In silico Screening of the Potential Colorectal Cancer Inhibitor from Okra Pods Extract and Iron Nanoparticle (FeNP)

 

Rangga Adhi Prastika, Suhailah Hayaza, Azka Muhammad Nurrahman,

Raden Joko Kuncoroningrat Susilo

Nanotechnology Engineering, Faculty of Advanced Technology and Multidiscipline,

Universitas Airlangga, Surabaya 60115, Indonesia.

 

ABSTRACT:

Colon cancer is a deadly cancer that affects the colorectal region. This cancer is the second highest leading cause of cancer related death just behind lung cancer. According to the World Health Organization (WHO) there are around 1,9 million new colon cancer cases and more than 930 thousands deaths caused by colon cancer during 2020. Conventional methods to treat colon cancer, such as operation, chemotherapy and radiotherapy have drawbacks. One potential alternative to treat colon cancer is to target the Human Uridine Phosphorylase 1 (hUPP1) enzyme which plays a role in the growth of colon cancer. The use of nutraceutical products from okra pods extract conjugated with various FeNP is predicted to inhibit the growth of colon cancer. This research aims to analyze the nature of active compounds from okra fruit as well as the conjugation effect of various FeNP. Methods performed in silico with drug-likeness approach, molecular docking, and molecular dynamics simulation. In drug-likeness, addition FeNP have affected pharmacokinetics properties of the compounds where the addition of Fe, FeO, and Fe₃O₄ demonstrated better antineoplastic and anti-inflammatory (intestinal) activity compared to Fe₂O₃. Molecular docking shows that all compounds have qualified the lipinski rules with binding energy and RMSD that indicated strong and stable interactions against hUPP1 enzyme. Molecular dynamic simulation and MM-GBSA shows great stability and a consistent interaction between okra pods extract compounds toward hUPP1 enzyme during simulation. This result shows that compounds from okra pods extract can potentially be used to treat colon cancer.

 

 

INTRODUCTION: 

Colon cancer is a deadly cancer that affects the colorectal region. This cancer is the second highest leading cause of cancer related death just behind lung cancer¹. According to the World Health Organization (WHO) there are around 1,9 million new colon cancer cases and more than 930 thousands deaths caused by colon cancer during 2020.

 

Several common symptoms of colon cancer are abdominal pain, rectal bleeding, diarrhea, constipation and anemia². Conventional methods to treat colon cancer, such as operation, chemotherapy and radiotherapy have drawbacks³. Operation is an invasive procedure, while chemotherapy and radiotherapy are not effective because cancer cell can survive from drug exposure through several mechanisms⁴. Therefore, there needs to be a new alternative for colon cancer treatment.

 

Biomarker targeting is an alternative that can be used to treat colon cancer. A few examples of colon cancer biomarkers are EGFR, KRAS, NRAS, BRAF and HER2 receptor⁵. Enzyme Human Uridine Phosphorylase 1 (hUPP 1) is an enzyme that has the potential to become a colon cancer biomarker⁶. This enzyme has a role in managing uridine homeostasis and pyrimidine metabolism for colon cancer. Not only that, this enzyme is an oncogene of colon cancer and has a role for colon cancer growth⁷.

 

Natural compounds have become a strong alternative to conventional cancer drugs. Natural compounds can come from animals or plants. An example of an animal containing compounds to treat is Arbacia lixula, which have compounds capable of inhibiting the growth of non-small cell lung cancer.  There are several plant that have bioactive compounds with potential for cancer treatment, such as Pinus merkusii and Rubia tinctorum^8,9. Other than that, there have been researches conducted on the use of bioactive compound to treat colon cancer. Fadholly et al. (2022) demonstrated that Nangirin, a compound from citrus species is capable of inhibiting colon cancer growth by inducing apoptosis through caspase-3 expressions¹⁰. Abelmoschus esculentus or okra has a potential of becoming an alternative colon cancer drug. Okra have several medical properties, such as antioxidant, antineoplastic, antitumor and antimicrobial^11,12. The antitumor properties of okra are due to the flavonoid content it has, which constitutes 12.54% of the total compound of okra¹³. Flavonoids are capable of disrupting the growth of colon cancer by breaking down the mitochondria through apoptosis, senescence and autophagy¹⁴. Active compounds of okra can also be found in all of its parts like its pods.

