INTRODUCTION:

Rheumatoid arthritis is a chronic inflammatory and autoimmune disease characterized by the presence of destructive joints, proliferation of synovial cells, inflammation and pannus formation (1). In RA, the subintimal area contains a heavy inflammatory infiltrate, consisting of lymphocytes, macrophages, fibroblasts, dendritic cells , and mononuclear cells contributing to the chronic inflammatory response (2). ROS and RNS may recruit chemotactic factors at the site of inflammation thereby worsening the disease severity (3)(4).

ROS comprises free radicals such as hydroxyl radical (OH⁻), singlet oxygen (¹O2), nitric oxide (NO), superoxide anion (O₂⁻) and hypochlorite ion (OCl⁻) (5). ROS have been implicated as second messengers and are known to regulate various molecular events including gene expression, DNA synthesis and cellular proliferation (6)(7). There is increasing evidence that reactive oxygen species is associated with various diseases and disorders.

Tumor necrosis factor-alpha (TNF-α) is identified as a pivotal cytokine involved in the pathogenesis of RA and has been shown to induce intracellular ROS formation from neutrophils (8). TNF-α is a key cell signaling protein (cytokine) mediating inflammatory events when primed with inflammatory stimuli such as LPS. TNF alpha exerts an anti-inflammatory effect that results in the activation of the transcription nuclear factor kappa B (NF-κB), leading to the production of various inflammatory mediators like cytokines, chemokines, and proteases (9). The overproduction of this cytokine is linked with many inflammatory disorders, including rheumatoid arthritis (10). TNF-α blockers are clinically efficient for treating rheumatoid arthritis; however, the obtainable inhibitors are costly biological drugs that demand non-oral means of administration and do not produce desired result to all patients. Therefore, TNF-α is being considered a key target for the development of new candidate medications to treat inflammatory diseases and the identification of viable lead compounds that may act as antagonists of this cytokine by oral route is presently required.

C. urens is a dietary and traditional medicinal plant in Srilanka used as jaggery. Currently, studies have indicated that the extract of C.urens exerts many bio-activities, including antioxidant, antibacterial, antidiabetic, properties. Our previous study involving LC/MS and HPLC analysis confirmed the presence of umbelliferone and rutin in Caryota.urens leaf hydroalcoholic extract (11). Rutin is a polyphenolic compound and are known to be present in almost all plant species. It is also known as Quercetin rutinoside and shows a wide range of biological activities like hypoglycaemic, antidiabetic, free radical scavenging, antioxidant and anti-inflammatory effect (12). Rutin protects cellular oxidative enzyme machinery as supported by the increased levels of cellular enzymatic and non-enzymatic antioxidants (13). Umbelliferone also known as 7-hydroxy coumarin is a bioactive natural product of the coumarin family. Umbelliferone shows good antioxidant, anti-inflammatory, anti-hyperglycaemic and anti-tumor activities (14). There has been growing interest in the natural product based drug discovery and the intend of this study was to evaluate if rutin and umbelliferone – natural constituents of Caryota urens exerts anti inflammatory activity by performing insilico molecular docking analysis with TNF alpha.

METHOD:

Preparation of plant material:

Plant material collected was washed to remove any dirt and dried for 15 days. The dried plant material was made into coarse powder and was further subjected to extraction process.

Two hundred grams of the leaf powder was soaked in 70% ethanol (1600ml) and distilled water for 3 days with intermittent stirring (15). At the end of the process, the extract was filtered. The filtrate was concentrated by evaporating to dryness and stored at 4°C until when needed.

Protein preparation:

Crystal structure of the complex TNF–alpha (PDB ID-2AZ5) obtained from the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB) (RCSB, www.rcsb.org/) was used as receptor. Structurally 2AZ5 has 148 amino acids with 4 chains A, B, C, D and has a resolution of 2.1Å.

