TCP3: QUALITATIVE & QUANTITATIVE PROTEOMIC ANALYSIS OF LYSINE MODIFICATIONS

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Technology Core Projects 3: Quantitative mass spectrometry of protein modifications and detecting protein modifications in complex mixtures - Akhilesh Pandey, Assistant Professor, Dept. of Biological Chemistry and IGM. The aim is to develop quantitative methods for mapping/detecting sites of acetylation, methylation, ubiquitylation and SUMOylation in proteins. In addition, his methods can be routinely applied to protein mixtures by prefractionation (MUDPIT) methodologies. Dr. Pandey also heads a major effort in protein bioinformatics. This involves a critical databasing effort with the Institute of Bioinformatics in Bangalore, India. 1. To develop stable isotope labeling in cell culture (SILAC) method to obtain quantitative information at the protein and peptide level from multiple samples (up to four different states) in a single experiment. This will allow for simultaneous relative quantitation of i) protein abundance in all 4 states, and, ii) post-translational modifications in all 4 states. 2. To develop mass spectrometric methods for improved detection of acetylated, methylated and ubiquitylated peptides. We will develop precursor ion scanning as well as neutral loss scanning methods to selectively identify peptides carrying an acetyl, methyl or ubiquitin moieties from complex mixtures. 3. To develop software tools for analysis of quantitation and to create a repository of all lysine modifications in yeast and humans. To generate pathways involving lysine modifications in a standardized format and to generate mapping of PTMs on the protein sequences in XML file format to enable better searching of protein databases using mass spectrometry-derive