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Introduction to Pharmaceutical Biotechnology, Volume 2 (Second Edition)
Figure 8.1. Basic protocol for proteomics.
8.2 Types of proteomics
8.2.1 Structural proteomics
In the postgenomic era, structural proteomics is one of the promising areas of
investigation, and explains the structure–function relationships of uncharacterized
gene products based on the three-dimensional protein structure (figure 8.4)[4]. It
suggests the biochemical and cellular roles of unannotated proteins and thus
classifies possible drug design and protein engineering targets [8]. Recently, several
innovative groups in structural proteomics research have attained proof of structural
proteomic theory by forecasting the three-dimensional structures of theoretical
proteins that correctly recognized the biological functions of those proteins.
Structural proteomics can show the three-dimensional structure and nature of
protein complexes present in a specific cell/organelle [5]. The final goal of structural
proteomics is to form an organization of structural data that will assist in predicting
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Target identification and selection
Target isolation and purification
Structure determination
Analyze structure for potential ligand binding
Docking of small molecules using
Biochemical assays and further testing
Lead optimization to improve potency
Cytotoxicity tests, pharmacokinetics studies &
toxicological investigations
sites
computational methods
Drug candidate
Figure 8.2. Basic procedure involved in proteomics.
Figure 8.3. Different areas in proteomics.
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Figure 8.4. Basic procedure involved in structural proteomics.
the possible structure and possible function of virtually any protein from information of its coding sequence. This proteomics branch can also help in accumulating
data about protein–protein interactions and about the construction of cells, to
describe how the expression of certain proteins results in a cell ’ s unique features.
Recent mass spectrometry tools have offered a useful platform that can be combined
with several procedures to examine protein structure and dynamics. Moreover
nuclear magnetic resonance (NMR)-based structural proteomics coupled with x-ray
crystallography can provide a comprehensive structural database to predict the basic
biological functions of hypothetical proteins identified by genome projects.
8.2.2 Functional proteomics (strategy)
As the number of genome sequencing projects is increasing, there is a parallel
exponential growth in the number of protein sequences whose function is still
unidentified (figure 8.5)[6]. Functional proteomics is a developing research area in
the field of proteomics whose methods are geared towards two main objectives: the
interpretation of the biological function of unidentified proteins and the description
of cellular mechanisms at the molecular level [7].
Functional proteomics uses proteomics tools to examine the features of the
molecular protein networks involved in a living cell. Documentation and investigation of molecular protein networks involved in the nuclear pore complex in yeast
is one of the recent accomplishments of functional proteomics. This accomplishment
allows understanding of the translocation of molecules from the nucleus to the
cytoplasm and vice versa [8].
8.2.3 Expression proteomics
Expression proteomics is the investigation of dysregulated proteins as a function of
stimulation or condition (disease, time, drug, etc). In other words it is the
quantitative investigation of protein expression between samples differing in a
number of variables. It can also be defined as the examination of protein expression
at a larger scale. The arrangement of expression of the entire proteome or a portion
of it (a subproteome) between samples can be ascertained with the help of this
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Figure 8.5. Functional proteomic analysis.
method. Expression proteomics is quite beneficial in finding disease-specific proteins.
For example, over-expression or under-expression of proteins in cancerous cells and
normal cells derived from a cancer patient and a normal individual, respectively, can
be examined using several tools such as 2DE, mass spectrometry, microarrays, etc.
This approach can recognize the growth of cancers and allows improvement of
drugs in the management of cancer.
8.3 Basic techniques involved in proteomics
8.3.1 Sequence alignment (algorithms)
Fast emerging new sequencing tools offer information on an unparalleled scale. A
principal challenge to the examination of these data is sequence alignment, wherein
sequence reads must be matched to a reference [9]. An extensive variety of alignment
algorithms and software have been developed over the past few years [9]. These
techniques involve the study of the sequences of the DNA, RNA or protein to detect
sites of resemblance that may be a result of functional, structural or evolutionary
relationships between the sequences. Nucleotide or amino acid residues which are
aligned sequences are characteristically presented as rows within a matrix. The
residues gaps are incorporated between these, so that identical or similar features are
associated in successive columns. The fastest way to allocate a new gene or cDNA is
to perform pairwise assessment (a procedure of relating objects in pairs to judge
which object is favored) with cDNA and EST sequences, and recognize gene
sequences procured in databanks using alignment techniques such as BLAST
(Basic Local Alignment Search Tool) or FASTA (a DNA and protein sequence
alignment software package). However, this method is really only beneficial when
there is >30% sequence uniqueness between the new gene and the databank
sequence. If a similar gene/protein is found, it proposes the possible role of the
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product of new gene. However, additional experiments will be essential to determine
the real in vivo function of the new gene product. To find resemblances below 30%
sequence uniqueness, numerous sequence assessments are made by means of an
algorithm such as PSI-BLAST (Position-Specific Iterated Blast). However, such a
search may provide false positive findings. PSI-BLAST develops a position-specific
scoring matrix or outline from the various sequence alignments detected above a
given score threshold by protein–protein BLAST [10].
