Journal of Advanced Pharmaceutical Technology & Research

ORIGINAL ARTICLE
Year
: 2022  |  Volume : 13  |  Issue : 3  |  Page : 207--215

Dipeptidyl peptidase IV inhibition of phytocompounds from Artocarpus champeden (Lour.) Stokes: In silico molecular docking study and ADME-Tox prediction approach


Supandi Supandi1, Mesy Savira Wulandari2, Erwin Samsul2, Azminah Azminah3, Reza Yuridian Purwoko4, Herman Herman2, Hadi Kuncoro2, Arsyik Ibrahim2, Neneng Siti Silfi Ambarwati5, Rosmalena Rosmalena6, Rizqi Nur Azizah7, Swandari Paramita8, Islamudin Ahmad2,  
1 Department of Pharmaceutical Analysis, Faculty of Pharmacy and Science, Universitas Muhammadiyah Prof. Dr. HAMKA, South Jakarta, Indonesia
2 Department of Pharmaceutical Sciences, Faculty of Pharmacy, Universitas Mulawarman, Samarinda, East Kalimantan, Indonesia
3 Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Surabaya, Surabaya, Indonesia
4 Research Center for Pre-Clinical and Clinical Medicine, Indonesian Research and Innovation Agency, East Jakarta, Jakarta, Indonesia
5 Department of Cosmetology, Engineering Faculty, Universitas Negeri Jakarta, East Jakarta, Jakarta, Indonesia
6 Department of Medical Chemistry, Faculty of Medicine, Universitas Indonesia, South Jakarta, Indonesia
7 Laboratory of Biopharmacy and Pharmacology, Faculty of Pharmacy, Universitas Muslim Indonesia, Makassar, South Sulawesi, Indonesia
8 Department of Community Medicine, Faculty of Medicine, and Research Center of Natural Products from Tropical Rainforest, Universitas Mulawarman, Samarinda, East Kalimantan, Indonesia

Correspondence Address:
Dr. Islamudin Ahmad
Jl. Kuaro Gn. Kelua, Samarinda 75119 East Kalimantan
Indonesia

Abstract

The present study examines the potential activity prediction based on free binding energy (ΔG) and interaction confirmation of phytocompounds from Artocarpus champeden (Lour.) Stokes with macromolecule protein receptor of dipeptidyl peptidase IV (DPP-IV) using in silico molecular docking studies and physicochemical and pharmacokinetic properties (ADME-Tox) prediction approaches. The active subsites of the DPP-IV receptor macromolecule protein Protein Data Bank (ID: 1 × 70) were docked using Autodock v4.2.6 (100 docking runs). A grid box of 52 × 28 × 26 Å points spaced by 0.37 Å was centered on the active site of x = 40.926 Å; y = 50.522 Å; z = 35.031 Å. For ADME-Tox prediction, Swiss ADME online-based application programs were used. The results show that 12 pythocompounds from A. champeden have the potential as DPP-IV inhibitors based on ΔG value and interaction conformation. There are five pythocompounds with lower ΔG values and inhibition constants than the native ligand and seven pythocompounds with ΔG values and inhibition constants close to the native ligand. The 12 compounds form an interaction conformation at the active subsites of the DPP-IV receptor. At the same time, the results of the ADME-Tox prediction analysis showed that the 12 compounds had different physicochemical and pharmacokinetic properties.



