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					 High Throughput Screening of Anthrax
Lethal Factor Inhibitors and Their Analogs
  A Dissertation submitted to the Amity University in partial fulfillment of the

                 degree of Master of Science in Bioinformatics



                       Ajay Ikkurthi

              Department of Bioinformatics


Virtual Screening of Human Transketolase with Zinc database: An Approach
for QSAR properties and Pharmacophore group Design” submitted by   Ajay Ikkurthi
(Roll. No: MSI/09/113)

         I, Ajay Ikkurthi (Roll. No: MSI/09/113), hereby declare that the M.Sc. project dissertation
entitled “Virtual     Screening of Human Transketolase with Zinc database: An
Approach for QSAR properties and Pharmacophore group Design” is the original
work done by me in the Department of Bioinformatics, Silicon Bioinformatics (a unit of Muvva
Biosolutions Pvt.Ltd) Hyderabad and to the best of my knowledge similar work has not been submitted
earlier for the award of any degree or diploma to the University or any other institution.

         This project is submitted in partial fulfillment for the requirement for the award of the degree of
M.Sc. Bioinformatics to the AMITY University.

                                                                    (Ajay Ikkurthi)



I am glad to express my immense sense of gratitude and indebtedness to my major advisor,
Neetu Jabalia, of Biotechnology, Amity Institute of Biotechnology(AIB), for her valuable
guidance, encouraging attitude, constructive criticism and suggestions during the course of the
term paper.

        I express my sincere thanks to the Librarian(AIB), for allowing me to utilized the relevant
journals and references from the library during the course of my term paper.

Date:    8-nov-2010                                                         Ajay Ikkurthi

CHAPTER    TITLE                 PAGE NO

   1.     Objective & Scope         7

   2.     Introduction             9-10

   3.     Review of Literature    12-17

   4.     Materials & Methods    19-32

   5.     Results & Discussion    34-37

   6.     Conclusion                39

   7.     Summary                   41

          Bibliography            43-45


                                       CHAPTER 1

                                 OBJECTIVE & SCOPE

       Transketolase, an enzyme of both the pentose phosphate pathway in animals and the
Calvin cycle of photosynthesis, catalyzes two important reactions, which operate in opposite
directions in these two pathways. In the first reaction of the pentose phosphate pathway, the
cofactor thiamine diphosphate accepts a 2-carbon fragment from a 5-carbon ketose (D-xylulose-
5-P), then transfers this fragment to a 5-carbon aldose (D-ribose-5-P) to form a 7-carbon ketose
(sedoheptulose-7-P). The abstraction of two carbons from D-xylulose-5-P yields the 3-carbon
aldose glyceraldehyde-3-P. In the Calvin cycle, transketolase catalyzes the reverse reaction, the
conversion of sedoheptulose-7-P and glyceraldehyde-3-P to pentoses, the aldose D-ribose-5-P
and the ketose D-xylulose-5-P.

    The objective of this study is to screen various lead like compounds from Zinc Database and
based on Gold Fitness the compounds were submitted to pharmacophore group identification and
QSAR properties. In the study GOLD, Ligandscout and Hyperchem was used for Screening
,pharmacophore ,QSAR properties analyses a tool for drug discovery .

       The scope of the study extends to predict the feasibility of the compounds as leads for
Drug Development.
                                         CHAPTER 2


          Transketolase, an enzyme of both the pentose phosphate pathway in animals and the
Calvin cycle of photosynthesis, catalyzes two important reactions, which operate in opposite
directions in these two pathways. In the first reaction of the pentose phosphate pathway, the
cofactor thiamine diphosphate accepts a 2-carbon fragment from a 5-carbon ketose (D-xylulose-
5-P), then transfers this fragment to a 5-carbon aldose (D-ribose-5-P) to form a 7-carbon ketose
(sedoheptulose-7-P). The abstraction of two carbons from D-xylulose-5-P yields the 3-carbon
aldose glyceraldehyde-3-P. In the Calvin cycle, transketolase catalyzes the reverse reaction, the
conversion of sedoheptulose-7-P and glyceraldehyde-3-P to pentoses, the aldose D-ribose-5-P
and the ketose D-xylulose-5-P.

   The second reaction catalyzed by transketolase in the pentose phosphate pathway involves
the same thiamine diphosphate-mediated transfer of a 2-carbon fragment from D-xylulose-5-P to
the aldose erythrose-4-phosphate, affording fructose 6-phosphate and glyceraldehyde-3-P.
Again, in the Calvin cycle exactly the same reaction occurs, but in the opposite direction.
Moreover, in the Calvin cycle this is the first reaction catalyzed by transketolase, rather than the

    In mammals, transketolase connects the pentose phosphate pathway to glycolysis, feeding
excess sugar phosphates into the main carbohydrate metabolic pathways. Its presence is
necessary for the production of NADPH, especially in tissues actively engaged in biosyntheses,
such as fatty acid synthesis by the liver and mammary glands, and for steroid synthesis by the
liver and adrenal glands. Thiamine diphosphate] is an essential cofactor, along with calcium.

