IDENTIFICATION OF POTENTIAL INHIBITOR AGAINST EXFOLIATIVE TOXIN A ASSOCIATED WITH STAPHYLOCOCCUS AUREUS INFECTION – A MOLECULAR DOCKING APPROACH

IDENTIFICATION OF POTENTIAL INHIBITOR AGAINST EXFOLIATIVE TOXIN A ASSOCIATED WITH STAPHYLOCOCCUS AUREUS INFECTION - A MOLECULAR DOCKING APPROACH

P. Vijayalakshma ¹, J. Selvarajb ¹ and M. Rajalakshmi ^{* 2}

Department of Biotechnology & Bioinformatics, Holy Cross College (Autonomous), Tiruchirapalli - 620002, Tamil Nadu, India.

Department of Biochemistry, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai - 600077, Tamil Nadu, India.

ABSTRACT: Staphylococcus aureus is a frequent colonizer of the human body as well as a serious human pathogen. It can cope with stress factors and acquire resistance to antibiotics, thus rendering treatment difficult. Discovering new treatments for this disease is urgently required, especially in view of the emergence of multiple drug resistant organisms and to reduce the total duration of current treatments. Exfoliative toxin A (ETA), produced by Staphylococcus aureus causes scalded skin syndrome which is characterized by the separation of layers of skin. It affects newborns and adults primarily. Hence, therapies for Staphylococcal scalded skin syndrome (SSSS) are critically needed. Seeking a more effective compound for the treatment has been one of the great interests. In the present study, compounds from different plant extracts are permissible to interact with ETA using Schrodinger. The best effective compound gymnemic triacetate was selected from the list of different compounds based on its docking score and binding energy. The result showed that the herbal compound gymnemic triacetate possesses anti-bacterial activity by blocking exfoliative toxin A.

Staphylococcus aureus, Exfoliative toxin, Scalded skin syndrome, Docking

INTRODUCTION: Staphylococcus aureus (M. R. S. A) is a well know disease-causing bacterium, and disease management has become a harmful subject worldwide ¹_. The pathogen city of the bacteria contains skin and soft tissue infections (SSTI), bone, joint and implant infections, pneumonia, septicemia and various toxicoses such as toxic shock syndrome, scalded skin syndrome,

Bloodstream infections, osteomyelitis, septic arthritis and device related infections, necrotizing fasciitis and abscesses ². The human pathogen S. aureus exudes a number of biologically related toxins. Among them, the exfoliative toxins (ETs), ¹ serotypes A (ETA) and B (ETB), causes staphylococcal scalded skin syndrome (SSSS). SSSS is differentiated by the exact division of intraepidermal layers of the skin at the desmosomes.

The disease frequently affects newborns and can exfoliate 50% or more of the skin. Exfoliative toxin play as a serine protease, which targets Dsg-1 (desmoglein-1), formed only in the skin. The role of Dsg-1 is to continue the keratinocyte cell-cell adhesion. Cleavage of Dsg-1 would direct to partition of skin keratinocytes, a result that would form the kind of division of layers of epidermal tissue seen in scalded skin syndrome and bullous impetigo. Natural products have traditionally and constantly been considered for hopeful new leads in pharmaceutical development ³. The active substances present in lots of medicinal plants might be used as a beneficial substitute for staphylococcal infection ⁴. A computer-aided method is an initial approach to screening new potential agents, and a discipline is a rising approach as it reduces lots of complexity of the drug discovery procedure. The selection of lead molecules with good pharmacological properties and drug-likeness is a too long process in the drug development process. In-silico molecular docking in one of the most potent method to find out new ligand for proteins of known structure and thus play main role in structure based drug design. Researchers frequently applied docking computer programs to discover the binding affinity for molecules that fit a binding site on the protein. In the present study, we mainly focused on identifying the potential herbal inhibitor for staphylococcal target exfoliative toxin using a molecular docking approach. The data obtained from in silico studies were later used for in-vitro studies. Gymnemic diacetate, Gymnemic triacetate was isolated from the leaf of Gymnema sylvestre, terpenoid, lupeol , friedelin   from Elephantopus scaber, polysaccharide from Tinospora cordifolia, costunoloide, eremanthin from Costus speciosus, and catechin  isolated from Cassia fistula, gallic acid and octyl gallate from Terminalia bellirica, were are some of the phytotherapeutic agents isolated in our lab and used for present study Table 1.

TABLE 1: NAME OF THE COMPOUNDS AND PLANT SOURCES

MATERIALS AND METHODS:

Ligand Preparation: The 11 natural compounds which were isolated in our lab from different medicinal plants were selected for the present study. Checmsketch version 11.01 (http:// www.acdlabs.com) was used to draw the structure of the molecules. Ligprep module in Schrödinger was helped to assign the correct bond order of the compounds. All the structures were converted to mae format (Maestro, Schrodinger, Inc.) and optimized by way of the Optimized Potentials for Liquid Simulations (OPLS 2005) force field with a default setting ⁵.

