Proteomics

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ECAN Genesis 2000 robot preparing Ciphergen SELDI-TOF protein chips for proteomic pattern analysis. Cancer prognosis by identifying protein patterns of specific cancers using devices such as this is an emerging field, with a limited track record of success and several high profile failures.^{[citation needed]}

Proteomics is the large-scale study of protein, particularly their structures and functions. This term was coined to make an analogy with genomics, and while it is often viewed as the "next step", proteomics is much more complicated than genomics. Most importantly, while the genome is a rather constant entity, the proteome differs from cell to cell and is constantly changing through its biochemical interactions with the genome and the environment. One organism has radically different protein expression in different parts of its body, in different stages of its life cycle and in different environmental conditions.

The word is derived from PROTEins and by genOME, since proteins are expressed by the genome. The proteome refers to all the proteins produced by an organism, much like the genome is the entire set of genes. The human body may contain more than 2 million different proteins, each having different functions. Thus, proteomics is the study of the composition, structure, function, and interaction of the proteins directing the activities of each living cell. As the main components of the physiological pathways of the cells, proteins serve as vital functions in the body.

The task of analysing an organism's genome is relatively straightforward. However the task of analysing an organism's proteome is anything but simple, in large part because of the complexity of proteins relative to nucleic acids. Despite this difficulty, scientists are still extremely interested in the field of proteomics. For one, the level of transcription of a gene gives only a rough idea of the real level of expression of that gene. An mRNA may be produced in abundance, but degraded rapidly, or translated inefficiently, so the amount of protein produced is minimal.

For another, many proteins experience posttranslational modifications that have a profound effect on their activities; some proteins are not active until they become phosphorylated. Furthermore, many transcripts give rise to more than one protein, through alternative splicing or alternative posttranslational modifications. Finally, many polypeptides form large complexes with other polypeptides, and the true expression of each polypeptide’s function occurs only in the context of the complex.

The entirety of proteins in existence in an organism throughout its life cycle, or on a smaller scale the entirety of proteins found in a particular cell type under a particular type of stimulation, are referred to as the proteome of the organism or cell type respectively.

Since proteins play a central role in the life of an organism, proteomics is instrumental in discovery of biomarkers, such as markers that indicate a particular disease.

With completion of a rough draft of the human genome, many researchers are now looking at how genes and proteins interact to form other proteins. A surprising finding of the Human Genome Project is that there are far fewer protein-coding genes in the human genome than there are proteins in the human proteome (~20,000 to 25,000 genes vs. ~1,000,000 proteins). The large increase in protein diversity is thought to be due to alternative splicing and post-translational modification of proteins. This discrepancy implies that protein diversity cannot be fully characterized by gene expression analysis alone, making proteomics a useful tool for characterizing cells and tissues of interest.

To catalog all human proteins and ascertain their functions and interactions presents a daunting challenge for scientists. An international collaboration to achieve these goals is being co-ordinated by the Human Proteome Organisation (HUPO).

[edit] Studying Proteomics

Most proteins do not function in isolation, but collaborate with other proteins. One goal of proteomics is to identify the proteins that interact with one another. This frequently can give important clues about the functions of newly discovered proteins. Several techniques are available to probe these protein-protein interactions. Traditionally, yeast two-hybrid analysis has been done, but now other methods are available. These include protein microarrays, immunoaffinity chromatography followed by mass spectrometry, and combinations of experimental methods such as phage display and computational methods. One of the most useful fruits of such analyses is the discovery of functions for new proteins.

Current research in proteomics requires first that proteins be resolved, sometimes on a massive scale. The best tool available for separation of many proteins at once is 2-D gel electrophoresis. The first separation in this two step process is based upon differing isoelectric points. Once proteins are separated and quantified, they have to be identified. Individual spots are cut out of the gel and cleaved into peptides with proteolytic enzymes. These peptides can then be identified by mass spectrometry, specifically matrix-assisted laser desorption-ionization time-of-flight (MALDI-TOF) mass spectrometry system. In this procedure, a peptide is placed on a matrix, which causes the peptide to form crystals. Then the peptide on the matrix is ionized with a laser beam and an increase in voltage at the matrix is used to shoot the ions toward a detector in which the time it takes an ion to reach the detector depends on its mass. The higher the mass, the longer the time of flight of the ion. In a MALDI-TOF mass spectrometer, the ions can also be deflected with an electrostatic reflector that also focuses the ion beam. Thus, the masses of the ions reaching the second detector can be determined with high precision and these masses can reveal the exact chemical compositions of the peptides, and therefore their identities.