   Transketolase is abundantly expressed in the mammalian cornea by the stromal keratocytes
and epithelial cells and is putatively considered one of the corneal crystallins.[1]
Species distribution

   Transketolase is widely expressed in a wide range of organisms including bacteria, plants, and
mammals. The following human genes encode proteins with transketolase activity:

      TKT (transketolase)
      TKTL1 (transketolase-like protein 1)
      TKTL2 (transketolase-like protein 1)


       The entrance to the active site for this enzyme is mainly made up of several arginine,
histidine, serine, and aspartate side chains, with a glutamate side chain playing a secondary role.
These side chains, specifically Arg359, Arg528, His469, and Ser386, are conserved within each
transketolase enzyme and interact with the phosphate group of the donor and acceptor substrates.
Because the substrate channel is so narrow, the donor and acceptor substrates cannot be bound
simultaneously. Also, the substrates conform into a slightly extended form upon binding in the
active site to accommodate this narrow channel.

       Although this enzyme is able to bind numerous types of substrates, such as
phosphorylated and nonphosphorylated monosaccharides including the keto and aldosugars
fructose, ribose, etc., it has a high specificity for the stereoconfiguration of the hydroxyl groups
of the sugars. These hydroxyl groups at C-3 and C-4 of the ketose donor must be in the D-threo
configuration in order to correctly correspond to the C-1 and C-2 positions on the aldose
acceptor.[2] Also they stabilize the substrate in the active site by interacting with the Asp477,
His30, and His263 residues. Disruption of this configuration, both the placement of hydroxyl
groups or their stereochemistry, would consequently alter the H-bonding between the residues
and substrates thus causing a lower affinity for the substrates.

       In the first half of this pathway, His263 is used to effectively abstract the C3 hydroxyl
proton which thus allows a 2-carbon segment to be cleaved from fructose 6-phosphate.[3] The
cofactor necessary for this step to occur is thiamin pyrophosphate (TPP). The binding of TPP to
the enzyme incurs no major conformational change to the enzyme; instead, the enzyme has two
flexible loops at the active site which make TPP accessible and binding possible.[2] This thus
allows the active site to have a "closed" conformation rather than a large conformational change.
Later in the pathway, His263 is used as a proton donor for the substrate acceptor-TPP complex
which can then generate erythrose-4-phosphate.

          The histidine and aspartate side chains are used to effectively stabilize the substrate
within the active site and also participate in deprotonation of the substrate. Specifically, the His
263 and His30 side chains form hydrogen bonds to the aldehyde end of the substrate, which is
deepest into the substrate channel, and Asp477 forms hydrogen bonds with the alpha hydroxyl
group on the substrate, where it works to effectively bind the substrate and check for proper
stereochemistry. It is also thought that Asp477 could have important catalytic effects because of
its orientation in the middle of the active site and its interactions with the alpha hydroxyl group
of the substrate. Glu418, which is located in the deepest region of the active site, plays a critical
role in stabilizing the TPP cofactor. Specifically, it is involved in the cofactor-assisted proton
abstraction from the substrate molecule.[2]

          The phosphate group of the substrate also plays an important role in stabilizing the
substrate upon its entrance into the active site. The tight ionic and polar interactions between this
phosphate group and the residues Arg359, Arg528, His469, and Ser386 collectively work to
stabilize the substrate by forming H-bonds to the oxygen atoms of the phosphate.[2] The ionic
nature is found in the salt bridge formed from Arg359 to the phosphate group.


         The catalysis of this mechanism is initiated by the deprotonation of TPP at the
thiazolium ring. This carbanion then binds to the carbonyl of the donor substrate thus cleaving
the bond between C-2 and C-3. This keto fragment remains covalently bound to the C-2 carbon
of TPP. The donor substrate is then released, and the acceptor substrate enters the active site
where the fragment, which is bound to the intermediate α-β-dihydroxyethyl thiamin diphosphate,
is then transferred to the acceptor.[2]
Experiments have also been conducted which test the effect replacing alanine for the amino acids
at the entrance to the active site, Arg359, Arg528, and His469, which interact with the phosphate
group of the substrate. This replacement creates a mutant enzyme with impaired catalytic

