Insilico Modelling Of Drugs Against Fungal Enzymes Biology Essay

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The main objective of this paper is to find out the drugs which have antifungal activity. Fungal diseases have always created a lot of problems in both humans and plants. It has become important to find drugs which can help us overcome these problems. Fenugreek is a food with traditional use in inflammation. Hence it is the most potential source of antifungal drugs due its varied potentiality. The main aim of the study is to find out the function of Diosgenin, a plant saponin, as a potential antifungal drug. The receptor molecules were five fungal enzymes derived from various fungal sources. For In-silico modelling we used software known as AUTODOCK. The ligand Diosgenin showed minimum binding energy (-8.37 kcal/mol) with Pectin Methylesterase (PDBID 1XG2), (-8.18 kcal/mol) with Native Cel7A (PDBID 1Q9H), (-7.28 kcal/mol) with Endoxylanase 11a (PDBID 1H1A) and (-6.17 kcal/mol) with Ferullic acid esterase (PDBID 1USW). The drug interacted with many amino acids of which Glutamate and Asparagine were found to occur commonly. From the above results we can conclude that plant saponin Diosgenin has potential to act as a antifungal drug against fungal enzymes.

KEYWORDS: Plant saponins , fungal enzymes, Diosgenin, In-silico modelling, Autodock.

INTRODUCTION

During the past two decades, there has been a dramatic rise in the incidences of life-threatening systemic fungal infections to both humans and plants. What makes finding a cure more difficult is the increasing antibiotic resistance of fungal pathogens. Many sources have been explored to find anti fungal drugs.Plant saponins are widely distributed amongst plants and have a wide range of biological properties [5]. One such potential source of antifungal drugs is Trigonellafoenum-graecum i.e. fenugreek. Fenugreek has been known since ancient times for its anti-inflammatory properties. The component of fenugreek important to study anti fungal activity is saponins which have a wide range of activity ranging from anti-carcinogenic to anti-viral.All these are thought to be related to surfactant function but have not been proved yet. All the fungal enzymes under study are Hydrolase producers. Hydrolase is an enzyme which catalyses the reaction of breaking down of molecules by cleavage of chemical bonds for example hydrogen bonds of various molecules such as proteins, nucleic acids,starch,cellulose, fats etc. This enzyme is very important for fungal pathogens to establish themselves and cause a disease. The main aim of this paper is to find out the possible anti fungal activity of plant saponins against specific fungal enzymes. The plant saponin under study in this paper is DIOSGENIN. Various fugal enzymes are considered from different fungal sources. This can be accomplished by docking the drug i.e. the saponins against the fungal enzymes using in-silico modelling. In silico modelling is a computational technique wherein interaction between two molecules is studied. This is done by using software called AUTODOCK and the process of interaction is called docking. We applied Autodock 4.2 software to check possible interactions .Docking indicates the ideal binding of the drug to the molecule forming a stable complex.The energy minimized ligands and the chosen receptor protein was subjected to docking using the Auto Dock tool and the compounds having the least binding affinity were sorted out.The putative geometries of the ligandâ€"enzyme complexes were then obtained by docking program [6]. This docking program is called as PYMOL. The interactions between drug and ligand can be physically seen using this software. Over the last years the PyMOL molecular graphics system [2] has evolved from being a powerful molecular viewer with exceptional 3D-capabilities into a platform for several programs and applications which make use of PyMOL’s versatile visualization properties.[4] It tells about the activity of the drug. This is a upcoming area and is extensively used and has given satisfactory results.

In the present paper we have studied the interaction of the plant saponin: Diosgenin against some specific fungal enzymes showing hydrolase activity.

FIGURE 1 : STRUCTURE OF DIOSGENIN

2. MATERIALS

LIGAND:-

The ligand used in this docking is Diosgenin. Diosgenin is a plant saponin found in fenugreek which has wide range of activity. The structure of the molecule was taken from NCBI and seen in CORINA online demo software and the molecule was downloaded as a PDB file.

TARGET ENZYMES:-

5 hydrolase enzymes were selected as targets for PDB studies. The structure of the target enzymes was downloaded from the protein database. ANISOU and HETAM sequences were deleted and molecules were prepared for docking.

SOFTWARE:-

The software used for docking is AUTODOCK 4.2. Autodock is a software which performs docking and prepares a PDB file which can be further analyzed using a software called PYMOL which shows the interaction between the macromolecules and a ligand chosen for study.