How to cite this article:
Supandi S, Wulandari MS, Samsul E, Azminah A, Purwoko RY, Herman H, Kuncoro H, Ibrahim A, Silfi Ambarwati NS, Rosmalena R, Azizah RN, Paramita S, Ahmad I. Dipeptidyl peptidase IV inhibition of phytocompounds from Artocarpus champeden (Lour.) Stokes: In silico molecular docking study and ADME-Tox prediction approach.J Adv Pharm Technol Res 2022;13:207-215


How to cite this URL:
Supandi S, Wulandari MS, Samsul E, Azminah A, Purwoko RY, Herman H, Kuncoro H, Ibrahim A, Silfi Ambarwati NS, Rosmalena R, Azizah RN, Paramita S, Ahmad I. Dipeptidyl peptidase IV inhibition of phytocompounds from Artocarpus champeden (Lour.) Stokes: In silico molecular docking study and ADME-Tox prediction approach. J Adv Pharm Technol Res [serial online] 2022 [cited 2022 Nov 27 ];13:207-215
Available from: https://www.japtr.org/text.asp?2022/13/3/207/349839


Full Text



 Introduction



Artocarpus champeden (Lour.) Stokes belongs to the Moraceae family, locally known as “Chempedak,” an annual fruit plant with a tall, strong woody tree. This fruit plant is a native that grows wild in tropical forests, mainly in India, Vietnam, Myanmar, Thailand, Malaysia, and Indonesia.[1] This plant is widespread in Sumatran, Kalimantan, Sulawesi, Maluku, and West Papua in Indonesia. Traditionally, this plant treats diarrhea, fever, malaria, and diabetes mellitus. However, no scientific evidence has been reported of A. champeden as a potential antidiabetic agent to the best of our knowledge. Therefore, our team is interested in researching the potential of this plant.

Meanwhile, several studies have isolated and identified phytocompounds found in A. champeden.[2],[3],[4],[5] However, data regarding the potential pharmacological activity of phytocompounds from A. champeden is still minimal, mainly as antidiabetic, whereas it has traditionally been used for generations. This series of work fills research gaps by examining the potential activity and interactions of phytocompound from A. champeden using the in silico molecular docking study and ADME-Tox prediction approach.

In silico molecular docking is a modeling method based on computer simulation to search for possible bindings of the test ligand and receptor-interacting under topographical conditions and the match between both molecules with the conformation that has the best interaction.[6],[7],[8],[9] ADME-Tox prediction is performed using an online-based application such as SWISSADME, which aims to study physicochemical and pharmacokinetic properties.[10],[11] Some studies that have been reported successfully related to the use of these application programs include ADMET analysis of three relevant natural components of the medicinal plant,[12] ADMET prediction of mangosteen derivates,[13] ADME-Tox prediction of phytocompounds from Merremia peltata,[14] and drug-likeness prediction of bioactive compounds from Punica granatum L.[15]

The current study predicts the interaction conformation and the potential activity of phytocompounds from A. champeden with macromolecules protein of dipeptidyl peptidase IV (DPP-IV) as a receptor target, hoping to fill research gaps on an in silico assay scale, thereby accelerating the development of further studies.

 Materials and Methods



Hardware and software

The analysis of molecular docking was carried out by a computer HP Pavilion, Autodock-v4.2.6, AutodockTools, ChemOffice-Pro-v15.00 PerkinElmer, Phyton Molecular Viewer (PMV-1.5.6), OpenBabel GUI, Accelrys Discovery Studio Visualizer 4.0. Software, and SWISSADME (http://www.swissadme.ch/) online tools program.

In silico molecular docking study

Native ligand and receptor preparation

The protein structure of macromolecule DPP-IV complexes with native ligand sitagliptin Protein Data Bank (PDB ID: 1 × 70, with 2.1Å resolution) was downloaded from the Research Collaboratory for Structural Bioinformatics PDB via the website: https://www.rcsb.org/. Macromolecule DPP-IV receptors and native ligand were separated using PMV-1.5.6. Gasteiger charges were added to each ligand atom. Water molecules were eliminated from each protein receptor and protonated. Then, a native ligand and protein receptor was prepared and converted in the PDBQT format (.pdbqt) using AutodockTools and OpenBabel programs.[7],[16]

