Literature Review

Chapter - III Review of Literature
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Identification and Validation of Drug Targets
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Chapter III
REVIEW OF LITERATURE
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3.1 MOLECULAR DRUG TARGETS
An assessment of the number of molecular targets that represent an
opportunity for therapeutic intervention is crucial to the development of
post-genomic research strategies within the pharmaceutical industry. Now
that we know the size of the human genome, it is interesting to consider
just how many molecular targets this opportunity represents. It is vital that
the position that we understand the properties that is required for a good
drug, and therefore must be able to understand what makes a good drug
target (Hopkins and Groom, 2002).
Drug research has contributed more to the progress of medicine
during the past century than any other scientific factor. The advent of
molecular biology and, in particular, of genomic sciences is having a deep
impact on drug discovery. Genome sciences, combined with bioinformatic
tools, allow us to dissect the genetic basis of multifactorial diseases and to
determine the most suitable points of attack for future medicines, thereby
increasing the number of treatment options. The dramatic increase in the
complexity of drug research is enforcing changes in the institutional basis
of this interdisciplinary endeavor. The biotech industry is establishing itself
as the discovery arm of the pharmaceutical industry. In bridging the gap

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between academia and large pharmaceutical companies, the biotech firms
have been effective instruments of technology transfer, (Drews J. 2000).
3.2 TARGET PREDICTION METHODS AND STRATEGIES
Therefore, scientists interested in discovering antibiotics must extract
useful information from genomes through comparative, functional, or
structural genomics in order to simplify drug target selection. The advent of
bacterial whole-genome sequences and establishment of useful genomic
analyses comes at a crucial time for antibiotic development. The
information gained from genome sequencing projects has already had a
major impact on both basic microbiology and its industrial applications, and
has rapidly changed the way research is conducted in this field.
No less remarkable, however, is the versatility shown by
microorganisms in overcoming the effects of antibiotics. Chemical
modification of existing antibiotics and development of inhibitors of
resistance genes, will have a significant impact on antibacterial therapy in
the immediate future. However, it is evident that this field requires
additional targets, innovative assay development strategies and new
chemical entities (Schmid, 1998). While the introduction of innovative
chemistry will probably be triggered by combinatorial chemistry (Myers,
1997) and novel resources for natural products (Nisbet and Moore, 1997),
there are high expectations for microbial genomics in accelerating target
discovery and assay development.

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3.3 TARGET SELECTION METHODS
Computational techniques for the identification of potential drug
targets based on genomic data have been reviewed recently (Schmid,
1998; Galperin, and Koonin, 1999, Moir et al., 1999; Freiberg,. 2001). In a
few studies, in silico analysis was successfully combined with experimental
techniques (Arigoni et al., 1998; Freiberg et al., 1998; Chalker et al., 2001).
For example, Arigoni and coauthors used comparative genome analysis to
identify previously uncharacterized genes as potential broad-spectrum
targets by emphasizing genes which are;
 Broadly conserved in various bacteria, including pathogens,
 Not conserved in humans; and
 Likely to encode soluble proteins.
The essentiality of selected genes was further assessed by directed
knockouts in E. coli and Bacillus subtilis (Arigoni et al., 1998). Most in silico
target identification techniques are focused on formal comparative
sequence analysis, without attempting to assess conservation of the overall
biological context in various pathogens and the human host. Comparative
analysis of pathways and biological subsystems may significantly improve
our ability to select potential targets. Antimicrobial drugs should be
essential to the pathogen, have a unique function in the pathogen, be
present only in the pathogen, and be able to be inhibited by a small
molecule.

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The target should be essential, in that it is a part of a crucial cycle in
the cell, and its elimination should lead to the pathogen’s death. The target
should be unique: no other pathway should be able to supplement the
function of the target and overcome the presence of the inhibitor. If the
macromolecule satisfies all the outlined criteria to be a drug target but
functions in healthy human cells as well as in a pathogen, specificity can
often be engineered into the inhibitor by exploiting structural or biochemical
differences between the pathogenic and human forms. Finally, the target
molecule should be capable of inhibition by binding of a small molecule.
Enzymes are often excellent drug targets because compounds are
designed to fit within the active site pocket.
3.3.1 Essential gene selection
Traditionally, the search for novel genes required for bacterial
survival or virulence was based on several genetic methods involving
random mutagenesis of a bacterial genome followed by screening for the
relevant phenotype (Berg and Berg, 1996). Previously, the most successful
strategy for finding novel genes essential for viability was the isolation of
conditionally lethal mutants. A conditionally lethal mutation causes the
gene product to function normally under one set of environmental
conditions (the permissive condition - e.g. growth at 37.8 C for Escherichia
coli), but fail to function under another set of conditions (the non-permissive
condition – e.g. growth at 42.8 C for E. coli). The genes that can mutate to

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conditional lethality (i.e. mutations that are lethal to a bacterium under non-
permissive conditions) are generally genes that are essential for viability.
However, such temperature-sensitive mutants do have their limitations
(Schmid, 1998). It has been estimated that approximately one-third of
proteins are difficult to mutate into a thermolabile form. In addition, genes
have been identified that are required for viability at high temperatures, and
this will lead to false-positive assignment of some genes as being essential
under all growth conditions (Schmid, 1989). Despite the widespread
success of using conditional mutants to identify essential genes, it can be
assumed that a significant number of essential genes still remain
undiscovered by this technology. Until recently, transposon mutagenesis
(i.e. the inactivation of genes by insertion of a transposable genetic
element) of bacterial genomes was not a powerful approach for the
identification of essential genes, because the frequency and randomness of
insertions was too low.
A breakthrough in using transposon mutagenesis to map genes
required for cellular viability was achieved by combining in vitro
transposition with natural transformation of certain bacterial species
(Akerley et al., 1998).
3.3.2 Structural genomics based target selection
Despite the exponential growth of sequence information from a large
diversity of organisms, each newly sequenced bacterial genome continues