Water molecules and hetero atoms were removed from the protein and missing polar hydrogen bonds were added. To ensure proper formal charge and force field treatment, metal ionization state is corrected and saved as PDBQT using Autodock 4.2.

Prediction of active site:

Detection of active sites in the protein surface is the first step in docking process. Amino acids residues within the active site of TNF-alpha are determined using CASTp (Computed Atlas of Surface Topography of proteins). It is a web based tool that identifies active site pockets, pocket mouth openings and cavities located on protein’s surface. It also defines the area and volume of the pocket.

TNFα with pocket ID of 2, area of 104.507 and a volume of 35.048 was used in this study. The possible binding site residues of TNF alpha identified by CASTp were as follows. ChainA: Val91, Asn92, Leu93, Phe124.ChainB: His15, Val17, Ala18, Pro20, Arg32 Ala33, Asn34, Ala35, Phe144, Glu146, Ser147, Gly148, Gln149 and Val150.

Preparation of ligand:

The chemical structure of HPLC confirmed compounds namely, umbelliferone and rutin and a known inhibitor of TNF alpha-SPD304 were downloaded from Pubchem. Open babel was used to convert canonical smiles to .pdb files. Energy minimization of compounds was done to attain a stable conformation and to eliminate any bond angle and bond length biases. These energy minimized compounds were used as a input for Autodock 4.2.

Molecular docking:

Both the ligand and the TNF alpha prepared were loaded onto Autodock 4.2 platform for docking. AutoDock was designed to predict interactions between ligand and a protein with known three dimensional structure. Autodock performs automated docking of ligand according to grid, while the grids are pre calculated by autogrid. Grid box for docking simulations were set using MGL tool. The grid box parameters was generated to cover all the active site amino acids of the protein and the Grid maps were set using Autogrid 4.2. The grid origin was set at -19.124 74.068 44.059 for x, y and z coordinates respectively.

Once the grid maps were constructed, auto dock was run using Lamarckian genetic algorithm (16). A maximum of 100 iterations were considered for autodock, so as to have all the intreractions of the ligands. Prepared ligands (umbelliferone and rutin) were docked within the defined grid region of the macromolecule. The interactions of the ligands with receptor proteins such as Pi stacking, H-bonding, and hydrophobic were analyzed. The results of the docking simulation and the interaction with the selected ligand molecules are discussed in the following sections.

RESULTS AND DISCUSSION:

Molecular docking is the most frequently used technique to predict interaction between a small molecule drug ligand and a macromolecule. The small molecule inhibitor usually fits within protein’s cavity identified by search algorithm. Monte Carlo stimulation and Lamarckian genetic algorithm is the common algorithm used by autodock. The algorithm enumerates all possible optimal conformations for a protein-ligand complex, predicting the best fit between the receptor and the ligand molecule. Docking analysis ranked the compounds based on the binding energies or fitness score. A complex with the overall minimum energy is considered the best docking pose of ligand with the macromolecule (17).

There are several reports on targeting TNF alpha. The present study screened for the ability of umbelliferone and rutin to inhibit TNF alpha interaction and downstream signaling. The active site amino acids involved in the interaction between TNF alpha and small molecule inhibitors were identified.

Docking analysis:

The docked ligands show binding free energy of -5.09 for rutin and -4.41 for umbelliferone, with the best-docked ligand being rutin. The best docking pose of rutin show 5 hydrogen bonding with the chain A: TYR151; Chain B: SER60, GLY121 and TYR151. Besides these major interactions, other non-bonded interactions were observed at Chain A: LEU57, TYR59, SER60, GLN61, LEU120, GLY122; Chain B: LEU57,TYR59, SER60, GLN61, LEU94, LYS98, TYR119, LEU120, GLY122, ILE155; Chain D: LEU55.

The other ligand umbelliferone, with a dock score of -4.41, showed 1 H-bond with Chain B: PHE144. Besides these major interactions, other non-bonded interactions were observed at ChainA: ASN92; Chain B: VAL17, ALA18, PRO20, SER147, GLN149, TYR151.

