Proteomics overview, study plan, and research plan (Robocentric biotech development)

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Proteomics is the large-scale study of proteins, according to Wikipedia. Wikipedia also explains the following about proteomics.

Proteins are vital parts of living organisms, with many functions such as the formation of structural fibers of muscle tissue, enzymatic digestion of food, or synthesis and replication of DNA. In addition, other kinds of proteins include antibodies that protect an organism from infection, and hormones that send important signals throughout the body.

The proteome is the entire set of proteins produced or modified by an organism or system. Proteomics enables the identification of ever-increasing numbers of proteins. This varies with time and distinct requirements, or stresses, that a cell or organism undergoes.

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Proteomics is an interdisciplinary domain that has benefited greatly from the genetic information of various genome projects, including the Human Genome Project. It covers the exploration of proteomes from the overall level of protein composition, structure, and activity, and is an important component of functional genomics.

Proteomics generally denotes the large-scale experimental analysis of proteins and proteomes, but often refers specifically to protein purification and mass spectrometry. Indeed, mass spectrometry is the most powerful method for analysis of proteomes, both in large samples composed of millions of cells and in single cells.

Complexity of the problem in proteomics.

After genomics and transcriptomics, proteomics is the next step in the study of biological systems. It is more complicated than genomics because an organism’s genome is more or less constant, whereas proteomes differ from cell to cell and from time to time. Distinct genes are expressed in different cell types, which means that even the basic set of proteins produced in a cell must be identified.

In the past this phenomenon was assessed by RNA analysis, which was found to lack correlation 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 cell’s physiological state. Proteomics confirms the presence of the protein and provides a direct measure of its quantity.

Post-translational modifications. Not only does the translation from mRNA cause differences, but many proteins also are subjected to a wide variety of chemical modifications after translation. The most common and widely studied post-translational modifications include phosphorylation and glycosylation. Many of these post-translational modifications are critical to the protein’s function.

Phosphorylation. One such modification is phosphorylation, which happens to many enzymes and structural proteins in the process of cell signaling. 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. Ubiquitin is a small protein that may be affixed to certain protein substrates by enzymes called E3 ubiquitin ligases. Determining which proteins are poly-ubiquitinated helps understand how protein pathways are regulated. This is, therefore, an additional legitimate “proteomic” study. Similarly, once a researcher determines which substrates are ubiquitinated by each ligase, determining the set of ligases expressed in a particular cell type is helpful.

Additional modifications. In addition to phosphorylation and ubiquitination, proteins may be subjected to (among others) methylation, acetylation, glycosylation, oxidation, and nitrosylation. Some proteins undergo all these modifications, often in time-dependent combinations. This illustrates the potential complexity of studying protein structure and function.

Distinct proteins are made under distinct settings. A cell may make different sets of proteins at different times or under different conditions, for example during development, cellular differentiation, cell cycle, or carcinogenesis. Further increasing proteome complexity, as mentioned, most proteins are able to undergo a wide range of post-translational modifications.

Therefore, a “proteomics” study may become complex very quickly, even if the topic of study is restricted. In more ambitious settings, such as when a biomarker for a specific cancer subtype is sought, the proteomics scientist might elect to study multiple blood serum samples from multiple cancer patients to minimise confounding factors and account for experimental noise. Thus, complicated experimental designs are sometimes necessary to account for the dynamic complexity of the proteome.

Limitations of genomics and proteomics studies. Proteomics gives a different level of understanding than genomics for many reasons.

the level of transcription of a gene gives only a rough estimate of its level of translation into a protein. An mRNA produced in abundance may be degraded rapidly or translated inefficiently, resulting in a small amount of protein.

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.

many transcripts give rise to more than one protein, through alternative splicing or alternative post-translational modifications.

many proteins form complexes with other proteins or RNA molecules, and only function in the presence of these other molecules.

protein degradation rate plays an important role in protein content.

Reproducibility. One major factor affecting reproducibility in proteomics experiments is the simultaneous elution of many more peptides than mass spectrometers can measure. This causes stochastic differences between experiments due to data-dependent acquisition of tryptic peptides. Although early large-scale shotgun proteomics analyses showed considerable variability between laboratories, presumably due in part to technical and experimental differences between laboratories, reproducibility has been improved in more recent mass spectrometry analysis, particularly on the protein level. Notably, targeted proteomics shows increased reproducibility and repeatability compared with shotgun methods, although at the expense of data density and effectiveness.

Data quality. Proteomic analysis is highly amenable to automation and large data sets are created, which are processed by software algorithms. Filter parameters are used to reduce the number of false hits, but they cannot be completely eliminated. Scientists have expressed the need for awareness that proteomics experiments should adhere to the criteria of analytical chemistry (sufficient data quality, sanity check, validation).

Methods of studying proteins include protein detection with antibodies (immunoassays), antibody-free protein detection (with detection methods, and separation methods), hybrid technologies, current research methodologies, high-throughput proteomic technologies (with mass spectrometry and protein profiling, affinity proteomics, protein chips, reverse-phased protein microarrays), and protein detection via bioorthogonal chemistry.

Practical applications of proteomics include new drug discovery, interaction proteomics and protein networks, expression proteomics, biomarkers, proteogenomics, and structural proteomics.

Bioinformatics for proteomics (proteome informatics) include protein identification, protein structure, post-translational modifications, and computational methods in studying protein biomarkers.

Emerging trends in proteomics include systems biology, and human plasma proteome.

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