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Chapter-4

DOCKING

4.1 DOCKING

Docking is often used to foretell the binding orientation of small molecule

drug candidates to their protein targets in order to in turn predict the affinity and

activity of the small molecule. Hence docking plays an important role in the rational

design of drugs.

4.1.1 Pdb Sum

PDB sum is the database which was used to provide and give summary of

every macromolecular structure deposited in the Protein Data Bank (PDB).

Figure 4. 1: Image showing the PDB SUM of the Glutathione S-transferase sequence

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4.1.2 D Jigsaw

It builds three-dimensional models for proteins based on homologues of

known structure. It’s a protein comparative modeling server for predicting the

structure and function of our protein sequence. 3D-JIGSAW takes part in the Critical

assessment of fully automated structure prediction servers. Version 2.0 is out, with a

new interactive mode to build models, a domain-oriented template search procedure

(Domain Fishing), alignment accuracy measures and a new energy minimization

algorithm to refine models.

4.1.3 CPH Models 3.0

It is a protein homology modeling server and in this, the template recognition

is based on profile-profile alignment guided by secondary structure and exposure

predictions. Automated neural-network based protein modelling server for tertiary

structure prediction.

4.1.4 Drug Bank

The Drug Bank database is a unique bioinformatics and cheminformatics

resource that combines detailed drug i.e. chemical, pharmacological and

pharmaceutical data with comprehensive drug target with sequence, structure, and

pathway information. The database contains nearly 4800 drug entries including

>1,350 FDA-approved small molecule drugs, 123 FDA-approved biotech

(protein/peptide) drugs, 71 nutraceuticals and >3,243 experimental drugs.

Additionally, more than 2,500 non-redundant protein (i.e. drug target) sequences are

linked to these FDA approved drug entries. Each Drug Card entry contains more than

100 data fields with half of the information being devoted to drug/chemical data and

the other half devoted to drug target or protein data..

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4.1.5 Zinc Database Search

ZINC, a free database of commercially-available compounds for virtual

screening. ZINC contains over 13 million purchasable compounds in ready-to-dock,

3D formats. ZINC is provided by the Shoichet Laboratory in the Department of

Pharmaceutical Chemistry at the University of California, San Francisco (UCSF).

Figure 4. 2: Image showing ZINC data base home page.

4.1.6 Isis Draw

ISIS/Draw is a program from MDL that is free for non-commercial use. We

can use it to draw chemical structures, and export them for viewing as 3D models.

ISIS means Integrated Scientific Information System. ISIS/Draw is mainly a 2D

drawing program, it has some 3D rotation features and can interface with Rasmol for

3D visualization and rendering. ISIS/Draw also includes structure and reaction

validation features and can calculate elementary properties such as formula and

molecular weight.

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In this study the bivalent inhibitors of Glutathione S transferase were drawn by

using ISIS/Draw 2.3 and that ligands were used for docking.

Figure 4. 3: Image showing ISIS Draw home page.

4.1.7 Tsar

TSAR software of version 3.3 was used to study the QSAR derivatives. It has

TSAR project window, to which molecular data is entered through import/export file

system. Multiple regression analysis is done by taking physiochemical properties and

biological activity. Then a graph was plotted in between actual values and predicted

values. A description of the basic operation of TSAR and fundamental aspects of the

application with which we need to be familiar, includes the TSAR interface in

knowing how to work with projects, data and views. When we work with TSAR

graphical interface, the first screen that is displayed is the main TSAR window. This

is called a

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project window. Any data that we handle in TSAR is organized into projects and you

view that data using the project Window. Menu bar displays menu items that give

access to drop-down menus. Toolbar contains action buttons that provide shortcuts to

the most frequently used menu options. View tabs allow us to move between different

views of the currently displayed project. Status bar displays general messages about

the status of the current project and displays single progress messages. Scroll bar

allow us to move around the window area and display information that is beyond the

window border.

Figure 4. 4: Image showing TSAR Homepage for 2D to 3D Conversion

4.1.8 Active Site Identification

Active site is identified using CASTp Server (Computed Atlas of Surface

Topography of Proteins) binding sites and active sites of proteins and DNAs are often

associated with structural pockets and cavities. CASTp server uses the weighted

Delaunay triangulation and the alpha complex for shape measurements. It provides

identification and measurements of surface accessible pockets as well as interior

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inaccessible cavities, for proteins and other molecules. It measures analytically the

area and volume of each pocket and cavity, both in solvent accessible surface (SA,

Richards' surface) and molecular surface (MS, Connolly's surface). It also measures

the number of mouth openings, area of the openings, and circumference of mouth lips,

in both SA and MS surfaces for each pocket.

