Proteome mining

Both approaches are applied in both proteome mining (Ch. 18.3), protein-expression profiling (Ch. 18.4), and PTM-mapping (Ch. 19). 

The power of the currently available methods for proteome mining can be illustrated with data reported on the identification of 800 proteins from 50 milhon E. coli K12 cells using multiple fractionation, RPLC-MS-MS, and various ways of database searching [37]. With MASCOT searching, 754 proteins were identified from 1326 peptides and 2167 MS-MS spectra, which means on average 1.75 peptides and 2.87 spectra per protein. Abont 17% of the E. coli K12 proteome was covered in this way. 

The focus in this text is on the application of LC-MS technology. Three general approaches to proteome mining can be discriminated, which are related to the three general approaches to protein identification bottom-up, top-down, and shotgun. The latter essentially is a bottom-up approach, but is discussed separately. 

The MudPIT procedure is a powerful approach to comprehensive proteome mining. In a comparison of the biphasic MudPIT approach (Figure 18. la) with either ID-RPLC-MS or triphasic MudPIT (Figure 18. Ib), 26, 55, and 62 proteins were identified from a complex protein mixture associated with bovine brain microtubules using the ID, 2D, and 3D approach, respectively [46]. The 3D approach enabled the identification of some hydrophilic peptides that were missed by the 2D approach. Even a tetraphasic MudPIT with longer columns was described [47]. 

More recently, attention was also paid to the potential use of predicted retention times to strengthen the confidence in protein identification [38, 40-41, 48-49], It also emphasizes the still restricted perspective we have on the proteome by means of the current technology. Whereas this is not (yet) a major problem in proteome mining, it may significantly influence the results in protein-expression profiling (Ch. 18.4) and/or protein-related biomarker discoveiy (Ch. 18.6). 

Alternatively, high-speed RPLC was reported by means of a short 50-pm-lD column packed with 0.8-pm porous Cig-modified silica particles in combination with TOF (0.2-s acquisition) orFT-ICR-MS (0.3-0.6 s acquisition) [56]. Applications in proteome mining of Shewanella oneidensis were shown 600 proteins were identified from 2000 peptides within 3 min. 

Proteome Mining Identifying known and unknown proteins in complex mixtures... 

Very recently, in-depth proteome mining of C. roseus suspension cell was conducted. Among the total 1663 identified proteins, 63 enzymes were identified to be potentially involved in secondary metabolism, of which 22 were involved in the TIA biosynthetic pathway and 16 were predicted as putative transporters [59]. In this study, four known enzymes in TIA pathway were confirmed, and gene candidates in other seven reaction steps, mainly in the secologanin (precursor of TIA) biosynthetic pathway, were proposed based on the proteomics data in combination with analogy with other species. 

Champagne, A., Rischer, H., Oksman-Caldentey, K.M., and Boutry, M. (2012) In-depth proteome mining of cultured Catharanthus roseus cells identifies candidate proteins involved in the synthesis and transport of secondary metabolites. Proteomics, 12, 3536-3547. 

Nirmalan NJ, Harnden P, Selby PJ, et al. Mining the archival formalin-fixed paraffin-embedded tissue proteome opportunities and challenges. Mol. Biosyst. 2008 4 712-720. 

Today, most data are entered into corporate databases which consider the need of the user and the purpose of data. They are structured, searchable, contain both raw and metadata. Decision-making tools can mine these databases and if necessary combine data from various sources, including genetic, proteomic, clinical and chemical databases. 

Maccarrone G, Milfay D, Birg 1, Rosenhagen M, Grimm R, Bailey J, Zolotarjova N, Turck CW (2004) Mining the hiunan CSF proteome by immunodepletion and shotgun mass spectrometry. Electrophoresis 25 2402-2412... 

Data-Mining for Cancer Proteomics Using 2D-DIGE Data and Clinicopathological Information... 

All four of the proteomic technologies mentioned above (2D-PAGE, HPLC, MS, and protein arrays) are dependent on the use of bioinformatics as a tool for data mining and elucidation. Often, MS runs generate lists of thousands of potential peptide biomarkers, and only with the help of dedicated software tools can the data be analyzed. Computer searches involving databanks of peptides and proteins are used to compare the lists of masses of the proteolytic peptides to theoretical proteolytic products. Matches between the observed mass and the calculated mass can serve as a way of identifying proteins of interest [28-32],... 

Computation proteome annotation is the process of proteome database mining, which includes structure/fold prediction and functionality assignment. Methodologies of secondary structure prediction and problems of protein folding are discussed. Approaches to identify functional sites are presented. Protein structure databases are surveyed. Secondary structure predictions and pattern/fold recognition of proteins using the Internet resources are described. 

SciNova Informatics, an Indian company, has developed a data mining product Prometheus targeted towards genomics, proteomics and small molecule research In Life Sciences. 

Leonard SE, Reddie KG, Carroll KS (2009) Mining the thiol proteome for sulfenic acid modifications reveals new targets for oxidation in cells. ACS Chem Biol 4 783-799... 

Hobson, D.J., Rupa, P., Diaz, G.J., Zhang, H., Yang, M., Mine, Y., Turner, P.V., Kirby, G.M. 2007. Proteomic analysis of ovomucoid hypersensitivity in mice by two-dimensional difference gel electrophoresis (2D-DIGE). Food Chem Toxicol 45 2372-2380. 

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