Multitarget Ensemble Docking of Potent Anticancer and Antioxidant Active Compounds from the Acacia auriculiformis and Acacia crassicarpa

 

Yanico Hadi Prayogo¹, Setyanto Tri  Wahyudi², Irmanida Batubara^3,4, Rita Kartika Sari^1,3, Wasrin Syafii^1*

¹Department of Forest Products, Faculty of Forestry and Environment, IPB University, Indonesia

²Department of Physics, Faculty of Mathematic and Natural Science, IPB University, Indonesia

³Tropical Biopharmaca Research Center, IPB University, Indonesia

⁴Department of Chemistry, Faculty of Mathematic and Natural Science, IPB University, Indonesia

 

ABSTRACT:

Bioactive chemicals derived from Acacia auriculiformis and A. crassicarpa have the potential to be developed as sources of anti-cancer raw materials and antioxidants, given that these plants are fast-growing species with medicinal capability. The in silico method was successful in identifying these bioactive chemicals for the preliminary study. Using an in silico approach, this work aimed to identify the most potent compounds as inhibitors of six cancer and stress oxidative therapy-targeted proteins from these two distinct Acacia species. Seventeen out of the 37 compounds examined exhibited low affinity and satisfied the drug-likeness criterion. Five active chemicals were identified by redocking analysis: auriculoside, 3-(3,4-dihydroxybenzyl)-7-hydroxychroman-4-one, kaempferol 7-O-glucoside, quercetin 7-O-glucoside, and keto-teracacidin. According to simulations of molecular dynamics, molecular motion occurs with a root mean square deviation of less than four and generates at least eleven receptor conformations for 0 to 100 ns. Auriculoside showed the lowest average binding energy against four receptors in colorectal and breast cancer, as determined by ensemble docking, followed by 3-(3,4-dihydroxybenzyl)-7-hydroxychroman-4-one, quercetin 7-O-glucoside, and kaempferol 7-O-glucoside. Auriculoside shown multitarget inhibitory effect against colorectal cancer by inhibiting cyclin dependent kinase-6 and breast cancer by inhibiting epidermal growth factor receptor and mammalian target of rapamycin. Auriculoside has the powerful ability to inhibit glycogen synthase kinase-3 beta, hence regulating oxidative stress. Kaempferol 7-O-glucoside and quercetin 7-O-glucoside also exhibited a possible single protein targeting method against breast cancer. These findings are essential for future research targeted at developing these plants as potent natural therapeutic raw materials and for isolating or synthesizing compounds with anticancer and oxidative stress-regulating antioxidant properties.

 

 

 

INTRODUCTION: 

A. auriculiformis and A. crassicarpa are two of the several Acacia species native to Indonesia. Acacia species are renowned for their rapid growth. These plants can also thrive in marginal habitats, particularly A. crassicarpa, which can thrive in peatlands¹.

 

In Indonesia, A. auriculiformis is widely used as sawn wood and cultivated for energy wood, while A. crassicarpa is grown in industrial forest plantations for pulp production^2,3. Because it is easy to grow and cultivate, it has a good chance of becoming a source of raw materials for medicines.

 

Several previous studies have shown that these two plants have good pharmacological activity. Acacia auriculiformis shows potential antidiabetic, antioxidant, antimutagenic, antibacterial, anti-inflammatory, and anti-cancer properties^4–12. Information regarding the pharmacological activity of A. crassicarpa is still very limited. The antioxidant activity is very good compared to other types of Acacia¹³. The diverse pharmacological activities are related to the bioactive compounds contained. To date, studies regarding the bioactive compounds responsible for this pharmacological activity are not yet known, nor are those related to the molecular mechanism of these bioactive compounds. Studies on bioactive compounds of both types of plants that act as anti-cancer and antioxidants are the main focus of the study, and both are interrelated. Cancer is still the leading cause of death worldwide, according to WHO 2020, with a large number of breast and colon cancer patients. 

