The study of human diseases at the molecular level has benefitted greatly by rapid advances in technology for genome sequencing. However, major impediments for effective utilization of current and future genomic information relates to the major challenges that are inherent in functional assignment of genes. The field of proteomics has arisen to help address this need, since proteins are the predominant functional outcome of gene expression. Measurement of proteomic content, including protein expression levels, posttranslational modifications and protein interactions can yield critical insight relevant to gene function. The major challenges in proteomics research relate to the complexity of samples and the dynamic range over which measurements must be performed. In general terms, these demands are far greater than encountered in genomics since, protein abundances can vary much more than gene do, proteins have much wider diversity in physical properties than genes do, and proteins have no means for signal amplification, nor analogous Watson-Crick base pairing as genes do. Thus the field of proteomics employs technology largely based on mass spectrometry measurements of peptides since these measurements have shown capabilities for large-scale protein identification and quantitation. However, since each protein can produce on average, 50-100 peptides, measurements of peptide mixtures are far more complex than measurements on proteins. In addition, current data-dependent measurement strategies lead to significant compression of achievable dynamic range, since such MS/MS measurements are only normally feasible on peptides observed with higher abundance. Ideally, every peptide in a proteome-wide digest would be subjected to MS/MS to gain maximal information from proteomics experiments. This project will advance capabilities for large-scale proteomics through the development of a mass spectrometer array capable of MS/MS acquisition an order of magnitude faster than current state-of-the art technology. The MS array technology to be developed under this project will involve ion cyclotron resonance mass spectrometry that will yield higher mass resolving power, higher mass measurement accuracy as well as higher throughput acquisition. As a result, an order of magnitude or more peptides can be identified during a given experiment which will dramatically increase the information content of each proteomics analysis as well as the dynamic range of proteins that can be studied.

Public Health Relevance

Proteins are the functional molecules in all important processes in disease and normal healthy state. This project will develop new technology that will enable improved understanding of human diseases such as cancer, cardiovascular disease, and neurological diseases by allowing identification of at least an order of magnitude more proteins, posttranslational modifications and protein interactions in human cells, serum and other biological fluids.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM097112-02
Application #
8242054
Study Section
Instrumentation and Systems Development Study Section (ISD)
Program Officer
Edmonds, Charles G
Project Start
2011-04-01
Project End
2015-03-31
Budget Start
2012-04-01
Budget End
2013-03-31
Support Year
2
Fiscal Year
2012
Total Cost
$325,325
Indirect Cost
$132,870
Name
University of Washington
Department
Genetics
Type
Schools of Medicine
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
Chavez, Juan D; Weisbrod, Chad R; Zheng, Chunxiang et al. (2013) Protein interactions, post-translational modifications and topologies in human cells. Mol Cell Proteomics 12:1451-67
Weisbrod, Chad R; Chavez, Juan D; Eng, Jimmy K et al. (2013) In Vivo Protein Interaction Network Identified with a Novel Real-Time Cross-Linked Peptide Identification Strategy. J Proteome Res :
Weisbrod, Chad R; Eng, Jimmy K; Hoopmann, Michael R et al. (2012) Accurate peptide fragment mass analysis: multiplexed peptide identification and quantification. J Proteome Res 11:1621-32