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Proteomics is the study of protein structures and functions. Proteins can be chemically modified in different ways after synthesis and they are essential parts of living organisms. In Cancer Research, scientist believes that Proteomics is a fast growing science and it provides support in cancer diagnosis and further in treatment as well. Although, there are many known potential difficulties where the sensitivity, specificity, and reproducibility of the available molecular markers are unsatisfactory and do not match the expectations. But scientist still believe that with the improvement and advancement of proteomics technology, early diagnosis and treatment of cancer will be efficient and reliable in future. The human genome consists of complete set of genes that is required to build a functional human being. But the genome is just a source of information. In other words it is just a raw information and in order to function this information, it must be expressed in protein. The very first stage of gene expression is the transcription of genes and later it is followed by the translation of messenger RNA to actually produce proteins. The term proteome basically describes the entire set of proteins expressed by a given genome, cell, tissue, or organism at any one time. The complexity of proteome is much more higher than either the genome or the transcriptome. The reason of this is very simple, As each protein can be chemically modified in different ways after synthesis depending on its structrue and the way it folds. Most proteins have carbohydrate groups added to them. Some proteins have phosphorylated or acetylated or methylated groups added to them. The functions of the protein is totally dependent on the folded structure and the folded shape or structure of a proteins specified by its amino acid sequence. At cellular level, most of our cells have the same genome despite of the cell type, developmental stage or environmental conditions. But the proteome,being very dynamic, usually differs significantly in these differing circumstances and conditions. It can be due to different gene expression patterns or it can be different protein modification patterns.(1)

The study of proteom gives a better understanding of an organism than genomics. In a cell, proteins represent the actual functional molecules. The changes that happens at the DNA level, it affects the proteins. e.g. if mutations occur in the DNA, it is the proteins that are eventually affected. Same in case of Drugs, when they have favorable effects, they do so by having an interaction with proteins.(1)

Proteomics covers different aspects of protein function, It includes the following:

Structural proteomics, it includes the analysis of protein structures at large-scale. Where protein structure are compared and it helps to identify the functions of those genes which are newly discovered. The structural analysis also helps to understand that where drugs bind to proteins and also show where proteins interact with each other. This understanding is achieved using different technologies such as X-ray crystallography and NMR spectroscopy.(1)

Expression proteomics, includes the analysis of protein expression at larger scale. Where it helps to identify the main proteins that are found in a particular sample and those proteins which are differentially expressed in related samples, such as diseased vs healthy tissue. If a protein is found only in a diseased sample then it can be a useful drug target or diagnostic marker. Proteins with same or similar expression profiles may also be functionally related. There are technologies such as 2D-PAGE and mass spectrometry that are used in expression proteomics.(1)

Interaction proteomics, is the analysis of protein interactions at larger scale. The characterization of protein-protein interactions are useful to determine the protein functions and it also explains the way proteins assemble in bigger complexes. There are technologies such as affinity purification, mass spectrometry and the yeast two-hybrid system which are particularly useful in interaction proteomics.(1)

The proteome analysis techniques are not simple and straightforward as those which are used in transcriptomics. But the benefit of proteomics is that it deals with the real functional molecules of the cells. It is known that Strong gene expression results in an abundant mRNA but it does not necessarily mean that the corresponding protein is also abundant. In proteomics things are not so simple as one gene does not always produce the same protein. The genes usually consist of a series of sub structures, which are called exons. These sub structures can be joined in a variety of ways which helps to give momentum to whole series of very similar but different proteins. Further increasing complications, once proteins are made, they are ornamented with different other chemicals. These chemicals can be phosphate, sugars or fats. The affect of the decorations is so severe on the function of protein, for example phosphate normally behaves as an on-off switch and sugars usually tells the proteins where to go and attach in the cell. Therefore, it was comparitivly very simple and easy to sequence human genome as there are only 46 molecules and they are made up of 4 building blocks or letters (A,C,G,T) where as proteins have 20 building blocks and each of which can be customized or ornamented after the protein is built. Hence, proteome have to deal with ca. 30,000 genes which can be arranged to give some 800,000 proteins that can be modified and decorated with over 300 different chemicals. Additionally, proteomics also describe the nature of proteins, where they are being produced in a particular cell type and at a specific time. The way they are modified in the cell and the location where they are modified and also with whom they are in contact. Finally, the most difficult thing, is to determine the function of the protein.(1)