Role in disease
Transketolase activity is decreased in deficiency of thiamine, which is generally due to
malnutrition. Two diseases are associated with thiamine deficiency: beriberi and Wernicke-
Korsakoff syndrome. While no mutations could be demonstrated,[4] there is an indication that
thiamine deficiency only leads to Wernicke-Korsakoff syndrome in those whose transketolase
has a reduced affinity for thiamine.[5] In this way, the activity of transketolase is greatly hindered,
and consequently, the entire pentose phosphate pathway is inhibited.[6]
Diagnostic use
Red cell transketolase activity is reduced in deficiency of thiamine (vitamin B1), and may be
used in the diagnosis of Wernicke's encephalopathy and other B1-deficiency syndromes if the
diagnosis is in doubt.[7] Apart from the baseline enzyme activity (which may be normal even in
deficiency states), acceleration of enzyme activity after the addition of thiamine pyrophosphate
may be diagnostic of thiamine deficiency (0-15% normal, 15-25% deficiency, >25% severe
                                      CHAPTER 3

                              REVIEW OF LITERATURE

    Transketolase belongs to the family of thiamin diphosphate dependent enzymes. The aim of
this study was to establish a bacterial expression system for human transketolase in order to
investigate the functional characteristics of mammalian transketolases. The level of recombinant
human enzyme expressed in Escherichia coli was modest. Purification of recombinant
transketolase and separation from the E. coli enzyme has been greatly simplified by means of a
non-cleavable hexa-histidine tag. The highest specific activity was 13.5 U/mg and the Km values
were 0.27±0.02 and 0.51±0.05 mM for the substrates      -xylulose 5-phosphate and     -ribose 5-
phosphate, respectively. Binding of cofactors to the apoenzyme showed the expected hysteresis.
Without preincubation, the Km values for thiamin diphosphate and for Mg2+ were, respectively,
4.1±0.8 and 2.5±0.4 μM, but after 1 h of preincubation these values were 85±16 nM and
0.74±0.23 μM. The kinetic constants are similar to those of the native enzyme purified from
human erythrocytes. Despite the modest expression level the reported system is well suited to a
variety of functional studies [9].

       Using isoelectric focusing of human erythrocyte transketolase, the isoenzyme pattern
described recently was reexamined. Seven bands having pI values of 7.4–8.4 were common to
the central part of the transketolase isoenzyme pattern in 63 healthy subjects investigated and
were definitely reproduced, whereas four additional marginal bands (pI values of 7.2, 7.3, 8.6
and 8.8) were found with varying intensities in part of the samples and could not always be
reproduced. We conclude that the method used does not permit the distinction of transketolase
variants, that would allow to postulate a genetic polymorphism, based only on variation of the
marginal bands of the pattern [10].
       Active human transketolase is a homodimeric enzyme possessing two active sites, each
with a non-covalently bound thiamine diphosphate and magnesium. Both subunits contribute
residues at each site which are involved in cofactor binding and in catalysis. His-tagged
transketolase, produced in E. coli, was similar to transketolase purified from human tissues with
respect to Km apps for cofactor and substrates and with respect to cofactor-dependent hysteresis.
Mutation of aspartate 155, corresponding to a conserved aspartate residue among thiamine
diphosphate-binding proteins resulted in an inactive protein which could not bind the cofactor–
magnesium complex and which could not dimerize. The results are consistent with the
suggestion that aspartate 155 is an important coordination site for magnesium. In support of this
interpretation, binding of cofactor by wild type apo-transketolase required the presence of
magnesium. Additionally, monomeric apo-his-transketolase required both magnesium and
cofactor binding for dimer formation [11].

   Transketolase, the most critical enzyme of the non-oxidative branch of the pentose phosphate
pathway, has been reported as a new target protein for cancer research. However, since the
crystal structure of human Transketolase is unknown, no structure-based methods can be used to
identify new inhibitors. We performed homology modeling of human Transketolase using the
crystal structure of yeast as a template, and then refined the model through molecular dynamics
simulations. Based on the resulting structure we propose five critical sites containing arginines
(Arg 101, Arg 318, Arg 395, Arg 401 and Arg 474) that contribute to dimer stability or catalytic
activity. In addition, an interaction analysis of its cofactor (thiamine pyrophosphate) and a
binding site description were carried out, suggesting the substrate channel already identified in
yeast Transketolase. A binding free energy calculation of its cofactor was performed to establish
the main driving forces of binding. In summary, we describe a reliable model of human
Transketolase that can be used in structure-based drug design and in the search for new
Transketolase inhibitors that disrupt dimer stability and cover the critical sites found[12].
         Human leukocyte transketolase of fresh cell extracts has been analyzed by
isoelectrofocusing on agarose gels (pH 3–10). The enzyme was then transblotted on
nitrocellulose   and    detected   with    specific   anti-transketolase    IgG    coupled    to   an
avidin/biotinimmunoperoxidase system. Each sample yielded multiple enzyme forms, within a pI
range of about 7.4–8.4. Transketolase profile, however, was not identical in all extracts. There
are two mainly distinct patterns, showing qualitative and quantitative differences: a standard
profile, which is predominant, and a variant, found in three unrelated subjects out of the two
hundred and twenty. Standard and variant enzyme have similar Km values for ribose 5-P and
xylulose 5-P and the same mobility on SDS-PAGE[13].