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to reveal up to 50% of genes without significant similarity to protein of
characterized function (Stover et al., 2000). By focusing a structural
genomics project on such anonymous proteins, our goal is twofold. On one
hand, we expect a sizable fraction of these proteins to exhibit a
recognizable 3-D structure similarity with previously characterized protein
families. Structure determination is thus used as a technique of functional
genomics, allowing common functional attributes to be recognized beyond
the twilight zone of sequence similarity. On the other hand, focusing a
structural genomics effort on anonymous proteins should also enhance the
probability of discovering original folds that are highly valuable byproducts
for the academic community.
3.3.3 Phylogeny based target selection
Scientists can use phylogenetic groups that are based on the specific
folds shared by organisms. These fold and sequence families in bacterial
pathogens can be useful antibiotic targets (Gerstein, 2000). One can find a
fold common to an entire phylogenetic group in order to target all of the
organisms with a broad-spectrum antibiotic. Alternatively, one can find a
fold that is unique to one particular pathogen for an effective narrow-
spectrum antibiotic target.

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3.3.4 Structure based target selection
Structural methods are the ideal for selection of drug targets.
However, structural databases are not complete since quality protein-
crystals are difficult to form and hinders X-ray crystallography (Holm and
Sander, 1993). However, nuclear magnetic resonance can determine 3D
structure determination. Also, computational modeling is approaching
accurate functional predictions based on alignment of amino acid
sequences (Grigoriev and Kim, 1999).
3.3.5 Sequence motif based: Target selection
Motif analysis is another strategy to identify potential antibiotic
targets among genes with unknown functions. Many databases, including
PROSITE database, can search for motifs in a sequence (Hood, 1999).
The motifs may show the approximate biochemical function of the gene.
Next, gene fusion is a new computational method to infer protein
interactions from genome sequences. Proteins that interact with each other
tend to have homologs in other organisms that are joined into a single
protein chain. This method would give additional functional information for
target proteins.
3.3.6 Gene expression based target selection
Finally, drug targets can be characterized further by using gene
expression profiles: DNA microarrays, large-scale protein interaction

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mapping, and proteomics (Hood, 1999). Genes that are functionally related
are assumed to have similar gene expression profile patterns. Protein
synthesis patterns are also useful to analyze the antimicrobial effect certain
drugs would have on particular necessary or important proteins (Frosch and
Reidl, 1998).
3.3.7 Finding antimicrobial drug targets using genetic foot printing
Identification of unexplored cellular functions as potential targets is a
prerequisite for development of novel antibiotic chemotypes. Choosing an
optimal target function is a crucial step in the long and expensive process
of drug development and requires the best possible understanding of
related biological processes in bacterial pathogens and their hosts.
Extensive programs utilizing genomic information to search for novel
antimicrobial targets have been launched recently in industry and academia
(Timberlake and Gavrias, 1999; Benton et al., 2000, 2005; Palmer, 2000;
Nilsen, 2001). Complete genome sequences of multiple bacterial species,
including many major pathogens, have become available in the last few
years, with many more such projects under way (Bernal et al., 2001). The
abundance of genomic data has enabled the development of novel
postgenomic experimental and computational techniques aimed at drug
target discovery. Experimental approaches to genomewide identification of
genes essential for cell viability in several microbial species have been
reviewed recently (Schmid, 1998; Moir et al., 1999; Judson and Mekalanos,

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2000; Loferer et al., 2000; Rosamond and Allsop, 2000; Chalker et al.,
2001; Hamer et al., 2001). These techniques are based on either
systematic gene inactivation by directed mutagenesis on a whole-genome
scale or high-throughput random transposon mutagenesis. The major
advantage of the latter technique is the parallel analysis of thousands of
genes under multiple growth conditions. A transposon-based approach
termed “genetic foot-printing” was originally described for Saccharomyces
cerevisiae (Smith et al., 1995, 1996).
Genetic foot-printing is a three-step process:
 Random transposon mutagenesis of a large number of cells,
 Competitive outgrowth of the mutagenized population under various
selective conditions, and
 Analysis of individual mutants surviving in the population using direct
sequencing or various hybridization and PCR-based techniques.
Various modifications of genetic foot printing have been recently
applied to several microorganisms, including Mycoplasma genitalium and
Mycoplasma pneumoniae (Hutchison et al., 1999); Pseudomonas
aeruginosa (Wong and Mekalanos, 2000), Helicobacter pylori (Jenks et al.,
2001), and Escherichia coli (Badarinarayana, 2001; Hare et al., 2001).
Another version of this method, termed genomic analysis and mapping by
in vitro transposition, has been developed for Haemophilus influenzae

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(Akerley et al., 1998; Reich et al., 1999) and Streptococcus pneumoniae
(Akerley et al., 1998).
Direct application of this technique is usually limited to microbial
species with natural competence, since transposon mutagenesis is
performed in vitro on isolated DNA fragments, and mutations are introduced
into the genome by transformation with linear DNA fragments followed by
gene conversion. By targeting a specific genomic region, this approach
increases insertion density, improving resolution of genetic foot-printing. An
elegant extension of this method beyond naturally competent species was
described for P. aeruginosa (Wong and Mekalanos, 2000).
3.3.8 Target selection using comparative genomics
Bacterial genome sequencing has triggered a complementary
approach to target discovery that is directed rather than random, consisting
of comparative genomics combined with bacterial genetics (Arigoni et al.,
1998). Extensive genomic information gained from many evolutionarily
distant bacterial species has made the automated comparison of bacterial
genomes a powerful tool for categorizing genes and their respective
products (Mushegian and Koonin, 1996; Tatusov et al., 1997).
Focused lists of target candidates are generated by comparative
genomics that are then rapidly validated using bacterial genetics. The gene
categories generated by this approach enable such a preselection of target
candidates on a whole-genome scale; in other words, targets can be

