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CHAPTER 4

MOLECULAR DOCKING STUDIES OF THE ISOLATED

ANTIMICROBIAL AND ANTI-INFLAMMATORY

COMPOUNDS

4.1 INTRODUCTION

Docking is a method which predicts the preferred orientation of one

molecule to a second when bound to each other to form a stable complex in three dimensional space which is important in many areas specifically, in cellular biology. The function of proteins is a result of their interaction (i.e. docking) with other proteins as well as other molecular components. Therefore if we could predict how proteins interact (dock) with other molecules we could possibly infer how to inhibit

their function. The inhibiting function is of particular interest to drug and pharmaceutical companies. Thus the results of docking can be extremely beneficial in finding drugs that are effective against particular diseases. Knowledge of the preferred orientation in turn may be used to predict the strength of association or binding affinity between two molecules using scoring functions [414].

Structure-based design has emerged as a new tool in medicinal

chemistry. A prerequisite for this new approach is an understanding of the principles of molecular recognition in protein-ligand complexes. If the three-dimensional structure of a given protein is known, this information can be directly exploited for the retrieval and design of new ligand. Structure-based ligand design is an iterative approach. First of all, it requires the crystal structure or a model derived from the crystal structure of a closely related homolog of the target protein,

preferentially complexed with a ligand. This complex unravels the binding mode and conformation of a ligand under investigation and indicates the essential aspects determining its binding affinity. It is then used to generate new ideas about ways of improving an existing ligand or of developing new alternative bonding skeletons [415].

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Molecular docking studies have helped in identification of potential

small molecules in drug discovery for various diseases [416]. Recently Izumizono et

al. (2011) [417] demonstrated the usefulness of docking in short-listing potential

candidates and subsequently confirmed its efficiency by in vitro testing on M.

tuberculosis. We employed the same principle of molecular docking and followed it

up with the use of stringent scoring functions to enhance the accuracy of our results.

The set of molecules identified by us in this study are very likely to serve as

potential leads in the search for new drugs in the areas of immunoinflmmatory and

microbial diseases.

Natural products from plants may be a new source of antimicrobial

agents with possibility of having novel mechanism of action which is different from

the existing synthetic drugs [418]. Penicillin-binding proteins (PBPs) are a group

of proteins that are characterized by their affinity for and binding of penicillin and

the name reflects the way by which the protein was discovered. They are the normal

constituent of many bacteria; And all �-lactam antibiotics (except for tabtoxinine-�-

lactam, which inhibits glutamine synthetase) bind to PBP to prevent the bacterium

from constructing a cell wall [419]. Bioactive natural products play important roles

in altering several biological processes and many have been involved in the

alleviation and control of inflammation-related diseases. These actions include both

gene expression modulation of pro-inflammatory enzymes, such as cyclooxygenase

2 (COX-2), and direct inhibitory effect by binding to this protein. However, it is also

well known that some anti-inflammatory molecules carry out their action by directly

inhibiting inflammatory proteins such as cyclooxygenase 2 (COX-2) [420]. This

enzyme catalyzes the first step in the synthesis of prostaglandins, thromboxanes and

other eicosanoids in several inflammatory processes.

Computational chemistry offers the possibility to explore these

interactions through protein - ligand docking procedures. Docking methods are thus

valuable tools for drug development, and most current approaches assume a rigid

receptor structure to allow virtual screening of large numbers of possible ligands and

putative binding sites on a receptor molecule [421].

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Novel compounds with significant medicinal properties have gained

much interest in therapeutic approaches for treating various inflammatory disorders

like arthritis, oedema and snake bites and the post-envenom consequences. The

importance of natural products in modern medicine has been well recognized [422].

Natural compounds isolated from plant sources have an extensive past and present

use in the treatment of diverse diseases, including inflammatory and microbial

diseases. More than 20 new drugs, launched worldwide between 2000 and 2005,

originate from natural products. Scrutiny of medical indications by source of

compounds has demonstrated that natural products and related drugs are used to

treat 87% of all categorized infectious and non-infectious human diseases [423].

They also serve as compounds of interest both in their natural form and also as

templates for synthesizing derivatives with higher activity and lower toxicity.

Trianthema decandra is a prostrate herb found in tropical conditions,

almost throughout the world. Phytosterol (TDCV) and flavonoid (TDCVI) isolated

from T. decandra exhibited antimicrobial activity in vitro while terpenoid (TDCIV)

and saponin (TDCVII) showed anti-inflammatory activity in vitro and in vivo. The

mechanisms of the anti-bacterial and anti-inflammatory effects of these compounds

are not yet clearly understood. To understand the possible modes of action of these

moleculer docking methods were employed.

4.2 REVIEW OF LITERATURE

Bioinformatics tools have become very important to pinpoint the targets for

different ligands and vice versa [424]. Using bioinformatics tools, we evaluated the

binding of the isolated compound flavonoid, phytosterol, terpenoid and saponin to some

of the target proteins related to antimicrobial and anti-inflammatory such as penicillin

binding protein, cyclooxygenase - 1, cycloxygenase - 2 and Phospholipase A2.