 

A common problem encountered for plant extracts is poor stability in the biological environment and incapable of reaching the delivery site due to the metabolic system and poor solubility. Several ways to solve this problem is by using plant extract to synthesize metal nanoparticles or by conjugating it with metal nanoparticles. Metal nanoparticles help with biomarker targeting and can provide a gene silencing effect¹⁵. Iron nanoparticle (FeNP) is a metal nanoparticle which has a great ability to target cancer cells and cause apoptosis. FeNP typically exists in oxide forms, such as ferrous oxide (FeO), magnetite (Fe₃O₄), maghemite (γ-Fe₂O₃) and hematite (α-Fe₂O₃). Different forms of FeNP have different properties. For example, magnetite and maghemite are ferromagnetic while hematite is not magnetic. In general, FeNP is shown to exhibit excellent anticancer properties to various types of cancer, such as liver cancer, brain tumors and prostate cancer^16,17. FeNP exhibits anticancer properties to several mechanism. FeNP plays a major role in the production of Reactive Oxygen Species (ROS) which can induce oxidative stress in cancer cell¹⁸. FeNP can also induce ferroptosis which is an iron-dependent, oxidation-regulated cell death by the accumulation of peroxide lipids within the cell. Another metal nanoparticle conjugation can be studied with in silico approach. In a research conducted by Katsipis et al., (2021), curcumin was conjugated with gold nanoparticles using in silico approach to study its anticancer properties^19,20. The result of in silico can be a strong foundation on the development of in vitro and in vivo methods. In this research, in silico will be conducted to determine the properties and the inhibition mechanism of okra pods extract compounds conjugated with FeNP towards hUPP 1 enzyme for colon cancer treatment.

 

MATERIALS AND METHODS:

In this research, the preparation of biomarker compounds was carried out by geometry optimization based on Avogadro software. Drug-likeness test was performed on the site at https://www.way2drug.com/passonline/index.php.  All ligand models were obtained from the pubchem database at https://pubchem.ncbi.nlm.nih.gov/ and RCSB PDB receptor model database with ID 3NBQ at https://www.rcsb.org/ as reference sequence Q16831 UniProtKB Data Base (https://www.uniprot.org/). The parameter models were optimized by adding hydrogen atoms, remove water, and partical charge using the AM1-BCC method within the AMBER ff14SB force field. Molecular docking tests were performed using Autodockvina with integrated in UCSF Chimera v 1.17.3.  Therefore, Lipinski's rule of five (RO5) validation at http://www.scfbio-iitd.res.in/software/drugdesign/lipinski.jsp, receptor binding pocket preparation at https://prankweb.cz/. In addition, UCSF Chimera and Discovery Studio Visualizer software were used to prepare ligand and receptor interactions. In the final step, Molecular Dynamic Simulation tests were conducted using GROMACS 2023.2 and MM-GBSA approach.

 

RESULT AND DISCUSSION:

Preparation of Marker Compound in Okra Pod Plant Extract and FeNP Addition:

Marker compound preparation is a major process in drug research that aims to identify, separate, and purify active compounds from plant extracts or other natural materials. In this study, referring to previous studies, it contains a variety of active compounds possessed by okra pod plant extracts^21,22, then compiled into a table 1. Based on this research, optimization was carried out with addition of FeNP. This optimization aims to obtain a more stable molecular structure configuration and lower energy after binding with FeNP. Optimization process involves computational calculations to minimize the potential energy of the molecular system so as to obtain the most thermodynamically stable structural conformation^23,24.

 

 

 

Table 1. Structure Parameters of Phenolic Compound Content in Okra Pod Extract and FeNP Addition

Compound	Formula ID	Formula	Formula Optimization (kJ/mol)	Formula Complex				Formula Complex Optimization (kJ/mol)
				Fe	FeO	Fe2O3	Fe3O4	Fe	FeO	Fe2O3	Fe3O4
2'-O-Methyl Isoliquiritigenin	5319688	C16H14O4	1687.94	C16H13FeO4	C16H13FeO5	C16H13Fe2O7	C16H13Fe3O8	1684.62	1685.26	1682.81	1863.79
Caffeic acid	689043	C9H8O4	1764.4	C9H6FeO4	C9H6FeO5	C9H6Fe2O7	C9H6Fe3O8	2055.79	1832.29	1897.55	2074.15
Ferulic acid	445858	C10H10O4	1778.12	C10H9FeO4	C10H9FeO5	C10H9Fe2O7	C10H9Fe3O8	1776.1	1775	1779.24	1957.52
Kaempferol	5280863	C15H10O6	2193.57	C15H9FeO6	C15H9FeO7	C15H9Fe2O9	C15H9Fe3O10	2190.26	2189.89	2187.86	2368.53
Quercetin	5280343	C15H10O7	2205.11	C15H8FeO7	C15H8FeO8	C15H8Fe2O10	C15H8Fe3O11	2488.13	2264.58	2586.03	3392.12