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defined according to the required characteristics for a given antibacterial
treatment. For example, genes that have orthologs in many evolutionarily
distant organisms are target candidates for broadspectrum applications.
Similarly, genes can be selected that are present only in a small subset of
the bacterial species sequenced to date, thereby representing possible
targets for narrow-spectrum antibacterial compounds. This target category
is of particular importance for the treatment of chronic infections, as
narrow-spectrum drugs would reduce both the spread of drug resistance
and the side effects caused by destruction of the commensal bacterial flora,
both of which are major disadvantages of long-term treatment with broad-
spectrum antibiotics. Targets can also be selected according to their
putative functions, although it should be noted that this approach, in
particular, is highly dependent on accurate functional annotation of
genomes and experimental validation is still a necessity (Arigoni et al.,
1998).
The comparative analysis of the genomes of Chlamydia trachomatis
and Chlamydia pneumoniae has generated testable hypotheses of genes
that might be responsible for the differences in tropism and pathologies
between these two organisms (Kalman et al., 1999).
In parallel to analyzing differences in closely related genomes by
sequencing, DNA-array technologies have enabled the possibility of
comparing genomes by hybridization. As exemplified by the comparative

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hybridization analysis of Mycobacterium tuberculosis and Mycobacterium
bovis by Bacille Calmette-Guérin (BCG), genomic regions that are different
between pathogenic and non-pathogenic variants of a species can be
rapidly identified (Behr et al., 1999). These regions contain genes that are
likely to be of relevance for the development of antibiotics and/or vaccines.
In addition to categorizing genes solely by sequence based
comparisons (e.g. BLAST - basic linear alignment search tool) (Altschul
et al., 1990), the coding sequences elucidated in new sequencing projects
can be compared with reference databases of cellular pathways created
mainly using the biological information known about E. coli and Bacillus
subtilis (Karp et al., 1999). Using this method, the metabolic capabilities of
a newly sequenced organism can be assessed, and putatively essential
pathways and missing components of pathways identified.
3.3.9 Genes of unknown function as drug targets
About 25-40% of the genes in a bacterial genome usually do not find
matches with known genes (Smith, 1996). This is mainly because the
earlier functions of most of the genes were not identified, also currently fast
automated annotations methods enable rapid identification of most of the
gene sequences and its functions. Furthermore, sequence homology is
based on the assumption that similar sequences will share similar functions
- a presupposition that does not hold true in many cases where similar
sequences are structurally and functionally diverse.

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One of the most intriguing results from the bacterial sequencing
projects completed so far is that a significant fraction of the genes have
unknown function (Hinton, 1997); in other words, these genes have been
identified solely by sequencing and have not been previously characterized
by any genetic or biochemical approach (Blattner et al., 1997). Even in well
studied model organisms such as E. coli, <38% of all genes surprisingly fall
into this category (Blattner et al., 1997; Hinton, 1997). Given the drawbacks
listed earlier of random screens for temperature sensitive mutations, it can
be speculated that, despite the extent of genetic studies on E. coli and B.
subtilis, many targets still remain undiscovered among the genes of
unknown function. Indeed, in a pilot study that investigated 26 FUN
(Function unknown) genes that are broadly conserved among diverse
bacterial species (including currently the smallest bacterial genome of
Mycoplasma genitalium), six novel genes essential for the growth of E. coli
and B. subtilis were identified, Arigoni et al., 1998. One of these genes was
earlier eliminated from a postulated minimal gene set required for life based
on its annotation as a host-interacting protein (Mushegian and Koonin,
1996).
3.3.10 Proteins as drug targets
Proteins continue to assume significant attention from the
pharmaceutical and biotechnology industries as a valuable source of
potential drug targets (Deisenhofer and Smith, 2001). Proteins provide the

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critical link between genes and disease, and as such are the key to the
understanding of basic biological processes including disease pathology,
diagnosis, and treatment. Researchers have discovered many potential
therapeutic targets, and there are currently more than 700 products in
various phases of development. However, translating the study of proteins
into validated drug targets poses substantial challenges. Genome
sequences instruct cells on how and when to make proteins.
The proteins in turn are the active players in the cell. Proteins form
the machinery of cells, allow cells to communicate, and can control growth
or death of an organism. Because of their role in cells, most of the drug
targets are proteins. Drugs work by binding specifically to a protein.
Extensive knowledge about the function of a protein can guide the selection
of targets for pharmaceutical chemists. Studying the complex domain of
200,000-300,000 distinct and interactive proteins poses substantial
challenges. Most target proteins for drug development participate in key
regulatory steps in the human body or in an infectious organism. As such,
they tend to be present in few copies only and often within specific cells.
Their isolation and purification using traditional preparative biochemical
means and in quantities required for routine assays has been a formidable
challenge. This situation has been radically changed by the ability to clone
and express proteins. Thus many key target proteins are now becoming
available in sufficient amounts to make them amenable not only to

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biological assays but also to NMR studies in solution and to crystallization
for X-ray analysis.
The number of protein structures solved using X-ray or NMR has
begun to rise sharply and more than 40,000 protein three-dimensional
structures have been deposited in the Protein Data Bank till date
(December 2006). Various classes of proteins can be categorized as
potential drug targets. Small molecules such as drugs, insecticides or
herbicides usually exert their effects by binding to protein targets. In the
past, many of these molecules were found empirically with little or no
knowledge of the mechanism of action involved. In many cases, the targets
that are modified by these substances were identified in retrospect.
Interestingly, the majority of drugs currently in use modulate either
enzymes or receptors, most of them G-protein-coupled receptors.
Protein structures are a rich source of information about membership
of families and super-families. It is such divergently evolved proteins that
need to be recognized as they are most likely to exhibit similar structure
and function. A classical example is the recognition of HIV proteinase as a
distant member of the pepsin/renin super-family and the subsequent
modeling of its three-dimensional structure and the design of inhibitors
(Blundell, 1988).
In general, putative relatives are identified, the sequences aligned,
and the three-dimensional structures are modelled. This is usually helpful in