4.2.1 Packages

Software packages available for docking are listed in Table 4.1.

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Table 4.1 Docking Software Package

Docking Program Developed By Reference

ClusPro Camacho group at Boston University 425Smooth Dock

Carlos J. Camacho and P. Christoph Champ at University of Pittsburgh

426

Auto Dock Olson group at The Scripps Research Institute 427Dock Kuntz and Shoichet groups at the University of

California, San Francisco 428

EhiTS SimBioSys in Toronto, Canada 429Glide Schrodinger USA 430

4.2.1.1 Docking Analysis Using MAESTRO

Maestro is the graphical user interface for all Schrödinger’s products

CombiGlide™, Epik™, Impact™, Liaison™, Ligprep™, MacroModel™, Phase™,

Prime™, QikProp™, Qsite™, and Strike™. It contains tools for building displaying

and manipulating chemical structures; for organizing, loading and storing these

structures and associated data; and for setting up, monitoring and visualizing the

results of calculations on these structures [431].

4.2.1.2 Docking Method – GLIDE (Grid Based Ligand Docking with

Energetics)

Glide searches for favorable interactions between one or more typically

small ligand molecules and a typically large receptor molecule, usually a protein.

Each ligand must be a single molecule, while the receptor may include more than

one molecule such as protein and cofactor. GLIDE can be run in rigid or flexible

docking modes; the later automatically generates conformation for each input

ligand. The combination of positions and orientation of the ligand relative to the

receptor, along with its conformation in flexible docking, is referred to as a ligand

pose. The ligand poses that GLIDE generates pass through a series of hierarchical

filters that evaluate the ligand interaction with the receptor. The initial filters test the

spatial fit of the ligand to the defined active site, and examine the complementarily

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of ligand-receptor interactions using the GRID based method patterned after the

empirical ChemScore function [432].

Ligand poses that pass these initial screens enter the final stage of the

algorithm, which involves evaluation and minimization of a grid approximation to

the OPLS-AA non bonded ligand-receptor interaction energy. Final scoring is then

carried out on the energy-minimized poses. Schrödinger’s proprietary GLIDE score

multi ligand scoring function is used to score the poses. If GLIDE score was

selected as the scoring function, a composite Emodel score is then used to rank the

poses of each ligand and to select the poses to report to the user. Emodel combines

Glide score non-bonded interaction energy, and for flexible docking, the excess

internal energy of the generated energy conformation [433]. Figure 4.1 shows the

Glide docking hierarchy.

Figure 4.1 Glide docking hierarchy

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High Throughput Virtual Screening (HTVS)

High-throughput virtual screening (HTVS) docking is intended for the

rapid screening of very large numbers of ligands. HTVS has much more restricted

conformation sampling than SP docking, and cannot be used with constraints, score-

in-place, or rigid docking. Advanced settings are not available for HTVS, but are

fixed at predetermined values [434].

Flexible docking

Under Options in the Docking section of the Settings folder, one can

choose whether ligands are docked flexibly, rigidly, or not at all (score in place), and

set options for conformation generation. These options are described below. Dock

flexibly is the default option, and directs Glide to generate conformations internally

during the docking process; this procedure is known as flexible docking [435].

Induced Fit Docking

Glide docking uses the assumption of a rigid receptor, although scaling

of van der Waals radii of nonpolar atoms, which decreases penalties for close

contacts, can be used to model a slight “give” in the receptor and/or ligand. This

may not be sufficient to treat systems where ligand binding includes substantial

conformation changes in the receptor (“induced fit”) Schrödinger has developed a

procedure for such cases which uses Prime and Glide to perform induced fit docking

[436].

The induced fit docking allows the receptor to alter its binding sites so

that it conforms more closely to the shape and binding mode of the ligand [437]. The

ability to model induced fit docking has two applications:

1. Generation of an accurate complex structure for a ligand known to

be active but cannot be docked in an existing (rigid) structure of the

receptor.

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2. Rescue of false negatives (poorly scored true binders) in virtual

screening experiments, where instead of screening against a single

conformation of the receptor, additional conformations obtained

with the induced fit protocol are used.

4.2.3 Molecular docking

Molecular docking is a method which predicts the preferred orientation

of one molecule to a second when bound to each other to form a stable complex.

Most commonly, one of the molecules is a small organic compound (called ligand)

such as a drug and the second is the drug's biological target such as a protein

receptor. Molecular docking can be thought of as a problem of “lock-and-key”,

where one is interested in finding the correct relative orientation of the “key”

(ligand) which will open up the “lock” (protein) [438]. During the process, the

ligand and the protein adjust their conformations to achieve an overall “best-fit” and

this kind of conformational adjustments resulting in the overall binding is referred to

as “induced-fit” [439].

Two approaches to the molecular docking are particularly popular:

1. Shape complementarity - protein and ligand are shape

complementary.