A known inhibitor of TNF alpha, SPD-304 was used to validate the docking results. The small molecule inhibitor with a binding free energy of -22.04 showed 1 conventional hydrogen bond with ChainB: LYS98, and 6 carbon hydrogen bond with Chain A:GLY121; Chain B: TYR59, SER60, LEU120, GLY121, TYR151. Other minor interactions were observed with ChainA: TYR59, SER60, GLN61, LYS98, LEU120, TYR151; Chain B: GLN61, PRO117, ILE118, TYR119, GLY121; Chain D: LEU55.

3D pose of the ligand bound protein was visualized using pymol. Discovery studio was used to deduce 2D interactions. The results of the molecular docking and interactions of ligand (C.urens compounds) with TNF-alpha protein are listed in Table 1. The 3D and 2D representation of the best-scored pose of rutin, umbelliferone and SPD-304 with TNF-α is shown in Figure1, 2 and 3 respectively.

Interaction analysis showed, that rutin formed 6 hydrogen bond interaction with TNF alpha whereas umbelliferone had only 1 hydrogen bond. Detailed analysis of interacting residues revealed that all the inhibitors interacted with TYR151, critical amino residue of TNF alpha. The interactions between rutin and TNF alpha were similar to that of SPD 304(18). Our findings are comparable to the previous representations by various authors (19)(18).

The key residues/common binding residues of the inhibitors involved in the binding were TYR119 and TYR151. These key residues of TNF alpha interact with inhibitors and may prevent the formation of TNF-alpha trimer. The active form of TNF-alpha is a trimer, mediates its action by binding to TNF receptor(20) and activates the downstream signaling pathways leading to inflammation (21). Thus the present data of pharmacophore modeling confirms that rutin and umbelliferone that are naturally abundant in C.urens may act as inhibitors of TNF alpha and thus serve as an interesting stratergy for controlling arthritis.

CONCLUSIONS:

Our insilico predictions confirmed the potential of polyphenols (rutin and umbelliferone) in C.urens to dock with TNF alpha. The ligands showed low binding free energy, indicating that the docked conformers were in their most favorable conformation. Our results also suggest the presence of rutin and umbelliferone in C.urens could be responsible for its anti inflammatory activity. Further assays are in progress to understand its anti arthritic activity. The results presented here may guide to develop novel inhibitors, that helps in dissociation of one of the sub units of TNF alpha and thereby inhibit protein function.

ACKNOWLEDGEMENT:

The authors are grateful to VISTAS, Pallavaram for the facilities.

CONFLICT OF INTEREST:

Authors declare no conflict of interest

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Received on 19.11.2019 Modified on 31.12.2019

Research J. Pharm. and Tech 2020; 13(9):4269-4273.

DOI: 10.5958/0974-360X.2020.00753.2

Ligand	Binding energy (ΔG) [kcal/mol]	Residues mediated by hydrogen bonds	Residues mediated by non-bonded contacts	No. of H-bond
Rutin	-5.09	Chain A: TYR151; Chain B: SER60, GLY121and TYR151.	Chain A: LEU57, TYR59, SER60, GLN61, LEU120, GLY122; Chain B: LEU57,TYR59, SER60, GLN61, LEU94, LYS98, TYR119, LEU120, GLY122, ILE155; Chain D: LEU55	5
Umbelliferone	-4.41	Chain B: PHE144	ChainA: ASN92; Chain B: VAL17, ALA18, PRO20, SER147, GLN149, TYR151	1
SPD-304	-22.04	Conventional hydrogen bond with ChainB: LYS98, and 6 carbon hydrogen bond with Chain A:GLY121; Chain B: TYR59, SER60, LEU120, GLY121, TYR151.	ChainA: TYR59, SER60, GLN61, LYS98, LEU120, TYR151; Chain B: GLN61, PRO117, ILE118, TYR119, GLY121; Chain D: LEU55	1