Protein Preparation: 3D structure of exfoliative toxin (PDB code: 1AG) was downloaded from PDB database.  Before doing the docking analysis, the target protein was prepared with help of protein preparation wizard in Maestro 9.1 ⁶. The protein preparation process contains two steps. In the first step side chains were neutralized neither near the binding site nor participate in salt bridge formation. In the second step, H was added, water molecules were removed and the structure was minimized till the average root mean square deviation of the non-hydrogen atoms achieved 0.3 A °.

Glide Docking and Scoring Function: Glide calculations were performed with Impact version v18007 (Schrodinger, Inc.) ^7-9. This is a grid-based docking and its form energetically favorable interaction between the ligand and receptor molecules 2007. It suggests the performance of test calculations with diverse scaling factors like van der Waal radii of the receptor and ligand atom, as steric repulsive contact could be exaggerated, leading to refusal of the right binding form of active compounds. After confirming that the proteins and ligands were in the right form for docking studies, the Grid was formed using the grid-receptor generation option. To reduce the possibility for the non-polar part of the receptor, we scaled van der Waal radii of receptor atoms by 1.00 A ° with a partial charge cutoff of 0.25. The compounds were docked in the binding site by the Glide ‘standard precision’ (SP) and Glide ‘extra precision’ (XP) mode ^{9, 10.}

Glide creates confirmation within and goes by these through a sequence of filters. The first place the ligand center at a variety of grid positions of a 1 A ° grid and turns it approximately the three Euler angles. In this stage, simple score values and geometrical filters weed out doubtful binding modes.

The next filter phase engages a grid-based force field assessment and modification of docking results with torsional and rigid body actions of the ligand. The OPLS-2005 force field was used for this purpose. A little number of existing docking solutions can then be subjected to a Monte Carlo procedure to try and reduce the energy score. The final energy evaluation is done with glide score, and a single best pose is generated as the output for a particular ligand.

Gscore = a * vdW + b* Coul+ Lipo + Hbond +Metal +BuryP + RotB+ Site

Where, vdW is van der Waal energy, Coul is Coulomb energy, Lipo is lipophilic contact term, HBond is hydrogen- bonding term, Metal is a metal-binding term, BuryP is a penalty for buried polar groups, RotB is a penalty for freezing rotatable bonds, Site is polar interactions at the active site, and the coefficients of vdW and Coul are: a = 0.065, b = 0.130.

Binding Energy Calculation: The binding calculations are more perfect than the docking studies ¹¹, Prime MM/GBSA module was used to calculate the binding energy of the complex. In the presence of partial charges, the calculation was carried out.  The following equation was used to calculate the binding energy

DGbind = DE+ DGsolv + DGSA

Where, DE= Ecomplex _ Eprotein _ Eligand; (Ecomplex, Eprotein, and Eligand are the minimized energies of the protein–inhibitor complex, protein, and inhibitor, respectively. DGsolv is the generalized born electrostatic solvation energy of the complex. DGSA is a non-polar contribution to the solvation energy due to the surface area) Prime uses a surface generalized born model employing a Gaussian surface instead of a vdW surface for better representation of the solvent-accessible surface area ^{12, 13}.

ADME Calculation: Qikprop module of Schrödinger was used to calculate the ADME properties of selected compounds. Both principle and physiochemical descriptors were calculated ¹⁴. Before performing the calculation, all the ligands were neutralized. This step is important because QikProp is not able to neutralize a structure, and so no properties will be calculated in normal mode ¹⁵. In normal mode, it predicts the principle descriptors and physicochemical properties of all ligands with a full analysis of the Molecular weight, H bond donor, H bond acceptor, QP logpw, Qplogkp, % human oral absorption. It also estimates the suitability of the whole compounds based on Lipinski’s rule of 5, which is essential for rational drug design ^{16, 17}.

RESULTS AND DISCUSSION: In this study, we have conducted a molecular docking and binding energy calculation between the target protein and 11 plant compounds which were obtained from 6 different medicinal plants. The insilico results revealed that Gymnemic triacetate from Gymnema Sylvestre exhibited better binding affinity with the target protein than the other compounds.

Validation of the Docking Protocol: To study the molecular base of contact and binding affinity of plant compounds in the active site of exfoliative toxin molecular docking was carried out. The selected plant compounds were docked towards the exfoliative toxin A (PDB id -1AGJ). All the compounds were docked in a glide environment; the result shows the good interaction of every compound, with the exofloative toxin a protein. Based on the docking score, the ligand was ranked. The docking results revealed that all the ligands were docked into the active site of the target protein. The characteristics of the binding sites play a vital role in the ligand binding of target protein.