One of the most promising developments to come from the study of human genes and proteins has been the identification of potential new drugs for the treatment of disease. This relies on genome and proteome information to identify proteins associated with a disease, which computer software can then use as targets for new drugs. For example, if a certain protein is implicated in a disease, the 3D structure of that protein provides the information a computer programs needs to design drugs to interfere with the action of the protein. A molecule that fits the active site of an enzyme, but cannot be released by the enzyme, will inactivate the enzyme. This is the basis of new drug-discovery tools, which aim to find new drugs to inactivate proteins involved in disease. As genetic differences among individuals are found, researchers will use these same techniques to develop personalized drugs that are more effective for the individual.

A computer technique which attempts to fit millions of small molecules to the three-dimensional structure of a protein is called Virtual ligand screening. The computer rates the quality of the fit to various sites in the protein, with the goal of either enhancing or disabling the function of the protein, depending on its function in the cell. A good example of this is the identification of new drugs to target and inactivate the HIV-1 protease. The HIV-1 protease is an enzyme that cleaves a very large HIV protein into smaller, functional proteins. The virus cannot survive without this enzyme; therefore, it is one of the most effective protein targets for killing HIV.

There are many distributed computing programs, such as the world community grid, which allows people from around the world to help scientists out with computing calculations. The software adds to the use of super computers by using the unused processing power of millions of home computers. The world community grid works on HIV, cancer, and protein folding. All three projects centre around protein modelling and protein modification models. Using the data gained from distributed computing models of proteins scientists can develop more specific and effective therapies. In addition, most enzymes act as part of complexes and networks, which also affect the way an enzyme acts in a cell. Understanding these complex networks will assist in developing drugs that affect the function of these complexes.

[edit] Proteomics And Biomarkers

Understanding the proteome, the structure and function of each protein and the complexities of protein-protein interactions will be critical for developing the most effective diagnostic techniques and disease treatments in the future.

An interesting use of proteomics is using specific protein biomarkers to diagnose disease. As certain diseases progress certain proteins are produced, using a number of different techniques we test for these proteins in order to diagnosis these diseases quickly. Some of these techniques include western blot, immunohistochemical staining and ELISA enzyme linked immunosorbent assay.

The following are some of the diseases have characteristic biomarkers that physicians can use for diagnosis.

• In Alzheimer’s, elevations in beta secretase creates amyloid/beta-protein, which causes plaque in the brain’s of Alzheimer patient, which in turn causes dementia. Targeting this enzyme will decrease the amyloid/beta-protein and therefore decrease the progression of the disease. A procedure to test for the increase in amyloid/beta-protein is by using immunohistochemical staining. This procedure utilizes antibodies binding to specific antigens or biological tissue of amyloid/beta-protein.
• Heart disease is also commonly assessed using several key protein based biomarkers. Standard protien biomarkers for CVD include interleukin-6, interleukin-8, serum amyloid A protein, fibrinogen, and troponins. cTnI cardiac troponin I increases in concentration within 3-12 hours of initial cardiac injury and can be found elevated days after an acute myocardial infarction. A number of commercial antibody based assays as well as other methods are used in hospitals as primary tests for acute MI.
• Proteomic analysis of kidney cells and cancerous kidney cells is producing promising leads in terms of finding biomarkers for Renal Cell Carcinoma and developing assays to test for this disease.