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proposing binding sites and molecular functions if key residues are
conserved. Combined algorithms have been reported; for example,
GenTHREADER uses the sequence comparison method to generate the
sequence-structure alignment and then evaluates the alignment using
threading potentials
3.3.11 Molecular drug targets and structure based drug design
Discussion of the use of structural biology in drug discovery began
over 35 years ago, with the advent of knowledge of the 3D structures of
globins, enzymes and polypeptide hormones. Early ideas in circulation
were the use of 3D structures to guide the synthesis of ligands of
haemoglobin to decrease sickling or to improve storage of blood (Goodford,
et al., 1980), the chemical modification of insulins to increase half-lives in
circulation (Blundell et al., 1972) and the design of inhibitors of serine
proteases to control blood clotting (Davie et al., 1991). An early and bold
venture was the UK Wellcome Foundation programme focussing on
haemoglobin structures established in 1975. (Beddell et al., 1976)
However, X-ray crystallography was expensive and time consuming. It was
not feasible to bring this technique ‘in-house’ into industrial laboratories,
and initially the pharmaceutical industry did not embrace it with any real
enthusiasm.
In time, knowledge of the 3D structures of target proteins found its
way into thinking about drug design. Although, in the early days, structures

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of the relevant drug targets were usually not available directly from X-ray
crystallography, comparative models based on homologues began to be
exploited in lead optimization in the 1980s. (Blundell et al., 1996).
An example was the use of aspartic protease structures to model renin,
a target for antihypertensives (Sibanda et al., 1983).
It was recognized that 3D structures were useful in defining
topographies of the complementary surfaces of ligands and their protein
targets, and could be exploited to optimize potency and selectivity
(Campbell, 2000). Eventually, crystal structures of real drug targets
became available; AIDS drugs, such as Agenerase and Viracept, were
developed using the crystal structure of HIV protease (Lapatto et al., 1989)
and the flu drug Relenza was designed using the crystal structure of
neuraminidase (Varghese, 1999). There are now several drugs on the
market that originated from this structure-based design approach (Hardy
and Malikayil, 2003); list >40 compounds that have been discovered with
the aid of structure-guided methods and that have entered clinical trials.
The structure-based design methods used to optimize these leads into
drugs are now often applied much earlier in the drug discovery process.
Protein structure is used in target identification and selection (the
assessment of the ‘druggability’ or tractability of a target), in the
identification of hits by virtual screening and in the screening of fragments.
Additionally, the key role of structural biology during lead optimization to

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engineer increased affinity and selectivity into leads remains as important
as ever.
3.3.12 Common drug targets
The introduction of genomics, proteomics and metabolomics has
paved the way for biology-driven process, leading to plethora of drug
targets. The list of potential drug targets encoded in a genome includes
most natural choice of virulent genes and species-specific genes. Other
options include targeting RNA, enzymes of the intermediary metabolism,
systems for DNA replication, translation apparatus or repair and membrane
proteins.
3.3.13 Species-specific genes as drug targets
Comparative analysis of the complete genome sequences of bacterial
pathogens available in the public databases offers the first insights into
drug discovery approaches of the near future (Galperin and Koonin, 1999).
An interesting approach to the prediction of potential drug targets
designated as the differential genome display has been proposed by Bork
and co-workers (Huynen et al., 1997). This approach relies on the fact that
genome of parasitic microorganisms are generally much smaller and code
for fewer proteins than the genomes of free-living organisms. The genes
that are present in the genome of a parasitic bacterium, but absent in a
closely related genome of free living bacterium, are therefore likely to be

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important for pathogenecity and can be considered as potential drug
targets. Exhaustive comparison of H. influenzae and E. coli gene products
identified 40 H. influenzae genes that have been exclusively found in
pathogens and thus constitute potential drug targets.
3.3.14 Nucleic acid as drug targets
Nucleic acids are the repository of genetic information. DNA itself has
been shown to be the receptor for many drugs used in cancer and other
diseases. These work through a variety of mechanisms including chemical
modification and cross linking of DNA (cisplatin) or cleavage of the
DNA (bleomycin). Much work either by intercalation of a polyaromatic
ring system into the double stranded helix (actinomycin-D, ethidium) or
by binding to the major and minor grooves of DNA (e.g., netropsin)
(Haq, 2002) has been reported. DNA has been shown to be the target for
chemotherapy with efforts to design sequence-specific reagents for gene
therapy.
3.3.15 RNA as drug target
Recent advances in the determination of RNA structure and function
have led to new opportunities that will have a significant impact on the
pharmaceutical industry. RNA, which, among other functions, serves as a
messenger between DNA and proteins, was thought to be an entirely
flexible molecule without significant structural complexity.

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However, recent studies have revealed a surprising intricacy in RNA
structure. This observation unlocks opportunities for the pharmaceutical
industry to target RNA with small molecules. Perhaps more importantly,
drugs that bind to RNA might produce effects that cannot be achieved by
drugs that bind to proteins (Ecker and Griffey, 1999). Proof of the principle
has already been provided by success of several classes of drugs obtained
from natural sources that bind to RNA or RNA-protein complexes.
3.3.16 Membranes as drug targets
Membranes are significant structural elements, both in defining the
boundaries of a cell as well as providing interior compartments within the
cell associated with particular functions. Cell membranes themselves can
also act as targets for molecular recognition. An understanding of the
structural and dynamic functions of the membranes (e.g., plasma
membranes and intercellular membranes) may add to a more rational
design of drug molecules with improved permeation characteristics or
specific membrane effects. Many general anesthetics are believed to work
by their physical effects when dissolved in membranes.
Several classes of antibiotics like gramicidin A, antifungal like
alamethicin and toxins such as mellitin found in bee venoms have direct
effects on planar lipid bilayers, causing transmembrane pores.

