The problems of peptide identification and protein identification are of fundamental importance in proteomics. We propose to study a new multi-pronged framework for the de novo peptide sequencing and protein identification under uncertainty problems using tandem mass spectroscopy. The proposed approach will be based on fundamental advances in mathematical modeling via mixed integer optimization, as well as theory and algorithms for optimization under uncertainty. We expect that significant advances will be introduced in theory and algorithmic enhancements. We put forward the following four specific aims:
Specific Aim 1 : Investigate and develop a novel de novo computational approach for the peptide identification based on information of the ion peaks in the peptide spectrum and a mixed-integer optimization modeling and algorithmic framework.
Specific Aim 2 : Investigate novel de novo methods for the identification of peptides in complex protein mixtures which will account for experimental uncertainty in the calculation of the mass/charge ratios of the ion peaks.
Specific Aim 3 : Study and develop a new hybrid in silico method which will combine the de novo approach of Specific Aim 1 with database methods for the peptide identification.
Specific Aim 4 : Investigate and develop a new approach for the protein identification which will combine the advances in Specific Aims 1-3 with database homology based methods. Preliminary studies are reported in Specific Aims 1, 2, and 3 (sections C.1, C.2, D.1, D.2, D.3), and the results, via comparative studies and computational efficiency, demonstrate the potential of the proposed research for high throughput peptide and protein identification.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
5R01LM009338-03
Application #
7626030
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Ye, Jane
Project Start
2007-05-01
Project End
2011-04-30
Budget Start
2009-05-01
Budget End
2010-04-30
Support Year
3
Fiscal Year
2009
Total Cost
$260,347
Indirect Cost
Name
Princeton University
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
002484665
City
Princeton
State
NJ
Country
United States
Zip Code
08544
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