Stowers Institute for Medical Research
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Multidimensional Protein Identification Technology
Multidimensional Protein Identification Technology (MudPIT)

MudPIT is a chromatography-based proteomic technique where a complex peptide mixture is prepared from a protein sample and loaded directly onto a triphasic microcapillary column packed with reversed phase, strong cation exchange, and reversed phase HPLC grade materials. The column is placed directly in-line with a tandem mass spectrometer.

Quantitative Proteomics
Label free quantitation by normalized spectral counts

We have developed the normalized spectral abundance factor (NSAF) approach for label free quantitative proteomics (Zybailov et al., 2006). We have shown that using GeneChip tools such as PLGEM enhanced our ability to statistically determine protein expression chnages in NSAF datasets (Pavelka et al., 2008). We have recently improved upon NSAF with dNSAF in which peptides/spectral counts that are shared between proteins are distributed based on the number of unique peptides/spectral counts detected for each protein (Zhang et al., 2010).

Dynamic Offline Lock Mass (Zhang et al., 2011)

We continually seek to improve upon instrument settings and data processing methods to optimize spectral counting based quantitative proteomics. We have developed a mathematical way to determine the optimal dynamic exclusion duration for LC-MS/MS platforms (Zhang et al., 2009). To calibrate high accuracy datasets, we have recently developed the Dynamic Offline Lock Mass (DOLM) method and its accompanying software, RAWDistiller (Zhang et al , 2011).

Equipment used for MudPIT

  • LTQ Linear Ion Trap Mass Spectrometers
  • LTQ-Orbitrap Hybrid Mass Spectrometers
  • Velos-Orbitrap Hybrid Mass Spectrometers
  • All mass spectrometers equipped with nanospray ionization sources interfaced with Agilent 1100 Quartenary HPLC pumps or Eksigent NanoLC 2D systems
  • 252-node Linux cluster

  • We utilize multidimensional protein identification technology (MudPIT) and quantitative proteomics approaches as the foundation for our research and collaborations. We also actively carry out research to improve aspects of these general approaches, with a particular focus on improving quantitative proteomics methods.