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3.3.17 GPCR as drug targets
G protein-coupled receptors (GPCRs) are membrane embedded
proteins, responsible for communication between the cell and its
environment (Horn et al., 1998). As a consequence, many major diseases,
such as hypertension, cardiac dysfunction, depression, anxiety, obesity,
inflammation, and pain, involve malfunction of these receptors (Wilson and
Bergsma, 2000), making them among the most important drug targets for
pharmacological intervention (Rattner et al., 1999; Sautel and Milligan,
2000; Schoneberg et al., 2000). Thus, whereas GPCRs are only a small
subset of the human genome, they are the targets for ~50% of all recently
launched drugs (Klabunde and Hessler, 2002).
G protein-coupled receptors (GPCRs), form one of the major groups
of receptors in eukaryotes; they possess seven transmembrane α-helical
domains, as confirmed by analysis of the crystal structure of Rhodopsin
(Palczewski et al., 2000). The study of GPCRs, and the way that they are
activated by their ligands, is of great importance in current research aiming
at the design of new drugs. The importance of GPCRs in pharmaceutical
industry, is reflected in the fact, that an estimated 50% of current
prescription drugs target GPCRs (Drews, 2000; Hopkins and Groom, 2002;
Ma and Zemmel, 2002). Characteristically, the human genome possesses
approximately 700–800 GPCRs (Wise et al., 2004). Traditional medicinal
chemistry enzyme targets include kinases, phosphodiesterases, proteases

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and phosphotases. In view of their widespread distribution and importance
in health and disease, it is not surprising that GPCRs are the most
successful class of target proteins for drug discovery research.
3.3.18 Surrogate markers
One approach towards the development of a generic assay is the
identification of surrogate markers. Within the context of antibacterial drug
discovery, surrogate markers are defined as genes that are deregulated as
a specific response towards the inactivation of a given essential target.
Eventually, inactivation of the respective target should occur through the
action of a small molecule. However, for the identification of surrogate
markers, target inactivation can be achieved through in vivo expression of
small-peptide inhibitors (i.e. surrogate ligands) or by shifting a conditional
mutant towards non-permissive conditions. Besides isolating temperature-
sensitive alleles of the respective gene, conditional mutants can be
generated by positioning a complementary copy of the essential gene
under the control of a tightly regulated promoter (Arigoni et al., 1998).
The availability of such conditional mutants enables the analysis of
the phenotypic consequences for a bacterial cell caused by the inactivation
of an essential gene. Samples harvested before and after depletion of the
target of interest can be compared to investigate its molecular effect on the
bacterial cell. Characterization of these types of responses using genomic
technologies such as RNA expression profiling or proteome analysis

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enables the identification of genes/proteins that are deregulated as a
consequence of target inhibition. Hence, such deregulated genes/proteins
are indicative of the biological activity of a given target and are defined as
surrogate markers. One crucial issue regarding this approach is whether
such target- or pathway-specific responses can be detected or whether
generic stress responses will dominate across most mutants. Analyses by
the current authors of several conditional mutants showed that specific
deregulation of protein expression can be observed together with common
responses.
3.3.18.1 Identification of surrogate markers leads to an assay for
compound screening
Genes that are identified as surrogate markers can be linked to a
reporter gene and its deregulation can then be assayed as a measure of
target/pathway inhibition. Such a target-specific whole cell assay will
combine the advantage of selection for cell permeable compounds inherent
in killing assays with the target-specificity and sensitivity achieved by in
vitro assays. Hence, the important additional advantage is that no detailed
functional information concerning the target is required.
3.3.19 Surrogate ligands
Another generic assay development strategy is the identification of
surrogate ligands for a given target. Surrogate ligands are short peptides
that bind with high affinity (low micromolar to nanomolar) to a target protein

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and thereby inhibit its function. Based on such surrogate ligands, in vitro
assays can be designed whereby compound libraries are screened for
small molecules that competitively displace the peptide and thus occupy
the peptide’s binding site on the target protein. There are several
approaches for isolating surrogate ligands from random peptide libraries,
the most prominent being phage display technology (Paige, 1999;
Wrighton, 1996).
Here, a library of random peptides is displayed on the surface of
filamentous phages. Binding peptides are isolated by a procedure called
‘biopanning’, where the purified target protein is immobilized on a solid
support and phages carrying binding peptides are isolated from the phage
library by repeated cycles of adhesion and washing. Sequencing the
appropriate segment of the DNA of each captured phage provides the
primary sequence of peptides that binds to the target. Isolation of high-
affinity binding peptides using phage display has been described for
several different protein classes, as will now be summarized. Agonists that
activate the cytokine erythropoietin (EPO) receptor were isolated from
random phage display peptide libraries (Wrighton, 1996).
These agonists were represented by a 14-amino acid consensus
sequence of cyclic peptides containing a disulphide bond. The amino acid
sequences of these peptides were not found in the primary sequence of
EPO. Furthermore, the signalling pathways activated by these peptides

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appeared to be identical to those induced by the natural ligand. Another
example of isolating small-peptide ligands for a protein of therapeutic
relevance was the nuclear hormone receptor for oestrogen (Paige, 1999).
There is great potential for the isolation of binding peptides that serve as
lead structures for many functionally diverse proteins, thus qualifying this
strategy for assay development of targets where functional information is
lacking. However, these experimental strategies aim to isolate surrogate
ligands in vitro.
In many instances, it is desirable to validate that the respective
peptide(s) have a significant effect on the target protein in vivo.
Thioredoxin, isolated from E. coli, serves as a structural framework for
presenting peptides in vivo (Colas et al., 1996). Using the yeast two-hybrid
approach, a peptide aptamer was isolated that binds, and competitively
inhibits, the cyclin-dependent kinase 2 (Cdk2)26.
Expression of this peptide in human cells slows their progression
through the G1 phase of the cell cycle. Furthermore, expression of
inhibitory peptide aptamers directed against the essential cyclin-dependent
kinases, DmCdk1 and DmCdk2 in Drosophila, caused adult eye defects
typical of those caused by cell cycle inhibition (Kolonin and Finley, 1998).
These findings demonstrate that in vivo validation of surrogate peptide
ligands is possible even in complex systems such as cultured mammalian
cells or Drosophila. For bacterial targets that are essential for viability, it

