure of the target protein consisting of TNF-α (PDB ID: 1TNF), IL-6 (PDB ID: 1ALU), IL-8 (PDB ID: 1IL8) was obtained from the RCSB PDB database with ID and samples in pdb format. The 3D structure of the target ligand and protein is displayed using PyMol 2.5 version software in the form of sticks, cartoons, spheres, and transparent surfaces with coloring selection based on the type of molecule are presented in Figure 1.

 

Figure 1. Visualisasi 3D ligan dan protein. (A) 6-Octadecenoic acid (B) 8-Octadecenoic acid (C) 9-Octadecenoic acid (D) Coumaran-5,6-Diol-3-One (E) 4H-1-Benzopyran-4-one (F) 1,2-Benzisothiazol-3-amine (G) Quinoline, 6-(4,5-diphenylimidazol-2-yl) (H) As-Indacen-1(2H)-one (I) 2,4-Cyclohexadien-1-one (J) 1,4-Phthalazinedione (K) Benzo[h]quinoline (L) Thymol (N) Phytol (M) Phenol (O) Galangin (P) TNF-α (Q) IL-6 (R) IL-8.

 

Druglikeness is a qualitative analysis that is used to determine the level of similarity of natural ingredients compounds with the performance of drug molecules through several specific parameters such as physicochemical properties and probability tests with specific parameters23. The physicochemical properties of the compounds containing Daun Wungu (Graptophyllum pictum L. Griff) as predicted by the Swiss ADME server were identified according to the formula, weight, heavy atoms, fraction csp3, hydrogen bonds between acceptors and donors, molar refractivity, total polar surface area (TPSA) are presented in Table 1. Parameters on physicochemical properties can be used for additional data in the prediction of druglikeness. The results of the identification of drug-like molecules based on the parameters of Lipinski, Ghose, Veber, Egan, Muegge, and the value of bioavailability, all compounds containing Daun Wungu are predicted to work as drug-like molecules are presented in Table 2. Compounds that are included in the drug-like molecule category can generally affect the activity of the target protein but the exact binding mechanism is not known24.

 

Table 1. Physicochemical properties of Daun Wungu compounds.

Compounds

Formula

Weight (g/mol)

Heavy Atoms

Fraction

Csp3

Rotatable Bonds

H Acceptors

H

Donors

Molar Refractivity

TPSA

(Ų)

6-Octadecenoic acid

C18H34O2

282.46

20

0.83

15

2

1

89.94

37.30

8-Octadecenoic acid

C18H34O2

282.46

20

0.83

15

2

1

89.94

37.30

9-Octadecenoic acid

C18H34O2

282.46

20

0.83

15

2

1

89.94

37.30

Coumaran-5,6-Diol-3-One

C8H6O4

166.13

12

0.12

0

4

2

40.25

66.76

4H-1-Benzopyran-4-one

C9H6O2

146.14

11

0.00

0

2

0

42.48

30.21

1,2-Benzisothiazol-3-amine

C7H6N2S

150.20

10

0.00

0

1

1

44.02

67.15

Quinoline, 6-(4,5-diphenylimidazol-2-yl)

C24H17N3

347.41

27

0.00

3

2

1

110.20

41.57

As-Indacen-1(2H)-one

C16H20O

228.33

17

0.56

0

1

0

71.11

17.07

2,4-Cyclohexadien-1-one

C6H6O

94.11

7

0.17

0

1

0

28.09

17.07

1,4-Phthalazinedione

C8H4N2O2

160.13

12

0.00

0

4

0

46.61

58.86

Benzo[h]quinoline

C13H9N

179.22

14

0.00

0

1

0

59.25

12.89

Thymol

C10H14O

150.22

11

0.40

1

1

1

48.01

20.23

Phytol

C20H40O

296.53

21

0.90

13

1

1

98.94

20.23

Phenol

C6H6O

94.11

7

0.00

0

1

1

28.46

20.23

Galangin

C15H10O5

270.24

20

0.00

1

5

3

73.99

90.90

 

Table 2. Drug-like molecule prediction of Daun Wungu compounds.

