Identification of polyphenolic compound inhibitors for 1, 3 β D- glucan synthase in the treatment of dermatophytoses by molecular docking

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1 International Journal of Advanced Research in Medical Sciences 2017 Volume 1 Issue 1 Pages: 1-5 Original Research Paper Identification of polyphenolic compound inhibitors for 1, 3 β D- glucan synthase in the treatment of dermatophytoses by molecular docking Aarthi R, Iyanar Kannan*, Sukumar RG, Prevathi RK, Shantha S Department of Microbiology, Tagore Medical College and Hospital, The Tamilnadu MGR Medical University, Rathinamangalam, Chennai , India. Corresponding author: dr.ikannan@tagoremch.com ABSTRACT The fungal skin infections are commonly seen in all age groups and are difficult to cure 100%. The fungal skin infections caused by dermatophytes (dermatophytoses) are treated with topical antifungal agents and synthetic antifungal drugs. These drugs take long time to exert their effect and also with several side effects such as anorexia, constipation, headache, hepatitis, pruritis, and exanthema, inhibition of steroid hormone synthesis includes nausea, dizziness and gastrointestinal upset. The 1, 3-β D Glucan is a major structural polymer of yeast and fungal cell walls and is synthesized from UDP-glucose by the multi-subunit enzyme 1, 3-β D Glucan synthase (GS). As the GS is an enzyme, is worth to look into structural analogues that can bind to its active site there by inhibiting its action. The three dimensional structure of GS is predicted by homology modelling. The structure of various polyphenolic compounds was obtained from ZINC database. The docking of ligands was performed using AutoDock 4.0 software. Based on molecular docking and ADMET properties, it is concluded that Quercetin, Ellagic acid and Resveratrol are excellent drug like molecules in binding glucan synthase. Keywords: Dermatophytes, 1, 3-β D Glucan synthase (GS), polyphenolic compounds, molecular docking, Autodock vina INTRODUCTION The fungal skin infections are commonly seen in all age groups and are difficult to cure 100%. The frequent fungal etiological agents that cause the fungal skin infection are candida albicans and dermatophytes. The dermatophytes are keratinophilic fungi which invade the keratin of skin, hair and nail [1]. The fungal skin infections caused by dermatophytes (dermatophytoses) are treated with topical antifungal agents and synthetic antifungal drugs [2]. The antifungal drugs belong to six main categories namely polyenes, azoles, allylamine and morpholine drugs, antimetabolite drugs, echinocandins and the griseovolvin [3]. As the fungi are eukaryotes, the antifungal drugs are Aarthi et al. IJARMS 2017; 1(1):1-5 1

2 targeted only against cell wall which is absent in mammalian cell. Among the antifungal drugs, echinocandins such as anidulafungin, caspofungin and micafungin are water soluble lipopeptide molecules specifically target cell wall 1, 3-β D-glucan synthase (GS) [4]. The GS is involved in cell division and cell wall integrity. The antifungal agents like echinocandins are given by injection for its action. These drugs take long time to exert their effect and also with several side effects such as anorexia, constipation, headache, hepatitis, pruritis, and exanthema, inhibition of steroid hormone synthesis includes nausea, dizziness and gastrointestinal upset [5]. The 1, 3-β D Glucan is a major structural polymer of yeast and fungal cell walls and is synthesized from UDP-glucose by the multi-subunit enzyme 1, 3-β D Glucan synthase (GS). As the GS is an enzyme, is worth to look into structural analogues that can bind to its active site there by inhibiting its action. Molecular docking is the insilico technique that has helped in drug development for various diseases [6]. The first insilico aided drug that came commercially is dorzolamide in 1996 [7]. Since then many drugs are developed successfully and are being used as the treatment for various diseases including cancer. In the present study, an attempt will be made to find the drug like molecules that can inhibit GS from the library of polyphenolic compounds present in various plants from pubchem and ZINC databases. The docking of ligands was performed using AutoDock 4.0 software. METHODOLOGY Prediction of three dimensional structures The three dimensional structure of GS is predicted by homology modelling. It is the method to determine 3D structure of protein with the help of 3D structure of homologous proteins. Softwares used were Modeller 9.11 and Easy modeller 2.0 GUI. First the primary structure of GS protein is retrieved from UniProtKB database ( ). The primary structure in FASTA format is submitted in BLASTp (blast.ncbi.nlm.nih.gov/) to find the homologous proteins. The proteins of low e value are selected. The 3D structures of the homologous proteins that are selected are retrieved from RCSB database ( The 3D structure of homologous proteins is submitted along with the primary structure of GS protein to Modeller software through GUI Easy Modeller. The predicted 3D structure is then validated with the Ramachandran plot. It is also further validated in ProQ online tool ( Active site prediction The possible binding sites of GS are searched using binding site prediction 3DLIGANDSITE, an online tool ( ) [8]. The best flexible binding sites were selected for this study. Generation and optimization of Ligand The structure of 25 polyphenolic compounds was obtained from ZINC Aarthi et al. IJARMS 2017; 1(1):1-5 2