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can be easily tested whether the peptide ligand binds to a functionally
relevant site on the protein (i.e. the respective peptides have to be lethal or
inhibit growth on induction of expression in E. coli).
3.4 DRUG TARGET PREDICTION - IN VITRO, IN VIVO AND IN SILICO
3.4.1 Genomic Studies and Identification of Diseased Gene
With the wealth of available genomic information, it is possible now to
test hundreds of thousands of DNA markers in human, mouse, and other
species for association with disease. The variations in DNA impact complex
physiologic processes flows through transcriptional and other molecular
networks. As it is possible to monitor these changes at transcript levels,
combining them with the DNA variation, transcription, and phenotypic data
will lead towards identification of the associations between DNA variation
and disease. Now it’s possible to assess more than 1,000,000
polymorphisms or the expression of more than 25,000 genes in each
participant of a clinical study at reasonable costs. However, there are
difficulties in identifying the most likely disease-related genes.
The application of statistical techniques in biology always contributed
to substantial results from the times of Mendel. The currently evolving
statistical procedures that operate on networks will be critical to extracting
information related to complex phenotypes like disease. The challenge is
adopting novel integrative genomics approaches for dissecting disease
traits. This will significantly enhance the identification of key drivers of

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disease beyond what could be achieved by genetic association studies
alone. Numerous studies have been undertaken adopting these
technologies to identify polymorphisms in genes that associate with
diseases like age-related macular degeneration (Edwards et al., 2005;
Haines, 2005; Klein, 2005), diabetes (Grant, 2006; Sladek, 2007), and
obesity (Herbert, 2006). The susceptibility gene (ApoE) for the disease
Alzheimer’s was identified nearly 15 years ago (Peacock et al., 1993).
The advent of DNA microarrays techniques has radically changed the
way we study genes. This enables us to have a perspective of their role in
everything from the regulation of normal cellular processes to complex
diseases like obesity and diabetes. Practically, microarrays allow
researchers to screen thousands of genes for differences in expression at
various experimental conditions (Schadt and Lum, 2006). Performing these
experiments and comparing them with a normal and diseased condition will
unravel the genes associated with the disease. Using these technologies,
gene transcripts associated with complex disease phenotypes (Karp et al.,
2000; Schadt et al., 2003) and disease subtypes are identified (Van’t Veer
et al., 2002; Mootha et al., 2003; Schadt et al., 2003).
3.4.2 Pharmacogenetics for Improving Drug discovery and Development
Pharmacogenetics is defined as the use of biological markers like
DNA, RNA or protein to predict the efficacy of a drug and the likelihood of
the occurrence of an adverse event in individual patients (Roses, 2000).

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The variable response to drugs results from the variation in the human
genome. Driven by the advancements in molecular biology, pharmaco-
genetics has evolved within the past 40 years from a niche discipline to a
major driving force of clinical pharmacology. More obviously,
pharmacogenetics has changed the practices and requirements in
preclinical and clinical drug research; large clinical trials without a
pharmacogenomic add-on appear to have become the minority. The
science of pharmacogenetics originated from the analysis of a few rare and
sometimes serendipitously found extreme reactions (phenotypes) observed
in some humans; these phenotypes were either inherited diseases or
abnormal reactions to drugs or other environmental factors. In 2008, we
have witnessed about 12 million single nucleotide polymorphisms in the
human genome and a large amount of other types of genomic variation.
The impact of inherited chemical individuality in metabolic enzymes has
been well known for more than 100 years.
3.4.3 Pharmacokinetics for Improving Drug discovery and Development
For a maximum ROI, pharmaceutical industry should improve success
rates and reduce candidate attrition. Early identification of potential drug
attrition candidates leads to overall cost reduction (Di Masi, 2002). To
accomplish this, all potential stages and causes of attrition that have led to
drug development failure in the past has to be clearly identified. Published
survey on the causes of failure in drug development points to inappropriate

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pharmacokinetics parameters (Prentis et al., 1998). Hence pharmacokinetic
factors are very crucial for the assessment of safety during early drug
development.
Drug discovery employing pharmacogentic principles provides
treatments customized for individuals or specific subpopulations with
minimized adverse effects (Ozdemir et al., 2001). If the target protein is
polymorphic it leads to varied drug responses. Polymorphisms results due
to single nucleotide polymorphisms (SNPs), gene deletions and gene
duplications. Recently, pharmaceutical companies are focused on
screening compounds that are substrates solely for a polymorphic enzyme
to avoid the wider inter-subject variability in exposure, and hence safety
and efficacy (Rodrigues and Rushmore, 2002).
3.4.4 Virtual screening
Target-based virtual screening methods depend on the availability of
structural information of the target, that being either determined
experimentally or derived computationally by means of homology modeling
techniques (Shoichet, 2004; Klebe, 2006). These methods aim at providing,
on one hand, a good approximation of the expected conformation and
orientation of the ligand into the protein cavity (docking) and, on the other
hand, a reasonable estimation of its binding affinity (scoring). Despite its
appealing concept, docking and scoring ligands in target sites is still a
challenging process after more than 20 years of research in the field

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(Kitchen et al., 2004) and the performance of different implementations has
been found to vary widely depending on the given target (Cummings et al.,
2005). To alleviate this situation, the use of multiple active site corrections
has been suggested to remedy the ligand dependent biases in scoring
functions and the use of multiple scoring functions (consensus scoring) has
been also recommended to improve the enrichment of true positives in
virtual screening (Charifson et al., 1999). Also, as the number of protein–
ligand complexes available continues to grow, docking methods are
beginning to incorporate all the information derived from the conformation
adopted by protein-bound ligands as a knowledge-based strategy to correct
some of the limitations of current scoring functions and actively guide the
orientation of the ligands into the protein cavity.
In spite of all these limitations, target-based virtual screening has
gained a reputation in successfully identifying and generating novel
bioactive compounds. As an example, the use of a knowledge-based
potential (SMoG) in protein-ligand docking, resulted in the identification of
new picomolar ligands for the human carbonic anhydrase-II (Grzybowski
et al., 2002). Docking methods have also resulted in the discovery of novel
inhibitors for several kinase targets, including cyclin-dependent kinases,
epidermal growth factor receptor kinase and vascular endothelial growth
factor receptor 2 kinase among others (Muegge and Enyedy, 2004). Finally,
the application of docking methods to targets for which experimentally