Compounds

Lipinski

Ghose

Veber

Egan

Muegge

Bioavailability

Probability

6-Octadecenoic acid

Yes

No

No

No

No

0.85

Drug-like molecule

8-Octadecenoic acid

Yes

No

No

No

No

0.85

Drug-like molecule

9-Octadecenoic acid

Yes

No

No

No

No

0.85

Drug-like molecule

Coumaran-5,6-Diol-3-One

Yes

No

Yes

Yes

No

0.55

Drug-like molecule

4H-1-Benzopyran-4-one

Yes

No

Yes

Yes

No

0.55

Drug-like molecule

1,2-Benzisothiazol-3-amine

Yes

No

Yes

Yes

No

0.55

Drug-like molecule

Quinoline, 6-(4,5-diphenylimidazol-2-yl)

Yes

No

Yes

No

No

0.55

Drug-like molecule

As-Indacen-1(2H)-one

Yes

Yes

Yes

Yes

No

0.55

Drug-like molecule

2,4-Cyclohexadien-1-one

Yes

No

Yes

Yes

No

0.55

Drug-like molecule

1,4-Phthalazinedione

Yes

No

Yes

Yes

No

0.55

Drug-like molecule

Benzo[h]quinoline

Yes

Yes

Yes

Yes

No

0.55

Drug-like molecule

Thymol

Yes

No

Yes

Yes

No

0.55

Drug-like molecule

Phytol

Yes

No

No

No

No

0.55

Drug-like molecule

Phenol

Yes

No

Yes

Yes

No

0.55

Drug-like molecule

Galangin

Yes

Yes

Yes

Yes

Yes

0.55

Drug-like molecule

 


Prediction of ligand binding mechanism with protein can be done through molecular docking simulation, this research uses PyRx 0.9.9 version software for docking. The simulation aims to determine the level of binding ability of a ligand to the protein domain based on the binding affinity value of the ligand-protein stable complex and generate negative energy. Binding affinity is formed when there is an interaction between protein and ligand, this energy is formed through a reversible reaction at constant temperature and pressure according to the laws of thermodynamics25. The grid in the docking simulation plays a role in directing the binding of the ligand to the proteins26. This study used a grid arrangement on different target, namely TNF-α Center (Ĺ) X: 19,968 Y: 49,675 Z: 39,930, Dimension (Ĺ) X: 80,739 Y: 58,243 Z: 58,256, IL-6 Center (Ĺ) X: 2,675 Y: -20,084 Z: 8,908, Dimension (Ĺ) X: 58.092 Y: 62.897 Z: 43139, and IL-8 Center (Ĺ) X: -0.859 Y: -7.374 Z: 1.905 Dimension (Ĺ) X: 63,313 Y: 56,673 Z: 52,220.

 

The docking simulation in this study aims to identify the comparison of the binding activity of compounds containing in Daun Wungu (Graptophyllum pictum L. Griff) on the three proteins. The docking simulation results that Quinoline, 6-(4,5-diphenylimidazole-2-yl) has the most negative binding affinity value for all target proteins, namely TNF-α, IL-6, and IL-8 are presented in Table 3. Quinoline, 6-(4,5-diphenylimidazole-2-yl) is predicted to have a stronger binding energy than other compounds in Daun Wungu (Graptophyllum pictum L. Griff) and has the potential to affect the biological activity of the three target proteins. Visualization of the molecular complex docking of Quinoline, 6-(4,5-diphenylimidazole-2-yl) with TNF-α, IL-6, and IL-8 displayed in transparent surfaces, cartoons, and sticks structures are presented in Figure 2.

 

Table 3. The result of molecular docking of all compounds contained in Daun Wungu (Graptophyllum pictum L. Griff) leaf with TNF-α, IL-6 and IL-8.