2. Simulations – protein and the ligand are separated, and ligand finds

its position into the protein active site after transformations and

internal changes of its structure.

To obtain correct screen of the protein paired with ligands two elements

are required:

i) Search algorithm - a method that searches the space of all possible

orientations and conformations of the protein paired with the

ligand. Some examples of such methods include molecular

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dynamic simulations, shape-complementarity methods and genetic

algorithms [440].

ii) Scoring function - a method that is used predict the strength of the

non-covalent interaction between two molecules after they have

been docked. Three general classes of scoring functions include

force filed, empirical functions, knowledge -based functions [441].

As the result of a docking an optimized conformation for both the protein

and ligand, and relative orientation between protein and ligand is obtained such that

the free energy of the overall system is minimized.

4.2.4 The Bacterial cell wall

Nearly all species of bacteria synthesize and maintain a cell wall, which

acts as a rigid barrier between the cell and the environment. Bacteria are broadly

divided into two classes based on their susceptibility to Gram stain. Gram-positive

bacteria have a thick cell wall and lack an outer membrane, while Gram-negative

bacteria have two cell membranes, with a relatively thinner cell wall layer between

them (Plate 4.1). In both classes of bacteria, the cell wall is responsible for

maintaining bacterial cell shape and protecting the cell from the effects of osmotic

pressure.

Plate 4.1 A comparison of the cell envelopes of (a) Gram-positive bacteria and

(b) Gram – negative bacteria [442]

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The cell wall is a three-dimensional polymer consisting of

polysaccharide and short oligopeptides, and is therefore often referred to as

peptidoglycan. The glycan strands are long, linear chains consisting of alternating

amino sugars N-acetylglucosamine (NAG) and N-acetylmuramic acid (NAM) which

are connected by �-1,4-glycosidic bonds. The lactyl group of the muramic acid

residue is the site of covalent attachment of the N-termini of the peptides which

form the bridges that cross-link adjacent peptidoglycan strands. While the glycan

strands vary little between bacterial species, the peptide cross bridges vary

considerably. A common stem peptide consists of L-alanyl-D-�-glutamyl-L-lysyl-D-

alanyl-D-alanine, as in Streptococcus sp., but the D-�-glutamyl- and L-lysyl-

positions vary between species. In E. coli, for example, the peptide is L-alanyl-D-

glutamyl-meso-aminopimelyl-D-alanyl-D-alanine. These strands of peptidoglycan

are covalently bound to one another via the bridging peptides and, thus, the bacterial

cell wall is one large, continuous polymer [443].

4.2.5 The Penicillin-Binding Proteins

The final step in bacterial cell wall biosynthesis is the transpeptidation

reaction, which forms the covalent bonds that cross-link adjacent strands of

peptidoglycan. These reactions are catalyzed by a family of enzymes called the

penicillin-binding proteins (PBPs), which are also called transpeptidases, D-alanyl-

D-alanine peptidases, or DD-peptidases. These enzymes catalyze the nucleophilic

attack of the amino group of the third residue in the pentapeptide of the acyl

acceptor chain on the penultimate D-alanine of the acyl donor peptide chain [444].

The basic mechanism of the cross-linking reaction is well known and is

shown in Figure 4.2. To initiate the reaction, the PBP active site serine hydroxyl

attacks the carbonyl carbon of the penultimate D-alanine of the acyl donor peptide,

forming a tetrahedral intermediate, which quickly collapses to the more stable acyl-

enzyme intermediate, with the terminal D-alanine serving as the leaving group. The

acyl-enzyme intermediate can either be attacked by the free amine of the third

residue of an acceptor stem peptide (transpeptidation) or by a water molecule

(carboxypeptidation) to release the free enzyme. The product of the transpeptidation

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reaction is a new cross-link in the bacterial cell wall, while the carboxypeptidation

reaction serves to trim the terminal D-alanine [445].

Figure 4.2 Mechanism of the transpeptidation and carboxypeptidation

reactions catalyzed by the penicillin - binding protein.

As might be expected for a family of enzymes that catalyze the same

reaction, the PBPs are all evolutionarily related, and it will be useful to examine how

each one is different. The various PBPs have been broadly classified based on their

size and amino acid sequence. The high-molecular weight (HMW) PBPs range from

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100 to 60 kD and the low-molecular weight (LMW) PBPs range from 60 to 25 kD.

Both the high- and low-molecular weight PBPs are divided into three classes, A, B

and C and each class contains subclasses based amino acid sequence, as determined

by Ghuysen [446]. The HMW PBPs of class A are the only PBPs that contain both

transglycosylation and transpeptidation domains. The transglycosylase domain is

responsible for the lengthening of the linear glycan chain from lipid II precursor

molecules. Class B HMW PBPs contain a transpeptidase domain and another

domain, lacking transglycosylase activity, whose function is still unclear but may be

involved in protein-protein recognition [447]. All LMW PBPs contain only the

transpeptidase domain. All HMW PBPs are anchored in the cell membrane by a

string of hydrophobic amino acid residues at the N-terminal end of the protein, while

many LMW PBPs are not membrane-bound. It seems that the HMW PBPs are

multifunctional enzymes, whose effective inhibition results in cell death. In contrast

LMW PBPs are nonfunctional and nonessential; their inhibition results in the

modification of cell wall cross-linking [447].