[edit] Branches of proteomics

1. Protein separation. All proteomic technologies rely on the ability to separate a complex mixture so that individual proteins are more easily processed with other techniques.
2. Protein identification. Well-known methods include low-throughput sequencing through Edman degradation. Higher-throughput proteomic techniques are based on mass spectrometry, commonly peptide mass fingerprinting on simpler instruments, or De novo repeat detection sequencing on instruments capable of more than one round of mass spectrometry. Antibody-based assays can also be used, but are unique to one sequence motif.
3. Protein quantification. Gel-based methods are used, including differential staining of gels with fluorescent dyes (difference gel electrophoresis). Gel-free methods include various tagging or chemical modification methods, such as isotope-coded affinity tags (ICATs), metal coded affinity tags (MeCATs) or combined fractional diagonal chromatography (COFRADIC)[1]. In metabolic labeling cells incorporate heavy stable isotopes present in their growth media (e.g. stable isotope labeling with amino acids in cell culture or SILAC). Modern day gel electrophoresis research often leverages software-based image analysis tools primarily to analyze bio-markers by quantifying individual, as well as showing the separation between one or more protein "spots" on a scanned image of a 2-DE product. Additionally, these tools match spots between gels of similar samples to show, for example, proteomic differences between early and advanced stages of an illness.
4. Protein sequence analysis. This is more of a bioinformatic branch, dedicated to searching databases for possible protein or peptide matches by algorithms such as SEQUEST, but also functional assignment of domains, prediction of function from sequence, and evolutionary relationships of proteins.
5. Structural proteomics. This concerns the high-throughput determination of protein structures in three-dimensional space. Common methods are x-ray crystallography and NMR spectroscopy.
6. Interaction proteomics. This concerns the investigation of protein interactions on the atomic, molecular and cellular levels. see related article on Protein-protein interaction prediction.
7. Protein modification. Almost all proteins are modified from their pure translated amino-acid sequence, so-called post-translational modification. Specialized methods have been developed to study phosporylation (phosphoproteomics) and glycosylation (glycoproteomics).
8. Cellular proteomics. A new branch of proteomics whose goal is to map the location of proteins and protein-protein interactions in whole cells during key cell events. Centers around the use of techniques such as X-ray Tomography and optical fluorescence microscopy.
9. Experimental bioinformatics. A branch of bioinformatics, as it is applied in proteomics, coined by Mathias Mann. It involves the mutual design of experimental and bioinformatics methods to create (extract) new types of information from proteomics experiments.

[edit] Key technologies used in proteomics

• One- and two-dimensional gel electrophoresis are used to identify the relative mass of a protein and its isoelectric point.
• X-ray crystallography and nuclear magnetic resonance are used to characterize the three-dimensional structure of peptides and proteins. However, low-resolution techniques such as circular dichroism, Fourier transform infrared spectroscopy and small angle x-ray scattering can be used to study the secondary structure of proteins.
• Tandem mass spectrometry combined with reverse phase chromatography or 2-D electrophoresis is used to identify by database search tools such as SEQUEST or de novo algorithms and quantify all the levels of proteins found in cells.
• Mass spectrometry (no-tandem), often MALDI-TOF, is used to identify proteins by peptide mass fingerprinting. This technology is also used in so-called "MALDI-TOF MS protein profiling" where samples (i.e. serum) are prepared by either protein chips (SELDI-TOF MS), magnetic beads (The Bruker Daltonics protein profiling platform) or with other methods of sample treatment, such as liquid chromatography, size-exclusion and immunoaffinity. Protein peaks of interest must be identified by tandem mass spectrometry. Protein profiling with MALDI-TOF MS could be of high use in clinical diagnostics, but so far there has been little succes with advancing MALDI-TOF MS protein profiling into clinical validation due to high analytical variation.
• Affinity chromatography, yeast two hybrid techniques, fluorescence resonance energy transfer (FRET), and Surface Plasmon Resonance (SPR) are used to identify protein-protein and protein-DNA binding reactions.
• X-ray Tomography used to determine the location of labelled proteins or protein complexes in an intact cell. Frequently correlated with images of cells from light based microscopes.
• Software based image analysis is utilized to automate the quantification and detection of spots within and among gels samples. While this technology is widely utilized, the intelligence has not been perfected yet. For example, the leading software tools in this area tend to agree on the analysis of well-definedm well-separated protein spots, but they deliver different results and tendencies with less-defined less-separated spots - thus necessitating manual verification of results.

