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7.22: Genomics and Proteomics - Biology

7.22: Genomics and Proteomics - Biology


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7.22: Genomics and Proteomics

17.5 Genomics and Proteomics

By the end of this section, you will be able to do the following:

Proteins are the final products of genes, which help perform the function that the gene encodes. Amino acids comprise proteins and play important roles in the cell. All enzymes (except ribozymes) are proteins that act as catalysts to affect the rate of reactions. Proteins are also regulatory molecules, and some are hormones. Transport proteins, such as hemoglobin, help transport oxygen to various organs. Antibodies that defend against foreign particles are also proteins. In the diseased state, protein function can be impaired because of changes at the genetic level or because of direct impact on a specific protein.

A proteome is the entire set of proteins that a cell type produces. We can study proteoms using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins. Proteomics is the study of proteomes' function. Proteomics complements genomics and is useful when scientists want to test their hypotheses that they based on genes. Even though all multicellular organisms' cells have the same set of genes, the set of proteins produced in different tissues is different and dependent on gene expression. Thus, the genome is constant, but the proteome varies and is dynamic within an organism. In addition, RNAs can be alternately spliced (cut and pasted to create novel combinations and novel proteins) and many proteins modify themselves after translation by processes such as proteolytic cleavage, phosphorylation, glycosylation, and ubiquitination. There are also protein-protein interactions, which complicate studying proteomes. Although the genome provides a blueprint, the final architecture depends on several factors that can change the progression of events that generate the proteome.

Metabolomics is related to genomics and proteomics. Metabolomics involves studying small molecule metabolites in an organism. The metabolome is the complete set of metabolites that are related to an organism's genetic makeup. Metabolomics offers an opportunity to compare genetic makeup and physical characteristics, as well as genetic makeup and environmental factors. The goal of metabolome research is to identify, quantify, and catalogue all the metabolites in living organisms' tissues and fluids.

Basic Techniques in Protein Analysis

The ultimate goal of proteomics is to identify or compare the proteins expressed from a given genome under specific conditions, study the interactions between the proteins, and use the information to predict cell behavior or develop drug targets. Just as scientists analyze the genome using the basic DNA sequencing technique, proteomics requires techniques for protein analysis. The basic technique for protein analysis, analogous to DNA sequencing, is mass spectrometry. Mass spectrometry identifies and determines a molecule's characteristics. Advances in spectrometry have allowed researchers to analyze very small protein samples. X-ray crystallography, for example, enables scientists to determine a protein crystal's three-dimensional structure at atomic resolution. Another protein imaging technique, nuclear magnetic resonance (NMR), uses atoms' magnetic properties to determine the protein's three-dimensional structure in aqueous solution. Scientists have also used protein microarrays to study protein interactions. Large-scale adaptations of the basic two-hybrid screen (Figure 17.17) have provided the basis for protein microarrays. Scientists use computer software to analyze the vast amount of data for proteomic analysis.

Genomic- and proteomic-scale analyses are part of systems biology , which is the study of whole biological systems (genomes and proteomes) based on interactions within the system. The European Bioinformatics Institute and the Human Proteome Organization (HUPO) are developing and establishing effective tools to sort through the enormous pile of systems biology data. Because proteins are the direct products of genes and reflect activity at the genomic level, it is natural to use proteomes to compare the protein profiles of different cells to identify proteins and genes involved in disease processes. Most pharmaceutical drug trials target proteins. Researchers use information that they obtain from proteomics to identify novel drugs and to understand their mechanisms of action.

Scientists are challenged when implementing proteomic analysis because it is difficult to detect small protein quantities. Although mass spectrometry is good for detecting small protein amounts, variations in protein expression in diseased states can be difficult to discern. Proteins are naturally unstable molecules, which makes proteomic analysis much more difficult than genomic analysis.

Cancer Proteomics

Researchers are studying patients' genomes and proteomes to understand the genetic basis of diseases. The most prominent disease researchers are studying with proteomic approaches is cancer. These approaches improve screening and early cancer detection. Researchers are able to identify proteins whose expression indicates the disease process. An individual protein is a biomarker whereas, a set of proteins with altered expression levels is a protein signature . For a biomarker or protein signature to be useful as a candidate for early cancer screening and detection, they must secrete in body fluids, such as sweat, blood, or urine, such that health professionals can perform large-scale screenings in a noninvasive fashion. The current problem with using biomarkers for early cancer detection is the high rate of false-negative results. A false negative is an incorrect test result that should have been positive. In other words, many cancer cases go undetected, which makes biomarkers unreliable. Some examples of protein biomarkers in cancer detection are CA-125 for ovarian cancer and PSA for prostate cancer. Protein signatures may be more reliable than biomarkers to detect cancer cells. Researchers are also using proteomics to develop individualized treatment plans, which involves predicting whether or not an individual will respond to specific drugs and the side effects that the individual may experience. Researchers also use proteomics to predict the possibility of disease recurrence.

