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.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Instrumentation and Systems Development Study Section (ISD)
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Edmonds, Charles G
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University of Washington
Schools of Medicine
United States
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Park, Sung-Gun; Anderson, Gordon A; Bruce, James E (2018) Parallel detection in a single ICR cell: Spectral averaging and improved S/N without increased acquisition time. Int J Mass Spectrom 427:29-34
Mohr, Jared P; Perumalla, Poorna; Chavez, Juan D et al. (2018) Mango: A General Tool for Collision Induced Dissociation-Cleavable Cross-Linked Peptide Identification. Anal Chem 90:6028-6034
Chavez, Juan D; Bruce, James E (2018) Chemical cross-linking with mass spectrometry: a tool for systems structural biology. Curr Opin Chem Biol 48:8-18
Park, Sung-Gun; Anderson, Gordon A; Bruce, James E (2017) Parallel Spectral Acquisition with Orthogonal ICR Cells. J Am Soc Mass Spectrom 28:515-524
Chavez, Juan D; Schweppe, Devin K; Eng, Jimmy K et al. (2016) In Vivo Conformational Dynamics of Hsp90 and Its Interactors. Cell Chem Biol 23:716-26
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Chavez, Juan D; Eng, Jimmy K; Schweppe, Devin K et al. (2016) A General Method for Targeted Quantitative Cross-Linking Mass Spectrometry. PLoS One 11:e0167547
Schweppe, Devin K; Zheng, Chunxiang; Chavez, Juan D et al. (2016) XLinkDB 2.0: integrated, large-scale structural analysis of protein crosslinking data. Bioinformatics 32:2716-8
Park, Sung-Gun; Anderson, Gordon A; Navare, Arti T et al. (2016) Parallel Spectral Acquisition with an Ion Cyclotron Resonance Cell Array. Anal Chem 88:1162-8
Navare, Arti T; Chavez, Juan D; Zheng, Chunxiang et al. (2015) Probing the protein interaction network of Pseudomonas aeruginosa cells by chemical cross-linking mass spectrometry. Structure 23:762-73

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