Each bacterial species expresses several PBPs to carry out the various

tasks of cell wall synthesis and maintenance, and cell division. The array of PBPs in

each species is usually a selection of a few enzymes of each class, and within the

cell, each PBP is expressed in varying amounts. In addition to catalyzing the

formation of cell wall cross-bridges, the PBPs bind and react with �-lactam

antibiotics[448]. Indeed, they were first discovered and termed penicillin-binding

proteins because of this phenomenon.

Penicillin is capable of reacting with the PBPs by nature of its structural

similarity to the terminal D-alanyl-D-alanine of the peptidoglycan stem peptide, the

natural substrate of the PBPs. Years before the targets of penicillin were identified,

Tipper and Strominger (1965) [449], noticed this structural similarity and postulated

that penicillin was a potent inhibitor of bacterial cell wall synthesis. This hypothesis

has been demonstrated to be true, and penicillin does in fact, react with PBPs in

exactly the same way peptidoglycan substrates do.

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4.2.6 �-lactam antibiotics

The extensive use of common antibiotics such as penicillin and

cephalosporin in medicine has resulted in an increasing number of resistant bacteria

through mutation and gene transfer.

The bacteria may escape the effect of �-lactam antibiotics by reducing

the permeability of the cell wall (gram negative bacteria), by producing �-

lactamases, by reducing the affinity for �-lactam in their penicillin-binding proteins,

or by developing tolerance to �-lactam antibiotics. A combination of these resistance

mechanisms may be found in the most resistant bacteria, such as Pseudomonas,

Serratia and Enterobacter; bacteria often involved in nosocomial infections.

Bacteria can become resistant against �-lactam antibiotics by expressing �-lactamase

[450].

In recent years several natural monocyclic �-lactam were shown to

exhibit high antibacterial activity, suggesting that a suitably substituted monocyclic

2-azetidinone ring might perhaps be the minimum requirement for biological

activity [451].

The discovery and development of the �–lactam antibiotics are among

the most powerful and successful achievements of modern science and technology.

In particular penicillin and cephalosporins represent the world’s major

biotechnology products with worldwide sales of ~US$ 15 billion per year or ~65%

of the total world market for antibiotics [452]. The � -Lactam ring (2-azetidinone)

plays a key role in the most widely employed class of antimicrobial agents. The

substituents attached to the ring allow selective activity.

4.2.6.1 Penicillin-binding proteins (PBPs) are targets of � - lactam

antibiotics.

� - lactam exerts their lethal action on growing bacterial cells only. All

�-lactam which work by interfering with the synthesis of the bacterial cell wall by

inhibiting penicillin-binding proteins (PBPs) are called DD-Transpeptidases. The

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layer of the bacterial cell wall that confers strength is the peptidoglycan, a meshwork

of strands of peptide and glycan that can be covalently cross-linked [453]. The

bacterial cell wall protects and determines the shape of organism.

The �-lactam acts as pseudo substrate of the PBPs. After the initial

recognition of antibiotic and the formation of Michaelis complex, it is assumed that

a covalent tetrahedral intermediate is formed, as the antibiotic is covalently bond to

the active site serine residue. After the cleavage of the C-N lactam bond, an acyl

intermediate is formed and is subsequently broken down during deacylation. The

acyl intermediate has a half-life of 15-20 hours in the case of cefatoxime-PBP2X

[454]. The inactivation of the PBPs eventually leads to a cell wall which is unable to

withstand osmotic forces and bacteriolysis. This has a lethal effect on bacteria

particularly on Gram positive ones.

4.2.7 Cyclooxygenases (COX-1 and COX-2) in Inflammation

Cyclooxygenases (Prostaglandin G/H synthase) are endogenous enzymes

in cellular prostaglandin biosynthetic pathways and catalyze reactions in which

arachidonic acid is converted to the endoperoxide intermediate, prostaglandin H-1

[455]. COX appends two oxygen molecules to arachidonic acid and triggers a set of

reactions that will eventually create inflammatory response. This enzyme exists in at

least two isoforms, the constitutively produced COX-1(PGHS 1) and the inducible

COX-2 (PGHS 2). Both iso-enzymes are located on the lumenal surface of the

endoplasmic reticulum and on the inner and outer membranes of the nuclear

envelope [456]. COX-1 is formed in many different cells to create prostaglandins

and used for basic “housekeeping” messages throughout the body that is responsible

for the physiological production of prostaglandins [457]. COX-2 is only formed in

special cells and is used for signalling both in pain and in inflammation. The over

expression of COX-2 is rapidly up regulated during the course of inflammation,

following cellular stresses, and in response to growth factors, tumor promoters,

hormones, bacterial endotoxins, and inflammatory cytokines. COX-2 can be induced

in a number of cell types, including fibroblasts, endothelial cells, monocytes and

ovarian follicles [458].