The National Cancer Institute has developed programs to improve cancer detection and treatment. The Clinical Proteomic Technologies for Cancer and the Early Detection Research Network are efforts to identify protein signatures specific to different cancer types. The Biomedical Proteomics Program identifies protein signatures and designs effective therapies for cancer patients.


91 Genomics and Proteomics

By the end of this section, you will be able to do the following:

Proteins are the final products of genes, which help perform the function that the gene encodes. Amino acids comprise proteins and play important roles in the cell. All enzymes (except ribozymes) are proteins that act as catalysts to affect the rate of reactions. Proteins are also regulatory molecules, and some are hormones. Transport proteins, such as hemoglobin, help transport oxygen to various organs. Antibodies that defend against foreign particles are also proteins. In the diseased state, protein function can be impaired because of changes at the genetic level or because of direct impact on a specific protein.

A proteome is the entire set of proteins that a cell type produces. We can study proteoms using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins. Proteomics is the study of proteomes’ function. Proteomics complements genomics and is useful when scientists want to test their hypotheses that they based on genes. Even though all multicellular organisms’ cells have the same set of genes, the set of proteins produced in different tissues is different and dependent on gene expression. Thus, the genome is constant, but the proteome varies and is dynamic within an organism. In addition, RNAs can be alternately spliced (cut and pasted to create novel combinations and novel proteins) and many proteins modify themselves after translation by processes such as proteolytic cleavage, phosphorylation, glycosylation, and ubiquitination. There are also protein-protein interactions, which complicate studying proteomes. Although the genome provides a blueprint, the final architecture depends on several factors that can change the progression of events that generate the proteome.

Metabolomics is related to genomics and proteomics. Metabolomics involves studying small molecule metabolites in an organism. The metabolome is the complete set of metabolites that are related to an organism’s genetic makeup. Metabolomics offers an opportunity to compare genetic makeup and physical characteristics, as well as genetic makeup and environmental factors. The goal of metabolome research is to identify, quantify, and catalogue all the metabolites in living organisms’ tissues and fluids.

Basic Techniques in Protein Analysis

The ultimate goal of proteomics is to identify or compare the proteins expressed from a given genome under specific conditions, study the interactions between the proteins, and use the information to predict cell behavior or develop drug targets. Just as scientists analyze the genome using the basic DNA sequencing technique, proteomics requires techniques for protein analysis. The basic technique for protein analysis, analogous to DNA sequencing, is mass spectrometry. Mass spectrometry identifies and determines a molecule’s characteristics. Advances in spectrometry have allowed researchers to analyze very small protein samples. X-ray crystallography, for example, enables scientists to determine a protein crystal’s three-dimensional structure at atomic resolution. Another protein imaging technique, nuclear magnetic resonance (NMR), uses atoms’ magnetic properties to determine the protein’s three-dimensional structure in aqueous solution. Scientists have also used protein microarrays to study protein interactions. Large-scale adaptations of the basic two-hybrid screen ((Figure)) have provided the basis for protein microarrays. Scientists use computer software to analyze the vast amount of data for proteomic analysis.

Genomic- and proteomic-scale analyses are part of systems biology , which is the study of whole biological systems (genomes and proteomes) based on interactions within the system. The European Bioinformatics Institute and the Human Proteome Organization (HUPO) are developing and establishing effective tools to sort through the enormous pile of systems biology data. Because proteins are the direct products of genes and reflect activity at the genomic level, it is natural to use proteomes to compare the protein profiles of different cells to identify proteins and genes involved in disease processes. Most pharmaceutical drug trials target proteins. Researchers use information that they obtain from proteomics to identify novel drugs and to understand their mechanisms of action.


Scientists are challenged when implementing proteomic analysis because it is difficult to detect small protein quantities. Although mass spectrometry is good for detecting small protein amounts, variations in protein expression in diseased states can be difficult to discern. Proteins are naturally unstable molecules, which makes proteomic analysis much more difficult than genomic analysis.