Compounds

Binding Affinity

(kcal/mol)

TNF-α

IL-6

IL-8

6-Octadecenoic acid

-3.7

-4.7

-4.1

8-Octadecenoic acid

-5.5

-4.2

-4.1

9-Octadecenoic acid

-5.1

-4.5

-4.2

Coumaran-5,6-Diol-3-One

-6.5

-5.5

-4.8

4H-1-Benzopyran-4-one

-6.4

-5.2

-4.9

1,2-Benzisothiazol-3-amine

-5.6

-5.1

-4.7

Quinoline, 6-(4,5-diphenylimidazol-2-yl)

-9.0

-7.1

-7.6

As-Indacen-1(2H)-one

-7.8

-6.5

-6.4

2,4-Cyclohexadien-1-one

-4.7

-4.5

-4.0

1,4-Phthalazinedione

-7.0

-5.8

-5.4

Benzo[h]quinoline

-7.1

-6.2

-6.0

Thymol

-5.4

-5.0

-5.9

Phytol

-6.0

-4.4

-4.5

Phenol

-4.8

-4.5

-3.8

Galangin

-7.0

-6.2

-6.8

 

Figure 2. Three-dimensional structure of ligan-protein from the docking results. (A) Quinoline_TNF-α (B) Quinoline_IL-6 (C) Quinoline_IL-8.

 

The interaction between the ligands on the target protein occurs through weak bonds, it plays a role in triggering the activation of specific biological activity27,28. Quinoline compound, 6-(4,5-diphenylimidazole-2-yl) interacts with TNF- via positions Glu104, Gln102, Arg103, Glu104, Gln102, Cys101, and Pro100 with Van der Waals bonds, Pi bonds on Arg103 and Arg103, and hydrogen bonds at Gln102, at IL-6 via positions Glu106, Glu105, Glu42, Thr43, Gln156, Trp157, and Ser47 with Van der Waals bonds, Pi bonds at Asp160 and Arg104, and hydrogen bonds at Glu42, at IL-8 via the positions of Val58, Pro53, Glu70, Glu63, Gln59, and Glu70 with Van der Waals bonds, Pi bonds on Ile39, Val62, Gln59, Leu66, and Lys67 and hydrogen bonds on Glu63, then there are two unfavorable bonds formed on the N atoms that make up the query ligand are presented in Figure 3.

 

 

Figure 3. Two-dimensional visualization of ligan-protein interaction. (A) Quinoline_TNF-α (B) Quinoline_IL-6 (C) Quinoline_IL-8.

 

 

 

 

The validation of the docking results can be done through molecular dynamic simulations, it aims to determine the stability of the molecular complex formed29,30. The results of the molecular dynamic simulation analysis showed that Quinoline, 6-(4,5-diphenylimidazole-2-yl) compound could bind stably to the three target proteins with a lower RMSF of about 2 – 3 are presented in Figure 4. The following is a link to the CABS-flex 2.0 version of the molecular dynamic simulation results from this research. The molecular complex resulting from docking A (http://biocomp.chem.uw.edu.pl/CABSflex2/job/f92aba06dfdb35e/), the molecular complex from docking B (http:// biocomp.chem.uw.edu.pl/ CABSflex2/job/4ab8cf38b0b6dff/), C docked molecular complex (http://biocomp.chem.uw.edu.pl/CABSflex2/job/68f9ca4101c197e/).


 

Figure 3. RMSF from molecular dynamic simulation. (A) Quinoline_TNF-α (B) Quinoline_IL-6 (C) Quinoline_IL-8.

 


CONCLUSION:

Quinoline compound, 6-(4,5-diphenylimidazole-2-yl) contained in Daun Wungu (Graptophyllum pictum L. Griff) had a stronger binding energy to the target protein than others. The compound was predicted to trigger the inhibition of the activity of TNF- α, IL-6, and IL-8 proteins better because it could produce more negative binding energy, stronger bond interactions, and produce stable molecular complexes, but the results of this study still had to undergo further testing through in vitro and in vivo approaches to strengthen the evidence base.

 

CONFLICT OF INTEREST

The authors declare that there is no conflict of interest regarding the publication of this article.

ACKNOWLEDGMENTS

This work was supported by Universitas Airlangga.

 

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20.   Nugraha AP, Rahmadhani D, Puspitaningrum MS, Rizqianti Y, Kharisma VD, Ernawati DS. Molecular docking of anthocyanins and ternatin in Clitoria ternatea as coronavirus disease oral manifestation therapy. J Adv Pharm Technol Res. 2021; 12 (4): 362-367. doi: 10.4103/japtr.japtr_126_21.