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Non-steroidal anti-inflammatory drugs (NSAIDs) are COX (COX-1 and

COX-2) inhibitors and prevent PG synthesis, thus exhibiting analgesic, antipyretic

and anti-inflammatory actions. However, NSAIDs have a number of adverse effects

such as increased incidence of gastric ulceration and renal complications, associated

with the chronic administration of NSAIDs [459]. These are thought to arise as a

consequence of the inhibition of the constitutive isoform of COX and some of their

beneficial effects by inhibiting COX-2. The two COX isoforms are about 60%

homologous. The ability to inhibit selectively one isoform is attributed to the

different amino acids at position 523, isoleucine in COX-1 and valine in COX-2.

This seemingly minute difference in structure, results in a larger central channel in

the COX-2 isoform [460]. As a result, molecules, which are too large to enter the

COX-1 channel, are able to enter the COX-2 channel, based solely on size

discrimination at the active site. Exploitation of this phenomenon has been the key

in developing selective COX-2 inhibitors which reduce the undesired side effects

such as gastrointestinal disorders, ulcers and renal failure related with that of COX

1. So, selective COX 2 inhibitors such as coxibs were developed in the recent times

[461].

Several classes of compounds having selective COX-2 inhibitory activity

have been reported in the literature. The presence of anti inflammatory agents in

natural bioactive compounds has been considered relevant, because of their

beneficial effects on human health, as demonstrated in numerous studies [462].

Some of the natural bioactive compounds exert their anti-inflammatory activities by

modulating gene expression of diverse inflammation-related genes which carry out

their action by directly inhibiting inflammatory protein cyclooxygenase 2 (COX-2).

Although several natural products have been shown to modulate COX-2 expression

it is not clear if those are able to directly interact with the gene product or its

modulating transcription factors [463].

Computational chemistry offers the possibility to explore these inter

actions through protein–ligand docking procedures. Docking methods are valuable

tools for drug development, and most current approaches assume a rigid receptor

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structure to allow docking of large numbers of possible ligands and putative binding

sites on a receptor molecule [464].

4.2.8 Phospholipase A2

Phospholipases A2 (PLA2) enzymes play a major role in the formation of

pro-inflammatory and inflammatory mediators such as prostaglandins, leukotrienes,

platelet-aggregating factor and lysophospholipids [465]. These enzymes catalyse the

hydrolysis of sn-2 acyl bond of phospholipids to produce unsaturated fatty acids and

lysophospholipids. Arachidonic acid, one of the fatty acids released by the

hydrolysis can lead to the biosynthesis of eicosanoids and hydrolysis of cellular

phospholipids of the activated inflammatory cells and therefore acts as a very

important inflammatory precursor. Increased concentrations of eicosanoids are

found in the state of inflammation [466]. PLA2s are classified as intracellular and

extracellular. Intracellular PLA2s are of high molecular weight and involved in

phospholipid metabolism, signal transduction and other cellular processes [467].

Extracellular PLA2/secretory PLA2 (sPLA2) are of low molecular weight and found

in mammalian pancreatic juice, snake and insect venoms [468]. The release of

arachidonic acid by sPLA2 is followed by the eicosanoid production. Hence, sPLA2

are considered as “the inflammatory PLA2s” and act as target for anti-inflammatory

drugs. Thus, inhibition of PLA2 by various biologically active substances has gained

therapeutic importance. Most of the natural compounds and their derivatives

interfere directly or indirectly with specific molecules or mechanisms, such as

various inflammatory mediators (arachidonic acid metabolites, peptides, cytokines

etc.), production of second messengers (cGMP, cAMP, various protein kinases) and

in release of pro-inflammatory molecules [469].

4.2.9 Docking Studies using the Synthesized Compounds

The newly synthesized compounds are subjected to molecular docking

studies for the inhibition of the enzyme L-glutamine: D-fructose-6-phosphateamido-

transferase [GlcN-6-P] which is a new target for antifungals. Among the five

molecules taken for docking studies, 2-(8-quinolinyl)-4-(2,5-dichlorothienyl)-1,3-

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thiazole shows minimum binding and docking energy and may be considered as

good inhibitor of GlcN-6-P synthase [470].

Molecular docking studies further help in understanding the various

interactions between the ligand and enzyme active site in order to rationalize the

biological results obtained in our study. The determination of the three-dimensional

co-crystal structure of COX-2 complex with a selective inhibitor, SC-558 (PDB ID:

1CX2) has led to the development of a model for the topography of the NSAIDs

binding site in human COX-2 [471].