Cancer Proteomics

Researchers are studying patients’ genomes and proteomes to understand the genetic basis of diseases. The most prominent disease researchers are studying with proteomic approaches is cancer. These approaches improve screening and early cancer detection. Researchers are able to identify proteins whose expression indicates the disease process. An individual protein is a biomarker whereas, a set of proteins with altered expression levels is a protein signature . For a biomarker or protein signature to be useful as a candidate for early cancer screening and detection, they must secrete in body fluids, such as sweat, blood, or urine, such that health professionals can perform large-scale screenings in a noninvasive fashion. The current problem with using biomarkers for early cancer detection is the high rate of false-negative results. A false negative is an incorrect test result that should have been positive. In other words, many cancer cases go undetected, which makes biomarkers unreliable. Some examples of protein biomarkers in cancer detection are CA-125 for ovarian cancer and PSA for prostate cancer. Protein signatures may be more reliable than biomarkers to detect cancer cells. Researchers are also using proteomics to develop individualized treatment plans, which involves predicting whether or not an individual will respond to specific drugs and the side effects that the individual may experience. Researchers also use proteomics to predict the possibility of disease recurrence.

The National Cancer Institute has developed programs to improve cancer detection and treatment. The Clinical Proteomic Technologies for Cancer and the Early Detection Research Network are efforts to identify protein signatures specific to different cancer types. The Biomedical Proteomics Program identifies protein signatures and designs effective therapies for cancer patients.

Section Summary

Proteomics is the study of the entire set of proteins expressed by a given type of cell under certain environmental conditions. In a multicellular organism, different cell types will have different proteomes, and these will vary with environmental changes. Unlike a genome, a proteome is dynamic and in constant flux, which makes it both more complicated and more useful than the knowledge of genomes alone.

Proteomics approaches rely on protein analysis. Researchers are constantly upgrading these techniques. Researchers have used proteomics to study different cancer types. Medical professionals are using different biomarkers and protein signatures to analyze each cancer type. The future goal is to have a personalized treatment plan for each individual.


7.22: Genomics and Proteomics - Biology

Proteomics is the large-scale study of proteins, particularly their structures and functions. 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.

While proteomics generally refers to the large-scale experimental analysis of proteins, it is often specifically used for protein purification and mass spectrometry. After genomics and transcriptomics, proteomics is 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. The amount of protein produced for a given amount of mRNA depends on the gene it is transcribed from and the current physiological state of the cell.

Proteomics confirms the presence of the protein and provides a direct measure of the quantity present. Not only does the translation from mRNA cause differences, but many proteins are also subjected to a wide variety of chemical modifications after translation which are critical to the protein’s function such as phosphorylation, ubiquitination, methylation, acetylation, glycosylation, oxidation, and nitrosylation. Some proteins undergo ALL of these modifications, often in time-dependent combinations, aptly illustrating the potential complexity one has to deal with when studying protein structure and function.

Proteomics typically gives us a 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. 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. They only function in the presence of these other molecules. Finally, protein degradation rate plays an important role in protein content.

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 that only recognize certain proteins when they are tyrosine-phosphorylated, known as phospho-specific antibodies. There are also antibodies specific to other modifications. These can be used to determine the set of proteins that have undergone the modification of interest. For more quantitative determinations of protein amounts, techniques such as ELISAs can be used.

Most proteins function in collaboration with other proteins. 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, and chromatography followed by mass spectrometry, dual polarisation interferometry, Microscale Thermophoresis, and experimental methods such as phage display and computational methods.

Robotic preparation of MALDI mass spectrometry samples: Matrix-assisted laser desorption/ionization (MALDI) is a soft ionization technique used in mass spectrometry. It allows for the analysis of biomolecules and large organic molecules which tend to be fragile and fragment when ionized by more conventional ionization methods.

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 3-D 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. 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. Moreover, an interesting use of proteomics is using specific protein biomarkers to diagnose disease. A number of techniques allow testing for proteins produced during a particular disease, which helps to diagnose the disease quickly.


Understanding Tau’s Role in Origin and Spread of Alzheimer’s Disease (Eisenberg Lab)

The UCLA-DOE Institute is a team of research laboratories working on fundamental research and technology developments in broad DOE mission areas ranging from microbes, to biofuels and green chemistry, to the design of new biomaterials.