Bioinformatics for Proteomics

The biggest problem in proteomics research is related to data analysis. As there is a severe need to create an environment where different stake holders (computer scientists, biologists and the people who collect data) can work closely together. So that some necessary analytical tools can be developed and they can be used to interpret data. It is a known fact that the analysis and processing of proteomics data is very complex. The results, which are obtained on different platforms or in different laboratories, are unmanageable due to the lack of standards for data formats, data processing parameters and data quality assessment. Accurate and precise, consistent and reliable, and transparent data processing and analysis are vital parts of proteomics workflows. Now with the advancement of technology it is possible to produce big amount of data but currently there are massive challenges to discover that how to actually analyze this big amount of data and to produce real biological insights using this data.(4)

Proteomics and System Biology

Proteomics has recently come into the act as a promising force to transform biology and medicine. It is becoming increasingly apparent that changes in mRNA expression correlate poorly with protein expression changes. Proteins changes enormously in patterns of expressions across developmental and physiological responses. Proteins also face changes on the act of environmental perturbations. Proteins are the actual effectors driving cell behavior. The field of proteomics strives to characterize protein structure and function, protein-protein,protein-nucleic acid, protein-lipid, and enzyme-substrate interactions, protein processing and folding, protein activation, cellular and sub-cellular localization, protein turnover and synthesis rates, and even promoter usage. Integrating proteomic data with information such as gene, mRNA and metabolic profiles helps in better understanding of how the system works. (3)

Current Proteomic Technologies

Proteomics has gained steady momentum over the past decade with the evolution of several approaches, few which are new and the others, which builds on traditional methods. Mass spectrometry based methods and Micro arrays are the most commonly used technologies for the large-scale study of the proteins.

Mass Spectrometry and Protein Profiling

Robotic preparation of MALDI mass spectrometry samples on a sample carrier.

There are 2 mass spectrometry based methods currently used for protein profiling. The more established and widespread method uses high resolution, 2 dimensional electrophoresis to separate proteins from different samples in parallel, followed by selection and staining of differentially expressed proteins to be identified by mass spectrometry. Despite the advances in 2DE and its maturity, it has its limits as well. The central concern is the inability to resolve all the proteins within a sample, given their dramatic range in expression level and differing properties. (3)

The second quantitative approach uses stable isotope tags to differentially label proteins from two different complex mixtures. Here, the proteins within a complex mixture are labeled first isotopically, and then digested to yield labeled peptides. The labeled mixtures are then combined, peptides separated by multidimensional liquid chromatography and analyzed by tandem mass spectrometry. Isotope coded affinity tag (ICAT) reagents are the widely used isotope tags. In this method, the cysteine residues of proteins get covalently attached to the ICAT reagent, thereby reducing the complexity of the mixtures omitting the non-cysteine residues. Quantitative proteomics using stable isotopic tagging is an increasingly useful tool in modern development. Firstly, chemical reactions have been used to introduce tags into specific sites or proteins, for the purpose of probing specific protein functionalities. The isolation of phosphorylated peptides have been achieved using isotopic labeling and selective chemistries to capture the fraction of protein among complex mixture. Secondly, the ICAT technology was used to differentiate between partially purified or purified macromolecular complexes such as large RNA polymerase II pre initiation complex and the proteins complexed with yeast transcription factor. Thirdly, ICAT labeling was recently combined with chromatin isolation to identify and quantify chromatin-associated proteins. Finally ICAT reagents are useful for proteomic profiling of cellular organelles and specific cellular fractions. (3)

Protein Chips

Balancing the use of mass spectrometers in proteomics and in medicine is the use of protein micro arrays. The aim behind protein micro arrays is to print thousands of protein detecting features, for the interrogation of biological samples. Antibody arrays are an example in which a host of different antibodies are arrayed to detect their respective antigens from a sample of human blood. Another approach is the arraying of multiple protein types for the study of properties like protein-DNA, protein-protein and protein-ligand interactions. Ideally, the functional proteomic arrays would contain the entire complement of the proteins of a given organism. The first version of such arrays consisted of 5000 purified proteins from yeast deposited onto glass microscopic slides. Despite the success of first chip, it was a greater challenge for protein arrays to be implemented. Proteins are inherently much more difficult to work with than DNA. They have a broad dynamic range, less stable than DNA and their structure is difficult to preserve on glass slides, though they are essential for most assays. The global ICAT technology will have striking advantages over protein chip technologies. (3)