 

Table 2. Antineoplastic and Anti-inflammatory Activity (Intestinal) of Okra Pod Extract and FeNP Addition via PASS Score

Compound	Antineoplastic Activity				Anti-inflammatory (Intestinal) Activity
	Colorectal Cancer		Colon Cancer
	Pa	Pi	Pa	Pi	Pa	Pi
2'-O-Methyl isoliquiritigenin	0.375	0.025	0.364	0.024	0.653	0.003
Fe-2'-O-Methyl isoliquiritigenin	0.233	0.052	0.233	0.05	0.587	0.004
FeO-2'-O-Methyl isoliquiritigenin	0.233	0.052	0.233	0.05	0.587	0.004
Fe2O3-2'-O-Methyl isoliquiritigenin	0.178	0.074	0.17	0.07	0.557	0.004
Fe3O4-2'-O-Methyl isoliquiritigenin	0.233	0.052	0.233	0.05	0.587	0.004
Caffeic acid	-	-	-	-	0.648	0.003
Fe-Caffeic acid	-	-	-	-	0.512	0.006
FeO-Caffeic acid	-	-	-	-	0.54	0.005
Fe2O3-Caffeic acid	-	-	-	-	0.512	0.006
Fe3O4-Caffeic acid	-	-	-	-	0.512	0.006
Ferulic acid	0.194	0.067	0.176	0.068	0.661	0.003
Fe-Ferulic acid	-	-	-	-	0.596	0.004
FeO-Ferulic acid	-	-	-	-	0.596	0.004
Fe2O3-Ferulic acid	-	-	-	-	0.565	0.004
Fe3O4-Ferulic acid	-	-	-	-	0.596	0.004
Kaempferol	0.275	0.041	0.265	0.04	0.312	0.062
Fe-Kaempferol	0.143	0.094	0.135	0.089	0.274	0.093
FeO-Kaempferol	0.143	0.094	0.135	0.089	0.274	0.093
Fe2O3-Kaempferol	-	-	-	-	0.255	0.133
Fe3O4-Kaempferol	0.143	0.094	0.135	0.089	0.274	0.093
Quercetin	0.287	0.039	0.276	0.037	0.303	0.068
Fe-Quercetin	0.122	0.111	0.116	0.104	0.211	0.171
FeO-Quercetin	0.174	0.076	0.167	0.072	0.229	0.146
Fe2O3-Quercetin	0.122	0.111	0.116	0.104	0.211	0.171
Fe3O4-Quercetin	0.122	0.111	0.116	0.104	0.211	0.171

 

Drug-likeness Test via In Silico on Okra Pod Plant Extract and FeNP Addition:

Drug-likeness test is a method used in drug research to predict how "drug-like" a compound has the potential to become an effective and safe drug. The predicted activity of the drug-likeness test results in table 2., shows the antineoplastic and anti-inflammatory (intestinal) activity of the okra pod plant extract and the addition FeNP. A compound is considered experimentally active if the Pa value is greater than Pi (Pa>Pi)²⁵. Although the interpretation of Pa values less than 0.3(Pa<0.1) signifies experimentally low biological activity, it can also indicate the presence of biological activity that can occur. The effect of FeNP addition on antineoplastic and anti-inflammatory (intestinal) activity varies depending on the bioactive compound used. In some compounds, the addition of FeNP can reduce or increase antineoplastic and anti-inflammatory (intestinal) activity. For example, addition Fe, FeO and Fe₃O₄ have better activity compared with Fe₂O₃. This can be caused by several factors, such as interactions, structure change, Fe oxidation forms affect the biological activity of compounds, or even form new complexes with different activities. In addition, the addition of an organic or inorganic compound can also affect the bioavailability and pharmacokinetics of bioactive compounds in biological systems^26,27.

