Proteomics/Introduction to Proteomics/Relationship of Proteomics to Other Fields

Bioinformatics in Proteomics

A cabinet from the IBM Blue Gene L supercomputer.

The intersection of bioinformatics and proteomics allows for the generation and management of large amounts of data. High-throughput computational methods are an integral part of proteomics for data acquisition, storage, and analysis. The Internet allows easy sharing of data sets and information between researchers and laymen. Large public and private databases are now common, and scientists of all expertise levels from all over the world can contribute to them. The Protein Data Bank (PDB) is one such database, handling structure and sequence data for proteins with a determined crystal structure. One of the largest databases, by far, is the National Center for Biotechnology Information, or NCBI. It contains both curated literature and links to many databases and resources for proteomics and biology in general.

Predictions and data gained from these "in-silico" methods cannot always be used in place of laboratory data. Whether or not they can be used depends on the data type and the situation for which it is being used. As a general rule, however, computational bioinformatics methods should either be used to support existing laboratory data or to generate a hypothesis based on previously existing data. Within the field of proteomics, bioinformatics is often used in protein structure prediction , and the "holy grail" of proteomics/bioinformatics is to create a bioinformatics application that can predict the final structure of a protein by its amino acid sequence alone. While predictions of this sort would need to be verified like any other prediction from bioinformatics tools, these predictions, if possible, could speed up analysis of protein structure and function, which is in itself the focus of proteomics.

Genomics in Proteomics

A chromosome of a fruit fly.

Genomics, like bioinformatics, is related to proteomics both through physical laboratory work and computational data. Many of the same laboratory techniques used in proteomics are also used in genomics, although the focus of the two sciences is fundamentally different. Genomics focuses on an organism's genetic makeup, while proteomics focuses on gene products. In a sense, genomics is a stepping stone to proteomics, because the eventual goal of genomics is to determine how genes work, and one cannot find out that information without studying the proteins produced.

In some areas, proteomics can replace genomics in functional use. There are, at times, situations in which raw gene structural data is insufficient to test a hypothesis, and one must know how genes interact with each other to get a full picture of how they work. Proteomics allows this analysis to happen, in a much more in-depth level than can be achieved with genomics. Genomics is by no means an obsolete science, but in the cases of protein analysis, structure prediction, and function prediction, it can be outdone by proteomics. The most significant reason for this is that proteomics techniques revolve around separation methods (some of which are explored in detail later) which use the unique characteristics of the proteins, such as structure and molecular weight, to identify and purify them.

Metabolomics in Proteomics

Metabolomics is a much younger field than Proteomics and expands on many of its methods. The focus of Metabolomics is, much like Genomics, different from proteomics and can be considered an expansion off of both of the earlier fields. Metabolomics is an attempt to understand and quantify the Metabolome in order to chart the effects and form of the phenotype of an organism.

This field achieves what both genomics and proteomics are unable to, in giving a complete systems biology view of metabolic biological functionality from a molecular and biochemical standpoint. Previous attempts to achieve this through the prior fields were based on expanding off their data using chemical knowledge. This is not to say that Metabolomics is a replacement for either Proteomics or Genomics. On the contrary the three fields can glean great benefits from being integrated together.

Transcriptomics in Proteomics

The process of DNA transcription that creates the transcriptome.

Transcriptomics has a direct impact on proteomics as a science. Without mRNA in a cell (the focus of transcriptomics and the content of the transcriptome), no proteins can be made. Therefore, problems with the transcriptome can and often do translate into problems with an organism's proteome. Like proteomics, transcriptomics can be used to explore the expression level of genes and classify disease. Transcriptomics can be integrated with proteomics for a variety of applications, including drug discovery and functional genome annotation.

Phenomics in Proteomics

Phenomics is the study of the phenome, or the set of all phenotypes expressed by a cell on up to species. The phenome includes traits caused by the environment, giving it a scope somewhat beyond what the other –omics fields really provide. That doesn’t mean, however, that Phenomics is not related to Proteomics. The function of the proteome is responsible for a large section of phenotypic characteristics. So the information gained from proteomics is part of what makes Phenomics feasible as a scientific field.

References

1. ^ Graves, P. R. and Timothy A. J. Haystead. "Molecular Biologist's Guide to Proteomics". Microbiol Mol Biol Rev. 2002 March; 66(1): 39-63.
2. ^ Bioinformatics Wikipedia Page: "Analysis of Protein Expression", "Prediction of Protein Structure"
3. ^ This is similar to the protein prediction methods commonly referred to as the ab initio approach.
4. ^ Vemuri, Goutham N and Aristidou, Aristos A. "Metabolic Engineering in the -omics Era: Elucidating and Modulating Regulatory Networks". Microbiol Mol Biol Rev. 2005 June
5. ^ Twyman, Richard. "Transcriptomics". http://genome.wellcome.ac.uk/doc_wtd020758.html
6. ^ Wikipedia Article on the Phenome