Preparation of phytocompounds as a test ligand

In this study, the structure of phytocompounds from A. champeden was collected from some literature,[2],[3],[4],[5] as shown in [Figure 1]. Each phytocompounds were prepared as a test ligand using ChemDraw® Pro v15 to build a two-dimension structure of each phytocompounds. Chem three-dimensional (3D)® Pro v15 was converted to a 3D structure, minimized using the MMFF94 force field, and saved to PDB (.pdb).[8]{Figure 1}

Analysis of in silico molecular docking

According to its protocols, the analysis of in silico molecular docking of 41 phytocompounds from A. champeden was conducted using Autodock 4.2.6.[7] Using the Lamarckian Genetic Algorithm (LGA) based on the lowest free energy of binding (ΔG), the native ligand was simulated in various conformations for best binding to the protein DPP-IV receptor binding site. The parameters of LGA were: elitism of 1, crossover rate of 0.8, the mutation rate of 0.02, the population size of 150, energy evaluation of 2500,000, and 100 runs. Moreover, the grid box comprised of 52 × 28 × 26Å points spaced by 0.375Å was centered on the active site of x = 40.926Å; y = 50.522Å; z = 35.031Å (XYZ-coordinates) according to a previous study.[17] The grid condition was used for molecular docking analysis of 41 phytocompounds from A. champeden. The results of molecular docking data were visualized using Accelrys Discovery Studio Visualizer-4.0.[18]

Determination of ADME-tox prediction

According to the literature, ADME-Tox prediction of the best docking results was determined using SWISSADME online tools.[11] Briefly, each phytocompounds (PDB format) structure was converted in SMILES format using OpenBabel GUI. SWISSADME online tools program was used to determine ADME-Tox of 12 phytocompounds.[18]

 Results



In silico molecular docking study

Validation of molecular docking method

In the present study, the docking results of the native ligand (sitagliptin) demonstrated a root mean square deviation (RMSD) value of 0.55 Å (<2 Å) with a binding free energy (ΔG) value of −8.59 kcal/mol (inhibition constant of 508.58 nM) and clusters of 82% for 100 times running. [Figure 2] shows that the overlay position between the docking results and the original native ligand does not significantly different positions according to the RMSD value <2 Å, indicating that the grid size and grid center of the docking process was different valid.{Figure 2}

The docking results of 41 phytocompounds from A. champeden in [Table 1] show that five compounds had a lower ΔG value and inhibition constant than the native ligand. Seven compounds have ΔG value and inhibition constant close to the native ligand.{Table 1}

Studies on molecular interaction

[Figure 3] demonstrates visualization of native ligand interaction with active site residue of DPP-IV macromolecule receptors.{Figure 3}

In [Figure 4], it was shown that 12 phytocompounds have conformational interactions with subsites of the DPP-IV receptor.{Figure 4}

ADME-tox prediction

The ADME-Tox properties prediction of selected 12 phytocompounds from A. champeden according to the molecular docking study is presented in [Table 2]. The physicochemical properties prediction provides an overview of bioavailability levels of phytocompounds, as shown in [Figure 5].{Figure 5}{Table 2}

 Discussion



The result of re-docking of native ligand indicates the level of validity of grid box and box size used with an RSMD value of 0.55 Å (<2 Å), which refers to the previous study,[17],[19] indicating that the grid size and grid center of the docking process was different valid. The docking result demonstrated native ligand and test ligand interaction with the active site of DPP-IV receptor macromolecules. The DPP-IV receptor has some active site areas at subsites area of amino acid residues known as S1, S1', S2', S2, and S2 extensive.[20],[21],[22] The test ligand activity can generally be predicted based on interactions at subsites (S1, S2, and S2 ext.) of the DPP-IV receptor.[21],[22]

In this study, it was found that five phytocompounds had lower ΔG values than the native ligand, including 24-methylencycloartanon, cycloartenon, cycloartenol, β-sitosterol, and cycloeucalenol, and seven phytocompounds that had an ΔG value close to the native ligand include cudraflavon C, artoindonesianin A, 5'-hydroxycudraflavon A, artoindonesianin B, artoindonesianin R, artoindonesianin A3, and cyclocommunim. In addition, the 12 phytocompounds showed conformational interactions that were specific to the active subsite of the DPP-VI receptor. Each amino acid residue of the active subsites of the DPP-IV receptor can form seven different interaction conformations with the test ligand.[23]