       Characteristic alterations of transketolase (TK) in extracts from cultured Alzheimer
fibroblasts have previously been reported (Paoletti et al. (1990) Biochem. Biophys. Res.
Commun., 172: 396–401). These abnormalities, encountered in 9 out of 13 Alzheimer patients,
were revealed following isoelectric focusing and consisted of enzyme forms having unusually
high alkaline pI values (alkaline bands). The present work has shown that immunologically
detected alkaline bands were progressively expressed when Alzheimer fibroblasts were
incubated for three weeks without medium changes. Full expression of the altered enzyme
pattern was not linked to relative cell density in the petri dish; rather, it appeared to be dependent
directly on the time elapsed since cell confluence was reached. Alkaline bands could artificially
be induced also in both crude and pure TK preparations from normal cells by a treatment with
commercial proteases, particularly chymotrypsin. Moreover, specific inhibitors of endogenous
cysteine-proteases were capable of abolishing TK alkaline bands in Alzheimer fibroblasts thus
turning a pathological into a normal enzyme pattern. Results obtained suggest that Alzheimer
fibroblasts contain enhanced Ca2+-independent cysteine-proteolytic activities as compared to
normal and other pathological cells. These enzymes, exhibiting chymotrypsin-like activity, might
exert their degradative effects at the time of cell extraction using TK and probably other cell
components as potential substrates. However, peculiar TK abnormalities represent so far an
useful biochemical marker detectable in fibroblasts of living Alzheimer patients and closely
associated to this neurological disorder[14].

         Transketolase in cultured skin fibroblasts from three patients with Wernicke-Korsakoff
syndrome (GM7504, 7505 and 7506) and matched controls was analyzed enzymatically and
immunochemically with specific antisera generated against transketolase purified from human
liver or red blood cells. The transketolase activity decreased by 45% in fibroblasts from the three
Wernicke-Korsakoff patients, when compared to the activity in control cells. On immunoblots
after SDS-PAGE, fibroblasts from the Wernicke-Korsakoff patients exhibited a 69-kDa species,
a size similar to that of normal transketolase. The level of immunoreactivity was similar in the
patient and control cells. The immunoblots of isoelectric focusing gels showed a major species of
pI 8.6 with additional minor bands. However, the isolectric focusing pattern of transketolase
from the Wernicke-Korsakoff patients was also found in the majority of the control fibroblasts.
Thus transketolase in fibroblasts from these Wernicke-Korsakoff patients is catalytically
defective, but appears to be immunochemically normal[15].

          Measurements of the activity of transketolase in human erythrocyte lysates by an assay
coupled to NADH oxidation indicate that interactions of assay substrates with hemoglobin can
give rise to overestimations of transketolase activity. Three potential sources of error are
identified. Thus, in lysates containing methemoglobin, NADH oxidation can be due firstly to
methemoglobin reductase activity or secondly to the monooxygenase activity of methemoglobin,
for which the substrate can be ribose 5-phosphate, a substrate also of transketolase. Thirdly, the
addition of high concentrations of the transketolase cofactor, TDP, to an insufficiently buffered
reaction mixture can cause the aggregation and precipitation of hemoglobin: a phenomenon that
may be misconstrued as an enhanced increase in absorbance at 340 nm and hence as additional
transketolase activity. Although the present study concentrates on these potential artefacts in
assays of transketolase activity, the findings may well be relevant to the measurement of other
enzyme activities in hemolysates by procedures based ultimately on the rate of consumption or
production of NAD(P)H [16].

