INTRODUCTIONHISTORY FUNCTIONAL PROTEOMICSPROTEOMICS SOFTWARE PROTEOME MAPPINGTOOLS FOR PROTEOME ANALYSISAPPLICATIONS OF PROTEOMICSCONCLUSIONREFERENCES

1. FUNCTIONAL PROTEOMICS
By
KAUSHAL KUMAR SAHU
Assistant Professor (Ad Hoc)
Department of Biotechnology
Govt. Digvijay Autonomous P. G. College
Raj-Nandgaon ( C. G. )

2. INTRODUCTION
HISTORY
FUNCTIONAL PROTEOMICS
PROTEOMICS SOFTWARE
PROTEOME MAPPING
TOOLS FOR PROTEOME ANALYSIS
APPLICATIONS OF PROTEOMICS
CONCLUSION
REFERENCES
2

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Proteome is a relatively new
term that defines the
complete set of proteins
expressed during a cell’s
entire lifetime. In a narrower
sense, it also describes the set
of proteins expressed in a cell
at a given time.
The term ‘proteomics’
indicates PROTEins
expressed by a genOME and
is The systematic analysis of
protein profiles of tissue
(introduced by Wilkins in
1995).

4. 4
• Marc Wilkins coined the term proteome in
1994 in a symposium on "2D
Electrophoresis: from protein maps to
genomes" held in Siena in Italy.
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5. 55/10/2020
Functional proteomics refers to the use of proteomics
techniques to analyze the characteristics of molecular
protein-networks involved in a living cell. One of the recent
successes of functional proteomics is the identification and
analysis of molecular protein-networks involved in the
nuclear pore complex (NPC) in yeast. This success helps
understand the translocation of molecules from nucleus to
the cytoplasm and vice-versa.

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Proteomics software provides scientists with the
ability to conduct database searches of known
protein sequences utilizing batch or real time
processing. This software is capable of controlling
automated hardware, i.e., robotics, as well as
facilitating data transfer operation.

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Proteins in an organism change during growth, disease, and the
death of cells and tissues, modifications of proteins that occur
during and after their synthesis, such as the attachment of sugar
residues or lipids, change the proteome complement.
There are five main steps in proteome analysis:
 Sample collection, handling and storage.
 Separation of individual proteins by 2-D polyacrylamide gel
electrophoresis (2-D PAGE).
 Identification by mass spectrometry or N-terminal sequencing of
individual proteins recovered from the gel.
 Protein characterization.
 Storage, manipulation, and comparison of data using bioinformatics.

8. TOOLS FOR PROTEOME ANALYSIS
Microarrays
DNA microarray technology can be used to
accomplish this because mRNA and protein
concentrations are often correlated. It can measure
even poorly expressed genes, ensuring a
comprehensive assessment of which genes are
expressed in which tissue. However, since mRNA and
protein levels do not always correlate in the cell and
many regulatory processes occur after transcription, a
direct measure of relative protein abundance is more
desirable.
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9. MASS SPECTROMETRY APPROACH
A variety of technologies are available to measure differences in
cellular protein abundance. One of such methods is
electrophoresis or chromatography coupled with mass
spectrometry (MS). In this method, mixtures of proteins in
cellular extracts are resolved and then individual proteins are
identified using MS peptide fingerprinting. Although in theory
MS approaches have the potential to characterize the entire
protein complement of a cell, in practice it has proved difficult
to identify proteins of low abundance, because cell extracts,
and the resulting mass spectra, are dominated by a few
hundred very abundant proteins.
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• Protein-protein interactions are at the heart of
most cellular processes, including carbohydrate
and lipid metabolism, cell-cycle regulation, protein
and nucleic acid metabolism, and cellular
architecture. A complete understanding of cellular
function depends on a full characterization of the
complex network of cellular protein-protein
associations.

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For two proteins A, B, if you find a “fused” protein
AB in some organism, this suggests that A and B
are functionally related (often in the same
biochemical pathway). For example GyrA and
GyrB gyrase subunits in E.coli, fused to make
topoII in yeast.