 

Molecular Docking Test on Okra Pod Plant Extract and FeNP Addition:

In silico molecular docking tests essentially predict the probability of success or failure of a molecule in drug development based on its drug properties^28,29. Obviously, the molecular docking test was conducted in accordance with Lipinski's rules of five (Ro5) such as molecular weight (MW) <500g/mol, log P<5, RB<10, polar surface area (PSA) <140, and Hb acceptors <10 and Hb donors <5. This rule states that molecules that obey two or more of the five rules have a high probability of having good drug properties, such as adequate absorption and permeability^30,31. In this research (Table 3), all compounds have qualified the Lipinski rule. Addition of FeNP increases the Molecular Weight (MW) due to addition of the mass Fe or O atoms in the compound, while the increased polarity due to interaction with Fe decreases the log P value. These changes can affect the pharmacokinetic properties of the compounds, such as solubility in the body, which is important in drug applications.

 

Prediction of ligand binding sites on protein structures has been widely practiced in various fields, especially in scientific research and drug development. Prankweb prediction-based approaches can identify potential binding sites on target proteins both from the structure of the protein, geometric, region, and physicochemical amino acids of a protein³². Based on Figure 1a, prankweb prediction shows several possible regions as binding pockets. Interestingly, each binding pocket shows a different amino acid placement. In this research, pocket 1 was used as the active site. This is based on the higher probability score as the active site compared to the other binding pockets. In addition, a high pocket score also indicates that the pocket has a suitable size, shape, and chemical properties for ligand binding, which is the hallmark of an active site^33,34. In addition, the mechanism of molecular docking (Figure 1b), showed bioactive compounds can interact with hUPP1 enzyme in binding active site

 

Table 3. Phenolic Compound Test Results of Okra Pod Extract and FeNP Addition via Lipinski RO5

Compound (pH>7)	MW	Hb Donor	Hb Acceptors	Log P	PSA	Drug-like Molecule
2'-O-Methyl Isoliquiritigenin	270	2	4	3.002	76.130	Yes
Fe-2'-O-Methyl Isoliquiritigenin	288	1	4	2.257	74.659	Yes
FeO-2'-O-Methyl Isoliquiritigenin	304	1	4	2.104	74.659	Yes
Fe2O3-2'-O-Methyl Isoliquiritigenin	355	1	4	1.646	74.659	Yes
Fe3O4-2'-O-Methyl Isoliquiritigenin	390	1	4	1.341	74.659	Yes
Caffeic acid	180	3	4	1.196	46.441	Yes
Fe-Caffeic acid	197	1	4	0.109	44.013	Yes
FeO-Caffeic acid	213	1	4	-0.044	44.013	Yes
Fe2O3-Caffeic acid	264	1	4	-0.501	44.013	Yes
Fe3O4-Caffeic acid	299	1	4	-0.806	44.013	Yes
Ferulic acid	194	2	4	1.499	51.329	Yes
Fe-Ferulic acid	212	1	4	0.792	49.528	Yes
FeO-Ferulic acid	228	1	4	0.639	49.528	Yes
Fe2O3-Ferulic acid	279	1	4	0.182	49.528	Yes
Fe3O4-Ferulic acid	314	1	4	-0.123	49.528	Yes
Kaempferol	286	4	6	2.305	72.386	Yes
Fe-Kaempferol	304	3	6	1.559	70.915	Yes
FeO-Kaempferol	320	3	6	1.407	70.915	Yes
Fe2O3-Kaempferol	371	3	6	0.949	70.915	Yes
Fe3O4-Kaempferol	406	3	6	0.644	70.915	Yes
Quercetin	302	5	7	2.011	74.05	Yes
Fe-Quercetin	319	3	7	0.924	71.622	Yes
FeO-Quercetin	335	3	7	0.772	71.622	Yes
Fe2O3-Quercetin	386	3	7	0.314	71.622	Yes
Fe3O4-Quercetin	421	3	7	0.009	71.622	Yes

 

Figure 1. (a) Binding pocket active site on hUPP1 enzyme (b) Schematic representation of ligand complex in molecular docking test