ALGAL FUNCTIONAL GENOMICS

Using state of the art methodologies for genome-wide approaches to probe the enzymology, cell biology and metabolism of organisms that contribute to carbon capture and primary productivity

MICROBIAL METABOLISM, CHEMISTRY AND COMMUNITIES

Deciphering the metabolism of syntrophic microbial communities and the biogenesis of the cellulosome appendage of the model cellulose degrading microbe, Clostridium thermocellum

SYNTHETIC BIOCHEMISTRY: ENZYME AND PATHWAY DESIGN

Developing new technologies to carry out important biosynthetic and biodegradation reaction schemes in vitro through the design of novel pathways and enzyme systems

ATOMIC IMAGING TECHNOLOGIES AND NOVEL STATES OF MATTER FOR PROTEINS

Pioneering the new method of micro-electron diffraction (microED) revealing microscale substructure within protein crystals at unprecedented levels of detail

UCLA-DOE Teaching Outreach

The Institute mentors and provides learning resources for students from around the world


Genomics, Proteomics and Metabolomics Approaches for Predicting Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients

Background: There is a continuous rise in the prevalence of Diabetes Mellitus Type 2 (T2DM) worldwide and most patients are unaware of the presence of this chronic disease at the early stages. T2DM is associated with complications related to long-term damage and failure of multiple organ systems caused by vascular changes associated with glycated end products, oxidative stress, mild inflammation, and neovascularization. Among the most frequent complications of T2DM observed in about 20-40% of T2DM patients is Diabetes Nephropathy (DN).

Methods: Literature search was done in view of highlighting the novel application of genomics, proteomics and metabolomics, as the new prospective strategy for predicting DN in T2DM patients.

Results: The complexity of DN requires a comprehensive and unbiased approach to investigate the main causes of disease and identify the most important mechanisms underlying its development. With the help of evolving throughput technology, rapidly evolving information can now be applied to clinical practice.

Discussion: DN is also the leading cause of end-stage renal disease, and comorbidity independent of T2DM. In terms of the comorbidity level, DN has many phenotypes therefore, timely diagnosis is required to prevent these complications. Currently, urine albumin-to-creatinine ratio and estimated glomerular filtration rate (eGFR) are gold standards for assessing glomerular damage and changes in renal function. However, GFR estimation based on creatinine is limited to hyperfiltration status therefore, this makes albuminuria and eGFR indicators less reliable for early-stage diagnosis of DN.

Conclusion: The combination of genomics, proteomics, and metabolomics assays as suitable biological systems that can provide new and deeper insights into the pathogenesis of diabetes, as well as to discover prospects for developing suitable and targeted interventions.

Keywords: Diabetes diabetes nephropathy genomics metabolomics proteomics system biology.


Welcome!

Bioinformatics, genomics, and proteomics are rapidly advancing fields that integrate the tools and knowledge from biology, chemistry, computer science, mathematics, physics, and statistics in research at the intersection of the biological and informational sciences. Inspired by the enormous amount of biological data that are being generated from the sequencing of genomes, these new fields will help us pose and answer biological questions that have long been considered too complex to address. Research in genomics, proteomics, and bioinformatics will also significantly impact society affecting medicine, culture, economics, and politics.

Bioinformatics

Also known as Computational Biology, this is the use of mathematics, statistics and computer programming to solve biological problems. Problems that are addressed in bioinformatics include protein and nucleotide sequence alignments, protein structure prediction, evolution modeling, prediction of gene expression and prediction of protein-protein interactions. The study of bioinformatics has vastly increased the power of genomic and proteomic research.

Genomics

Genomics is the study of organisms’ genomes and their expression. Genomics deals with how genes are organized within the genome, the management of DNA by the organism and modifications in the genome through evolution. Study in genomics has the potential of offering new therapies for treating many diseases.

Proteomics

The Natural complement to genomics, proteomics is the study of all of the proteins in an organism (proteome) and their interactions with their environment. The nature of proteins makes proteomics far more complex than genomics. Study in proteomics includes investigations in quantitation, structure (3-dimensional and sequence analysis), function, interactions, and modifications of proteins. In industry, protein separation and purification of proteins are also important aspects of proteomics.

BiGP at Williams

The Bioinformatics, Genomics, and Proteomics curriculum involves faculty from the biology, chemistry, computer science, mathematics/statistics, and physics departments and is designed to provide students with an understanding of these revolutionary new areas of investigation. There are many routes into this interdisciplinary field, so taking introductory courses in the related disciplines will open doors to the BiGP focused courses. Students interested in graduate work in bioinformatics, genomics, and proteomics should take BIGP courses and their prerequisites. Interested students are also encouraged to participate in independent research with members of the advisory faculty as they explore the development of these new fields.