4.2.10 Objectives of the Study

The main aim of the study is to analyze the binding affinity of the

isolated compound from T. decandra with its target protein using docking studies

and to carry out Molecular docking studies (in silico) with the X-ray crystal

structures of PBP, COX-1, COX-2 and PLA2-inhibitor complexes available in the

Protein Data Bank (PDB; http://www.pdb.org/pdb) to illustrate the interactions

exhibited by TDCIV, TDCV, TDCVI and TDCVII with the target protein. The

specific objective of the study is to identify potential targets for the isolated

compounds using in silico docking to understand the possible hypothetical mode of

action underlying the antimicrobial and anti-inflammatory activities.

4.3 MATERIALS AND METHODS

4.3.1 Molecular Modelling Studies

Induced Fit Docking studies have been carried out using GLIDE [472]

software v5.5, developed by Schrodinger, running on Red Hat Enterprise Linux 5

(RHEL5) workstation. Maestro v9.0 Graphical User Interface (GUI) workspace was

used for all the steps involved in ligand preparation, protein preparation and Induced

Fit Docking (IFD).

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4.3.2 Preparation of the Ligand

The ligand used in this study was prepared using Ligprep module of v2.3

of Schrodinger Suite 2009. Ligprep follows OPLS-AA (Optimized Potential Liquid

Simulations for All Atoms) force fields for energy minimization. The protein taken

for the study was 2Y4A (Penicillin Binding Protein), 3N8X (COX-1), 1PXX

(COX-2) and 1KPM (PLA2) retrieved from PDB database. The optimized structure

was then energy minimized to remove the steric clashes between the atoms. The

energy minimization was done till it reached a Root Mean Square Deviation

(RMSD) cutoff of 0.18 Å and the resulting structure was used for docking [422].

4.3.3 Induced Fit Docking (IFD)

IFD of the prepared ligand with the prepared protein was performed

using IFD protocol of GLIDE v5.5 from Schrodinger Suite 2009 [422]. Both the

ligand and the receptor were flexible which enabled the ligand to dock at the

receptor’s binding site and generate multiple poses of the receptor-ligand complex.

Each docking included unique structural conformations of the receptor needed to fit

the ligand pose. The IFD gives the best structure of the docked complex based on

the Glide score (G-score) of the dockings.

4.3.3.1 Induced Fit Docking Protocol

The prepared protein is docked using induced fit protocol using the

following system.

� Constrained minimization of the receptor (Glide protein

preparation, refinement only) with an RMSD cutoff of 0.0018 �

� Initial Glide docking of each ligand using a softened potential (Van

der Waals radii scaling). By default, a maximum 20 poses per

ligand are retained, and by default poses to be retained must have a

Coulombic-vdW score less than 100 and an H-bond score is less

than –0.05.

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� One round of Prime side-chain prediction for each protein/ligand

complex, on residues within a given distance of any ligand pose

(default 5 Å).

� Prime minimization of the same set of residues and the ligand for

each protein/ligand complex pose. The receptor structure in each

pose now reflects an induced fit to the ligand structure and

conformation.

� Glide re-docking of each protein/ligand complex structure within a

specified energy of the lowest-energy structure (default 30

kcal/mol). The ligand is now rigorously docked, using default Glide

settings, into the induced-fit receptor structure.

� Minimization of the re-docked ligand within the protein.

� Estimation of the binding energy (Glide Energy) for each output pose.

4.4 RESULTS AND DISCUSSION

To predict the antimicrobial data on a structural basis, docking studies were carried out using GLIDE program, the scoring functions and hydrogen bonds formed with the surrounding amino acids are used to predict their binding modes, their binding affinities and orientation of these compounds TDCV and TDCVI at the active site of the Penicillin Binding Protein.

The docking study proposes that the antimicrobial compounds like flavonoid (TDCVI) and phytosterol (TDCV) isolated from T. decandra have a similar affinity towards the PDB ID: 2Y4A (Penicillin Binding Protein) bound to the nucleophilic serine of the active site and mimicking the transition state of the enzymatic reaction. Docking was performed for flavonoid compound and penicillin binding protein. Total poses obtained for the flavonoid compound is 15. Out of 15 docked complexes of the ligand, we got 2 best docked poses 1& 2 showing favourable H-bond interactions with the aminoacid residues of the receptor molecule. For pose 1 the Glide energy obtained was - 48.43 and the docking score (K calmol-1) was - 8.12. There were 6 hydrogen bond interactions with LEU 349, SER 49, SER 298, SER 298, ARG 351 and ASN 300 (Plate 4.2). Table 4.2 shows the docking score details of TDCVI.

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Plate 4.2 The docked complex of the ligand TDCVI in the active site residue

cleft of the protein PBP

Plate 4.3 The docked complex of the ligand TDCVI in the active site residue

cleft of the protein PBP

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For pose 2 of TDCVI the Glide energy obtained was -45.23 with a

docking score (K cal mol -1) of -7.62 and there were 2 hydrogen bond interactions

with ASN 300 and SER 298 (Plate 4.3).

Penicillin Binding Protein: 2Y4A

Table 4.2 Docking score details of TDCVI with PBP.