Table 4. Detail of binding pocket in hUPP1 enzyme

Pocket Rank	hUPP 1 (PDB:3NBQ)
	1	2	3
Pocket score	24.03	4.26	1.38
Probability score	0.876	0.188	0.017
Amino acid count	20	14	6
Amino acid residue	GLY60, SER61, ARG64, HIS108, GLY109, MET110, ARG138, ILE139, GLY140, THR141, SER142, GLY143, PHE213 ,GLN217, GLU248, MET249 ,GLU250, LEU272. ILE281 ,GLN292	LEU21, LEU22, ASN23, SER116, HIS120, ILE123 LYS124, PHE167 GLN169, ARG178, VAL253, ALA256 MET257, CYS261	GLU215, GLY216, GLY218, TYR227, ASP231, TYR235

 

Visualization of molecular docking (Figure 2), indicated interaction of bioactive compounds in okra pod plant extract against hUPP 1 enzyme. Interaction can be reviewed through the binding energy and RMSD. Binding energy is a value of the total bond energy required or generated during the process of complex formation between ligand and receptor. The more negative of the binding energy, the stronger interaction between ligand and receptor^35,36. Furthermore, RMSD in molecular docking must be <2Å, indicating that the predicted compound pose closely matches the experimental for evaluated reliability and accuracy³⁷. Binding energy has a correlation with RMSD value in molecular docking results, where the addition of FeNP produces different energy variations according to the scoring function. Interactions between compound and FeNP are influenced by several complex factors, including dielectric constant that affects electrostatic interactions, geometric structure and charge distribution on the nanoparticle surface^38–40. Each compound can show different interaction patterns with FeNP depending on its chemical structure and ability to form specific interactions with the nanoparticle surface, which ultimately determines drug loading efficiency and release characteristics. Furthermore, amino acid residues such as THR141, SER142, PHE213, GLN217, MET249, and GLU250 are residues that are often involved in interactions with bioactive compounds from okra pod plant extracts and the addition of FeNP.

 

Compound		Fe	FeO	Fe2O3	Fe3O4
2'-O- Methyl isoliquiritigenin	BE = -7.6, RMSD = 1.417	BE = -7.0, RMSD = 1.381	BE = -7.1, RMSD = 1.392	BE = -6.7, RMSD = 1.223	BE = -7.7, RMSD = 0.839
Caffeic acid	BE = -6.3, RMSD = 1.419	BE = -6.8, RMSD = 1.294	BE = -6.0, RMSD = 1.81	BE = -6.7, RMSD = 1.545	BE = -6.8, RMSD = 1.717
Ferulic acid	BE = -6.1, RMSD = 1.288	BE = -6.1, RMSD = 1.615	BE = -6.1 RMSD = 1.215	BE = -6.6 RMSD = 1.77	BE = -6.9, RMSD = 1.279
Kaempferol	BE = -7.4, RMSD = 1.76	BE = -7.7, RMSD = 1.257	BE = -8.2, RMSD = 1.447	BE = -7.1, RMSD = 1.784	BE = -7.7, RMSD = 0.623
Quercetin	BE = -7.4, RMSD = 1.284	BE = -7.7, RMSD = 1.381	BE = -7.7, RMSD = 1.937	BE = -7.9, RMSD = 1.657	BE = -8.2, RMSD = 1.564

*BE = Binding Energy (kcal.mol^-1)

Figure 2. Visualization of Molecular Docking for Bioactive Compounds from Okra Pod Extract and FeNP Addition against hUPP 1 enzyme

 

 

Figure 3. Superposition of Bioactive Compound of Okra Pod Plant Extract against hUPP 1 enzyme (a)2’-O-Methylisoliquiritigenin (b)Caffeic Acid (c)Ferulic Acid (d)Kaempferol (e)Quercetin.

 

 

Figure 4. Virtual MDs on Complex Ligands (a) 2’-O-Methylisoliquiritigenin (b)Caffeic Acid (c)Ferulic Acid (d)Kaempferol (e)Quercetin

 

Molecular Dynamics Simulation (MDs) Test on Okra Pod Plant Extracts against hUPP 1

Groningen Machine for Chemical Simulations (GROMACS) is a molecular dynamics simulation software used to model the behavior and dynamics of molecules in chemical and biological systems^41,42. The molecular dynamics simulation superposition can be seen in Figure 3, which includes five bioactive compounds of okra pods such as 2'-O- Methylisoliquiritigenin, caffeic acid, ferulic acid, kaempferol, and quercetin on the active site of the hUPP1 enzyme. The red colored lines show the orientation and active position of each ligand in the enzyme binding site formed.

