Specific Aims:

The specific aims of Q-MOP are to establish workflows and bioinformatics tools that will allow to quantitatively measure proteins. With these developments complete and quantitative data series for specific protein complexes, signaling pathways or protein networks will be recorded. If required, protein abundance will be determined along with spatio-temporal information, i.e. in a specific subset of cells of a tissue, organ or organism. This allows one to address for instance how the positional information of cells affects their ability to respond to developmental or environmental cues.

To determine protein abundance levels a minimal set of proteotypic peptides (PTPs) that collectively and unambiguously identify this protein is used. For a significant fraction of the proteins of an organism PTPs are experimentally identified during the first phase of proteome analysis. For the proteins that remain unidentified during this initial phase Q-MOP is developing, implementing and validating computer-based prediction tools that complete the experimental set of PTPs. A complete PTP list that is based on a significantly improved prediction tool is envisaged to be released soon for the two model organisms Drosophila melanogaster and Caenorhabditis elegans. In a last step, reproducible and robust assays are generated for every PTP and deposited in an open-source atlas.

Projects:

The Arabidopsis thaliana Proteome

• Prof. Ueli Grossniklaus (Institute of Plant Biology, UZH)
• Prof. Wilhelm Gruissem (Institute of Plant Sciences, ETH)
  
• Participants
  
  

The Caenorhabditis elegans Proteome

• Prof. Michael Hengartner (Institute of Molecular Life Sciences, UZH)
  
• Participants
  
  

The Drosophila melanogaster Proteome

• Prof. Konrad Basler (Institute of Molecular Life Sciences, UZH)
  
• Dr. Erich Brunner (Institute of Molecular Life Sciences, UZH)
  
• Participants
  
  

Technology development in high throughput data processing, analysis and mining workflows (Q-MOP/FGCZ)

• Dr. Christian Ahrens (Q-MOP) - Data analysis and mining.
  Development of analysis-driven experimentation (ADE), and a deterministic peptide classification and protein inference solution.
  
• Dr. Ermir Qeli (Q-MOP) - Data analysis, mining and visualization.
  Development of improved algorithms for prediction of proteotypic peptides.
  
  
• Prof. Ralph Schlapbach (FGCZ)
  
• Dr. Christian Panse (FGCZ) - Data processing & management.