Pose No Docking

Score

Glide

energy Interaction Distance Å

1 -8.12 -48.43 LEU 349 (O-H---O)

SER 49 (O-H---O)

SER 298 (O-H---O)

SER 298 (O-H---O)

ARG 351 (N-H---O)

ASN 300 (O-H---O)

3.38

2.84

2.82

2.98

3.01

3.50

2 -7.62 -45.23 ASN 300 (O-H---O)

SER 298 (O-H---O)

2.80

2.81

Docking was performed for TDCV and penicillin binding protein. Total

poses obtained for phytosterol compound was 15. Out of 15 docked complexes of

the ligand, we got 1 best docked pose 1 showing favourable H-bond interactions

with the aminoacid residues of the receptor molecule. For pose 1 the Glide energy

obtained was -34.32 with a docking score (K cal mol-1) of - 4.34. There was only 1

hydrogen bond interaction with ASP 163 (Plate 4.4). Table 4.3 shows the docking

score details of TDCV.

213

Plate 4.4 The docked complex of the ligand TDCV in the active site residue

cleft of the protein PBP

Penicillin Binding Protein: 2Y4A

Table 4.3 Docking score details of TDCV with PBP.

Pose No Docking Score Glide energy Interaction Distance Å

1 - 4.34 - 34.32 ASP 163(O-H---O) 2.722 - 4.75 - 30.04 THR 393(O-H---O) 2.85

The anti-inflammatory compounds terpenoid and sapogenin (TDCIV and

TDCVII) were evaluated in silico (docking) to recognize their hypothetical binding

mode using the X-ray crystal structure of PLA2 (PDB ID: 1KPM), COX-1 (PDB ID:

3N8X) crystal structure of cyclooxygenase-1 and COX-2 (PDB ID: 1PXX) crystal

structure of cyclooxygenase - 2 respectively and also to rationalize their structure

activity relationships. The GLIDE based molecular docking results are reasonably

fair in understanding the binding interactions and in vitro anti-inflammatory activity

of these compounds to PLA2, COX-1 and COX-2 enzyme.

214

Docking was performed for terpenoid (TDCIV) and protein COX-1.

Total poses obtained for terpenoid with protein COX-1 was 10. Among them pose 4

is the best. For pose 1 the Glide energy obtained was -21.23 with a docking score

(K cal mol-1) of -5.66. There was 1 hydrogen bond interaction with MET 522

(Plate 4.5). For pose 2 the Glide energy obtained was -25.75 with a docking score

(K cal mol -1) of -5.81 and there was 1 hydrogen bond interaction with ARG 120 and

for pose 4 Glide energy obtained was -27.75 with a docking score of -5.93. There

were 2 hydrogen interactions with HIE 90 and SER 353 (Plate 4.6). Table 4.4 shows

the docking score details of TDCIV with COX-1.

Plate 4.5 The docked complex of the ligand TDCIV in the active site residue

cleft of the protein COX-1.

215

Plate 4.6 The docked complex of the ligand TDCIV in the active site residue

cleft of the protein COX-1.

PDB ID: 3N8X (COX-1 Protein)

Table 4.4 Docking score details of TDCIV with COX-1.

Pose No Docking Score Glide energy Interaction Distance Å

1 -5.66 -21.23 MET 522(O-H---O) 2.75

2 -5.81 -25.75 ARG 120(N-H---O) 2.99

4 -5.93 -27.75 HIE 90(N-H---O)

SER 353(O-H-----O)

2.91

3.04

Docking was performed for TDCIV and protein COX-2 with Diclofenac

bound to the cyclooxygenase active site. Total poses obtained for terpenoid with

COX-2 was 17. For pose 1 the Glide energy obtained was -23.46 with a docking

score (K cal mol-1) of -5.67 (Plate 4.7). There was 1 hydrogen bond interaction with

216

TYR 348. For pose 4 the Glide energy obtained was -25.25 with a docking score (K

cal mol -1) of -5.54 and there were 2 hydrogen bond interactions with GLN 192 and

PHE 518. For pose 10 Glide energy obtained was -26.55 with a docking score of -

5.26. There were 2 hydrogen interactions with GLN 192 and PHE 518. Table 4.5

shows the docking score details of TDCIV with COX-2.

Plate 4.7 The docked complex of the ligand TDCIV in the active site residue

cleft of the protein COX-2.

PDB: 1PXX (COX-2 Protein)

Table 4.5 Docking score details of TDCIV with COX-2.

Pose No Docking Score Glide energy Interaction Distance Å

1 -5.67 -23.46 TYR 385(O-H---O) 3.06

4 -5.54 -25.25 GLN 192(O-H---O) PHE 518 (N-H---O)

2.872.83

10 -5.26 -26.55 GLN 192(O-H---O) PHE 518(N-H-----O)

3.003.08

217

Docking was performed for TDCIV and complex of protein PLA2 and

vitamin E. Total poses obtained for terpenoid with PLA2 was 17. For pose 1 the

Glide energy obtained was -32.04 with a docking score (K cal mol-1) of -5.15. There

were 2 hydrogen bond interactions with HIS 48 and ASP 49 (Plate 4.8). For pose 2

the Glide energy obtained was -28.36 with a docking score (K cal mol -1) of - 4.50

and no hydrogen bond interactions were seen. Table 4.6 shows the docking score

details of TDCIV with PLA2.