METHODOLOGY

A standard protocol is followed using Autodock 4.2 operating the LINUX operating system. Autodock 4.2 is widely distributed docking software which performs flexible docking of ligands into a protein structure. The ligand and the target enzymes are prepared and docking is done following a standard protocol. The parameters are set by default. Each docking experiment gives 10 preferable conformations out of which the conformation with least binding energy that is highest negative value is selected to draw a complex and ensure the interactions. The interacting amino acids are labelled and interacting sites are marked.

PICTORIAL REPRESENTATION OF LIGAND MOLECULE INTERACTIONS

FIG 1: INTERACTION (BLUE) BETWEEN CELL7A (GREEN) AND LIGAND DIOSGENIN (RED), BINDING ENERGY -8.18.

FIG 2: INTERACTION (BLUE) BETWEEN ENDOXYLLANASE (PINK) AND LIGAND DIOSGENIN (RED), BINDING ENERGY -7.28

FIG 3: INTERACTION (BLUE) BETWEEN FERULLIC ACID ESTERASE (CYAN) AND LIGAND DIOSGENIN (RED), BINDING ENERGY -6.17

FIG 4: INTERACTION (BLUE) BETWEEN N-ACETYLGALACTOSAMINE (BROWN) AND LIGAND DIOSGENIN (RED), BINDING ENERGY -6.78

FIG 5: INTERACTION (BLUE) BETWEEN PECTIN METHYLESTERASE (PURPLE) AND LIGAND DIOSGENIN (RED), BINDING ENERGY -8.37

RESULTS AND DISCUSSION

RESULT TABLE:-

FUNGAL TARGET ENZYMES

PDBID

BINDING ENERGY

RECEPTOR MOLECULES

VDW ENERGY

Cel7A

1Q9H

-8.18

TRY51

-8.43

Endoxylanase 11a

1H1A

-7.28

GLU178, ASN45, GLN122

-7.53

Ferullic acid esterase

1USW

-6.17

GLU182, PRO181

-6.44

N-acetylglucosamidase

1C91

-6.78

ASP117

-6.9

Pectin methylesterase

1XG2

-8.37

ASN201

-8.67

TABLE : The above table shows the interacting macromolecules with their pdbid , binding energy, interacting amino acids and van-der-waals energy.

On the basis of pymol interactions the molecules were seen interacting with the ligand at a few amino acid sites. The ligand Diosgenin was seen interacting with the enzyme Cel7A which is a Cellbiohydrolase,( figure 1) with the reacting amino acids as ASN201 of the protein. In the case of enzyme Endoxylanase 11A (figure 2) the ligand was seen interacting with GLN122, GLU178, GLY179 amino acids of the protein. The ligand showed interaction with Ferullic acid esterase (figure 3) at GLU182 and PRO181 amino acid sites on the protein. In the case of N-acetylglucosamidase (figure 4) ligand Diosgenin showed interaction with amino acid residue ASP117 in the protein. With the last molecule Pectin methylesterase (figure 5) Diosgenin showed interaction at ASN201 amino acid residue site. Also this result shows that Pectin methylesterase has the least binding energy and Ferullic acid esterase has the highest binding energy. Another very evident point that can be noticed according to the results is the presence of ASN (Asparagine) and GLU (Glutamate) in interactions with different enzymes.

In comparison to earlier works that is interaction of 5-(2,4-dimethylbenzyl)pyrrolidin-2-one with antifungal drug target enzymes by In-silico molecular docking studies where activity of ligand as a potential drug against fungal enzymes has been shown but the major difference is that the ligand taken in this case is derived from a novel Streptomyces VITSVK5 spp [3]. But in our case the ligand considered is a plant saponin derived from an Indian medicinal plant Trigonellafoenum-graecum i.e. fenugreek. The saponin considered is Diosgenin which by experimental results has shown hydrolase inhibitory actions against fungal hydrolase enzymes.

CONCLUSION

Therefore the present study on in-silico modelling of antifungal drugs against fungal enzymes shows us the potential functionality of the plant saponins Diosgenin as an antifungal drug acting specifically as an inhibitor of enzyme hydrolase. Also the results show us the two possible conserved amino acid residues which take part in ligand-enzyme interaction. These conserved sites do not change loci over generations and are responsible for some important metabolic or structural functions and help in determining specific locations which in turn helps in identification and analysis. Therefore conclusion derived from the present study done is the potential ability of the ligand Diosgenin to act as a potential drug against fungal enzymes specifically hydrolases and also it tells us about the presence of some conserved amino acid sequences which are carried over generations.

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