3 database. The ligands were saved in mol 2 format. The OPEN BABEL software ( was used to convert mol format to pdb format. Rapid virtual screenings of these compounds were performed in the docking tool igemdock v2.0 [9]. A population size of 150 was set with 70 generation and one solution for quick docking. Based on the binding energy the ligands were selected for further study. The selected ligands were then analyzed for drugeleva e es ased L s s le f f ve. Other drug like properties were analysed using OSIRIS Property Explorer ( o/) and Mol soft: Drug-Likeness and molecular property explorer ( On the basis of binding affinity and drug like properties, the ligands were selected and were taken for further molecular docking study. accommodate ligands to move freely. The best conformation is chosen with the lowest docked energy, after the docking search was completed. RESULTS AND DISCUSSION Three structure prediction The primary structures of 1,3 beta glucan synthase(gs) was retrieved from UniprotKB database. The 3D structures of 1,3 beta glucan synthase(gs) was successfully predicted by homology modelling. The predicted 3D structure of 1,3 beta glucan synthase(gs) is given in figure 1. Its 3D structure is viewed as PDB file with Rasmol structure colour scheme. Alpha helices are coloured magenta, beta sheets are coloured yellow, turns are coloured pale blue, and all other residues are coloured white. Protein-ligand docking The docking of ligands was performed using AutoDock 4.0 software. Docking was performed to obtain a population of possible conformations and orientations for the ligands at the binding site and also its binding energy. Using the software, polar hydrogen atoms were added to the GS and its non polar hydrogen atoms were merged. All bonds of ligands were set to be rotatable. All calculations for proteinligand flexible docking were done using the Lamarckian Genetic Algorithm (LGA) method. The grid box with a dimension of 126 x 126 x 126 points was used so as to cover the entire binding site and Figure 2: The 3D structure of 1,3 beta glucan synthase(gs) viewed with Rasmol structure colour scheme The Ramachandran plot was generated from the predicted 3D structure of 1,3 beta glucan synthase(gs) to validate it. The Aarthi et al. IJARMS 2017; 1(1):1-5 3

4 Ramachandran plot is shown in figure 2. From the figure is seen that most of the residues clustered tightly in the mostfavoured regions with very few outliers showing that the predicted structure is good. shows the docking pose of 3 selected molecules with chitinase. Table 1: The Binding energy values obtained by igemdock S.No Ligand Total energy in (kcal/mol) 1. Ellagic acid Quercetin Resveratrol Figure 2: Ramachandran plot of 1,3 beta glucan synthase(gs) The predicted structure was further validated by ProQ online tool. A total of 25 polyphenolic compounds were obtained from ZINC database. It was converted to pdb format using OPEN BABEL software. All the 25 compounds were then subjected to virtual rapid screening with igemdock software and three compounds were found to have good fit with a low binding energy with both the targets. After the confirmation of ADME properties, the three ligands were then subjected to further molecular docking with igemdock subjecting to accurate docking (very slow docking) by setting population size of 800 is set with 80 generation and 10 solutions. The results were projected in the Table 1. Figure 3 Figure 3: The docking pose in igemdock of 3 selected molecules. The selected three compounds were then subjected to further docking with Autodock vina tool. The Table 2 shows the energy values of the compounds with the two targets and figure 4 shows the docking poses of ligands with GS. Aarthi et al. IJARMS 2017; 1(1):1-5 4

5 Table 2: The Binding energy values obtained by Autodock vina S.No Ligand Total energy in (kcal/mol) 1. Ellagic acid Quercetin Resveratrol -9.4 Figure 3: The docking pose in Autodock vina of 3 selected molecules. CONCLUSION Based on molecular docking and ADMET properties, it is concluded that Quercetin, Ellagic acid and Resveratrol are excellent drug like molecules in binding glucan synthase. REFERENCES 1. Weitzman I, Summerbell RC. The Dermatophytes. Clinical microbiology reviews, 1995; 8(2): Dixon DM, Walsh TJ Antifungal Agents. In: Baron S, ed. Medical Microbiology. Galveston: University of Texas Medical Branch Ch Bennett JE Antifungal agents. In, Brunton LL, Chabner BA, Knollman BC, eds. Goodman & G lma s he ha mac l g cal basis of therapeutics. New York: McGraw-Hill Douglas C. Fungal beta (1, 3)-Dglucan synthesis. Med Mycol, 2001; 39 (suppl 1): Grant SM, Clissold SP. Itraconazole: A review of its pharmacodynamic and pharmacokinetic properties and therapeutic use in superficial and systemic mycosis. Drugs, 1989; 37(3): Rajamani R, Good AC: Ranking poses in structure-based lead discovery and optimization: current trends in scoring function development. Curr Opin Drug Discov Devel 2007, 10: Kubinyi H. "Chance favors the prepared mind--from serendipity to rational drug design". J Recept Signal Transduct Res 1999; 19 (1 4): Wass MN, Kelley LA, Sternberg MJ, 3D LigandSite: predicting ligand-binding sites using similar structures.nar. 2010; 38: Yang JM, Chen CC, "GEMDOCK: A generic evolutionary method for molecular Docking." Proteins: Structure, Function, and Bioinformatics, 2004; 55: Aarthi et al. IJARMS 2017; 1(1):1-5 5