Plate 4.8 The docked complex of the ligand TDCIV in the active site residue

cleft of the protein PLA2.

PDB ID: 1KPM (PLA2)

Table 4.6 Docking score details of TDCIV with PLA2

Pose No Docking Score

Glide energy Interaction Distance Å

1 -5.15 -32.04 HIS 48(N-H---O)

ASP49(O-H---O)

2.68

2.86

2 -4.50 -28.36 - -

218

Docking was performed for sapogenin (TDCVII) and protein COX-1.

Total poses obtained for sapogenin with protein COX-1 was 25. Among them pose

11 is the best. For pose 11 the Glide energy obtained was -44.39 with a docking

score (K cal mol-1) of -6.83. There were 3 hydrogen bond interactions with HIS 43,

THR 62 and ILE 125 (Plate 4.9). Table 4.7 shows the docking score details of

TDCVII with COX-1.

Plate 4.9 The docked complex of the ligand TDCVII in the active site residue

cleft of the protein COX-1

PDB ID: 3N8X (COX-1 Protein)

Table 4.7 Docking score details of TDCVII with COX-1

PoseNo

Docking Score

Glideenergy

Interaction Distance Å

11 -6.83 -44.39

HIS 43 (O-H…O)

THR 62 (O-H…O)

ILE 124 (O-H…O)

2.98

2.98

3.08

219

Docking was performed for TDCVII and protein COX-2. Total poses

obtained for sapogenin with COX-2 was 3. Among them best pose obtained in

pose 1. For pose 1 the Glide energy obtained was -47.10 with a docking score

(K cal mol-1) of -14.15 (Plate 4.10). There were 3 hydrogen bond interactions with

MET 522, ALA 527 and ARG 523. Table 4.8 shows the docking score details of

TDCVII with COX-2.

Plate 4.10 The docked complex of the ligand TDCVII in the active site residue

cleft of the protein COX-2

PDB: 1PXX (COX-2 Protein)

Table 4.8 Docking score details of TDCVII with COX-2

Pose No Docking

ScoreGlide

energy Interaction Distance Å

1 -14.15 -47.10 MET522 (O-H…O)

ALA527 (O-H…O)

ARG513 (O…H-N)

2.8

2.7

2.2

220

Docking was performed for TDCVII and complex of protein PLA2. Total

poses obtained for sapogenin with PLA2 was 27. Out of 27 docked complexes of the

ligand, we got 1 best docked ligands at pose 27 showing more H-bond interactions

with the aminoacid residues of the receptor molecule. For pose 27 the Glide energy

obtained was -40.41 with a docking score (K cal mol-1) of -4.34. There were 5

hydrogen bond interactions with CYS 61, GLY 30, ASP 49, PRO 56 and HIS 48

(Plate 4.11). Table 4.9 shows the docking score details of TDCVII with PLA2. As

it’s well known, hydrogen bonding plays an important role for the structure and

function of biological molecules, especially for inhibition in a complex.

Plate 4.11 The docked complex of the ligand TDCVII in the active site residue

cleft of the protein PLA2

221

PDB ID: 1KPM (PLA2)

Table 4.9 Docking score details of TDCVII with PLA2

Pose. No Docking

ScoreGlide

energy Interaction Distance Å

27 -4.34 -40.41 CYS 61 (O-H…O) GLY 30 (O-H…O)

ASP 49 (O-H…OH) PRO 56 (O-H…O) HIS 48 (O-H…OH)

2.32.82.82.92.8

4.5 CONCLUSION

With the in vitro antimicrobial activity results and in vitro and in vivo

anti-inflammatory results the proposed structures of the isolated compounds in hand,

it is thought worth-while to carry out in silico studies. Penicillin Binding Protein

(PBP) was chosen to carryout in silico studies using the characterized antimicrobial

compounds, while PLA2, COX-1 and COX-2 were chosen for the characterized

anti-inflammatory compounds.

The present study demonstrated that the antimicrobial compounds

flavonoid and phytosterol (TDCVI and TDCV) isolated from T. decandra bind with

the selected template Penicillin Binding Protein (PDB ID: 2Y4A). The

anti-inflammatory compounds terpenoid and saponin (TDCIV and TDCVII) from

were evaluated in silico (docking) to recognize their hypothetical binding mode

using PLA2 (PDB ID: 1KPM), COX-1 (PDB ID: 3N8X) and COX-2 (PDB ID:

1PXX) to rationalize their structure activity relationships. This study could be

utilized for the designing of effective drug for the treatment of inflammatory

diseases. This study would also initiate the research on discovery and design of more

effective drug.