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determined structures are not available yet has gained considerable
attention in recent years, particularly for the many targets of therapeutic
relevance belonging to the super-family of G-protein-coupled receptors
(GPCRs) (Bissantz et al., 2003). In these cases, structural information is
generated computationally by modelling the structure of the target of
interest on the basis of a template structure of a related target, including
often information on ligands as restraints (Evers et al., 2003). Such
strategies have resulted in the successful identification of novel antagonists
for the neurokinin-1 and the a1A-adrenergic receptors (Evers and Klebe,
2004; Evers and Klabunde, 2005).
One of the earliest developed initiatives is the computer system
PASS (Poroikov et al., 2000), which is based on the analysis of structure-
activity relationships for a training set of compounds consisting of about 35,
000 biologically active compounds extracted from the literature. The system
provides a prediction of the activity spectra of substances for more than
500 biological activities.
3.4.5 Present state of the art: Computer-aided drug design
Given the vast size of organic chemical space (Kuntz, 1992), drug
discovery cannot be reduced to a simple “synthesize and test” drudgery.
There is an urgent need to identify and/or design drug-like molecules
(Walters et al., 1998) from the vast expanse of what could be synthesized.
In silico methods have the potential to reduce both time and cost in

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developing suggestions on drug/lead-like molecules. Computational tools
have the advantage for delivering new lead candidate more quickly and at
lower cost. Drug discovery in the 21st
century is expected to be different in
at least two distinct ways: development of individualized medicine departing
from genomic information and extensive use of in silico simulations to
facilitate target identification, structure prediction and lead/drug discovery.
The expectations from computational methods for reliable and expeditious
protocols for developing suggestions on potential leads are continuously on
the increase. Several conceptual and methodological concerns remain
before an automation of drug design in silico could be contemplated.
Computational methods are needed to exploit the structural
information to understand specific molecular recognition events and to
elucidate the function of the target macromolecule. This information should
ultimately lead to the design of small molecule ligands for the target, which
will block/activate its normal function and thereby act as improved drugs.
As structural genomics, bioinformatics, and computational power continue
to explode with new advances, further successes in structure-based drug
design are likely to follow. Every year, new targets are being identified;
structures of those targets are being determined at an amazing rate, and
capability to capture a quantitative picture of the interactions between
macromolecules and ligands is accelerating.

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3.4.6 Genomics and drug development
So far, bacterial genomics has had a major impact on identification
and validation of targets and on assay development technologies for high-
throughput screening. However, genomic technologies will also be crucial
to subsequent stages of drug development such as lead optimization,
toxicology and clinical studies. One technology with particularly high
potential in these areas is the determination of cellular gene expression
patterns using DNA arrays (Maier et al., 1997).
The global changes in gene expression of a given cell as a response
to the effect of a compound can be viewed as a reflection of the mechanism
by which a compound acts on the cell. In other words, compounds with
similar effects on the cell’s physiology could produce related changes in
gene expression patterns. Using high density oligonucleotide expression
arrays representing nearly all the yeast genes, novel kinase inhibitors
isolated from combinatorial chemical libraries were characterized by
investigating their effect on yeast gene expression on a genome-wide scale
(Gray et al., 1998).
Hannes Loferer et al. (2000) experimented two inhibitors with an
identical in vitro inhibitory spectrum (Cdc28p and Pho85p) were compared
with a structurally related compound that showed no inhibitory in vitro
activity. Results showed 3% of the genes showed a greater than twofold
change in transcript level when treated with each of the kinase inhibitors,

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whereas only 0.03% were affected following treatment with the control
compound. Part of the set of genes affected by the inhibitors were loci
involved in cell cycle progression and phosphate metabolism, consistent
with the spectrum of kinases that were inhibited in vitro. Very few of the
genes induced by the inhibitors were affected by the control compound,
suggesting that many of the drug-sensing mechanisms might respond to
signals associated with the function rather than the structure of the drug
(Gray et al., 1998).
Pharmaceutical and biotechnological companies are screening large
numbers of chemical libraries for compounds with antibacterial activity.
Downstream development of these primary hits into leads would be greatly
facilitated if efficient technologies for mode-of-action studies were
available. Recently, the effect of the anti-tuberculosis drug isoniazid on
global gene expression profiles was investigated. One of the key findings
was the induction of a set of genes encoding the pathway known to be
affected by the drug (synthesis of the outer lipid envelope of mycobacteria)
(Wilson, 1999).
Also it is evident that its possible to correlate changes in gene
expression patterns with a drug’s mode of action. However, for analyzing
entirely new chemical entities only described by their bactericidal or
bacteristatic activity, extensive databases of the effects of reference
compounds (of known mode of action) and conditional mutants on transcript

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profiles will have to be generated. The degree of resolution to which the
mechanism of action of a currently undescribed compound can be
pinpointed by gene expression profiling remains to be determined.
Another application of the same principle is the assessment of the
risk of developing early hits from drug screening. For example,
characterization using gene expression profiling of an anti-arteriosclerotic
compound that, in cell culture, drastically reduced levels of low-density
lipoprotein, revealed that the effect of this compound on gene expression
strongly resembled that of a completely different class of compounds that
was already shown to be toxic (Service, 1998). Thus, resources were not
wasted on unsuitable drug candidates. Such approaches are also being
discussed in the field of general toxicology and have already been defined
as the subdiscipline of toxicogenomics (Nuwaysir et al., 1999).
Together with advances in the human genome project and genomic
technologies, the development of anti-infective drugs in the future will
become more efficient and targeted by defining patient sub-populations
according to their suitability for a given treatment. This era of genetic
susceptibility to infectious disease will streamline clinical trials by using
more focused patient populations. For example, a small deletion in the
gene for the interferon g(IFNg) receptor (IFNGR1) was found to be
associated with dominant susceptibility to infections caused by BCG that is