You can request calculation for a particular molecule. The results will be

shown on the screen or emailed to you. The emailed results include measured

parameters for pockets, cavities and mouth openings, as well as listing of wall atoms

and mouth atoms for each pocket. In addition, a downloadable PyMOL plug-in will

help you to visualize the pocket of your interest.

Figure 4. 5: Image Showing CASTp server home page

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4.1.9 Weblab Viewer lite

WebLabViewer provided a very easy-to-use, user-friendly approach for

molecule-display. WebLab ViewerLite analyzes organic and inorganic structures,

proteins, DNA/RNA, and crystals. WebLabViewer has been a cut-down version of a

commercial viewer/editor-program now sold by Accelrys. Apparently Accelrys has

discontinued the free version of what has now become DS Viewer Pro. WebLab

ViewerLite is developed by Molecular Simulations Inc. and is used by 36 users of

Software Informer.

The most popular versions of this product among our users are: 3.1, 3.2 and

4.0. The list of features is more or less similar to that of the two other viewing-

packages (Rasmol and Chime), in addition, WebLabViewer could generate and

display surfaces and features a very easy to use interface. Display soft surfaces and

solvent accessible surfaces. Visualize organic and inorganic crystal structures in a

variety of display styles. Display proteins using C-alpha Wire, C-alpha stick, Line

Ribbon, Flat Ribbon, Solid Ribbon, Tube and Schematic display styles. Color by

Amino Acid, Amino Acid chain, pKa, hydrophobicity, and secondary type.

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Figure 4. 6: Image showing WebLab ViewerLite with protein structure

4.1.10 Mvd (Molegro Virtual Docker)

Molegro Virtual Docker is an integrated platform for predicting protein –

ligand interactions. It handles all aspects of the process, from preparing the molecules

to determining the potential binding site of the target protein and predicting the

binding mode of the ligand. Molegro Virtual Docker offers high-quality docking

based on a novel optimization technique combined with a user interface experience

focusing on usability and productivity. Mol Dock is based on a new heuristic search

algorithm that combines differential evolution with a cavity prediction algorithm. The

docking scoring function of Mol Dock is an extension of the piecewise linear

potential (PLP) including new hydrogen bonding and electrostatic terms. To further

improve docking accuracy, a re-ranking scoring function is introduced, which

identifies the most promising docking solution from the solutions obtained by the

docking algorithm. The docking accuracy of Mol Dock has been evaluated by

docking flexible ligands to 77 proteins.

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One application of molecular docking is to design pharmaceuticals in silico by

optimizing lead candidates targeted against proteins. The lead candidates can be found

using a docking algorithm that tries to identify the optimal binding mode of a small

molecule (ligand) to the active site of a macromolecular target. Thus, the purpose of

drug discovery is to derive drugs that more strongly bind to a given protein target than

the natural substrate. By doing so, the biochemical reaction that the target molecule

catalyzes can be altered or prevented.

4.1.10.1 Importance of Molegro Virtual Docker

High docking accuracy: The docking engine has been proven to correctly identify

binding modes with high accuracy. Molegro Virtual Docker is shown to outperform

other docking programs with regard to identification of correct binding modes.

Easy-to-use interface: The built-in wizards enable the user to easily setup and

perform docking runs. Advanced visualization and analysis tools are provided to

examine ligand-receptor interactions and fine-tune found docking solutions.

Scoring Function: The MolDock scoring function (MolDock Score) used by MVD is

derived from the PLP scoring functions originally proposed by Gehlhaar, later

extended by Yang. The MolDock scoring function further improves these scoring

functions with a new hydrogen bonding term and new charge schemes. The docking

scoring function, Escore, is defined by the following energy terms: where Einter is the

ligand-protein interaction energy. The summation runs over all heavy atoms in the

ligand and all heavy atoms in the protein including any cofactor atoms and water

molecule atoms that might be present. The EPLP term is a piecewise linear potential

described below. The second term describes the electrostatic interactions between

charged atoms. It is a Coulomb potential with a distance-dependent dielectric constant

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given by: D(r) = 4r. The numerical value of 332.0 fixes the units of the electrostatic

energy to kilocalories per mole.

4.1.11 Tsar

Conversion of 2d to 3d structure

The conversion of 2D structure into 3D structure can be done by using

Tsar Software. This conversion is very useful for 3D visualization of 2D

structure.

