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==The Dynamic Proteome==
==The Dynamic Proteome==
Revision as of 11:54, 16 September 2010
The Dynamic Proteome
Our aim is to create predictive models of cellular behavior, with a focus on modeling signal transduction dynamics.
Protein Protein Interactions
Proteins interact with one another in order to transmit signals from the outside environment (signal transduction), form complex molecular machines, and localize to specific areas of the cell. Current methods for capturing protein-protein interactions (PPIs) lack context and/or specificity. We are developing methods that will enable us to capture in vivo PPIs involving new biochemical, mass spectrometric, and computational techniques. See PPIX project page.
Protein post-translational modifications
Proteins undergo state changes through post-translational modifications. These chemical modifications determine the activity of enzymes, the localization of proteins, and a protein's interactions. We are working to perfect high-throughput methodologies to measure global protein phosphorylation.
In collaboration with Emily Bates' lab, we are currently analyzing differential phosphorylation in brain tissue between migraine susceptible and wild-type mice. See Mouse Brain Phosphoproteomics project page.
Lipidomics & Proteomics of Asthma
Building on the work and expertise of the BYU Lipidomics Team, we are collaborating with Srirama Rao to apply lipidomic and proteomic techniques to mouse asthma models and human lung tissue. Specifically, we are asking:
- Can particular phosphatidyl ethanolamine/choline species be used as biomarkers?
- Can proteomics be coupled with lipidomics to provide mechanistic explanations for different lung phenotypes?
- Can these analyses be used on human biopsies to differentiate and ultimately explain asthma from different origins?
The integration of measures of protein state to create meaningful models of cellular behavior is an ongoing challenge in Systems Biology.
We are beginning work with Sean Warnick's lab to apply mathematical models to proteomic measurements in order to deduce protein network structure. To accomplish this, prescribed sets of time series data and over/under expression of particular nodes are used with Dynamical Structure Analysis (DSA), yielding a useful model of protein interactions and dynamics. We are addressing how to:
- Scale DSA to larger numbers of measurements.
- Deal with noise and signal loss in measures of concentration.
- Supplement network reconstruction with direct measures of protein interactions.
Open, Dynamic Tools for Proteomics
Mass spectrometry based proteomic data sets are challenging to analyze due to their enormous size, complexity, and changing specifications. The development of fast, easily modifiable software enables researchers to glean much additional information from existing data sets and rapidly test new analytical approaches.
We are the main support behind the mspire libraries, enabling programmatic access to a variety of mass spectrometry data. In particular, we offer:
- a unified interface offering random scan access to all versions of mzXML and the new mzML standard format.
- the only existing free & open source converter for Bioworks .srf files
- a universal converter from pepXML to the new mzIdentML standard
We are also currently working on:
- A dynamic, massively parallel data analysis pipeline, KatamariDotei ("clod identification").
- Integrates 4 different search engines (3 of which are free and/or open source).
- Utilizes Percolator's Support Vector Machines to distinguish genuine from spurious hits.
- Fast visualization of mass spectrometry data sets.