The ADME-Tox properties play a crucial role in the drug industry. They are generally used in drug development, mainly using the computer-aided drug design approach to reduce unwanted effects. 24-Methylencycloartanon has an MW value that is in the unacceptable range, while the others are in the acceptable range. Artoindonesianin A, artoindonesianin A3, artoindonesianin B, artoindonesianin R, cudraflavon C, cyclocommunin, and 5'-hydroxycudraflavon A obey the Lipinski rule, except six other compounds (RO5 value >0).[24] All phytocompounds showed the H-bond (acceptor and donor) and skin permeant value in the acceptable range. Based on the topological polar surface area (TPSA) value, which reveals that 24-methylencycloartanon, β-sitosterol, cycloartenol, cycloartenon, and cycloeucalenol have an excellent brain penetration (TPSA <70Å2), and seven other compounds have good gastrointestinal penetration (with TPSA <140Å2).[25] XLOGP3 shows the lipophilicity and polarity value prediction of phytocompounds. The higher the value, the lower the polarity.[26],[27] ESOL indicates the solubility levels of phytocompounds. The lower the values,[28] the lower solubility.[29] [Figure 5] demonstrated that the phytocompounds of artonindonesianin (A3, B, and R), β-sitosterol, cycloartenol, and 5'-hydroxycudraflavon A were the acceptable/optimal range of ADME-Tox/physicochemical space for oral bioavailability.

 Conclusion



Analysis of in silico molecular docking and ADME-Tox prediction were performed to study the potential pharmacological activity of phytocompounds from A. champeden as DPP-IV inhibitors. Our findings show that almost all phytocompounds have potential interaction with the receptor at the active subsites. Nevertheless, 12 phytocompounds have the most similar interaction with the DPP-IV receptor and have different physicochemical properties for bioavailability and pharmacokinetics prediction.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