        This review highlights recent research on the properties and functions of the enzyme
transketolase, which requires thiamin diphosphate and a divalent metal ion for its activity. The
transketolase-catalysed reaction is part of the pentose phosphate pathway, where transketolase
appears to control the non-oxidative branch of this pathway, although the overall flux of labelled
substrates remains controversial. Yeast transketolase is one of several thiamin diphosphate
dependent enzymes whose three-dimensional structures have been determined. Together with
mutational analysis these structural data have led to detailed understanding of thiamin
diphosphate catalysed reactions. In the homodimer transketolase the two catalytic sites, where
dihydroxyethyl groups are transferred from ketose donors to aldose acceptors, are formed at the
interface between the two subunits, where the thiazole and pyrimidine rings of thiamin
diphosphate are bound. Transketolase is ubiquitous and more than 30 full-length sequences are
known. The encoded protein sequences contain two motifs of high homology; one common to all
thiamin diphosphate-dependent enzymes and the other a unique transketolase motif. All
characterised transketolases have similar kinetic and physical properties, but the mammalian
enzymes are more selective in substrate utilisation than the nonmammalian representatives. Since
products of the transketolase-catalysed reaction serve as precursors for a number of synthetic
compounds this enzyme has been exploited for industrial applications. Putative mutant forms of
transketolase, once believed to predispose to disease, have not stood up to scrutiny. However, a
modification of transketolase is a marker for Alzheimer’s disease, and transketolase activity in
erythrocytes is a measure of thiamin nutrition. The cornea contains a particularly high
transketolase concentration, consistent with the proposal that pentose phosphate pathway activity
has a role in the removal of light-generated radicals [17].
       The pentose phosphate pathway (PPP) is an important metabolic pathway for yielding
reducing power in the form of NADPH and production of pentose sugar needed for nucleic acid
synthesis. Transketolase, the key enzyme of non-oxidative arm of PPP, plays a vital role in the
survival/replication of the malaria parasite. This enzyme in Plasmodium falciparum is a novel
drug target as it has least homology with the human host. In the present study, the P. falciparum
transketolase (PfTk) was expressed, localized and biochemically characterized. The recombinant
PfTk harboring transketolase activity catalyzed the oxidation of donor substrates, fructose-6-
phosphate (F6P) and hydroxypyruvate (HP), with         values of 2.25 and 4.78 mM, respectively.
p-Hydroxyphenylpyruvate (HPP) was a potent inhibitor of PfTk, when hydroxypyruvate was
used as a substrate, exhibiting a Ki value of 305 μM. At the same time, noncompetitive inhibition
was observed with F6P. The native PfTk is a hexamer with subunit molecular weight of 70 kDa,
which on treatment with low concentrations of guanidine hydrochloride (GdmCl) dissociated
into functionally active dimers. This protein was localized in the cytosol and nucleus of the
parasite as studied by confocal microscopy. A model structure of PfTk was constructed based on
the crystal structure of the transketolases of Saccharomyces cerevisae, Leishmania mexicana and
Escherichia coli to assess the structural homology. Consistent with the homology modeling
predictions, CD analysis indicated that PfTk is composed of 39% α-helices and 26% β-sheets.
The availability of a structural model of PfTk and the observed differences in its kinetic
properties compared to the host enzyme may facilitate designing of novel inhibitors of PfTk with
potential anti-malarial activity [18].

        Thiamin, or vitamin B1, is crucial for brain function. In its active form, thiamin
pyrophosphate (TPP), it is a co-enzyme for several enzymes, including transketolase.
Transketolase is an important enzyme in the non-oxidative branch of the pentose phosphate
pathway (PPP), a pathway responsible for generating reducing equivalents, which is essential for
energy transduction and for generating ribose for nucleic acid synthesis. Transketolase also links
the PPP to glycolysis, allowing a cell to adapt to a variety of energy needs, depending on its
environment. Abnormal transketolase expression and/or activity have been implicated in a
number of diseases where thiamin availability is low, including Wernicke-Korsakoff's Syndrome
and alcoholism. Yet, the precise mechanism by which this enzyme is involved in the
pathophysiology of these disorders remains controversial [19].

            Analytical isoelectric focusing and a stain for transketolase have been applied to
partially purified samples of human erythrocyte hemolysates and have detected individual
species of transketolase having pI values of 6.6, 7.3, 7.5, 7.8, 8.1, 8.2, 8.4 and 9.2. Six different
patterns of these species were detected in 25 healthy subjects. The species of pI 7.5, 7.8 and 8.1
were common to all six patterns. Species isolated by electrofocusing could be rerun in the same
system with identical pI value. The addition of thiamin diphosphate to the staining mixture
darkened some but not all bands of transketolase activity. Thus human erythrocyte transketolase
is heterogeneous and appears to share with human fibroblast transketolase heterogeneity for
affinity of the cofactor. This heterogeneity might need recognition when thiamin nutritional
sufficiency is assessed by the ‘ thiamin diphosphate effect’ on erythrocyte transketolase [20].
                                          CHAPTER 4

                             MATERIALS AND METHODS


                   System configuration

      Intel (R) Core 2 Duo - 2.93 GHz
      2 GB of RAM
      500 GB Hard Disk Drive
      1 MB cache
      1.44” Floppy Disk Drive
      17” Color Monitor
      128 MB AGP Card