The purpose of this conversion in this study was to do docking with

Molegro software (MVD2007).

Steps involved in this conversion

Open the Tsar and Molecules need to be converted were imported into

Tsar.

Click on the structure option in Tsar, and follow these three steps

1. Corina-Make3D,

2. Charge2-derive charges

3. Cosmic-optimize 3D

These three steps resulted in conversion of 2D structure into 3D.

The structure came with this Tsar was fully optimized and stable structure

After this we can export these 3D converted molecules into our files.

The three steps in tsar for 3d conversion

1. corina -Make 3D

2. charge2-Derive Charges

3. cosmic-Optimize 3D

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Figure 4. 7:Image Showing TSAR workspace with 2D to 3D conversion

4.2 METHODOLOGY OF DOCKING

Molegro Virtual Docker was used to perform docking. The Molegro Virtual

Docker window is shown below. All the compounds were docked and their respective

results were compared for the Mol dock Score and Ki values.

4.2.1 Basic Features

1) Import and export of industry standard file formats (PDB, Mol2, SDF)

2) Automated preparation of input structures (assign hydrogens, charges, bond orders,

hybridization, protonation templates)

3) Visualization styles (wireframe, ball-and stick, CPK, stick, cartoon, and surfaces)

4) Automatic prediction of potential binding sites (active site finding)

5) Flexible 3D-label system

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6) Docking scoring function (extended PLP score taking hydrogen bonding

directionality into account)

7) Docking search algorithm based on state-of-the-art genetic algorithms

8) Console command interface to allow for advanced user interaction

9) GUI wizards and on-line help

4.2.2 Validation Parameters

There are two types of validation parameters used here. They are RMSD

CALCULATION and RAMACHANDRAN PLOT

4.2.3 Benchmark Results

Bench mark results of MVD (Molegro Virtual Docker) software provides very

accurate predictions of ligand binding modes.It has been cited in more than 100

research papers, and is used by organizations across the world. [82]

DOCKING PROGRAM ACCURACY Program Accuracy

Molegro Virtual Docker 87.0%

Glide 81.8%

GOLD 1 78.2%

Surflex 75.3%

FlexX2 57.9%

4.2.4 The Steps Involved In Docking Were

1. Importing the molecules or ligands

2. Preparation of molecules

3. Cavity Detection of a Prepared Protein

4. Docking

5. Analysis of Results

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The protein and ligand molecules present in the PDB or Mol2 formats were

imported into the workspace of the Molegro Virtual Docker software. The molecules

were prepared after getting imported into the workspace of MVD. The cavities

present in the protein can be detected by the Detect Cavities option and the large

cavity was selected as the binding site for the ligand while performing docking. The

docking was performed using the docking wizard.

Step 1- Importing the molecules or ligands

MVD was open and imported the protein and ligand molecules, then it

removes water and other molecules which exceed in number.

Figure 4. 8: Image showing the Molegro Virtual Docker window

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Figure 4. 9: Images showing importing of Molecules

Figure 4. 10: Images showingImported Protein Structure (back bone view)

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Step 2 - Preparation of Molecules

Figure 4. 11: Images showing preparation of protein

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Step 3-Cavity Detection of a Prepared Protein

Figure 4. 12: Images showing the protein, ligand imported into the Molegro Virtual Docker and the cavities

were detected in the protein

Step 4- Docking

Docking was select and went docking wizard. Here gave the reference ligand

name and click on the next

This requires the scoring function (score& grid resolution)& binding

site[origin, centre(X, Y, Z) and radius] details. Then click on the next

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Figure 4. 13: Images showing the Executing of Docking Setup

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Figure 4. 14: Image showing the grid calculation

Figure 4. 15: Image showing the docking progress

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In this step it give the algorithmic settings as next

Algorithmic Settings: The following parameters were used for the guided differential

evolution algorithm:

Population size = 50, crossover rate = 0.9, scaling factor = 0.5, and max evaluations

=100 000.

Figure 4. 16:Image showing the Poses of Protein-Ligand Complex

These settings were found by trial and error in a few preliminary runs and

generally gave the best results across all the 77 complexes. Click on the next it will

carry to the pose clustering page. It has multiple poses .In this page we give the

RMSD threshold value .Click on the next it will carry to the errors &warnings page. It

shows the errors & cavity information.

Bivalent inhibitors of Glutathione S transferase Compound 6, compound 7,

compound 8, EAA, GABA were utilized in the docking process. The results of this

docking process were discussed in detail in results section.