Abstract

This grant proposal addresses the following important problems in computational proteomics: 1. Development of new filters for MS/MS database searches that will dramatically reduce the running time for protein identification and post-translationally modified proteins in particular. 2. Design of new algorithms for matching MS/MS spectra against the alternative splicing databases. 3. Development of algorithms for shotgun protein sequencing by clustering and assembly of overlapping spectra. Application of clustering and assembly of MS/MS spectra to analysis of post - translational modifications. Improving the state of the art in de novo sequencing through analysis of paired MS/MS spectra and generation of reliable sequence tags derived from paired spectra. 4. Development of computational tools for analyzing relative abundance of peptides in protein samples. ? Relevance: Mass spectromtery is a key technology for proteomics, and is increasingly used for research that directly impacts human health. Examples include, but are not limited to, discovery of protein bio-markers that can be used as diagnostics, and small peptides that can be used directly as therapeutics. However, computational analysis of mass spectromtery data remains a significant bottleneck. This proposed research addresses computational challenges in the analysis of mass spectromtery data. ? ? ? ?

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Research Project (R01)
Project #
2R01RR016522-04A1
Application #
7094573
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Sheeley, Douglas
Project Start
2001-12-01
Project End
2009-08-31
Budget Start
2006-09-05
Budget End
2007-08-31
Support Year
4
Fiscal Year
2006
Total Cost
$603,249
Indirect Cost

  Institution

Name
University of California San Diego
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
804355790
City
La Jolla
State
CA
Country
United States
Zip Code
92093

  Publications

Gupta, Nitin; Bark, Steven J; Lu, Weiya D et al. (2010) Mass spectrometry-based neuropeptidomics of secretory vesicles from human adrenal medullary pheochromocytoma reveals novel peptide products of prohormone processing. J Proteome Res 9:5065-75
Gupta, Nitin; Hixson, Kim K; Culley, David E et al. (2010) Analyzing protease specificity and detecting in vivo proteolytic events using tandem mass spectrometry. Proteomics 10:2833-44
Hook, Vivian; Bark, Steven; Gupta, Nitin et al. (2010) Neuropeptidomic components generated by proteomic functions in secretory vesicles for cell-cell communication. AAPS J 12:635-45
Gupta, Nitin; Pevzner, Pavel A (2009) False discovery rates of protein identifications: a strike against the two-peptide rule. J Proteome Res 8:4173-81
Kim, Sangtae; Gupta, Nitin; Bandeira, Nuno et al. (2009) Spectral dictionaries: Integrating de novo peptide sequencing with database search of tandem mass spectra. Mol Cell Proteomics 8:53-69
Bandeira, Nuno; Olsen, Jesper V; Mann, Jesper V et al. (2008) Multi-spectra peptide sequencing and its applications to multistage mass spectrometry. Bioinformatics 24:i416-23
Kim, Sangtae; Gupta, Nitin; Pevzner, Pavel A (2008) Spectral probabilities and generating functions of tandem mass spectra: a strike against decoy databases. J Proteome Res 7:3354-63
Castellana, Natalie E; Payne, Samuel H; Shen, Zhouxin et al. (2008) Discovery and revision of Arabidopsis genes by proteogenomics. Proc Natl Acad Sci U S A 105:21034-8
Gupta, Nitin; Benhamida, Jamal; Bhargava, Vipul et al. (2008) Comparative proteogenomics: combining mass spectrometry and comparative genomics to analyze multiple genomes. Genome Res 18:1133-42
Frank, Ari M; Pesavento, James J; Mizzen, Craig A et al. (2008) Interpreting top-down mass spectra using spectral alignment. Anal Chem 80:2499-505

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