12. • Genes of related function are often adjacent in the
genome-especially in prokaryotic operons. Besides
co-transcription regulation, this is also likely to be
an evolutionary effects of horizontal transfer-
probability of co- transfer is higher for linked
genes, so linkage of interacting proteins is
advantageous if horizontal transfer rates are
appreciable.
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13. 3.
• Many genes are not universally conserved, and are found in
some genomes and not others, because of gene loss and
horizontal transfer. Genes will tend to only occur in an
organism that is using them productively, so for a set of
genes that function in a particular pathway, there will tend
to be an all or none effect: either all the genes are in a given
organism, or none of them are. Thus clustering genes by
their binary pattern of occurrence in many genomes can
detect functionally related genes.
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A comparison of each protein in the proteome will all
other proteins distinguishes unique proteins that have
arisen from gene duplication, and also reveals the
number of protein families. The domain content of these
proteins may also be analyzed. In this analysis, each
protein is used as a query in a similarity search against
the remaining proteome, and the quality and length of
the alignments found rank the similar sequences.

15. • Cluster analysis sorts out the relationships among all
the proteins. Clustering organizes the proteins into
groups by some objective criterion. One such
criterion can be the statistical significance of their
alignment score. The lower the value, better is the
alignment. A second criterion can be the distance
between each pair of sequences in a MSA. The
distance is the number of amino acid changes
between the aligned sequences.
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cluster analysis

16. • This method is based on the distance criterion for
sequence relationships. First, a group of related
sequences found in the all-against-all proteome
comparison is subjected to a MSA. A distance matrix
that shows the number of amino acid changes between
each pair of sequences is then made. This matrix is then
used to cluster the sequences by a neighbour-joining
algorithm. This forms a phylogenetic tree called a
minimum spanning tree that minimizes the number of
amino acid changes that would generate the group of
sequences.
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Clustering by single linkage

17. • The all-against-all analysis provides an
indication as to the number of protein/gene
families in an organism. This number
represents the core proteome of the
organism from which all biological
functions have diversified. For example, in
H. influenzae, the total number of genes is
1709 and the number of gene families is
1425. Drosophila has 13,600 genes and
8065 gene families.
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Core Proteome

18. Between-proteome comparisons to identify
orthologs, gene families and domains
• Each protein in the proteome is used as a query in a
database similarity search against another proteome or
combined set of proteomes. When the proteome is not
available, an EST database may be searched for matches.
The search should identify highly conserved proteins of
similar domain structure and other similar proteins that
show variation in the domain structure. Pair of proteins in
two organisms that align along most of their lengths with a
highly significant alignment score are likely to be
orthologs. These proteins perform the core biological
functions shared by all organisms, including DNA
replication, transcription, translation, and intermediary
metabolism.
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19. Clusters of orthologous groups
• When entire proteomes of the two organisms are
available, a different approach can be undertaken.
Orthologs can be identified by using the protein
from one of the organisms to search for the
proteome of the other. The high-scoring matches
would identify the ortholog.
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20. • The term ‘protein chip’ encompasses a bewildering number of quit
different devices linked only by their overall function, which is the
large-scale analysis of proteins. These chips are generally prototypical
in nature, they exploit very new innovations in micro fluidics and
nanotechnology, and their full impact on proteomics and other areas of
biology is currently difficult to judge.
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• The most common type of analytical protein chip is the antibody array, which is
a coated glass slide or silicon wafer containing a high-density array of specific
antibodies.
• Antigen arrays are the converse of antibody arrays. They are spotted with
antigens and are used to capture antibodies from solution, e.g. for antibody
profiling in serum. The antigens may be proteins or other molecules such as
peptides or carbohydrates.
• Functional chips are arrayed with the proteins whose functions are under
investigation. Unlike analytical chips, which are used for expression profiling,
functional chips can be used to investigate many different properties of
proteins, including binding activity, the function of complexes and biochemical
functions.

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Proteomics is extremely valuable for understanding
biological processes and advancing the field of
system biology.
Proteomics attempts to catalog and characterize
proteins, compare variations in their expression
levels in health and disease, and identify their
functional roles.

24. 24
Principles of Gene Manipulation and Genomics; by primrose
& Twyman
Bioinformatics (concepts, skills & applications) : S.C. Rastogi,
Namita Mendiratta, Parag Rastogi (2004)
Bioinformatics : C.S.V. Murthy (first edition 2003)
www.wikipedia.com
REFERENCES
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