The hydrogen-bonding interactions between the compounds and target protein are mostly contributed by PHE 30, LYS 32, and HIS 165 Fig. 1. And the lengths of the hydrogen bonds formed by the drug molecule's target protein are listed in Table 2. It is seen that PHE 30 and LYS 32 alternatively form hydrogen-bonding interactions with the 11 drug molecules.

The hydrogen bonds formed by these two amino acid residues have comparatively shorter lengths than those formed by other residues, which provides a signal that these hydrogen bonds are stronger than others and play significant roles in the ligand binding. Hydrogen bonds play a vital role in the structure and function of biological molecules. Compared with all the docking results, Gymnemic triacetate shows a more number of H-bond and high glide scores. The docking results like atomic interaction and their docking scores glide energy, and their hydrogen bond details of the best four compounds were shown in Table 2. In the present study, Gymnemic triacetate, a novel compound isolated from the leaf of Gymnema Sylvestre shows the best docking score and docking energy with more interaction with the exfoliative toxin A.

FIG. 1: BINDING POSES OF THE FOUR COMPOUNDS EXFOLIATIVE TOXIN A

TABLE 2: GLIDE XP DOCKING RESULTS OF BEST COMPOUNDS WITH GLIDE SCORE, ENERGY AND THEIR ATOMIC INTERACTION

Binding Energy Calculation: Docking methods begin with a known protein structure and a known ligand structure and plan to quickly make an optimal protein ligand-bound conformation. The docked complexes were minimized with the help of local optimization features in prime in Prime MM-GBSA method. This method was helped to find out the binding free energy of set of compounds to receptor. The OPLS-AA force field and GBSA continuum solvent mode was helped to calculated the binding energies of the docked complex.

The interaction energy was calculated after a minimization was carried out on a docked ligand in which atoms within 7.5 Å from the ligand were free to move (other atoms were fixed). The interaction energy comprises an implicit solvation (H₂O) term. Van der Waals, solvation and electrostatic energy as well as solvent accessible surface area (SASA) were calculated for each minimized complex. Finally, based on the composite scoring, the complexes were ranked and listed in Table 3.

TABLE 3: CALCULATED ENERGIES OF SELECTED FOUR COMPOUNDS WITH EXFOLIATIVE TOXIN A

ADME Calculation: These four compounds selected from docking studies were additionally estimated for their drug-like actions through analysis of pharmacokinetic parameters necessary for absorption, distribution, metabolism, excretion, and toxicity (ADMET) by use of QikProp. Predicted properties of the entire four compounds were listed in Table 4. The molecular weight of the selected compounds existed in the range between 150 to 470. H bond donors were in the range between 3 and 5, whereas H bond acceptor of the chosen compounds found to be in the range between 8-15. The predicted water/gas partition coefficient was in the range 15 22 for most of the compounds. Predicted skin permeability & log Kp (QPlogKp) negatively ranged between 4-1, and predicted human oral absorption on 0–100% scale was found to be in the intermediate levels of 50 and 65. Table 4 explains the drug-likeness property of the compounds. The jointed approach of docking, binding energy calculation, and ADME prediction was used to identify the binding affinity of these compounds with the receptor and also validate them as potential candidates for second-generation drug discovery.

TABLE 4: DRUG LIKENESS PROPERTY CALCULATED BY QIKPROP SIMULATION

CONCLUSION: With the current torrent of data, computational modes have become crucial to biological examination. In the present study computational approach was helped to recognize the mechanism of interactions and binding capacity between exfoliative toxins A with selected natural compounds. Anti-diabetic activities of these 11 compounds were proved experimentally. In the present effort, we have tried to identify the antibacterial activity of these compounds through molecular docking analysis.

The examination of the best-docked ligands allowed us to know the binding manner of compounds used in this study and verify the role as anti-bacterial agent. Binding energies of the drug enzyme (receptor) connections are significant to explain how to fit the drug binds to the target macromolecule. Compared to all compounds, Gymnemic triacetate isolated from the leaf of Gymnema Sylvestre shows very good interaction with exfoliative toxin A.

So, these phytocompounds should be subjected to extra experimental analysis to validate these results.

ACKNOWLEDGEMENT: This work was carried out with the support of the Department of Biotechnology funded -Bioinformatics  (DBT-BIF) Centre, Holy Cross College, Bharathidasan University, Trichy, India

CONFLICTS OF INTEREST: Authors have no conflicts of interest.

