Conference Paper

Analysis and Classification of Proteomics Data, a Case Study.

Universita' degli Studi "Magna Græcia" di Catanzaro, Catanzaro, Calabria, Italy
DOI: 10.1109/CBMS.2006.43 Conference: 19th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2006), 22-23 June 2006, Salt Lake City, Utah, USA
Source: DBLP


This paper presents a methodology for analyzing and classifying proteins identified in biological samples. In particular, such methodology consists in normalizing and classifying quantity and quality of proteins identified by using tandem mass spectrometry. A case study is considered and a classification experiment for protein discriminant is also reported

6 Reads
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Subcellular proteomics, as an important step to functional proteomics, has been a focus in proteomic research. However, the co-purification of "contaminating" proteins has been the major problem in all the subcellular proteomic research including all kinds of mitochondrial proteome research. It is often difficult to conclude whether these "contaminants" represent true endogenous partners or artificial associations induced by cell disruption or incomplete purification. To solve such a problem, we applied a high-throughput comparative proteome experimental strategy, ICAT approach performed with two-dimensional LC-MS/MS analysis, coupled with combinational usage of different bioinformatics tools, to study the proteome of rat liver mitochondria prepared with traditional centrifugation (CM) or further purified with a Nycodenz gradient (PM). A total of 169 proteins were identified and quantified convincingly in the ICAT analysis, in which 90 proteins have an ICAT ratio of PM:CM>1.0, while another 79 proteins have an ICAT ratio of PM:CM<1.0. Almost all the proteins annotated as mitochondrial according to Swiss-Prot annotation, bioinformatics prediction, and literature reports have a ratio of PM:CM>1.0, while proteins annotated as extracellular or secreted, cytoplasmic, endoplasmic reticulum, ribosomal, and so on have a ratio of PM:CM<1.0. Catalase and AP endonuclease 1, which have been known as peroxisomal and nuclear, respectively, have shown a ratio of PM:CM>1.0, confirming the reports about their mitochondrial location. Moreover, the 125 proteins with subcellular location annotation have been used as a testing dataset to evaluate the efficiency for ascertaining mitochondrial proteins by ICAT analysis and the bioinformatics tools such as PSORT, TargetP, SubLoc, MitoProt, and Predotar. The results indicated that ICAT analysis coupled with combinational usage of different bioinformatics tools could effectively ascertain mitochondrial proteins and distinguish contaminant proteins and even multilocation proteins. Using such a strategy, many novel proteins, known proteins without subcellular location annotation, and even known proteins that have been annotated as other locations have been strongly indicated for their mitochondrial location.
    Molecular &amp Cellular Proteomics 02/2005; 4(1):12-34. DOI:10.1074/mcp.M400079-MCP200 · 6.56 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Classical proteomics combined two-dimensional gel electrophoresis (2-DE) for the separation and quantification of proteins in a complex mixture with mass spectrometric identification of selected proteins. More recently, the combination of liquid chromatography (LC), stable isotope tagging, and tandem mass spectrometry (MS/MS) has emerged as an alternative quantitative proteomics technology. We have analyzed the proteome of Mycobacterium tuberculosis, a major human pathogen comprising about 4,000 genes, by (i) 2-DE and mass spectrometry (MS) and by (ii) the isotope-coded affinity tag (ICAT) reagent method and MS/MS. The data obtained by either technology were compared with respect to their selectivity for certain protein types and classes and with respect to the accuracy of quantification. Initial datasets of 60,000 peptide MS/MS spectra and 1,800 spots for the ICAT-LC/MS and 2-DE/MS methods, respectively, were reduced to 280 and 108 conclusively identified and quantified proteins, respectively. ICAT-LC/MS showed a clear bias for high M(r) proteins and was complemented by the 2-DE/MS method, which showed a preference for low M(r) proteins and also identified cysteine-free proteins that were transparent to the ICAT-LC/MS method. Relative quantification between two strains of the M. tuberculosis complex also revealed that the two technologies provide complementary quantitative information; whereas the ICAT-LC/MS method quantifies the sum of the protein species of one gene product, the 2-DE/MS method quantifies at the level of resolved protein species, including post-translationally modified and processed polypeptides. Our data indicate that different proteomic technologies applied to the same sample provide complementary types of information that contribute to a more complete understanding of the biological system studied.
    Molecular &amp Cellular Proteomics 02/2004; 3(1):24-42. DOI:10.1074/mcp.M300074-MCP200 · 6.56 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We describe an approach for the accurate quantification and concurrent sequence identification of the individual proteins within complex mixtures. The method is based on a class of new chemical reagents termed isotope-coded affinity tags (ICATs) and tandem mass spectrometry. Using this strategy, we compared protein expression in the yeast Saccharomyces cerevisiae, using either ethanol or galactose as a carbon source. The measured differences in protein expression correlated with known yeast metabolic function under glucose-repressed conditions. The method is redundant if multiple cysteinyl residues are present, and the relative quantification is highly accurate because it is based on stable isotope dilution techniques. The ICAT approach should provide a widely applicable means to compare quantitatively global protein expression in cells and tissues.
    Nature Biotechnology 11/1999; 17(10):994-9. DOI:10.1038/13690 · 41.51 Impact Factor
Show more