 

In silico approaches can be used to select bioactive compounds and understand the molecular binding mechanism. Many previous works were done using this approach to predict biological interaction behaviour of active compounds related to antimicrobial^14,15, antiinflamatory^16,17, antituberculosis^18,19, dengue antivirus²⁰, and HIV protease inhibitor²¹. A previous study about selecting anti-cancer compounds from Ficus carica was achieved using an in silico approach²². This approach is also used to confirm the effectiveness of synthetic compounds related to anticancer candidate from carboxamide²³ and benzamide²⁴ derivatives. In another previous study, an in silico approach was also carried out to explore various flavonoid compounds as anti-cancers²⁵. The dynamic simulations combined with molecular docking offers the first step for selecting natural bioactive compounds as anticancer and antioxidant for further clinical and preclinical testing. Therefore, this study aimed to select the most active compounds from A. auriculiformis and A. crassicarpa through an in silico approach. We used dynamic modeling for creating receptor conformations and docking the compounds from the two Acacia species in order to determine the effect of different receptor conformations on the binding behavior of the compounds. This method was used to gain a greater comprehension of the prospective active compounds.

 

MATERIAL AND METHODS:

Ligand Preparation:

The ligands used were 37 compounds from plants A. crassicarpa and A. auriculiformis, which were collected from various previous studies (Table 1). The molecular structure of each compound was made using Chem3D Pro version 12.0.21076 (Chambridge Soft, UK). Each molecule was performed quantum-mechanical energy minimization of the molecular structure using ORCA software (Table S1). Then, further treatment for each terminating molecule, namely merge nonpolar hydrogen, compute gasteiger, and torque selection were performed in AutoDockTools version 1.5.6.

 

Table 1: List of identified compounds from A. auriculiformis and A. crassicarpa trees

Species (part)	Part	Compound names
A.crassicarpa	Wood	Melacacidin; isomelacacidin; taxifolin; catechin; 3-Hydroxy-7-methoxy baicalein; digitopurpone; onjixanthone II; quercetin. 13,26
A. auriculiformis	Wood	Teracacidin; 4’,7,8-trihydroxyflavanone; isoteracacidin; 4’,7,8-trihydroxyflavonol; 2,3-trans-3,4’,7,8-tetrahydroxyflavanone; 3-(3,4-dihydroxybenzyl)-7-hydroxychroman-4-one; 5,7,2’,5’-tetrahydroxyflavone; genistein; 2,3-cis-3,4’,7,8-tetrahydroxyflavanone (keto-teracacidin).9,13,27,28
	Leaf	3,7-diglucosidemyricetin ; 7-glucosidekaempferol; 3-glucosidekaempferol; Isorhamnetin; 7-glucosidequercetin; 2,4-ditert-butylphenol; stigmasterol; β-sitosterol ; γ-sitosterol; campesterol.29,30
	Bark	Betulin12
	Fruit	Acaciaside; proAcaciaside-I; proAcaciaside-II; Acaciamine. 31
	Fruit and seed	Acaciaside A; Acaciaside B.8,32
	Aerial part	α-spinasterol; auriculoside. 33
A. auriculiformis and A. crassicarpa	Wood	5,7,3’,5’-Tetrahydroxyflavanone.13

 

Receptor Preparation:

A total of 8 X-ray macromolecular structures were obtained from the protein data bank (Tabel 2; https://www.rcsb.org/). Three proteins are associated with treating colorectal cancer (cyclin-dependent kinase 6/CDK6,vascular endothelial growth factor receptor 2/VEGFR2, and B-Raf kinase/BRK). Three proteins were associated with breast cancer targeting protein (epidermal growth factor receptor/EGFR, estrogen receptor alpha/ERα, and mammalian target of rapamycin/mTOR). Two proteins were associated with oxidative stress in cells, myeloperoxidase/MOX and glycogen synthesis kinase 3 beta/GSK3β. Receptor preparation separated non-protein structures such as water, ions, etc. Kollman charges were assigned. All the preparation was performed using AutoDockTools version 1.5.6.

 

Tabel 2: Resolution, ramachandran analysis results and G-factor of eight receptors

Receptor (PDB ID)	Resolution	Ramachandran Analysis (%)	G-factor
CDK6 (5L2S)51	2.27	89.4	0.08
VEGFR2 (2OH4)52	2.05	91.2	0.19
BRK (4MNF)53	2.8	87.3	0.05
EGFR (3POZ)54	1.5	93.3	0.2
ERα (1SJ0)55	1.9	92.3	0.13
mTOR (4DRI)56	1.45	95.1	0.15
MOX (6WYD)57	2.55	87.9	0.06
GSK3β (4PTE)58	2.03	90.7	0.21

Virtual Screening:

Thirty-seven ligands were virtual screened using EasyDockVina v2.2 software and ADMETlab (https://admetmesh.scbdd.com). The affinity energy parameter based on analysis with AutoDockVina with EasyDockVina v2.2 software was used as a reference for selection. In addition, the selection considerations also looked at the medicinal chemistry parameters based on four types of rules, namely the Lipinski rule, Pfizer rule, GSK rule, and Golden Triangle. Ligands with low affinity and can fulfill four rules in medicinal chemistry parameters will be selected for further analysis.