Operating System                               :      LINUX EL – 4.0

Virtual Screening Software                     :      Dock 6.4

Homology Modeling Software                 :       MOE

Pharmacophore Software                     :       Ligandscout

QSAR Properties                            :       Hyperchem

Visualization Tool                             :     Chimera PyMOL

Databases                                      :     PDB, and Zinc Database
A. PyMOL [21] :

       PyMOL is an open-source, user-sponsored, molecular visualization system created by Warren
Lyford DeLano and commercialized by DeLano Scientific LLC, which is a private software company
dedicated to create useful tools that become universally accessible to scientific and educational
communities. It is well suited for producing high quality 3D images of small molecules and biological
macromolecules such as proteins. PyMOL is one of the few open source visualization tools available for
use in structural biology. The ‘Py’ portion of the software’s name refers to the fact that it extends, and is
extensible by, the Python Programming Language.


          The Protein Data Bank (PDB) is a repository for 3-D structural data of proteins and nucleic
acids. The data, obtained by X-ray crystallography or NMR spectroscopy and submitted by biologists and
biochemists from around the world, is submitted to this public domain and can be accessed free. The
WorldWide Protein Data Bank (wwPDB) consists of organizations that act as deposition, data processing
and distribution centers for PDB data. The founding members are Research Collaboratory for Structural
Bioinformatics (RCSB PDB, USA), Macromolecular structure Database-European Bioinformatics
Institute (MSD-EBI, Europe) and Protein Data Bank Japan (PDBj, Japan). The Biological Magnetic
Resonance Bank (BMRB, USA) group joined the wwPDB in 2006.

          The mission of the wwPDB is to maintain a single Protein Data Bank Archive of
macromolecular structural data that is freely and publicly available to the global community. The
PDB is a key resource in structural biology and is critical to more recent work in structural
genomics This database stores information about the exact location of all the atoms in a large
biomolecule (although, usually without the hydrogen atoms, as their positions are more of a
statistical estimate) If one is only interested in sequence data, such as amino acid sequence of a
particular protein or the nucleotide sequence as a particular nucleic acid, the much larger
databases from Swiss-Prot and the International Nucleotide S sequence Database Collaboration
should be used. Each structure published in PDB receives a four-character alphanumeric
identifier, its PDB ID. This should not be used as an identifier for biomolecules, since often
several structures for the same molecule (in different environments or conformations) are
contained in PDB with different PDB IDs
C.Zinc Database [23]:

The ZINC database is a curated collection of commercially available chemical compounds
prepared especially for virtual screening. ZINC is used by investigators (generally people with
training as biologists or chemists) in pharmaceutical companies, biotech companies, and research

ZINC is different from other chemical databases because it aims to represent the biologically
relevant, three dimensional form of the molecule.

ZINC is updated regularly and may be downloaded and used free of charge. It is developed by
John Irwin in the Shoichet Laboratory in the Department of Pharmaceutical Chemistry at the
University of California, San Francisco.

The latest release of the website interface is "ZINC 11"(2011). The database contents are
continuously updated. Static subsets are generated regularly and are dated.

D.Chimera [24]:

UCSF Chimera or Chimera is an extensible program for interactive visualization and analysis of
molecular structures and related data, including density maps, sequence alignments, docking
results, trajectories and conformational ensemblies. High-quality images can also be generated.

        Chimera is developed by the Resource for Biocomputing, Visualization and Informatics
(RBVI) at the University of California, San Francisco. Development of Chimera is funded by the
NIH National Center for Research Resources.

        While performing docking using DOCK 6.4 we prepare protein and ligands structures
using Chimera Software.
E. MOE[25]:

Molecular Operating Environment (MOE) provides a suite of applications for manipulating and analyzing
large collections of compounds. It is a fully integrated suite of computational chemistry, molecular
modeling and informatics software for life science applications. The suite's applications are written in an
embedded programming language, Scientific Vector Language (SVL), and can be easily customized since
the source code is provided in the distribution.

     In MOE input the protein sequence of human transketolase and design the structure for protein. We
get 11 structures the final 11th structure is taken as best one. On this 11th structure molecular dynamics is
done in order to stabilize the protein structure. Here, we get 201 structures of which final (201th) structure
is taken for docking

F. DOCK 6.4 [26]:

        DOCK addresses the problem of "docking" molecules to each other. In general,
"docking" is the identification of the low-energy binding modes of a ligand (small molecule),
within the active site of a receptor (macromolecule), whose structure is known. A compound that
interacts strongly or binds with the receptor associated with a disease may inhibit its function and
thus can act as a drug. Solving the docking problem computationally requires an accurate
representation of the molecular energetic as well as an efficient algorithm to search the potential
binding modes.

