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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 coined to make an analogy with genomics, the study of the genes. The word "proteome" is a portmanteau of "protein" and "genome". The proteome of an organism is the set of proteins produced by it during its life, and its genome is its set of genes.
Proteomics is often considered the next step in the study of biological systems, after genomics. It is much more complicated than genomics, mostly because while an organism's genome is rather constant, a proteome differs from cell to cell and constantly changes through its biochemical interactions with the genome and the environment. One organism has radically different protein expression in different parts of its body, different stages of its life cycle and different environmental conditions. Another major difficulty is the complexity of proteins relative to nucleic acids. E.g., in human there are about 25 000 identified genes but an estimated >500 000 proteins that are derived from these genes. This increased complexity derives from mechanisms such as alternative splicing, protein modification (glycosylation,phosphorylation) and protein degradation.
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, 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. Finally, many proteins form complexes with other proteins or RNA molecules, and only function in the presence of these other molecules.
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 the completion of a rough draft of the human genome, many researchers are 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 proteins in the human proteome (20,000 to 25,000 genes vs. > 500,000 proteins). The human body may even contain more than 2 million proteins, each having different functions. The protein diversity is thought to be due to alternative splicing and post-translational modification of proteins. The discrepancy implies that protein diversity cannot be fully characterized by gene expression analysis, thus proteomics is useful for characterizing cells and tissues.
To catalog all human proteins, their functions and interactions is a great challenge for scientists. An international collaboration with these goals is co-ordinated by the Human Proteome Organization (HUPO).
Additional recommended knowledge
Most proteins function in collaboration with other proteins, and one goal of proteomics is to identify which proteins interact. This often gives important clues about the functions of newly discovered proteins. 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, and combinations of experimental methods such as phage display and computational methods.
Current research in proteomics requires first that proteins be resolved, sometimes on a massive scale. Protein separation can be performed using two-dimensional gel electrophoresis, which usually separates proteins first by isoelectric point and then by molecular weight. Protein spots in a gel can be visualized using a variety of chemical stains or fluorescent markers. Proteins can often be quantified by the intensity of their stain. Once proteins are separated and quantified, they are 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. 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.
Protein mixtures can also be analyzed without prior separation. These procedures begin with proteolytic digestion of the proteins in a complex mixture. The resulting peptides are often injected onto a high pressure liquid chromatography column (HPLC) that separates peptides based on hydrophobicity. HPLC can be coupled directly to a time-of-flight mass spectrometer using electrospray ionization. Peptides eluting from the column can be identified by tandem mass spectrometry (MS/MS). The first stage of tandem MS/MS isolates individual peptide ions, and the second breaks the peptides into fragments and uses the fragmentation pattern to determine their amino acid sequences. Labeling with isotope tags can be used to quantitatively compare proteins concentration among two or more protein samples.
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.
There are many distributed computing programs, such as the world community grid, which allows people around the world to help scientists by 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.
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:
Proteomics uses various technologies:
|This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Proteomics". A list of authors is available in Wikipedia.