8.3.2 Protein structure (annotation resources)
An important reason three-dimensional protein structures are interpreted with
supportive or derived data is to determine the molecular background of the protein
function. In this endeavor, protein structure annotation databanks curate important
evidence and explanations, based on community-accepted standards, for the
∼100 000 three-dimensional investigational protein structures that allow further
understanding of the structure–function relationship [11].
A method to control the function of an orphan gene is to inspect the threedimensional structural arrangements of the protein, that the gene responsible for this
protein is expected to encode; such a protein is called a theoretical protein. This threedimensional structural arrangement is matched with the protein structures stored in
databases. One of the bases for this method is the fact that the three-dimensional
structures of proteins are better maintained than are their primary structures. For this
reason protein function depends on the three-dimensional structure instead of the
primary structure of the protein. It has been projected that 20%–30% of orphan genes
could be allocated function by defining the three-dimensional structures of the
proteins encoded by them, and equating these to protein structures in databanks.
For example, the three-dimensional structural configurations of hemoglobin and
myoglobin are very comparable, considering that they both are oxygen carriers.
However, a BLAST search will fail to detect resemblance between myoglobin and αor β-globin (both are hemoglobin polypeptides). Thus it has been projected that if a
theoretical protein has a similar structure to a known protein, there is a 66% chance
that it has a role related to that of the known protein. The structure of a theoretical
protein can be evaluated in the following three ways:
• If this target sequence displays >25% resemblance to a sequence whose
structure is already known (called a template sequence), relative modeling can
be employed to forecast the structure of the target protein sequences.
• When the uniqueness is <25%, the sequence can be matched to known
structures to examine the level to which the experimental data match the
values anticipated by theory.
• The target sequence structure can also be examined from the primary data
without reference to known structures. Protein structures are experimentally
evaluated using x-ray crystallography and NMR spectroscopy. Both of these
methods are slow, time consuming and require greater quantities (milligrams)
of the purified protein. However, current developments in both procedures
will allow their alteration for high throughput analyses.
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Figure 8.6. Procedure involved in protein structural investigation.
8.3.3 Protein structural investigation
Figure 8.6 provides a schematic representation of the process involved in protein
structural investigation.
Structural investigation can often disclose the general function of a protein when
sequence and structural assessments fail to suggest function. For example, protein
surface scanning may disclose splits that may signify ligand-binding sites. A protein
with a large split or cleft could be cautiously assigned the name ‘enzyme’. A greater
resolution in the description can be conceivable by comparing the shape of the split/
cleft to a library of small molecular shapes using drug design software, e.g. DOCK
and HOOK. These programs are capable of identifying possible ligands with binding
sites present on protein surfaces. Scanning of the protein surface also facilitates
identification of the domains most likely involved in interaction with other proteins.
Knowing gene function involves more than recognizing the gene products. After they
are produced, several gene products are altered by the breakdown of end groups (e.g.
signal sequences, propeptides or initiator methionine residues), by the accumulation of
chemical groups (such as methyl-, acetyl-, phosphoryl) or sometimes by adding
linkages to sugars and lipids. In addition to the wide variety produced by the alternate
splicing of mRNA, nearly a hundred mechanisms of post-translational modification
are recognized. Therefore, the human genome, which may have 35 000–40 000
protein-coding genes, may offer over 350 000 different gene products. The main aim of
proteomics is to provide data on each protein encoded in a genome, on its role,
structure, cellular localization, post-translational modifications, associations (shared
domains, evolutionary history) with other proteins and variants.