21.   Hollingsworth, SA, Dror RO. Molecular Dynamics Simulation for All. Neuron. 2018; 99(6): 1129-1143.  doi: 10.1016/j.neuron.2018.08.011.

22.   Wijaya RM, Hafidzhah MA, Kharisma VD, Ansori ANM, Parikesit AA. COVID-19 In Silico Drug with Zingiber officinale Natural Product Compound Library Targeting the Mpro Protein. Makara J Sci. 2021; 25(3): 162-171. doi: 10.7454/mss.v25i3.1244.

23.   Prahasanti C, Nugraha AP, Kharisma VD, Ansori ANM, Devijanti R, Ridwan TPSP, Ramadhani NF, Narmada IB, Ardani IGAW, Noor TNEBA. A bioinformatic approach of hydroxyapatite and polymethylmethacrylate composite exploration as dental implant biomaterial. J Pharm and Pharmacogn Res. 2021; 9(5): 746-754.

24.   Listiyani P, Kharisma VD, Ansori AN, Widyananda MH, Probojati RT, Murtadlo AA, et al. In Silico Phytochemical Compounds Screening of Allium sativum Targeting the Mpro of SARS-CoV-2. Pharmacognosy Journal. 2022; 14(3): 604-609. DOI: 10.5530/pj.2022.14.78

25.   Ansori ANM, Kharisma VD, Parikesit AA, Dian FA, Probojati RT, Rebezov M, Scherbakov P, Burkov P, Zhdanova G, Mikhalev A, Antonius Y, Pratama MRF, Sumantri NI, Sucipto TH, Zainul R. Bioactive Compounds from Mangosteen (Gracinia mangostana L.) as an Antiviral Agent via Dual Inhibitor Mechanism against SARS-CoV-2: An In Silico Approach. Pharmacogn J. 2022; 14(1): 85-90. doi: 10.5530/pj.2022.14.12.

26.   Antonius Y, Utomo DH, Widodo. Identification of potential biomarkers in nasopharyngeal carcinoma based on protein interaction analysis. International Journal of Bioinformatics Research and Applications. 2017; 13(4): 376-388. DOI: 10.1504/IJBRA.2017.087385

27.   Aini NS, Kharisma VD, Widyananda MH, Murtadlo AA, Probojati RT, Turista DD, et al. In Silico Screening of Bioactive Compounds from Syzygium cumini L. and Moringa oleifera L. Against SARS-CoV-2 via Tetra Inhibitors. Pharmacognosy Journal. 2022;14(4):267-272. DOI: 10.5530/pj.2022.14.95

28.   Hartati FK, Djauhari AB, Kharisma VD. Evaluation of Pharmacokinetic Properties, Toxicity, and Bioactive Cytotoxic Activity of Black Rice (Oryza sativa L.) as Candidates for Diabetes Mellitus Drugs by in silico. Biointerface Res App Chem. 2021; 11(4): 12301-12311. doi: 10.33263/BRIAC114.1230112311.

29.   Kharisma VD, Ansori ANM, Fadholly A, Sucipto TH. Molecular Mechanism of Caffeine-Aspirin Interaction in Kopi Balur 1 as Anti-Inflammatory Agent: A Computational Study. Indian J Forensic Med Tox. 2020; 14(4): 4041-4046.

30.   Aini NS, Kharisma VD, Widyananda MH, Murtadlo AA, Probojati RT, Turista DD, et al. Bioactive Compounds from Purslane (Portulaca oleracea L.) and Star Anise (Illicium verum Hook) as SARS-CoV-2 Antiviral Agent via Dual Inhibitor Mechanism: In Silico Approach. Pharmacognosy Journal. 2022;14(4):352-357. DOI: 10.5530/pj.2022.14.106

 

 

 

 

Received on 11.05.2022            Modified on 02.08.2022

Accepted on 05.10.2022           © RJPT All right reserved

Research J. Pharm. and Tech 2023; 16(5):2291-2296.

DOI: 10.52711/0974-360X.2023.00376