 

Docking Analysis:

Docking analysis of selected ligands was performed using autodock4 and autogrid4 on AutoDockTools software. Genetic algorithm parameters are used with the GA runs 100, population size 300, the long maximum number of evaluations (25000000), rate of gene mutation 0.02, and rate of crossover 0.8. The grid center coordinates values used for each protein were based on the position of the natural inhibitor/co-crystal ligand (Table 3). Before docking the 38 prepared ligands, the docking analysis began with method validation (docking using co-crystal ligands). Validation was done by looking at the root mean square deviation (RMSD). Validation was successful if it produced an RMSD value of less than 2 Å (Table 3).  

 

Table 3: Grid Center and RMSD value from each co-crystal ligand and its receptor

Receptor	PDB ID: name or InChlKey	Grid Center Coordinates (x,y,z)	RMSD (Å)
CDK6	6ZV: Abemaciclib	22.452, 38.566, -8.687	1.546
VEGFR2	GIG: BBGZUGGIEIYPCN-UHFFFAOYSA-N	3.173, 33.766, 17.175	0.567
BRK	29L: DEZZLWQELQORIU-RELWKKBWSA-N	7.023, 17.704, -46.954	1.281
EGFR	03P: ZYQXEVJIFYIBHZ-UHFFFAOYSA-N	18.746, 31.832, 11.626	1.682
ERα	E4D: NAULYXADLNIEES-RRPNLBNLSA-N	30.66, -1.067, 23.464	0.678
mTOR	RAP: rapamycin	35.45, 48.585, 35.327	1.050
GSK3β	2WF: PGKFRHOTYGEBDX-UHFFFAOYSA-N	-3.906, 0.965, -35.463	0.546
MOX	7GD: 7-benzyl-2H-triazolo[4,5-b]pyridin-5-amine	-21.38, 20.749, 16.044	1.161

 

Ensemble Docking:

a) Molecular Dynamic Simulations:

The eight proteins used in this study were simulated by molecular dynamics using the AMBER20 program. The simulation stages consist of minimization, heating, equilibration, and run production. The water and hydrogen molecules in the protein are removed with pdb4amber. Furthermore, the H++ website (http://biophysics.cs.vt.edu/) was used to calculate the pKa value of the ionized group in amino acid residues at pH 6.8 and the explicit solvent box topology used was cubic with the TIP3P water model and neutralized using Na+/Cl-. Files obtained from processing in H++ are reprocessed by the ambpdb and pdbamber scripts. Protein is then minimized through 5 stages, namely the initial four stages using restraint and the final stage using a conjugate gradient algorithm. Each stage uses 10000 cycles, with the first 500 steps using the steepest descent. The heating stage is carried out for one ns, with the first 500 ps used to increase the temperature linearly to 310 K, then held for 500 ps. In this step, the amino acids are constrained by 10 kcal mol^-1 A^-2 with a constant number of particles, volume, and temperature (NVT) ensemble. This equalization stage is divided into the NVT ensemble and the NPT (constant number of particles, pressure, and temperature) ensemble. The ensemble NPT was performed at a constant temperature of 310 K for 500 ps. Furthermore, the dynamics simulation was carried out using PMEMD.CUDA to evaluate the free movement of receptor molecules without restraint at a range of 0–100 ns. The simulation results will produce 11 confirmations for each receptor representing the conformation at 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 ns. The analysis of the simulation results is also shown by looking at the RMSD and root mean square fluctuation (RMSF) parameters.

 

b) Docking Analysis. Four compounds with the lowest binding free energy values were selected for further re-docking using 11 protein conformations at 0 – 100 ns captured from dynamic molecular simulation results. Autodock4 and autogrid4 were used to evaluate ligand interaction in the 0 – 100 ns range. The genetic algorithm parameter, grid center coordinates, and grid size follow those used previously in the docking analysis method.