8.3.4 Two-dimensional gel electrophoresis in proteomics
Gel-based proteomics is one of the most multipurpose approaches for fractionating
protein complexes [12]. Among these approaches, two-dimensional polyacrylamide
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Figure 8.7. Two-dimensional gel electrophoresis.
gel electrophoresis (2DE) provides a key orthogonal method. It is commonly used to
simultaneously fractionate, detect, and quantify proteins when coupled with mass
spectrometric identification or other immunological tests.
The basic tools in proteomics include separation and identification of proteins
that are isolated from cells. To achieve this, 2DE can be utilized. 2DE involves
placement of the protein extract on a polyacrylamide gel after which an electric
charge is applied across the gel to separate proteins based on their molecular weight
(figure 8.7). Once this is complete, the gel is turned 90°, and through a second phase
of electrophoresis, the proteins are separated in a second dimension based on their
molecular mass. Once the gels are stained, the proteins are exposed as spots;
different gels display 200–10 000 spots. To detect different proteins, spots are cut
from the gel and are allowed to digest with enzymes, e.g. trypsin, to offer a
characteristic set of fragments. These fragments are further studied by mass
spectrometry, which is known as peptide mass fingerprinting. To recognize the
proteins, the peptide’s mass is matched with the masses predicted from data in
genetic or protein databanks.
8.3.5 Domain fusion method (or rosetta stone method)
The examination of amino acid sequences from different individuals often provides
results in which two or more proteins encoded for discretely in a genome also act as
fusions, either in a similar genome or that of some other organism [13]. These fusion
proteins, called Rosetta stone sequences, support the linkage of dissimilar proteins,
and suggest the probability of functional interactions between the linked objects,
creating local and global relationships within the proteome [14]. The domain fusion
method searches for functionally active proteins that are distinct in a number of
organisms, but are fused into a single protein, a Rosetta stone protein, in some other
organisms. The basic principle of the Rosetta stone technique is as follows. The
protein A sequence from an organism is utilized to search for a homolog in a
different organism. This examination can recognize a single domain protein such as
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A. However, in a number of organisms, it may recognize a Rosetta stone protein,
such as A–B. As domains A and B are portions of a single protein, they are likely to
be functionally related. Now the sequence of domain B is used to search for its
homolog in the first organism. The homolog of the domain identified in this
organism will be functionally related to protein A. Thus if the function of protein
A were known, this strategy will reveal the function of protein/gene B, which was
formerly an orphan gene.
8.4 Complete proteome of Mycoplasma genitalium
M. genitalium is the smallest member of the Mollicutes, has a genome size of 580 kb
and the potential to express 480 gene products, and is thus an outstanding model to
assess [15]:
• The minimum metabolism necessary for a free-living cell.
• Proteomic tools and the information derived by proteome analysis.
Wasinger and co-workers utilized proteomics to offer a portrait of what type of
genes are expressed in the bacterium M. genitalium during exponential and stationary growth periods. M. genitalium is one of the simplest independent bacteria
with a reduced genome. Using 2DE, the researchers explored 427 protein spots in
exponentially growing cells. Of these, 201 were examined and recognized. The rest of
the spots present in fragments derived from larger proteins, variants of similar
proteins (isoforms) and post-translationally modified forms. The recognized proteins
included enzymes involved in DNA replication, transcription, translation, delivery
of materials across the cell membrane and energy metabolism. The study, however,
could not cover 158 known proteins (33% of the proteome) and 17 unknown
proteins. It was analyzed that there was a 42% reduction in the number of proteins
synthesized during the transition from the exponential growth phase to the stationary phase. Moreover, a number of new proteins appeared, and additional
proteins experienced intense variations because of nutrient exhaustion, enhanced
acidity of the growth medium and other adaptations against environmental changes.
In M. genitalium, a mere 33% of the proteome is expressed during maximal growth,
and the remaining 67% of the proteome are the proteins to be expressed under
different environmental conditions. Wasinger’s examination of the M. genitalium
proteome assisted in establishing the least number of expressed genes essential for
independent existence and the modifications in gene expression that accompany the
changeover from the exponential growth phase to the stationary phase. It was also
noticed that the extensive range of information offered by proteome analysis cannot
be derived only by genome sequencing.
