Study of posttranslational modifications in lenticular alphaA-Crystallin of mice using proteomic analysis techniques.

In the present work the complexity in the 2D-gel protein pattern of murin lenticular alphaA-Crystallin was analyzed. An in depth study of the different protein isoforms was done combining different proteomic tools. Lens proteins of four different ages, from embryo to 100-week-old mice, were separated by large 2D-PAGE, revealing an increase in the number and intensity of the spots of alphaA-Crystallin during the process of aging. For further analyses the oldest mice were chosen. Comparison and evaluation of two different staining methods proved Imidazole-Zinc to be a good alternative to the generally used Coomassie stain. The characterization of the different alphaA-Crystallin protein species was done using nanoLC-ESI-MS/MS (liquid chromatography electrospray ionisation tandem mass spectrometry). Data interpretation was done by database searching, manual validation and a new MS/MS-interpretation tool for posttranslational modifications--the PTM-Explorer. Using this way, eight different phosphorylation sites were identified and localized; the identification of four of them was not published so far. Furthermore, quantitative N-terminal acetylation of alphaA-Crystallin and variable C-terminal truncation was observed, also not published in this extent yet. The results of the mass spectrometric analysis were validated by immunoblotting experiments using two different alphaA-Crystallin specific antibodies. In addition, a fluorescent phospho-specific stain was used to detect the protein spots including phosphorylation groups. Re-separation 2D-PAGE was done to round off the present study and explain the appearance of some of the protein spots in the gel as artifacts of the 2D-PAGE separation.

Application of proteomics and protein analysis for biomarker and target finding for immunotherapy.

Regulatory T-cells play a central role in the maintenance of the immunological balance and are powerful inhibitors of T-cell activation both in vivo and in vitro. The enhancement of suppressor-cell function might be a target for immunotherapeutic approaches for the treatment of immune-mediated diseases like multiple sclerosis and Crohn's disease.The method of choice to elucidate the still unclear effector functions of regulatory T-cells is the differential proteome analyses performed with human and murine T-cell populations. To this end, whole-protein extracts of conventional and regulatory T-cells are separated by high-resolution two-dimensional gel electrophoresis according to Klose. The proteomes are analyzed by a 2DE gel image analysis software, ProteomWeaver. The protein spots that are found differentially expressed are picked from the gels and prepared for matrix-assisted laser desorption/ionization (MALDI) mass spectrometrical analysis automatically. The high-resolution 2DE-PAGE and the automated spot handling and protein identification allows one to rapidly find new potential candidate proteins that are of functional relevance for regulatory T-cells, to be used as targets for drug development or as biomarkers for research and diagnostic purposes.

Bioinformatics in proteomics.

Proteomics technologies are under continuous improvements and new technologies are introduced. Nowadays high throughput acquisition of proteome data is possible. The young and rapidly emerging field of bioinformatics in proteomics is introducing new algorithms to handle large and heterogeneous data sets and to improve the knowledge discovery process. For example new algorithms for image analysis of two dimensional gels have been developed within the last five years. Within mass spectrometry data analysis algorithms for peptide mass fingerprinting (PMF) and peptide fragmentation fingerprinting (PFF) have been developed. Local proteomics bioinformatics platforms emerge as data management systems and knowledge bases in Proteomics. We review recent developments in bioinformatics for proteomics with emphasis on expression proteomics.

Interpretation of mass spectrometry data for high-throughput proteomics.

Recent developments in proteomics have revealed a bottleneck in bioinformatics: high-quality interpretation of acquired MS data. The ability to generate thousands of MS spectra per day, and the demand for this, makes manual methods inadequate for analysis and underlines the need to transfer the advanced capabilities of an expert human user into sophisticated MS interpretation algorithms. The identification rate in current high-throughput proteomics studies is not only a matter of instrumentation. We present software for high-throughput PMF identification, which enables robust and confident protein identification at higher rates. This has been achieved by automated calibration, peak rejection, and use of a meta search approach which employs various PMF search engines. The automatic calibration consists of a dynamic, spectral information-dependent algorithm, which combines various known calibration methods and iteratively establishes an optimised calibration. The peak rejection algorithm filters signals that are unrelated to the analysed protein by use of automatically generated and dataset-dependent exclusion lists. In the "meta search" several known PMF search engines are triggered and their results are merged by use of a meta score. The significance of the meta score was assessed by simulation of PMF identification with 10,000 artificial spectra resembling a data situation close to the measured dataset. By means of this simulation the meta score is linked to expectation values as a statistical measure. The presented software is part of the proteome database ProteinScape which links the information derived from MS data to other relevant proteomics data. We demonstrate the performance of the presented system with MS data from 1891 PMF spectra. As a result of automatic calibration and peak rejection the identification rate increased from 6% to 44%.