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normally avirulent and used as a tuberculosis vaccine (Jouanguy et al.,
1999).
The increasing resistance of bacterial pathogens to present-day
antibiotics and the lack of a robust pipeline of innovative antimicrobial
substances demand innovative and more efficient approaches towards the
development of anti-infective drugs. Bacterial genomics has so far greatly
increased the rate with which novel targets are identified and validated.
Furthermore, the chances that ‘second generation’ genomic technologies
will accelerate target identification and generic assay development are
high. Genomics can also help to streamline later stages of the development
of antimicrobials, such as lead optimization, toxicology and clinical trials.
However, a concerted innovative application of genomic technologies and
chemistries are required to decrease the lag-period between lead
identification and marketing of a new drug.
3.4.7 Genomics and assay development
Traditionally, screening for novel antimicrobial compounds in industry
has been performed by testing large libraries of natural products for their
ability to kill bacteria. Many of the antibiotics used today were discovered
this way. This classical approach is again enjoying increased interest
because of improved chemical diversity through combinatorial chemistry
(Myers, 1997) and the exploitation of unexplored resources of natural
products (Nisbet and Moore, 1997). However, this approach has

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disadvantages such as low sensitivity and the fact that the targets of the
respective compounds are unknown. The latter point has also created
interest in applying genomic technologies to investigate the mode of action
of antibacterial compounds.
In an alternative approach, individual proteins involved in well-studied
essential pathways are purified and biochemical in vitro assays set up to
screen compound libraries for inhibitors. Empirically, many of the primary
hits of these screens will have to be modified to enable penetration into the
bacterial cell. Furthermore, the biological function (i.e. biochemical activity)
of the respective target protein needs to be known to enable this type of
assay development. As already outlined, the latest developments in whole
genome genetic analysis (e.g. GAMBIT or the B. subtilis functional analysis
program) will lead to the identification of most genes essential for growth
and survival of several bacterial species within the near future. However,
detailed functional information that will enable classical assay development
will only be available for a minority of new targets. Thus, to use this
extensive target information efficiently, innovative assay development
strategies are required that are applicable to a broad functional variety of
antibacterial targets.
3.4.8 Protein-protein interaction technologies
It can be anticipated that many validated drug targets from microbial
genomics will be identified in the near future for which detailed functional

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information is lacking. Although existing technologies enable assay
development based on such targets, large scale functional analysis helps to
prioritize targets for screening programs by positioning them in the context
of cellular pathways. One historically very successful method of gaining
functional information concerning proteins is the identification of protein-
protein interactions using the yeast two-hybrid system (Colas et al., 1996).
This technology utilizes the fact that the DNA binding and the trans-
activating domains of the yeast transcriptional activator Gal4 can be
separated, rendering the protein inactive. Activity of the separated domains
can be regained by physical proximity. Hence, protein fusions to these
domains are generated and physical interaction of the respective protein
fusion partners can be identified on the basis of transcriptional activation of
the reporter genes.
In the standard molecular biology laboratory, performing a yeast two-
hybrid screen is very labour-intensive, because of the high frequency of
false-positive hits that must be identified for every screen. The two largest
problems in laboratory-scale yeast two-hybrid screens are the appearance
of auto activating protein domains fused to the DNA-binding domain vector
and the effect of ‘sticky’ proteins that bind non-specifically to many other
proteins (Bartel et al., 1993).
Thus, solutions for efficiently eliminating these problematic clones are
essential if large-scale analyses of protein–protein interactions are to be

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feasible. Genome Pharmaceuticals Corporation (GPC) has developed an
efficient procedure for parallel identification and elimination of
autoactivators and sticky proteins. In this method, called PathCode™,
replicas of putatively interaction- positive yeast clones (as identified by
positive selection) are arrayed on nylon filters and allowed to grow on three
different selective media in parallel. One medium selects for the presence
of both plasmids and assays for protein interaction using the lacZ reporter.
The remaining two media each counterselect against one of the two
interaction plasmids and positively select for the other. The selection
(for and against) is reversed between the two media. Clones that are lacZ-
positive on any of the counterselective media are false-positive hits. Large
numbers of clones are analyzed using digital image analysis procedures
based on the software package BiochipExplorer™ (GPC). This technology
opens up the possibility of analyzing large numbers of ‘baits’ or even
performing library versus- library screens for entire bacterial genomes.
Assay formats can be developed that enable screening for small
molecules that inhibit and/or disrupt such protein–protein interactions
(‘reverse two-hybrid screening’) (Huang and Schreiber, 1997). Thus, the
investigation of protein–protein interactions using the yeast two-hybrid
system not only helps to gain functional information, but might directly be
the basis for drug screening. However, it remains to be elucidated how

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many of the antibacterial drug targets will be involved in protein–protein
interactions that can be disrupted by small molecules.
3.5 NOVEL GENOMICS APPROACH
Availability of genome sequences of pathogens has provided a
tremendous amount of information that can be useful in drug target and
vaccine target identification. One of the recently adopted strategies is
based on a subtractive genomics approach, in which the subtraction
dataset between the host and pathogen genome provides information for a
set of genes that are likely to be essential to the pathogen but absent in the
host. This approach has been used successfully in recent times to identify
essential genes in Pseudomonas aeruginosa. The same methodology was
used to analyse the whole genome sequence of the human gastric
pathogen Helicobacter pylori. The analysis revealed that out of the 1590
coding sequences of the pathogen, 40 represent essential genes that have
no human homolog. Further analyses of these 40 genes by the protein
sequence databases lists some 10 genes whose products are possibly
exposed on the pathogen surface. This preliminary work reported here
identifies a small subset of the Helicobacter proteome that might be
investigated further for identifying potential drug and vaccine targets in this
pathogen.
The completion of the human genome project has revolutionised the
field of drug-discovery against threatening human pathogens (Miesel et al.,

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2003). The strategies for drug design and development are progressively
shifting from the genetic approach to the genomic approach (Galperin and
Koonin, 1999). The search for potential drug targets has increasingly relied
on genomic approaches. Subtractive genomics has been successfully used
by authors to locate novel drug targets in Pseudomonas aeruginosa
(Sakharkar et al., 2004).
_____

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