8.5 Architecture and design of the nuclear pore complex
Three-dimensional examination of the nuclear pore complex discloses the fundamental, highly symmetric framework of this supramolecular assembly, how it is
attached in the nuclear membrane and how it is constructed from many distinct,
interconnected subunits [16]. The organization of the subunits within the membrane
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pore makes a large central channel, by which active nucleocytoplasmic transport is
known to occur, and eight smaller peripheral channels that are probable routes for
passive diffusion of ions and small molecules. The nuclear pore complex (NPC) is
made up of proteins embedded in the nuclear membrane. This complex joins the
cytoplasmic and nuclear compartments of the cell and permits transport of materials
between the nucleus and the cytoplasm. As this transportation contains mRNA
intended for the cytoplasm, nuclear pores are vital control points for regulating gene
expression. Their structural and functional sophistication has been an obstacle in
understanding their molecular organization. Genomics in association with proteomics has offered scientists techniques to investigate the three-dimensional construction of nuclear pores and to recognize their proteins. Depending on its size, the yeast
nuclear pore complex contains around 200 different proteins. By using a rat genome
databank as a resource, scientists were able to recognize potential nuclear pore
complex proteins, also called nucleoporins, by means of sequence resemblance to
already identified nuclear pore complex proteins from other organisms. In another
example, proteomics was employed to outline the molecular construction of a
nuclear pore complex. Accordingly, pore complexes were separated and purified,
and then nucleoporins were detected by means of mass spectrometry on peptide
digests of proteins purified from the NPCs. Yeast’s NPC only includes around 30
different proteins, but with each protein present in multiple copies. The nucleoporins
are prearranged into 16 subunits (eight on the pore’s nuclear side and eight on its
cytoplasmic side). Moreover, there are filament-base structures on both sides of the
pore, formed into a bag on the nuclear side. Using multiple tools such as mutant
analysis, microscopy and proteomics, the actual regions/sites of NPC proteins in the
three-dimensional arrangement of the pore have been mapped. The majority of
nucleoporins are proportionally distributed on the cytoplasmic and nuclear sides of
the pore. Five proteins are present only on one side or the other, and seven are
distributed more on one side than on the other. The function of nucleoporins in
nucleocytoplasmic transportation or the regulation of transport can be studied by
examining protein–protein interactions within the pore, as well as interactions of
pore proteins with transport proteins, signal molecules and other cellular
components.
8.6 Functional genomics and systems biology
Functional genomics is an efficient method for determining the roles of the novel
genes revealed by complete genome sequences (figure 8.8)[17]. Such a method
should take a hierarchical approach, as this will both bound the number of trials to
be done and allow a closer and closer estimate of the function of any individual gene
to be attained. Furthermore, hierarchical studies have, in their early stages,
remarkable integrative power and functional genomics aims at a comprehensive
and integrative view of the workings of living cells [18]. Functional genomics is an
area where the function of a gene product is determined. This includes answers to the
questions:
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Figure 8.8. Schematic representation of functional genomics.
• How are genes expressed?
• How are the gene sequence and structure related to the end product, and its
relationship with gene versus an individual from the same generation?
• Exactly how is a product associated with a sequence and structure, and how is
it associated with the end products of other genes of similar organisms?
• What are the implications of the environment over a gene and how does this
interaction help in understanding the rate of genetic variation? Gene–
environment interaction helps in understanding genetic influences modeled
as latent [19].
These questions can be answered by studying the following:
• Determining at what time and exactly at which place specific genes are
expressed on a genomic scale (called expression profiling). This permits the
identification of those particular genes that are over-expressed or underexpressed. This concept can be used to understand different biological
processes in health and disease. This concept can also be utilized to study
the expression in a particular tissue/organ at the molecular level. This type of
gene-based profiling, and more specifically recent RNA sequence based
profiling, can be used as an important tool for drawing correlations between
gene activity and various physiological or developmental states. It also helps
in creating collections of gene expression data across various cell types,
development times, varied species and against different stimuli [20].
• The replacement of a particular natural gene with mutated gene under in vitro
conditions to access its in vivo function (the knockout method) [21].
• The genetic relationship between proteins and other molecules (as first
suggested by Archibald Garrod in 1902, who predicted that genes control
the function of proteins during his study on patients suffering from
alkaptonuria).
Functional genomics aims towards answering such questions thoroughly for most of
the genes present in a genome, in comparison to classical strategies that report one
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