 

Virtual Molecular Dynamics (VMD) is a molecular visualization software used for observation and analysis of molecular dynamics simulation results⁴³. Based on Figure 4, the ligand complex refers to the interaction between the ligand and the target receptor, namely the hUPP 1 enzyme. The structure of each ligand is shown in stick representation.

 

Figure 5. The RMSD of the backbone conformation is shown as a function of 50 ns

 

Figure 6. The RMSF of the backbone conformation is shown as a function of 50 ns

 

Based on the RMSD analysis graph in Figure 5, it can be observed that the RMSD values of the backbone protein indicate stability throughout the 0-50 ns simulation. Higher RMSD values correspond to greater deviations from the initial structure^44,45. Based on the RMSD analysis graph in Figure 5, it can be observed that the RMSD values for all compound-protein complexes experience a significant increase at the beginning of simulation, which is around 0 to 2 nanoseconds (ns). This increase indicates a large adjustment and movement in the position of the atoms in the compound-protein complex at the start of the simulation. In addition, these adjustments also occur as the system attempts to achieve a more stable conformation after being initiated from the initial structure obtained from molecular docking. Therefore, this RMSD graph is used to monitor the stability and flexibility of the complex ligand structure during the molecular dynamics simulation.

 

Root Mean Square Fluctuation (RMSF) is an analysis used to measure the average fluctuation rate of each protein residue with respect to its structural average during the molecular dynamics simulation period. This high fluctuation reflects the flexibility and dynamic nature of the protein parts during the simulation. High fluctuations in certain regions can also affect the function and activity of the protein, such as in the process of ligand binding or interaction with other proteins^46,47. Regions that show high fluctuations can be attractive targets for drugs because the presence of flexibility can affect interactions with ligands or drug molecules^48,49. Based on Figure 6, the threshold RMSF of hUPP 1 enzyme is in the range of 0 nm to 0.35 nm which is identified as an indicator of significant changes in residue flexibility. The similar RMSF patterns for all five ligands suggest consistency in molecular interactions between hUPP1 enzyme and various active compounds from okra pod plant extracts.

 

Figure 7. Intermolecular hydrogen bonds along 50 ns simulation time

 

Hydrogen bonding serves to determine and understand the affinity and specificity of ligand binding to the target protein^50,51. The interaction that occurs in Figure 7, shows significant results with the results of molecular docking test. This is because the binding that occurs between the ligand and the hUPP1 enzyme involves the presence of conventional hydrogen bonds between amino acid residues in the enzyme and ligand.

 

Figure 8. Analysis along simulation time; (a)temperature (b) solvent‑accessible surface area (SASA)

 

Figure 9. Per-residue energy decomposition profiles using the MM-GBSA approach at 35-40 ns; (a)2'-O-Methyl isoliquiritigenin_hUPP1, (b)Caffeic Acid_hUPP1, (c)Ferulic Acid_hUPP1, (d)Kaempferol_ hUPP1, (e)Quercetin

 

Table 5. MM-GBSA energy components (kcal·mol⁻¹) over 35-40 ns, reported as standard error of the mean  (SEM).

Component	ΔEvdw	ΔEele	ΔGgas			ΔGsolv	ΔGbind
2'-O-Methyl isoliquiritigenin_hUPP1	-11.84 ± 0.05	-23.92 ± 0.05	-35.75 ± 0.07	27.24 ± 0.03	-2.35 ± 0.00	24.89 ± 0.03	-10.86 ± 0.08
Caffeic Acid_hUPP1	-23.33 ± 0.01	3.60 ± 0.011	-19.73 ± 0.12	12.57 ± 0.02	-3.47 ± 0.00	9.10 ±0.02	-10.63 ± 0.l2
Ferulic Acid_hUPP1	-10.60 ± 0.04	-16.14 ± 0.09	-26.75 ± 0.10	17.30 ± 0.03	-2.18 ± 0.00	15.12 ± 0.04	-11.63 ± 0.11
Kaempferol_ hUPP1	-22.25 ± 0.06	-24.64 ± 0.25	-48.88 ± 0.26	27.47 ± 0.01	-2.82 ± 0.00	24.65 ± 0.01	-22.23 ± 0.26
Quercetin_ hUPP1	-16.68 ± 0.03	-11.02 ± 0.28	-27.69 ± 0.28	23.23 ±0.03	-2.58 ± 0.00	20.66 ± 0.03	-7.04 ± 0.28

 

In addition, Figure 8 part (a-b), shows the stability and equilibrium of the complex ligand system in terms of several parameters that occur during the MD analysis process. Temperature analysis (Figure 8a), fluctuating from a range of 292 K to 307 K. This can occur due to several factors such as particle speed in maintaining temperature, conformational changes during simulation, to solvation and desolvation processes in water molecules around the ligand complex^52–54. The existence of temperature fluctuations in this study indicates success in molecular dynamics simulation, the system shows fluctuations appear consistent in the ligand complexes studied without significant deviations for certain compounds.

