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5. Proteomics

Proteomics is defined as the use of quantitative protein-level measurements of gene expression in order to characterize biological processes and elucidate the mechanisms of gene translation. The objective of proteomics is the quantitative measurement of protein expression particularly under the influence of drug or disease perturbations.(140)

Proteomics analysis of a mixture of proteins incorporates three procedures:

    1. Protein resolution.
    2. Protein identification.
    3. Protein quantitation.

Protein resolution is performed using two-dimensional polyacrylamide gel electrophoresis. Protein identification is accomplished using Edman degradation, mass spectrometry and Western immunoblotting. Protein quantitation is achieved using scanners and phosphorimagers.

Gene expression monitors gene transcription whereas proteomics monitors gene translation. Because of the additional requirements of the secondary translation stage, proteomics has more restrictive expression and post-translational modification conditions than gene expression. Proteomics poses greater stringency conditions than gene expression for the phenotypic expression of a candidate gene. Thus, proteomics provides a more direct response in functional genomics than the indirect approach provided by gene expression. Proteomics has been reviewed.(64,140-161)

5.1. Proteome Databases

Proteome databases provide integrated data management and analysis systems for the translational expression data generated by large-scale proteomics experiments. Proteome databases integrate the expression levels and properties of thousands of proteins with the thousands of genes identified on genetic maps and offer a global approach to the study of gene expression.

As described in the survey of Celis et al.,(162) proteome databases address five research problems that cannot be resolved by DNA analysis:

    1. Relative abundance of protein products.
    2. Post-translational modifications.
    3. Subcellular localizations.
    4. Molecular turnover.
    5. Protein interactions.

The creation of comprehensive databases of genes and gene products will lay the foundation for the further construction of comprehensive databases of higher-level mechanisms, e.g. regulation of gene expression, metabolic pathways and signalling cascades.(140)

Human proteome databases include eleven representative implementations:

  1. ANL Human Breast Epithelial Cell Protein 2DE Database.(163)
    2. http://www.anl.gov/BIO/PMG/projects/index_hbreast.html
  2. FindMod Tool.(164) http://www.expasy.ch/tools/findmod
  3. Heart High-Performance 2-DE Database.(165) http://www.mdc-berlin.de/~emu/heart/
  4. HEART-2DPAGE.(166) http://userpage.chemie.fu-berlin.de/~pleiss/dhzb.html
  5. HSC-2DPAGE.(167) http://www.harefield.nthames.nhs.uk/nhli/protein/
  6. Human 2-D Page Databases.(168) http://biobase.dk/cgi-bin/celis
  7. Joint Protein Structure Laboratory 2D-Gel.(169) http://www.ludwig.edu.au/jpsl/jpslhome.html
  8. NCI 2DWG Image Meta-Database.(170) http://www-lecb.ncifcrf.gov/2dwgDB/
  9. Prostate Expression Database.(171) http://chroma.mbt.washington.edu/PEDB/
  10. SWISS-2DPAGE.(172) http://www.expasy.ch/ch2d/
  11. WORLD-2DPAGE.(173) http://www.expasy.ch/ch2d/2d-index.html

Proteome databases have been reviewed.(140,162,174-175)

5.2. Proteome Database Mining

Proteome database mining is an emerging technology. Proteome database mining is used to identify intrinsic patterns and relationships in proteomics data. The identification of patterns in complex proteomic datasets provides two benefits:

    1. Generation of insight into gene translation and post-translational modification conditions.
    2. Characterization of multiple protein expression profiles in complex biological processes, e.g. pathological states.

Proteome database mining has been conducted on three experimental systems:

    1. Escherichia coli K-12 proteome.(176)
    2. Human lymphoid proteins.(177)
    3. Toxicity evaluation of drug candidates.(138)

6. Conclusions

The Human Genome Initiative is an international research program for the creation of high-resolution structural and functional maps of the human genome. Genome research projects generate enormous quantities of data. Database mining is the process of finding and extracting useful information from raw datasets. On the basis of the central dogma of molecular biology, computational genomics has identified a classification of three successive levels for the management and analysis of genetic data in scientific databases:

    1. Genomics.
    2. Gene expression.
    3. Proteomics.

Genome database mining is referred to as computational genome annotation. Computational genome annotation is the identification of the protein-encoding regions of a genome and the assignment of functions to these genes on the basis of sequence similarity homologies against other genes of known function. Gene expression database mining is the identification of intrinsic patterns and relationships in transcriptional expression data generated by large-scale gene expression experiments. Proteome database mining is the identification of intrinsic patterns and relationships in translational expression data generated by large-scale proteomics experiments. As the determination of the DNA sequences comprising the human genome nears completion, the Human Genome Initiative is undergoing a paradigm shift from static structural genomics to dynamic functional genomics. Thus, gene expression and proteomics are emerging as the major intellectual challenges of database mining research in the postsequencing phase of the Human Genome Initiative.

Genome, gene expression and proteome database mining are complementary emerging technologies with much scope being available for improvements in data analysis. Improvements in genome, gene expression and proteome database mining algorithms will enable the prediction of protein function in the context of higher order processes such as the regulation of gene expression, metabolic pathways and signalling cascades. The final objective of such higher-level functional analysis will be the elucidation of integrated mapping between genotype and phenotype.(64) Future research directions in genome database technologies have been reviewed.(57,178-179)

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