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Proteomics is the large-scale study of proteins, particularly their structures and functions. Proteins are vital parts of living organisms, as they are the main components of the physiological metabolic pathways of cells. The term "proteomics" was first coined in 1997 to make an analogy with genomics, the study of the genes. The word "proteome" is a blend of "protein" and "genome", and was coined by Marc Wilkins in 1994 while working on the concept as a PhD student. The proteome is the entire complement of proteins, including the modifications made to a particular set of proteins, produced by an organism or system. This will vary with time and distinct requirements, or stresses, that a cell or organism undergoes.
- 1 Complexity of the Problem
- 2 Limitations to genomic study
- 3 Methods of studying proteins
- 4 Establishing protein-protein interactions
- 5 Practical applications of proteomics
- 6 See also
- 7 References
- 8 Bibliography
- 9 External links
Complexity of the Problem[edit | edit source]
After genomics, proteomics is often considered the next step in the study of biological systems. It is much more complicated than genomics mostly because while an organism's genome is more or less constant, the proteome differs from cell to cell and from time to time. This is because distinct genes are expressed in distinct cell types. This means that even the basic set of proteins which are produced in a cell needs to be determined.
In the past this was done by mRNA analysis, but this was found not to correlate with protein content. It is now known that mRNA is not always translated into protein, and the amount of protein produced for a given amount of mRNA depends on the gene it is transcribed from and on the current physiological state of the cell. Proteomics confirms the presence of the protein and provides a direct measure of the quantity present.
Examples of post-translational modifications[edit | edit source]
Phosphorylation[edit | edit source]
More importantly though, any particular protein may go through a wide variety of alterations which will have critical effects to its function. For example during cell signaling many enzymes and structural proteins can undergo phosphorylation. The addition of a phosphate to particular amino acids—most commonly serine and threonine mediated by serine/threonine kinases, or more rarely tyrosine mediated by tyrosine kinases—causes a protein to become a target for binding or interacting with a distinct set of other proteins that recognize the phosphorylated domain.
Because protein phosphorylation is one of the most-studied protein modifications many "proteomic" efforts are geared to determining the set of phosphorylated proteins in a particular cell or tissue-type under particular circumstances. This alerts the scientist to the signaling pathways that may be active in that instance.
Ubiquitination[edit | edit source]
Ubiquitin is a small protein that can be affixed to certain protein substrates by enzymes called E3 ubiquitin ligases. Determining which proteins are poly-ubiquitinated can be helpful in understanding how protein pathways are regulated. This is therefore an additional legitimate "proteomic" study. Similarly, once it is determined what substrates are ubiquitinated by each ligase, determining the set of ligases expressed in a particular cell type will be helpful.
Additional modifications[edit | edit source]
Listing all the protein modifications that might be studied in a "Proteomics" project would require a discussion of most of biochemistry; therefore, a short list will serve here to illustrate the complexity of the problem. In addition to phosphorylation and ubiquitination, proteins can be subjected to methylation, acetylation, glycosylation, oxidation, nitrosylation, etc. Some proteins undergo ALL of these modifications, which nicely illustrates the potential complexity one has to deal with when studying protein structure and function.
Distinct proteins are made under distinct settings[edit | edit source]
Even if one is studying a particular cell type, that cell may make different sets of proteins at different times, or under different conditions. Furthermore, as mentioned, any one protein can undergo a wide range of post-translational modifications.
Therefore a "proteomics" study can become quite complex very quickly, even if the object of the study is very restricted. In more ambitious settings, such as when a biomarker for a tumor is sought - when the proteomics scientist is obliged to study sera samples from multiple cancer patients - the amount of complexity that must be dealt with is as great as in any modern biological project.
Limitations to genomic study[edit | edit source]
Scientists are very interested in proteomics because it gives a much better understanding of an organism than genomics. First, the level of transcription of a gene gives only a rough estimate of its level of expression into a protein. An mRNA produced in abundance may be degraded rapidly or translated inefficiently, resulting in a small amount of protein. Second, as mentioned above many proteins experience post-translational modifications that profoundly affect their activities; for example some proteins are not active until they become phosphorylated. Methods such as phosphoproteomics and glycoproteomics are used to study post-translational modifications. Third, many transcripts give rise to more than one protein, through alternative splicing or alternative post-translational modifications. Fourth, many proteins form complexes with other proteins or RNA molecules, and only function in the presence of these other molecules. Finally, protein degradation rate plays an important role in protein content.