 

Receptor-Ligand Binding Visualisation:

Visualization of the ligand-receptor interaction from the results of the docking analysis was carried out using Discovery Studio Visualizer version 21.1.0.20298

 

RESULTS:

The virtual screening result showed several compounds with the lowest affinity for each receptor (Table S2). Three proteins associated with colorectal cancer produce three potential compounds. The kaempferol 7-O-glucoside (-9.6kcal/mol); 4',7,8-trihydroxyflavonol (9.8 kcal/mol) and quercetin (9.8kcal/mol); auriculoside (-10.2kcal/mol) against CDK6, VEGFR2 and BRK, respectively. Alpha spinasterol (10.5kcal/mol), 3-deoxysappanone (-9.3kcal/mol), and Acaciamine (13.2 kcal/mol) are anti-EGFR, ER, and mTOR, respectively, for the ovarian cancer receptor target. Two other compounds have the potential to overcome oxidative stress, namely 3-deoxysappanone (-9.0kcal/mol) against MOX and betulin (-9.8kcal/mol) against GSK3β.

 

However, not all compounds selected based on the binding affinity score met the four rules in the medicinal chemistry parameters. Based on Table S3, 13 compounds were excluded due to the rejection in more than 2 out of 4 rules. These compounds were 3,4-diglucosidemyricetin, stigmasterol, β-sitosterol, γ-sitosterol, campesterol, α-spinaterol, betulin, Acaciaside, proAcaciaside I and II, Acaciamine, and Acaciaside A and B. Most of the rejected compounds are sterols/steroids and di-glycoside flavonoids due to the structure of these compounds, which have large molecular weight and bulk structures with many hydrogen donors and acceptors. The result of the virtual screening resulted in 17 compounds, which were a collection of five compounds with the lowest affinity for each protein and met the criteria for medicinal chemistry parameters.

 

 

 

Auriculoside, 3-deoxysappanone, kaempferol 7-O-glucoside, quercetin 7-O-glucoside, and keto-teracacidin showed good potential for binding to target receptors. Based on Table 4, quercetin 7-O-glucoside produced the lowest binding energy to VEGFR2 receptors (-9.63 kcal/mol). Auriculoside inhibited EGFR (-9.02 kcal/mol) and mTOR (9.22 kcal/mol). Kaempferol 7-O-glucoside compound had the lowest BE at CDK6 (-8.02 kcal/mol) and MOX (-8.85 kcal/mol). BRK and ERα receptors produced the lowest BE through interaction with 3-deoxysappanone of -9.12 and -9.52 kcal/mol, respectively. Keto-teracacidin has the lowest BE value at GSK3β (-7.96 kcal/mol). However, none of the ligands of A. auriculiformis and A. crassicarpa had a lower binding energy than their co-crystal ligand.

 

The ligand inhibition constant (Ki) at each receptor has a similar trend to BE. A low Ki value indicates good inhibitory power. The Ki values in this study ranged from 1.33x10^-9 to 16.25μM (Table 5). Compounds with low Ki values showed good inhibition of receptor activity. The four ligands with the lowest BE at each receptor also have low Ki, namely auriculoside (against EGFR and mTOR), 3-(3,4-dihydroxybenzyl)-7-hydroxychroman-4-one (against BRK and ERα), kaempferol 7-O-glucoside (against CDK6 and MOX), quercetin 7-O-glucoside (against VEGFR2), and keto-teracacidin against GSK3β.

 

Table 4 Binding energy of selected compounds againts cancer  and oxidative stress receptor