Genomics, Proteomics and Systems Biology Approaches

Each year the Science and SciLifeLab Prize for Young Scientists focuses on four important fields of life science research to select winners for the annual awards. The Grand Prize winner can be from any of the four categories, and additional winners are chosen from each of the remaining three life science categories.

Genomics, Proteomics and Systems Biology Approaches is one of this year’s categories.

Research in this category focuses on genomics, proteomics, integrative omics and systems biology approaches, including computational, to facilitate comprehensive understanding of living cells, organisms and species,

What are Genomics, Proteomics and Systems Biology?

Genomics is the study of all the genes that make up the human genome – the double-stranded DNA helix that defines the human body. This sequence of chemical base pairs is a kind of map of the genetic code that humans inherit. Studying the gene variations provides insight into disease management and environmental impacts on human health.

Proteomics is the study of the structures and functions of the entire set of proteins that are made, used and changed by the human body. The human genome contains about 21,000 protein-encoding genes, and each human gene can be the blueprint for creating hundreds of different proteins. While proteomics generally refers to the large-scale experimental analysis of proteins, in the clinical sense, it refers to using technologies such as protein purification and mass spectrometry on tissues such as blood.

Proteomics and genomics present possibilities for practical applications in areas such as personalized medicine, predictive medicine and targeted medicine. Proteomics, in particular, holds great promise for the future diagnosis and treatment of cancer. For example, by searching for “biomarkers” in the proteins of tissues and bodily fluids, scientists and clinicians can identify cells at risk for cancer growth.

Systems biology has been responsible for very important developments in the science environmental sustainability as well as in human health. Systems Biology takes a holistic approach to deciphering the complexity of biological systems that starts from the understanding that the networks that form the whole of living organisms are more than the sum of their parts. It integrates many scientific disciplines – biology, computer science, engineering, bioinformatics, physics to name a few – to predict how systems change over time and under varying conditions, and to develop solutions to the world’s most pressing health and environmental issues.

Some tools of proteomics

Technologies used to advance the understanding of protein biochemistry include:

Mass Spectrometry – an evolving technology that allows scientists to detect and quantify proteins in a complex biological matrix. This process is very precise, distinguishing proteins that differ in composition by a single hydrogen atom, the smallest atom.

Protein Microarrays – these are powerful tools for capturing and measuring proteins in a high throughput fashion. A protein microarray typically consists of a small piece of glass or plastic coated with thousands of “capture reagents” (molecules that can “grab” specific proteins). This technology allows scientists to isolate and study many potential biomarker proteins.

Nanotechnologies – these microscopic technologies (a nanometer is approximately 1/80,0000 the width of a human hair) can be used for the targeted delivery of drugs, energy-based therapeutics (such as heat or radiation) and imaging contrast agents. They can also be used in biosensors to measure minute quantities of biomarkers in biological fluids.

Bioinformatics –is a form of research that uses data modeling and database design combined with gene and protein expression analysis and predictions to model and analyze biological systems using software tools.


7.22: Genomics and Proteomics - Biology

Introduction
Genomics and proteomics are becoming powerful tools for revealing gene function and genomic organization in large scale. Genomics is the study of the entire genome, usually starting with whole genome sequencing. Proteomics is the study of the entire protein components within the cells.

Genomics
The genomics approach has three steps: BAC construction, shot-gun sequencing and sequence assembly. BACs are bacterial artificial chromosomes which can have large inserts of 100 to 300 kb. These large fragment are digested into smaller pieces (shot-gun) and the sequence is determined based on these small plasmids. The small plasmids overlap with each other, so do the BAC DNA. The sequence is then assembled using computer programs.

Functional genomics and proteomics
Functional genomics is about using the sequence data to explore how DNA and proteins work with each other and the environment to create complex, dynamic living systems. The directions include transcriptomics (analysis of transcripts of the whole cell), comparative genomics, structural genomics and proteomics. Transcriptome is mostly studied by a novel method called DNA microarray, where the DNA is spotted on piece of glass and the mRNA is labeled and used as probes. The hybridized signal is then compared to determine gene transcripts level under different condition.

Proteomics
The entirety of proteins 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, is called proteome. The study on proteome is called proteomics. The main tools for proteomics studies include 2D gel electrophoresis, MOLDI-TOF MS and protein array.

