REVIEW OF LITERATURE - Shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/5356/6/06... · 2015-12-04 · Likely to encode soluble proteins. The essentiality of selected genes was

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 prote in 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 paral lel 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 pharmaceutica l

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 immobi lized 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 near ly 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 identificat ion 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 profil ing 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.,

http://www.bioinfo.de/isb/2006/06/0005/main.html#Miesel


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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).

_____

http://www.bioinfo.de/isb/2006/06/0005/main.html#Galperin

http://www.bioinfo.de/isb/2006/06/0005/main.html#Galperin

http://www.bioinfo.de/isb/2006/06/0005/main.html#Sakharkar

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REVIEW OF LITERATURE - Shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/5356/6/06... · 2015-12-04 · Likely to encode soluble proteins. The essentiality of selected genes was