Ligand	Binding energy (kcal/mol)
	CDK6	VEGFR2	BRK	EGFR	ERα	mTOR	MOX	GSK3β
4’,7,8-trihydroxyflavanone	-7.87	-8.26	-7.87	-7.32	-8.66	-7.59	-7.67	-7.48
isoteracacidin	-6.53	-8.16	-7.00	-7.53	-7.66	-7.69	-7.26	-6.83
4’,7,8-trihydroxyflavonol	-7.49	-8.17	-7.19	-7.16	-7.75	-8.02	-7.41	-6.84
trans-3,4’,7,8-tetrahydroxyflavanone	-7.22	-8.39	-7.34	-7.38	-7.89	-8.07	-7.28	-6.93
melacacidin	-6.61	-8.15	-7.28	-7.19	-7.70	-7.12	-6.97	-6.85
taxifolin	-7.19	-8.75	-8.09	-7.44	-7.78	-7.23	-6.91	-6.91
catechin	-7.23	-8.25	-7.24	-7.05	-7.90	-6.96	-6.97	-7.50
keto-teracacidin	-7.06	-7.76	-7.42	-7.05	-8.29	-7.63	-7.21	-7.96
3-(3,4-dihydroxybenzyl)-7-hydroxychroman-4-one	-7.53	-9.16	-9.12	-7.75	-9.52	-8.19	-8.18	-7.02
5,7,2’,5’-tetrahydroxyflavone	-7.10	-8.24	-7.58	-8.00	-7.64	-7.14	-7.09	-7.66
genistein	-7.55	-8.24	-7.33	-7.14	-8.29	-7.15	-7.36	-6.84
5,7,3’,5’-tetrahydroxyflavanone	7.13	-8.44	-7.70	-7.50	-7.83	-7.59	-6.61	-7.56
digitopurpone	-6.87	-8.09	-6.67	-6.62	-7.65	-7.26	-7.83	-7.31
quercetin	-7.01	-8.60	-8.11	-7.28	-7.96	-6.96	-6.72	-6.97
kaempferol 7-O-glucoside	-8.02	-8.75	-8.82	-8.68	-6.65	-8.26	-8.85	-6.78
quercetin 7-O-glucoside	-6.69	-9.63	-7.89	-8.58	-6.84	-8.59	-7.82	-7.59
auriculoside	-7.66	-9.26	-8.89	-9.02	-8.10	-9.22	-7.68	-7.69
Co-crystal ligand	-8.79	-12.27	-11.22	-9.68	-13.57	-20.30	-9.26	-9.10

 

Table 5 Inhibition constant of selected compounds from A. auriculiformis and A. crassicarpa.

Ligand	Ki (μM)
	CDK6	VEGFR2	BRK	EGFR	ERα	mTOR	MOX	GSK3β
4’,7,8-trihydroxyflavanone	1.71	0.88	1.69	4.32	0.450	2.74	2.38	3.27
3,4’,7,8-tetrahydroxyflavanone	4.97	0.71	6.33	4.02	1.66	1.23	4.66	7.04
isoteracacidin	16.25	1.05	7.35	3.03	2.43	2.29	4.75	9.81
4’,7,8-trihydroxyflavonol	3.21	1.02	5.39	5.68	2.08	1.33	3.71	9.67
trans-3,4’,7,8-tetrahydroxy- flavanone	5.08	0.71	4.17	3.88	1.66	1.21	4.64	8.32
melacacidin	14.31	1.06	4.59	5.32	2.27	6.06	7.73	9.54
taxifolin	5.32	0.39	1.18	3.53	1.97	5.01	8.66	8.57
catechin	5.03	0.89	4.92	6.84	1.62	7.87	8.66	3.19
keto-teracacidin	6.65	2.03	3.64	6.81	0.834	2.55	5.17	1.47
3-(3,4-dihydroxybenzyl)-7-hydroxychroman-4-one	3.01	0.19	0.208	2.07	0.106	1.00	1.01	7.12
5,7,2’,5’-tetrahydroxyflavon	6.19	0.91	2.77	1.38	2.49	3.73	6.36	2.42
genistein	2.93	0.91	4.21	5.83	0.845	5.77	4.03	9.69
5,7,3’,5’-tetrahydroxyflavanone	5.93	0.65	2.25	3.18	1.82	2.72	14.30	2.88
digitopurpone	9.19	1.18	12.8	14.13	2.46	4.73	1.84	4.35
quercetin	7.23	0.50	1.13	4.63	1.47	7.91	11.90	7.76
kaempferol 7-O-glucoside	1.32	0.39	0.342	0.44	13.44	0.888	0.32	10.68
quercetin 7-O-glucoside	12.57	0.09	1.66	0.51	9.65	0.50	1.82	2.71
auriculoside	2.44	0.16	0.30	0.25	1.16	0.18	2.34	2.32
Co-crystal ligand	0.364	0.001	0.006	0.081	0.0001	1.3x10-9	0.164	0.214

 

 

Figure 1: Receptor-ligand interaction in colorectal cancer, ovarium cancer, and oxidative stress protein target.