Methods of studying proteins[edit | edit source]
Determining proteins which are post-translationally modified[edit | edit source]
One way in which a particular protein can be studied is to develop an antibody which is specific to that modification. For example, there are antibodies which only recognize certain proteins when they are tyrosine-phosphorylated; also, there are antibodies specific to other modifications. These can be used to determine the set of proteins that have undergone the modification of interest.
A more common way to determine post-translational modification of interest is to subject a complex mixture of proteins to electrophoresis in "two-dimensions", which simply means that the proteins are electrophoresed first in one direction, and then in another... this allows small differences in a protein to be visualized by separating a modified protein from its unmodified form. This methodology is known as "two-dimensional gel electrophoresis".
Recently, another approach has been developed called PROTOMAP which combines SDS-PAGE with shotgun proteomics to enable detection of changes in gel-migration such as those caused by proteolysis or post translational modification.
Determining the existence of proteins in complex mixtures[edit | edit source]
Classically, antibodies to particular proteins or to their modified forms have been used in biochemistry and cell biology studies. These are among the most common tools used by practicing biologists today.
For more quantitative determinations of protein amounts, techniques such as ELISAs can be used.
For proteomic study, more recent techniques such as Matrix-assisted laser desorption/ionization have been employed for rapid determination of proteins in particular mixtures.
Establishing protein-protein interactions[edit | edit source]
Most proteins function in collaboration with other proteins, and one goal of proteomics is to identify which proteins interact. This is especially useful in determining potential partners in cell signaling cascades.
Several methods are available to probe protein-protein interactions. The traditional method is yeast two-hybrid analysis. New methods include protein microarrays, immunoaffinity chromatography followed by mass spectrometry, dual polarisation interferometry and experimental methods such as phage display and computational methods.
Practical applications of proteomics[edit | edit source]
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, its 3D structure provides the information 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 expect to use these 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.
Biomarkers[edit | edit source]
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. A number of techniques allow to test for proteins produced during a particular disease, which helps to diagnose the disease quickly. Techniques include western blot, immunohistochemical staining, enzyme linked immunosorbent assay (ELISA) or mass spectrometry. The following are some of the diseases that have characteristic biomarkers that physicians can use for diagnosis.
Alzheimer's disease[edit | edit source]
In Alzheimer’s disease, elevations in beta secretase create amyloid/beta-protein, which causes plaque to build up in the patient's brain, which is thought to play a role in dementia. Targeting this enzyme decreases the amyloid/beta-protein and so slows the progression of the disease. A procedure to test for the increase in amyloid/beta-protein is immunohistochemical staining, in which antibodies bind to specific antigens or biological tissue of amyloid/beta-protein.
Heart disease[edit | edit source]
Heart disease is commonly assessed using several key protein based biomarkers. Standard protein biomarkers for CVD include interleukin-6, interleukin-8, serum amyloid A protein, fibrinogen, and troponins. cTnI cardiac troponin I increases in concentration within 3 to 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.
See also[edit | edit source]
- proteomic chemistry
- List of omics topics in biology
- Shotgun proteomics
- Top-down proteomics
- Bottom-up proteomics
- systems biology
- Functional genomics
Protein databases[edit | edit source]
- Protein Information Resource (PIR)
- Protein Data Bank (PDB)
- National Center for Biotechnology Information (NCBI)
- Human Protein Reference Database
- Proteopedia The collaborative, 3D encyclopedia of proteins and other molecules.
References[edit | edit source]
- Anderson NL, Anderson NG (1998). Proteome and proteomics: new technologies, new concepts, and new words. Electrophoresis 19 (11): 1853–61.
- Blackstock WP, Weir MP (1999). Proteomics: quantitative and physical mapping of cellular proteins. Trends Biotechnol. 17 (3): 121–7.
- P. James (1997). Protein identification in the post-genome era: the rapid rise of proteomics.. Quarterly reviews of biophysics 30 (4): 279–331.
- Marc R. Wilkins, Christian Pasquali, Ron D. Appel, Keli Ou, Olivier Golaz, Jean-Charles Sanchez, Jun X. Yan, Andrew. A. Gooley, Graham Hughes, Ian Humphery-Smith, Keith L. Williams & Denis F. Hochstrasser (1996). From Proteins to Proteomes: Large Scale Protein Identification by Two-Dimensional Electrophoresis and Arnino Acid Analysis. Nature Biotechnology 14 (1): 61–65.