Reverse Phased Protein Microarrays

This is a promising and newer microarray application for the diagnosis, study and treatment of complex diseases such as cancer. The technology merges laser capture microdissection (LCM) with micro array technology, to produce reverse phase protein microarrays. In this type of microarrays, the whole collection of protein themselves are immobilized with the intent of capturing various stages of disease within an individual patient. When used with LCM, reverse phase arrays can monitor the fluctuating state of proteome among different cell population within a small area of human tissue. This is useful for profiling the status of cellular signaling molecules, among a cross section of tissue that includes both normal and cancerous cells. This approach is useful in monitoring the status of key factors in normal prostate epithelium and invasive prostate cancer tissues. LCM then dissects these tissue and protein lysates were arrayed onto nitrocellulose slides, which were probed with specific antibodies. This method can track all kinds of molecular events and can compare diseased and healthy tissues within the same patient enabling the development of treatment strategies and diagnosis. The ability to acquire proteomics snapshots of neighboring cell populations, using reverse phase microarrays in conjunction with LCM, will have number of applications beyond the study of tumors. The approach can provide insights into normal physiology and pathology of all the tissues and will be invaluable for characterizing developmental processes and anomalies. (3)

Emerging trends in Proteomics

A number of emerging concepts have the potential to improve current features of proteomics. Obtaining absolute quantification of proteins and monitoring post translational modifications are the two tasks that impacts the understanding of protein function in healthy and diseased cells. Advances in quantitative proteomics would clearly enable more in-depth analysis of cellular systems. For many cellular events the protein concentrations do not change, rather their function is modulated by post transitional modifications (PTM). Methods of monitoring PTM are an underdeveloped area in proteomics. Selecting a particular subset of protein for analysis will substantially reduce protein complexity making it advantageous for diagnostic purposes where the blood is the starting material. Another important aspect of proteomics, yet not addressed, is that, proteomics methods should focus on studying proteins in the context of the environment. The increasing use of chemical cross linkers, introduced into living cell to fix protein-protein, protein-DNA and other interactions may ameliorate this problem partially. The challenge is to identify suitable methods of preserving relevant interactions. Another goal for studying protein is to develop more sophisticated methods to image proteins and other molecules in living cells and real time. (3)

Human Plasma Proteome

Characterizing human plasma proteome has become a major goal in proteomics arena. The plasma proteome is without doubt the most complex proteome in the human body. It contains immunoglobulin, cytokines, protein hormones, secreted proteins and indicative of infection on top of resident, hemostatic proteins. It also contains tissue leakage proteins due to the blood circulation through different tissues in the body. The blood thus contain information on physiological of all tissues and combined with its accessibility makes the blood proteome invaluable for medical purposes. With the recent advancement in proteomics, characterizing the proteome of blood plasma is a daunting challenge. Temporal and spatial dynamics further complicated the study of human plasma proteome. The turnover of some proteins is quite faster than others and protein content of artery may substantially vary from that of vein. All these differences make even the simplest proteomic task of cataloging the proteome seem out of reach. To tackle this problem, priorities needs to be established. Capturing the most meaningful subset of proteins among the entire proteome to generate diagnostic tool is one such priority. Secondly, since cancer is associated with enhanced glycosylation of proteins, methods that focus on this part of proteins will also be useful. It should be stressed again that multiparameter analysis will best reveal a pathological state. As these technologies improve, the disease profiles should be continually related to respective gene expression changes. (3)

References

(1) http://www.proteomic.org/html/proteomics_.html (2)http://www.merriam-webster.com/dictionary/proteomics.html (3)http://archive.cspo.org/outreach/md/docs/Weston-Systemsbiology.pdf (4)http://hgp.sagepub.com/content/1/1/239204.full