(a) kaempferol 7-O-glucoside-CDK6; (b) quercetin 7-O-glucoside-VEGFR2; (c) 3-(3,4-dihydroxybenzyl)-7-hydroxychroman-4-one-BRK; (d) auriculoside-EGFR; (e) 3-(3,4-dihydroxybenzyl)-7-hydroxychroman-4-one-ERα; (f) auriculoside-mTOR; (g) kaempferol 7-O-glucoside-MOX; (h) keto-teracacidin-GSK3β.

 

The interaction between the ligand-receptors produces several types of atomic interactions, namely hydrogen bonds, Van Der Waals, and interactions involving phi orbitals (Figure 1). Hydrogen bonds become bonds that always exist in the interaction of compounds with receptors. In addition, another unique interaction formed is py-sulfur, which is only found in the 3-(3,4-dihydroxybenzyl)-7-hydroxychroman-4-one-ERα interaction.

Significant fluctuations in the RMSF value for certain amino acids will indicate the movement of amino acids associated with the RMSD receptor at a certain time (Figure 2). When associated with RMSD values, the three receptors with low fluctuations (BRK, GSK3β, and MOX) also had RMSF values that did not experience differences between their amino acids.

 

Figure 2: RMSF value graph of each amino acid residues in eight receptors.

 

A high RMSD value indicates a high level of deviation and a less stable protein. The RMSD values for all receptors ranged from 0 to 4 (Figure 3). The BRK, GSK3β, and MOX receptors have lower ranges of RMSD fluctuations than other receptors. These three receptors have differences in the range of the lowest and highest RMSD values of 2.3518, 2.783, and 1.9535 Å, respectively. Meanwhile, the other four receptors, CDK6, VEGFR2, EGFR, and ERα, had differences in the range of the lowest and highest RMSD values of 3.5861, 3.4133, 3.1300 and 3.7895 Å, respectively.

 

Figure 3: RMSD score at 100 ns simulation on eight receptors

 

The analysis of ensemble docking with 11 receptor conformations in the range 0 – 100 ns showed the effect of the ligand on the BE value. The binding energy of the ligands increased and decreased in each conformation and occurred at all receptors. Based on the position of the graph (Figure 4), the binding energy observed was less than -10kcal/mol at all receptors. There was a compound that had intensively low binding energy values compared to other compounds. This was observed with auriculoside (code: cpd38), which had the lowest binding energy at four receptors (Figure 4).

 

Figure 4: The binding energy graph of selected compounds in ensemble docking at range of 0 – 100 ns. (a) CDK6; (b) VEGFR2; (c) BRK; (d) EGFR; (e) ERα; (f) mTOR; (g) MOX; (h) GSK3β. Cpd02: 4’,7,8-trihydroxyflavanone; cpd11: keto-teracacidin; cpd12: 3-(3,4-dihydroxybenzyl)-7-hydroxychroman-4-one; cpd13: 5,7,2’,5’-tetrahydroxyflavone; cpd14: genistein; cpd17: digitopurpone; cpd19: quercetin; cpd21: kaempferol 7-O-glucoside, cpd24: quercetin 7-O-glucoside, cpd38: auriculoside.

 

The binding energy value varies due to conformational changes during the dynamic simulation in the time range 0-100 ns. Based on Table 5, the lowest mean binding energy value was due to the interaction of kaempferol 7-O-glucoside on BRK. However, auriculoside produced the lowest mean binding energy by interaction with six receptors. The receptor conformation-ligan interactions that result in the lowest BE interaction were varied at each receptor. Molecular docking of the receptor ensemble also showed a difference in the interactions formed in the initial conformation (0 ns) compared to the conformation when the ligand reached the lowest binding energy value.

 

Table 6: Binding energy average score and list of interacted amino acid residue of the lowest binding energy ligand

