? The effectiveness of shotgun proteomics for samples from cancer samples has been curtailed by two key challenges. First, cancer is often accompanied by deficits in apoptosis, cell division, and DNA repair as well as inflammatory responses; these changes lead to hetergeneity in proteins due to mutation and chemical modification. Peptides that differ from reference sequences in databases are not identified by the standard database search algorithms. Second, the degree of homology among proteins in human sequence databases introduces significant problems when identified peptides are assembled to produce protein identifications; a peptide may be an exact match to dozens of protein sequences, leading to an amplification in the number of proteins reported by researchers. We propose an integrated set of algorithms designed to address these shortcomings. First, we will develop """"""""sequence tagging"""""""" software to infer partial sequences from tandem mass spectra by repurposing research in database search algorithms. Second, we will create algorithms to reconcile partial peptide matches to these spectra in order to identify peptides that vary from reference sequences by mutations and modifications. Third, we will develop a modular framework for assembling these peptide identifications into proteins that will incorporate estimated false positive rates and multiple forms of peptides. The algorithm will apply clustering technologies in the application of parsimony rules to reduce effects of database homology in protein list reporting. These open-source tools will be developed using standard file formats and be supported by code documentation to promote their widespread use. Proteomics can potentially make powerful contributions to clinical diagnosis and research, but the bioinformatics that enable this technology have critical shortcomings that prevent its efficient translation from a research tool to a clinical tool. We propose new systems for improving proteomics' application to clinical samples by improving identification of modified and mutant protein forms and managing sets of related proteins documentation to promote their widespread use. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA126218-02
Application #
7293600
Study Section
Special Emphasis Panel (ZCA1-SRRB-9 (O1))
Program Officer
Rodriguez, Henry
Project Start
2006-09-27
Project End
2011-07-31
Budget Start
2007-09-01
Budget End
2008-07-31
Support Year
2
Fiscal Year
2007
Total Cost
$297,369
Indirect Cost
Name
Vanderbilt University Medical Center
Department
Biochemistry
Type
Schools of Medicine
DUNS #
004413456
City
Nashville
State
TN
Country
United States
Zip Code
37212
Wang, Xiaojing; Zhang, Bing (2014) Integrating genomic, transcriptomic, and interactome data to improve Peptide and protein identification in shotgun proteomics. J Proteome Res 13:2715-23
Wang, Xiaojing; Liu, Qi; Zhang, Bing (2014) Leveraging the complementary nature of RNA-Seq and shotgun proteomics data. Proteomics 14:2676-87
Holman, Jerry D; Dasari, Surendra; Tabb, David L (2013) Informatics of protein and posttranslational modification detection via shotgun proteomics. Methods Mol Biol 1002:167-79
Ma, Ze-Qiang; Polzin, Kenneth O; Dasari, Surendra et al. (2012) QuaMeter: multivendor performance metrics for LC-MS/MS proteomics instrumentation. Anal Chem 84:5845-50
Martinez, Melissa N; Emfinger, Christopher H; Overton, Matthew et al. (2012) Obesity and altered glucose metabolism impact HDL composition in CETP transgenic mice: a role for ovarian hormones. J Lipid Res 53:379-89
Chambers, Matthew C; Maclean, Brendan; Burke, Robert et al. (2012) A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol 30:918-20
Wang, Xiaojing; Slebos, Robbert J C; Wang, Dong et al. (2012) Protein identification using customized protein sequence databases derived from RNA-Seq data. J Proteome Res 11:1009-17
Wilhelm, Mathias; Kirchner, Marc; Steen, Judith A J et al. (2012) mz5: space- and time-efficient storage of mass spectrometry data sets. Mol Cell Proteomics 11:O111.011379
Tabb, David L (2012) Evaluating protein interactions through cross-linking mass spectrometry. Nat Methods 9:879-81
Chen, Yao-Yi; Dasari, Surendra; Ma, Ze-Qiang et al. (2012) Refining comparative proteomics by spectral counting to account for shared peptides and multiple search engines. Anal Bioanal Chem 404:1115-25

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