- UNSW Staff Bio: Professor Marc Wilkins
- Simon Rogers, Mark Girolami, Walter Kolch, Katrina M. Waters, Tao Liu, Brian Thrall and H. Steven Wiley (2008). Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models. Bioinformatics 24 (24): 2894–2900.
- Vikas Dhingraa, Mukta Gupta, Tracy Andacht and Zhen F. Fu (2005). New frontiers in proteomics research: A perspective. International Journal of Pharmaceutics 299 (1–2): 1–18.
- Buckingham, Steven (2003). The major world of microRNAs. URL accessed on 2009-01-14.
- Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M. (2006). Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127 (3): 635–648.
- Archana Belle, Amos Tanay, Ledion Bitincka, Ron Shamir and Erin K. O’Shea (2006). Quantification of protein half-lives in the budding yeast proteome. PNAS 103 (35): 13004–13009.
Bibliography[edit | edit source]
- 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 RM (2004). Principles Of Proteomics (Advanced Text Series), Oxford, UK: BIOS Scientific Publishers. (covers almost all branches of proteomics)
- Naven T, Westermeier R (2002). Proteomics in Practice: A Laboratory Manual of Proteome Analysis, Weinheim: Wiley-VCH. (focused on 2D-gels, good on detail)
- Liebler DC (2002). Introduction to proteomics: tools for the new biology, Totowa, NJ: Humana Press. ISBN 0-585-41879-9 (electronic, on Netlibrary?), ISBN 0-89603-991-9 hbk
- Wilkins MR, Williams KL, Appel RD, Hochstrasser DF (1997). Proteome Research: New Frontiers in Functional Genomics (Principles and Practice), Berlin: Springer.
- Arora PS, Yamagiwa H, Srivastava A, Bolander ME, Sarkar G (2005). Comparative evaluation of two two-dimensional gel electrophoresis image analysis software applications using synovial fluids from patients with joint disease. J Orthop Sci 10 (2): 160–6.
- Rediscovering Biology Online Textbook. Unit 2 Proteins and Proteomics. 1997-2006.
- Weaver RF (2005). Molecular biology, 3rd, 840–9, New York: McGraw-Hill.
- Reece J, Campbell N (2002). Biology, 6th, 392–3, San Francisco: Benjamin Cummings.
- Hye A, Lynham S, Thambisetty M, et al. (November 2006). Proteome-based plasma biomarkers for Alzheimer's disease. Brain 129 (Pt 11): 3042–50.
- Perroud B, Lee J, Valkova N, et al. (2006). Pathway analysis of kidney cancer using proteomics and metabolic profiling. Mol Cancer 5: 64.
- Yohannes E, Chang J, Christ GJ, Davies KP, Chance MR (July 2008). Proteomics analysis identifies molecular targets related to diabetes mellitus-associated bladder dysfunction. Mol. Cell Proteomics 7 (7): 1270–85..
- Macaulay IC, Carr P, Gusnanto A, Ouwehand WH, Fitzgerald D, Watkins NA (December 2005). Platelet genomics and proteomics in human health and disease. J Clin Invest. 115 (12): 3370–7.
- Rogers MA, Clarke P, Noble J, et al. (15 October 2003). Proteomic profiling of urinary proteins in renal cancer by surface enhanced laser desorption ionization and neural-network analysis: identification of key issues affecting potential clinical utility. Cancer Res. 63 (20): 6971–83.
- Vasan RS (May 2006). Biomarkers of cardiovascular disease: molecular basis and practical considerations. Circulation 113 (19): 2335–62.
- “Myocardial Infarction”. (Retrieved 29 November 2006)
- Introduction to Antibodies - Enzyme-Linked Immunosorbent Assay (ELISA). (Retrieved 29 November 2006)
- Decramer S, Wittke S, Mischak H, et al. (April 2006). Predicting the clinical outcome of congenital unilateral ureteropelvic junction obstruction in newborn by urinary proteome analysis. Nat Med. 12 (4): 398–400.
- Mayer U (January 2008). Protein Information Crawler (PIC): extensive spidering of multiple protein information resources for large protein sets. Proteomics 8 (1): 42–4.
- Jörg von Hagen, VCH-Wiley 2008 Proteomics Sample Preparation. ISBN 978-3-527-31796-7
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