Receptor	Ligand	Average BE (kcal/mol)	H-bond site
			0 ns	Lowest BE
CDK6	Auriculoside	-6.96 ± 0.48	Ile159, Gln139, Glu11, Thr172, Asp153	Glu51, Arg130, Val132, Arg134, Gly155, Leu156
mTOR	Auriculoside	-8.75 ± 0.27	Tyr94, Gly65, Arg141, Glu137, Lys69, Ala93, Tyr92	Val67, Gln66, Glu137
GSK3β	Auriculoside	-7.43 ± 0.48	Asp98, Asn151, Gln150	Pro101, Ile27, Asn151, Val100
EGFR	Auriculoside	-8.56 ±  0.75	Ala22, Lys45, Asn142, Asp155, Asp137, Arg141	Arg141, Ala22, Asp137, Lys45, Asp155
MOX	3-(3,4-dihydroxybenzyl)-7-hydroxychroman-4-one	-8.31 ± 0.67	Glu235, Tyr327	Tyr327, Glu235, Tyr289, Phe86
ERα	3-(3,4-dihydroxybenzyl)-7-hydroxychroman-4-one	-7.41 ± 0.29	Ile80, Glu17, Gly84	Ile80, Arg88, Glu47, Leu21
VEGFR2	Quercetin 7-O-glucoside	-8.35 ± 0.37	Ile159, Arg161, Glu60	Leu25, Cys104, Glu102, Lys53, Asp180
BRK	Kaempferol 7-O-glucoside	-8.85 ± 0.31	Asn132, Ser88, Cys84	Ile144, Lys35, Ile79, Thr81, His126, Ile125

 

DISCUSSIONS:

This study performed multitargeted docking to obtain a compound capable of targeting more than one protein for cancer treatment and overcoming oxidative stress. CDK-6, VEGFR, and BRK proteins are target proteins in treating colorectal cancer. CDK-6 plays a role in cell cycle progression (phosphorylation of Rb protein), and inhibition of this protein can suppress the growth of COLO320 cells³⁴. VEGFR2 is a receptor of VEGF that has an important role in angiogenesis and specifically regulates differentiation in colon cancer cells (HCT116) ³⁵. BRAF kinase plays a role in cell proliferation and survival, and in the case of colorectal cancer, at least 20% of patients have a BRAF-V600E mutation³⁶. In the case of breast cancer, 70% of patients are ERα positive, and this protein plays a role in cell cycle regulation, thereby increasing the rate of proliferation and metastasis³⁷. The EGFR receptor plays a role in activating signaling steps in cells for cell proliferation, adhesion, and inhibition of apoptosis, and overexpression of these receptors occurs in cases of triple-negative breast cancer³⁸. mTOR is involved in cell growth, the phosphorylation of AKT, and the organization of cells, which are all treatment targets for breast cancer³⁹.

 

Antioxidants are closely related to oxidative stress conditions due to the overproduction of reactive oxygen species. The activities of myeloperoxidase and GSK3β proteins are related to signaling pathways involving reactive oxygen species. Myeloperoxidase, a heme peroxidase protein, catalyzes the production of hypohalous acid from the oxidation of halide/pseudohalide ions with the help of H₂O₂. This hypohalous molecule then interacts with other small molecules to form other reactive oxygen species closely related to oxidative stress conditions⁴⁰. GSK3β is a serine/threonine kinase that, in hyperactivated conditions, is widely associated with inflammation and oxidative stress^41,42.

 

Potential bioactive compounds based on molecular docking are flavonoids (Figure 5). The flavonoid group of compounds has been widely known to have functions related to nutraceuticals, pharmaceuticals, drugs, and cosmetics⁴³. Due to their antioxidant and anti-cancer properties, flavonoids can regulate tumors' inflammatory response and oxidative stress⁴⁴. The glycone forms (auriculoside, quercetin 7-O-glucoside, and kaempferol 7-O-glucoside) and aglycone form (keto-teracacidin) of flavonoids showed good binding activity to the receptor target. This phenomenon was also confirmed in the glycone and aglycone fractions of flavonoids from green tea and kaempferol and its derivatives, which have good antioxidant and anti-cancer activity^45,46. However, there is no information on the antioxidant and anti-cancer activity of the 3-(3,4-dihydroxybenzyl)-7-hydroxychroman-4-one compound. Other studies have shown that homoisoflavonoids, scillascillin, 7-O-methyl-8-demethoxy-3-hydroxy-3,9-dihydropunctatin and loureiriol, showed moderate cytotoxicity against drug-sensitive CCRF-CEM and multidrug-resistant CEM/ADR5000 leukemia cells and strong cytotoxicity against breast and prostate cancer lines^47,48.