        The current release is version 6.4 which was released in May 2010. The DOCK algorithm
addressed rigid body docking using a geometric matching algorithm to superimpose the ligand
onto a negative image of the binding pocket. DOCK 6 is written in C++ and is functionally
separated into independent components, allowing a high degree of program flexibility.
Accessory programs are written in C and Fortran 77.
       The program sphgen identifies the active site and other sites of interest and generates the
sphere centers that fill the site. The program grid generates the scoring grids. Then, the dock
program matches spheres (generated by sphgen) with ligand atoms and uses scoring grids (from
grid) to evaluate ligand orientations.

G.Ligandscout [27]:

        LIGANDSCOUT creates pharmacophores from structure-based complex data, and
allows sophisticated pharmacophore analysis to create selective pharmacophoric screening filters
for a specific target. Using pharmacophore perception rules that are based on several years of
experience in pharmacophore modeling, LIGANDSCOUT offers a large range of chemical
feature definitions including hydrogen bonding vectors, chargeable groups, aromatic plane
interactions and aromatic-positive ionizable interactions. LIGANDSCOUT starts with a
macromolecule/ligand complex and automatically detects bound ligands creating a standard
residue hull around non-stranded residues. The advanced ligand bond interpretation is based on
geometric interpretation as well as a matching algorithm to optimally distribute double bonds
among sp2 atoms

I. HyperChem [28]:

        Hypercube, Inc. produces two versions of the core HyperChem product: HyperChem 8.0
and HyperChem for MAC. HyperChem 8.0 includes the Chemist's Developer Kit, an advanced
customization tool; HyperNMR, for a priori simulation of NMR spectra; and HyperChem Data,
a chemical database program with over 10,000 molecules included. Also available is
HyperChem Lite, a very affordable program tailored for students, which is compatible with
Microsoft Windows versions 95, 98 and ME.

       Hypercube, Inc. has set new standards for ease of use and molecular modeling power on
PC-based systems. Our goal is to bring molecular modeling to all chemists and chemistry


        Initially the lead like compounds were downloaded from Zinc Database. The Zinc Database
compound were prepared for screening with Dock 6.4 Software.


        The sequence of Human transketolase was retrieved from NCBI Database. Later the sequence is
submitted to MOE for Construction the protein model to the sequence and the best model was submitted
to dynamics simulations. In this the lowest model was retrieved for further studies
                                         CHAPTER 5

                              RESULTS & DISCUSSION

Homology modeling & Dynamics Simulations:
         Protein sequence of Human transketolase which was having a length of 624 residues is
taken from NCBI database. This sequence is taken as target and a template is identified using
MOE Suite. Structure for the protein is generated using protein sequence in MOE Suite. We get
11 models here, out of which final (11th) model is considered as the final structure. On this final
protein structure molecular dynamics for 1 ps has been done using MOE Suite, to stabilize the
obtained protein structure. 201 structures will be generated in this step, and the final structure
(201) is taken as the stabilized protein structure.

Fig: 5.1 Human Transketolase Model Generated by MOE
Virtual Screening of Human Transketolase with Zinc

     Based on the characteristics of the protein, 24,000 ligands are downloaded from the ZINC
database with neutral pH. Then, screening and docking was done on these 45,000 ligands using
DOCK 6.4. The total time taken for the docking is 2, 22,960 seconds which is equal to 72 hours
approximately. Based on the grid score of DOCK 6.4, 20 ligands are considered as best.

 Docking Result:


     Compund ID:       ZINC22284426

     GOLD Score: 71.80

ARG'353--- O1 (2.55)

GLU,73 --- N3 (3.38)

Compund ID: ZINC21785764

GOLD Score:    70.83

GLU'406---N5 (2.74)

ARG'353---N3 (3.16)

ALA'351---N6 (3.24)

Compund ID:   ZINC12321548

GOLD Score:   70.58


ARG'353---O2 (3.13)

ARG'353---O1 (3.44)

Compund ID:   ZINC15906792

GOLD Score: 69.83

ARG'190--- O2 (2.86)

ARG'190--- O3 (2.91)

GLU'406--- O2 (2.02)

GLU'406--- O6 (3.05)

GLU'406--- O6 (2.76)

ARG'353--- O5 (2.74)

ARG'353--- N3 (3.16)

Compund ID:   ZINC04248140

GOLD Score: 69.60





Compund ID:   ZINC13303345

GOLD Score:   69.43

ARG'190---O3 (2.91)

ARG'353---N3 (3.13)

ARG'353---N2 (3.30)

ARG'76 ---O2 (2.75)

ARG'157---O2 (2.83)

ASP'157---N5 (2.97)