References

1de Almeida Lopes MM, de Souza KO, de Oliveira Silva E. Cempedak - Artocarpus champeden, in de Brito E, Rodrigues S, and de Oliveira Silva E. 1st Edition. Exotic Fruits Reference Guide. London: Academic Press: 2018; 121-7.
2Achmad SA, Hakim EH, Juliawaty LD, Makmur L, Suyatno S. A new prenylated flavone from Artocarpus champeden. J Nat Prod 1996;59:878-9.
3Hakim EH, Juliawaty LD, Syah YM, Achmad SA. Molecular diversity of Artocarpus champeden (Moraceae): A species endemic to Indonesia. Mol Divers 2005;9:149-58.
4Syah YM, Juliawaty LD, Achmad SA, Hakim EH, Ghisalberti EL. Cytotoxic prenylated flavones from Artocarpus champeden. J Nat Med 2006;60:308-12.
5Widyawaruyanti A, Subehan S, Kalauni SK, Awale S, Nindatu M, Zaini NC, et al. New prenylated flavones from Artocarpus champeden, and their antimalarial activity in vitro. J Nat Med 2007;61:410-3.
6Arciniega M, Lange OF. Improvement of virtual screening results by docking data feature analysis. J Chem Inf Model 2014;54:1401-11.
7Morris GM, Ruth H, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. Software news and updates AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 2009;30:2785-91.
8Wang L, Wu Y, Deng Y, Kim B, Pierce L, Krilov G, et al. Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc 2015;137:2695-703.
9Belew RK, Forli S, Goodsell DS, O'Donnell TJ, Olson AJ. Fragment-based analysis of ligand dockings improves classification of actives. J Chem Inf Model 2016;56:1597-607.
10Daina A, Michielin O, Zoete V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 2017;7:42717.
11Pires DE, Blundell TL, Ascher DB. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 2015;58:4066-72.
12Verma D, Mitra D, Paul M, Chaudhary P, Kamboj A, Thatoi H, et al. Potential inhibitors of SARS-CoV-2 (COVID 19) proteases PLpro and Mpro/3CLpro: Molecular docking and simulation studies of three pertinent medicinal plant natural components. Curr Res Pharmacol Drug Discov 2021;2:100038.
13Prasetyanti IK, Sukardiman S, Suharjono S. ADMET prediction and in silico analysis of mangostin derivatives and sinensetin on maltase-glucoamylase target for searching anti-diabetes drug candidates. Pharmacogn J 2021;13:883-889.
14Abdurrahman S, Ruslin R, Hasanah AN, Mustarichie R. Molecular docking studies and ADME-Tox prediction of phytocompounds from Merremia peltata as a potential anti-alopecia treatment. J Adv Pharm Technol Res 2021;12:132-9.
15Arunkumar J, Rajarajan S. Study on antiviral activities, drug-likeness and molecular docking of bioactive compounds of Punica granatum L. to Herpes simplex virus-2 (HSV-2). Microb Pathog 2018;118:301-9.
16O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: An open chemical toolbox. J Cheminform 2011;3:33.
17Ahmad I, Arifianti AE, Sakti AS, Saputri FC, Mun'im A. Simultaneous natural deep eutectic solvent-based ultrasonic-assisted extraction of bioactive compounds of cinnamon bark and sappan wood as a dipeptidyl peptidase IV inhibitor. Molecules 2020;25:E3832.
18Mahayasih PG, Harizal H, Herman H, Ahmad I. In silico identification of natural products from Centella asiatica as severe acute respiratory syndrome coronavirus-2 main protease inhibitor. J Adv Pharm Technol Res 2021;12:261-6.
19Kim BR, Kim HY, Choi I, Kim JB, Jin CH, Han AR. DPP-IV inhibitory potentials of flavonol glycosides isolated from the seeds of Lens culinaris: In vitro and molecular docking analyses. Molecules 2018;23:E1998.
20Arulmozhiraja S, Matsuo N, Ishitsubo E, Okazaki S, Shimano H, Tokiwa H. Comparative binding analysis of dipeptidyl peptidase IV (DPP-4) with antidiabetic drugs – An Ab initio fragment molecular orbital study. PLoS One 2016;11:e0166275.
21Luo F, Fu Y, Ma L, Dai H, Wang H, Chen H, et al. Exploration of dipeptidyl peptidase-IV (DPP-IV) inhibitory peptides from silkworm pupae (Bombyx mori) proteins based on in silico and in vitro assessments. J Agric Food Chem 2022;70:3862-71.
22Zhang X, Wang R, Cheng C, Zhang Y, Ma Y, Lu W. Identification of two novel dipeptidyl peptidase-IV inhibitory peptides from sheep whey protein and inhibition mechanism revealed by molecular docking. Food Biosci 2022;48:101733.
23Radifar M, Yuniarti N, Istyastono EP. PyPLIF: Python-based protein-ligand interaction fingerprinting. Bioinformation 2013;9:325-8.
24Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 2001;46:3-26.
25Daina A, Zoete V. A BOILED-egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem 2016;11:1117-21.
26Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 2002;45:2615-23.
27Ghaemi Z, Alberga D, Carloni P, Laio A, Lattanzi G. Permeability coefficients of lipophilic compounds estimated by computer simulations. J Chem Theory Comput 2016;12:4093-9.
28Daina A, Michielin O, Zoete V. iLOGP: A simple, robust, and efficient description of n-octanol/water partition coefficient for drug design using the GB/SA approach. J Chem Inf Model 2014;54:3284-301.
29Arnott JA, Planey SL. The influence of lipophilicity in drug discovery and design. Expert Opin Drug Discov 2012;7:863-75.