The MM-GBSA calculations provide the overall energetic contributions governing the binding affinity of each ligand against hUPP1. These calculations were based on the analysis of molecular dynamics trajectories, including RMSD, hydrogen bond interactions, and SASA for solvent exposure. Among the ligands (Table 5), Kaempferol_hUPP1 displayed the most favorable binding free energy (ΔG_bind = -22.23 kcal·mol^-1), whereas Quercetin_hUPP1 showed the least favorable value (-7.04 kcal·mol^-1). These findings are consistent with the residue-level decomposition profiles (Figure 9), which highlight critical amino acid residues contributing to ligand stabilization within the binding pocket.

Solvent-Accessible Surface Area (SASA) analysis plays an important role in analyzing the interaction between hUPP1 compounds and enzymes to the surrounding solvent molecules. Stable proteins usually have a more compact structure, with strong internal interactions. In complex surface dynamics simulations, these interactions play an important role in determining the accessibility of the solvent to the molecular surface, which in turn affects the SASA calculation^55,56. In addition, an increase in the SASA value during the simulation indicates structural relaxation and a decrease in protein stability. A more compact structure means a smaller surface area is exposed to the solvent, resulting in a lower SASA value⁵⁷. Figure 8b, shows the fluctuation of SASA for various ligand complexes during the 2 ns simulation, with values ranging from 155-165nm².

 

CONCLUSION:

Bioactive compounds in okra pod plant extracts can be potent therapeutic agents. Drug-likeness test showed that all types of FeNP conjugation are predicted to produce drug-like molecules as proven by the lipinski rules of 5. Other than that, the addition of FeNP conjugation is shown to affect the pharmacokinetics properties of okra pods extract. The addition of FeNP is shown to produce molecule with antineoplastic and anti-inflammatory (intestinal) activity in nearly all molecule except caffeic acid and ferulic acid. Fe, FeO, and Fe₃O₄ demonstrated better antineoplastic and anti-inflammatory (intestinal) activity compared to Fe₂O₃. Molecular docking also predict a binding affinity between FeNP and all the bioactive molecules as shown by the binding energy values. The MDs results showed the stability and consistency of the interaction between the active compounds of okra pods extracted against hUPP1 enzyme during the simulation. In addition, the MM-GBSA binding free energy (ΔG_bind) values further supported these observations, ranging from approximately -7.0 to - 22.2 kcal·mol⁻¹ across the tested compounds. Notably, Kaempferol_hUPP1 exhibited the most favorable binding energy (ΔG_bind = -22.23 ± 0.26 kcal·mol-1), while Quercetin_hUPP1 showed the least (ΔG_bind = -7.04 ± 0.28 kcal·mol^-1). This indicates that the potential of these compounds can be used as inhibitory agents of the hUPP 1 enzyme involved in the development of human colon cancer through the hUPP1 enzyme. However, this research is limited to in silico analysis without additional in vitro or in vivo experiments for confirmation, therefore hopefully further research is necessary.

 

FUNDING STATEMENT:

This study supported by Universitas Airlangga, Indonesia.

 

CONFLICT OF INTEREST:

The authors have no conflicts of interest regarding this investigation.

 

ACKNOWLEDGMENTS:

The authors would like to thank the Universitas Airlangga for providing financial support for this research.

 

DATA ACCESS STATEMENT:

Data supporting the research findings is available from the corresponding author upon reasonable request.

 

AUTHOR CONTRIBUTION:

All authors contributed and proofread the final manuscript.

 

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Received on 21.09.2024      Revised on 18.01.2025 Accepted on 29.03.2025      Published on 05.09.2025 Available online from September 08, 2025 Research J. Pharmacy and Technology. 2025;18(9):4215-4224. DOI: 10.52711/0974-360X.2025.00606 © RJPT All right reserved
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