 

 

Figure 5: Chemical structure of selected ligand with lowest binding energy

 

The RMSD and RMSF scores describe the stability and flexibility of the protein. The RMSD describes the movement of a conformational molecule, while the RMSF value describes the fluctuation of atoms or residues in the macromolecule, which describes the flexibility of the protein during the simulation time⁴⁹. These two descriptors are important to understand the receptor motion or conformational variation, which is then related to its interaction with the drug candidate or ligand. All proteins used in this study can be categorized as stable, with RMSD values less than four and low fluctuations in the RMSF of amino acid residues.

 

Ensemble docking is a basic study to identify the active compound and the best conformation capable of forming a good interaction with the ligand⁵⁰. Ensemble docking reveals auriculoside as a superior compound as an anti-cancer and antioxidant candidate drug, along with quercetin 7-O-glucoside, kaempferol 7-O-glucoside and 3-(3,4-dihydroxybenzyl)-7-hydroxychroman-4-one. Auriculoside showed the lowest average binding energy values on CDK6, mTOR, and EGFR, indicating good potential as a colorectal and breast anti-cancer. In addition, the potential for regulating oxidative stress of auriculoside was also demonstrated by the lowest mean binding energy values with GSK3β. Because it has the lowest binding energy versus ERα, 3-(3,4-dihydroxybenzyl)-7-hydroxychroman-4-one has also shown potential as a breast anti-cancer candidate compound. The potent oxidative regulation observed from 3-deoxysappanone via the MOX receptor. Meanwhile, kaempferol 7-O-glucoside and quercetin 7-O-glucoside have the potential to act as colorectal anti-cancer active compounds.

 

Based on a previous study, the four compounds with the best activity came from the A. auriculiformis plant. According to liquid chromatography-mass spectrometry, 3-(3,4-dihydroxybenzyl)-7-hydroxychroman-4-one was found in a heartwood methanol extract with a relative abundance of 1.62%¹³. Kaempferol 7-O-glucoside and quercetin 7-O-glucoside were identified in the leaves without specific information about their amount²⁹. Auriculoside has been isolated from the aerial part, but its content was not exactly known³³. In this study, the compounds from A. crassicarpa did not show significant potential. Compared to A. auriculiformis, the information regarding the phytochemical component of A. crassicarpa is limited. Furthermore, extensive exploration is needed.

 

CONCLUSION:

Ensemble docking can predict bioactive compounds with the best binding and inhibition to target proteins in the treatment of cancer and oxidative stress. Auriculoside, quercetin 7-O-kaempferol, kaempferol 7-O-glucoside, and 3-(3,4-dihydroxybenzyl)-7-hydroxychroman-4-one are predicted as anti-cancer and antioxidant active compounds. In particular, auriculoside showed multitarget inhibitory activity against colorectal cancer via CDK6 and breast cancer via EGFR and mTOR. Auriculoside has the potent activity of regulating oxidative stress through inhibition of GSK3β. A potential single protein targeting mechanism was also demonstrated by kaempferol 7-O-glucoside and quercetin 7-O-glucoside against breast cancer. These four compounds are from A. auriculiformis, but the amounts were not completely identified. Moreover, an exploration of the phytochemical compounds of A. crassicarpa is needed to obtain any other bioactive compounds that can have good biological activity. This study shows good potency in flavonoids and their derivatives from the plants A. auriculiformis and A. crassicarpa. As previously stated, this study serves as the foundation for more in-depth investigation using the four potential compounds in a preclinical to specific clinical approach. 

 

LIST OF ABBREVIATIONS:

CDK6: cyclin dependent kinase-6

VEGFR2: vascular endothelial growth factor receptor 2

BRK: B-Raf kinase

EGFR: epidermal growth factor receptor

ERα: estrogen receptor alpha

mTOR: mammalian target of rapamycin

GSK3β: glycogen synthase kinase 3 beta

MOX: myeloperoxidase

BE: binding energy

RMSD: root mean square deviation

RMSF: root mean square fluctuation

Ki: inhibition constant

 

CONFLICT OF INTEREST:

The authors have no conflicts of interest regarding this investigation.

 

ACKNOWLEDGMENTS:

The authors would like to thank the Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia, particularly the Directorate General of Higher Education, for funding this research through the Master Program Leading to Doctoral Degree for Outstanding Students or Pendidikan Magister Menuju Doktor untuk Sarjana Unggul (PMDSU) program.

 

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Accepted on 23.06.2023           © RJPT All right reserved

Research J. Pharm. and Tech 2024; 17(2):707-716.