  • Concept maps to depict the importance of genomics and proteomics in biological studies
  • Step by step sequencing mechanism and techniques
  • Step by step technique illustration of proteomics
  • Key concept sheets for concise summary of each section
  • Statistics about human genome is given
  • Details about DNA microarray

Genomics and human genome project

  • Genomics approach overview
  • BAC and BAC libraries
  • Shot-gun sequencing
  • Sequencing steps
  • Sequence assembly
  • Human genome project

Functional genomics and proteomics

  • Functional genomics overview
  • Transcriptomics
  • DNA microarray
  • Structural genomics
  • Comparative genomics
  • Introduction
  • Methods overview
  • 2D electrophoresis
  • MALDI-TOF MS
  • Protein array

See all 24 lessons in Genetics, including concept tutorials, problem drills and cheat sheets:
Teach Yourself Genetics Visually in 24 Hours


Proteomics: Meaning and Significance | Genetics

In this article we will discuss about:- 1. Meaning of Proteomics 2. Types of Proteomics 3. Significance.

Meaning of Proteomics:

The term proteomics was coined in mid 1990s at the back drop of successful genomics. In bioinformatics point of view proteomics is the databases of protein sequence, databases of predicted protein structures and more recently, databases of protein expression analysis. As more protein structures are identified, the relationship between structure and functions became easier to predict.

In addition, databases of protein structure and corporating tools facilitating the identification of common protein structure and their predicted functions. In this technique individually purified ligands such as proteins, peptides, antibodies, antigens, and carbohydrates are spotted on to a derivatized surface and are generally used for examining protein expression levels for protein profiling.

A major challenge facing plant biotechnology and other bioinformatics research community is the translation of complete genome DNA sequence data into protein structure and predicted functions. Such a steps will provide the key link between the genotypes of an organism and its expressed phenotype.

The growth of proteomics is a direct result of advances made in large scale nucleotide sequencing of expressed sequence tags (EST). Although mass spectrometry or more popularly MS technology has been considered as versatile tool for examining simultaneous expression of more than 1000 proteins and identification, mapping of post-translational modifications (Table 25.5).

These methods performed in a latest array of technology resulted in large-scale characterization of protein location, protein-protein interaction and protein functions.

Insilico methodologies are being developed to identify protein interaction from genome sequence. For example, 6809 putative protein-protein interaction has been identified in Escherichia coli and more than 45,000 have been identified in yeast and large number of these interactions is functionally related.

Types of Proteomics:

i. Structural Proteomics:

One of the main targets of proteomics investigation is to map the structure of protein complexes or the proteins present in a specific cellular organelle known as cell map or structural proteins. Structural proteomics attempt to identify all the proteins within a protein complex and characterization all protein-protein interactions. Isolation of specific protein complex by purification can simplify the proteomic analysis.

ii. Functional Proteomics:

It mainly includes isolation of protein complexes or the use of protein ligands to isolate specific types of proteins. It allows selected groups of proteins to be studied its chracteristics which can provide important information about protein signalling and disease mechanism etc.

Significance of Proteomics:

Bioinformatics has been widely employed in protein-profiling, where question of protein structural information for the purpose of protein identification, characterization and database is carried out. The spectrum of protein expressed in a cell type provides the cell with its unique identity. It explores how the protein complement changes in a cell type during development in response to environmental stress.

Protein microarrays facilitate the detection of protein protein interaction and protein expression profiling. Several protein microarray examples indicate that protein arrays hold great promise for the global analysis of protein-protein and protein-ligand interaction.

iii. Proteomics to a phosphorylation:

In post-translational modification of protein, mass spectrometer (MS) can be used to identify novel phosphorylation. Measure changes in phosphorylation state of protein takes place in response to an effective and determining phosphorylation sites in proteins.

Identification of phosphorylation sites can provide information about the mechanism of enzyme regulation and protein kinase and phosphotases involved. A proteomics approach for this process has an advantage that one can study all the phosphorylating proteins in a cell at the same time.

Proteome mining is a functional proteomic approach used to extract information from the analysis of specific sub-proteomics. In principle, it is based on the assumption. In principle, it is based oil the assumption that all drug like molecule selectively compete with a natural cellular ligand for a binding site on a protein target.


Watch the video: BY 210 Mukhtar; Genomics, Transcriptomics, and Proteomics (June 2022).


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