Functional proteomics, and tools

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

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 SYNOPSIS 2 Functional proteomics

3. INTRODUCTION 3 13/04/2013 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. HISTORY 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. 13/04/2013

5. 5 FUNCTIONAL PROTEOMICS 06/05/2020 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 .  

6. 13/04/2013 6 PROTEOMICS SOFTWARE 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.

7. 13/04/2013 7 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. PROTEOME MAPPING 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. 13/04/2013 8

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. 13/04/2013 9

10. STUDY OF PROTEIN-PROTEIN INTERACTIONS 13/04/2013 10 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.

11. 13/04/2013 11 1.Domain fusion NON-HOMOLOGY METHODS OF INFERRING PROTEIN-PROTEIN, INTERACTION 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. 2. Conservation of gene position 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. 13/04/2013 12

13. 3. Phylogenetic profiles 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. 13/04/2013 13

14. 13/04/2013 14 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 .     All-against-all self-comparison revels number of gene families and duplicated genes

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. 13/04/2013 15 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. 13/04/2013 16 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. 13/04/2013 17 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. 13/04/2013 18

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. 13/04/2013 19

20. Different types of chips 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. 13/04/2013 20

21. 13/04/2013 21 Antibody arrays 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 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 protein chips 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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23. 13/04/2013 23 CONCLUSION 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 13/04/20133