[edit] See also

• bioinformatics
• cytomics
• genomics
• List of omics topics in biology
• metabolomics
• lipidomics
• systems biology
• transcriptomics

[edit] Protein databases

• UniProt
• PIR
• Swiss-Prot
• PDB
• NCBI

[edit] References

• Belhajjame, K. et al. Proteome Data Integration: Characteristics and Challenges. Proceedings of the UK e-Science All Hands Meeting, ISBN 1-904425-53-4, September 2005, Nottingham, UK.
• Twyman, R. M. 2004. Principles of proteomics. BIOS Scientific Publishers, New York. ISBN 1-85996-273-4.(covers almost all branches of proteomics)
• Westermeier, R. and T. Naven. 2002. Proteomics in practice: a laboratory manual of proteome analysis. Wiley-VCH, Weinheim. ISBN 3-527-30354-5.(focused on 2D-gels, good on detail)
• Liebler, D. C. 2002. Introduction to proteomics: tools for the new biology. Humana Press, Totowa, NJ. ISBN 0-585-41879-9 (electronic, on Netlibrary?), ISBN 0-89603-991-9 hardback, ISBN 0-89603-992-7 paperback.
• Wilkins MR, Williams KL, Appel RD, Hochstrasser DF. Proteome research: new frontiers in functional genomics. Berlin Heidelberg, Springer Verlag; 1997, ISBN 3-540-62753-7.
• Arora, Pankaj S., et al. (2005). "Comparative evaluation of two two-dimensional gel electrophoresis image analysis software applications using synovial fluids from patients with joint disease". Journal of Orthopaedic Science 10 (2): 160-166. [2]
• Rediscovering Biology Online Textbook. Unit 2 Proteins and Proteomics. 1997-2006.
• Weaver. R.F. Molecular Biology. Third Edition. The McGraw-Hill Companies Inc. 2005. pgs 840-849.
• Campbell and Reece. Biology. Sixth Edition. Pearson Education Inc. 2002. pg 392-393.
• Hye A, Lynham S, Thambisetty M, et al. " Proteome-based plasma biomarkers for Alzheimer's disease." Brain 129: 3042-3050, (2006).
• Perroud B, Lee J, Valkova N, et al. "Pathway Analysis of Kidney Cancer Using Proteomics and *Metabolic Profiling." Biomed Central: 65-82, (24 November 2006).
• Macaulay IC, Carr P, Gusnanto A, et al. "Platelet Genomics and Proteomics in Human Health and Disease." The Journal of Clinical Investigation 115: 3370-3377, (December 2005).
• Rogers MA, Clarke P, Noble J, et al. "Proteomic Profiling of Urinary Proteins in Renal Cancer by Surface Enhanced Laser Desorption Ionization, and Neural-Network Analysis: Identification of Key Issues Affecting Clinical Potential Utility." Cancer Research 63: 6971-6983, (15 October 2003).
• Vasan RS. “Biomarkers of cardiovascular disease: molecular basis and practical considerations” Circulation. 2006;113:2335-2362.
• “Myocardial Infaction”. http://medlib.med.utah.edu/WebPath/TUTORIAL/MYOCARD/MYOCARD.html (Retrieved 29 Nov 2006)
• Word Community Grid. http://www.worldcommunitygrid.org (Retrieved 29 Nov 2006)
• Introduction to Antibodies - Enzyme-Linked Immunosorbent Assay (ELISA). http://www.chemicon.com/resource/ANT101/a2C.asp. (Retrieved 29 Nov 2006)

[edit] External links

• Exhaustive proteomics software, tools and databases list curated by hand.
• GPMDB is a database of results obtained from protein identification experiments.
• ProteomeCommons.org is a site with news, links, data and code for proteomics.
• PRIDE (PRoteomics IDEntifications database) is a centralized, standards compliant, public data repository for proteomics data.
• CPRMap - Clinical Proteomics Research Map
• Introduction to Proteomics - An interactive web feature that explains how proteins are sequenced and identified.
• MIT - Reduction in the number of human genes from previous estimates.
• Proteomic World - Resources for proteomics research.
• Yeast GFP Localization Database - Database of microscope images and quantitation for most of the yeast proteome.
• Sashimi is a SourceForge repository for open source proteomic software.
• PRIDE proteome database is a public repository for digital content relating to proteomics.
• IntAct Interaction Database is a public repository for manually curated molecular interaction data from literature.
• BioGRID: A General Repository for Interaction Datasets, is a public repository for manually curated molecular interaction data from literature.
• CPAS National Cancer Institute / Fred Hutchinson Cancer Research Center open source software platform for processing and mining proteomics data.
• msInspect NCI / FHCRC developed open source tool for mining LC-MS data, including peptide detection and quantitation.
• The 2006 Report from the National Academy of Sciences entitled Reaping the Benefits of Genomic and Proteomic Research: Intellectual Property Rights, Innovation, and Public Health, free to read and research online.
• Human Protein Atlas