REFERENCES:

1. Turner NA, Sharma-Kuinkel BK, Maskarinec SA, Eichenberger EM, Shah  P, Carugati  M,  Holland  TL, Vance G and Fowler JR: Methicillin-resistant Staphylococcus sureus. An Overview of Basic and Clinical Research Nature Review Microbiology 2019; 203-18.
2. Mkrtchyan HV, Xu Z, Yacoub M, Ter-Stepanyan MM, Karapetyan HD, Kearns AM, Cutler RD, Pichon B and  Ambardzumyan AD: Detection of diverse genotypes of Methicillin-resistant Staphylococcus aureus from hospital personnel and the environment in Armenia. Antimicrobial Resistance & Infection Control 2017; 6-19
3. Omprakash GB, Madhuri CS, Swamy SVM, Gholve Sachin B, Padmaja SG and Mahesh JB: Phytopharmaceuticals: An emerging platform for innovation and development of new drugs from botanicals. Journal of Drug Delivery and Therapeutics 2019; 1046-57.
4. Manandhar S, Luitel S and Dahal RK: In-vitro antimicrobial activity of some medicinal plants against human pathogenic bacteria. Journal of Tropical Medicine 2019; 1895340.
5. Aamir M, Singh VK, Dubey MK, Meena M, Pratap Kashyap S,  Katari  SK, Upadhyay RS, Umamaheswari A,  Surendra Singh  S: In-silico prediction, characterization, molecular docking, and dynamic studies on fungal sdrs as novel targets for searching potential fungicides against fusarium wilt in tomato. Frontirers Pharmacology 2018; 9: 1038.
6. Román J, Castillo A and Mahn A: Molecular docking of potential inhibitors of broccoli myrosinase. Molecules. 2018; 23(6): 1313.
7. Alogheli H, Olanders G, Schaal W, Brandt P and Karlén A: Docking of macrocycles: comparing rigid and flexible docking in glide. Journal of Chemical Information Modelling 2017; 57(2): 190‐
8. Kaushik AC, Kumar S, Wei DQ and Sahi S: Structure based virtual screening studies to identify novel potential compounds for GPR142 and their relative dynamic analysis for study of type 2 diabetes. Frontiers in Chemistry 2018; 6: 23.
9. Ponnulakshmi R, Shyamaladevi B, Vijayalakshmi P and Selvaraj J: In-silico and in-vivo analysis to identify the antidiabetic activity of beta sitosterol in adipose tissue of high fat diet and sucrose induced type-2 diabetic experimental rats. Toxicology Mechanism Methods. 2019; 29(4): 276‐
10. Antony P and Vijayan R: Identification of novel aldose reductase inhibitors from spices: a molecular docking and simulation study. PLoS One 2015; 10(9): 0138186.
11. Casalvieri KA, Matheson CJ, Backos DS and Reigan P: Molecular docking of substituted pteridinones and pyrimidines to the ATP-binding site of the N-terminal domain of RSK2 and associated MM/GBSA and molecular field datasets. Data in Brief 2020; 29: 105347.
12. Ignjatović M, Caldararu O, Dong G, Muñoz-Gutierrez C, Adasme-Carreño F and Ryde U: Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM and free-energy simulations. Journal of Computer Aided Molecular Design 2016; 707-30.
13. Dash R: Molecular docking and binding free energy analysis of rutin and apigetrin as galectin-1 inhibitors. SDR Journal of Computational Chemistry & Molecular Modelling 2018; 2.
14. Kalirajan R, Pandiselvi A, Gowramma B and Balachandran P: In-silico Design, ADMET Screening, MM-GBSA Binding Free Energy of Some Novel Isoxazole Substituted 9-Anilinoacridines as HER2 Inhibitors Targeting Breast Cancer. Current Drug Resign Review 2019; 118‐
15. Hussain A and Verma CK: Molecular docking and in silico ADMET study reveals 3-(5-{[4-(aminomethyl) piperidin-1-yl] methyl}-1h-indol-2-yl)-1h-indazole-6-carbonitrile as a potential inhibitor of cancer Osaka thyroid kinase. Biomedical Research 2017; 28- 13.
16. QikProp Version 3.3, Schrödinger, LLC, New York 2018.
17. Gudipati S, Muttineni R, Mankad AU, Pandya HA and Jasrai YT: Molecular docking based screening of Noggin inhibitors. Bioinformation 2018; 15‐
    

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    How to cite this article:

    Vijayalakshma P, Selvarajb J and Rajalakshmi M: Identification of potential inhibitor against exfoliative toxin a associated with Staphylococcus aureus infection - a molecular docking approach. Int J Pharm Sci & Res 2021; 12(3): 1515-20. doi: 10.13040/IJPSR.0975-8232.12(3).1515-20.

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