ASN'74---O1 (3.30)

PRO'72---O1 (2.39)

Compund ID:   ZINC17147726

GOLD Score: 69.08



Compund ID:   ZINC24624994
GOLD Score: 69.13

ARG'353--- N1 (3.12)

ARG'190--- O2 (3.04)

ARG'190--- O4 (3.12)

ARG'332--- O3 (2.94)

ASP'76--- N2 (3.20)

GLY'189--- O3 (2.49)

Ligand   scout      provides    the    following   pharmacophore   features   for   automated
pharmacophoric generation:

                 Depiction in Ligandscout                 Pharmacophore Feature

                                                    Hydrogen Bond Donor

                                                    Hydrogen Bond Donor

                                                    Hydrogen Bond Acceptor
Both Hydrogen Bond Donor &
Hydrogen Bond Acceptor

Positive Ionizable Area

Negative Ionizable Area

Hydrophobic Interactions

Aromatic Ring

Metal Binding Feature

Excluded Volume
        QSAR Properties

               Partial   Surface       Surface      Volume        Hydration        LogP   Refractivity   Polarizability   Mass
               Charges   Area          Area(Grid)                 Energy
ZINC22284426   0.00e     547.00Ao 2    595.51Ao 2   952.25Ao 3    -9.44Kcal/mol    4.38    49.94Ao 3     34.68Ao 3        321.44amu
ZINC21785764             480.89Ao 2    601.88Ao 2   966.53 Ao 3   -12.29Kcal/mol   3.94   39.51 Ao 3     35.02 Ao 3       343.38 amu
ZINC12321548             521.33 Ao 2   611.92Ao 2   984.05Ao 3    -12.13Kcal/mol   5.95   45.27Ao 3      36.13Ao 3        343.40amu
ZINC15906792             479.65Ao 2    582.11Ao 2   922.19Ao 3    -23.93Kcal/mol   3.46   29.26Ao 3      31.12Ao 3        333.28amu
ZINC04248140             447.62Ao 2    542.35Ao 2   878.23Ao 3    -8.28Kcal/mol    3.86   40.43Ao 3      32.86Ao 3        300.38amu
ZINC13303345             495.69Ao 2    584.01Ao 2   931.39Ao 3    -16.79Kcal/mol   5.10   21.91Ao 3      34.35Ao 3        334.31amu
ZINC17147726             391.27Ao 2    522.67Ao 2   875.92Ao 3    -8.02Kcal/mol    4.22   42.88Ao 3      37.46Ao 3        325.43amu
ZINC24624994   0.00e     518.02Ao 2    581.60Ao 2   930.41Ao 3    -14.36Kcal/mol   7.78   45.24Ao 3      31.20Ao 3        339.40amu
                                         CHAPTER 6


Upon Identifying the pharmacophore group and the region of most likely to interact and respond are
well depicted and graphed with molecular dynamics and simulations. The whole protein build of
predicted Transketolase Enzyme has been done with the core bioinformatics softwares involving Gold,
Autodock, Dock 6, Pymol, and Others respectively to observe the changes and regions of interest.

My paper has been produced as a fair attempt to understand the professional bioinformatics works
with respect to diagnosis, Building and observing the deceased region with extreme keen detail. The
High through put screening has been done with over 20,000 + compounds noting their Score in terms
of X, Y and Z axis to produce optimum fitness and to observe the right dynamics that may or will exist
inside the structure for the produced novel drug to enter and bind successfully. This structure study is
a detailed view of every angle of a protein structured that has to be studied satisfying all the subject
ethics and conditions.

All the methods and mechanisms used in the process of structure prediction, homology modeling and
drug designing are well recognized and used by the current industry to the very own core of
knowledge readily available.

                                     CHAPTER 7


                      The pharmacophoric concept plays an important role in ligand-based drug
design methods to describe the similarity and diversity of molecules. The pharmacophore
approach is extremely useful in library design for efficient hit discovery and to identify new
potential drugs. For the numerous therapeutically relevant drug targets with undetermined active
site geometries, pharmacophore modeling provides an effective mechanism for virtual screening.

                           Therefore, this study states the importance of pharmacophoric groups
and their use to enhance drug discovery process prior synthesis. This approach is to identify the
pharmacophoric groups of kappa opioid receptor agonists. Further, work can be extended to
develop new analogues of the kappa opioid receptor agonists based on the pharmacophoric
groups involved in the biological activity and can go for fragment based virtual screening.

Screening with the similar compounds that possible may have structure similarities gives a good
chance to interpret and implement the possible structure of the compound of your interest. All
the In-Silico techniques beginning of cell level to molecular level are highly sophisticated and
well understood with very least cost and best results and predictions.
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