Journal
AICHE JOURNAL
Volume 53, Issue 1, Pages 160-173Publisher
WILEY
DOI: 10.1002/aic.11061
Keywords
mixed-integer linear optimization (MILP); de novo peptide identification; tandem mass spectrometry (MS/MS)
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Funding
- NATIONAL LIBRARY OF MEDICINE [R01LM009338] Funding Source: NIH RePORTER
- NLM NIH HHS [R01 LM009338, R01 LM009338-01] Funding Source: Medline
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A novel methodology for the de novo identification of peptides by mixed-integer optimization and tandem mass spectrometry is presented in this article. The various features of the mathematical model are presented and examples are used to illustrate the key concepts of the proposed approach. Several problems are examined to illustrate the proposed method's ability to address (1) residue-dependent fragmentation properties and (2) the variability of resolution in different mass analyzers. A preprocessing algorithm is used to identify important m/z values in the tandem mass spectrum. Missing peaks, resulting from residue-dependent fragmentation characteristics, are dealt with using a two-stage algorithmic framework. A cross-correlation approach is used to resolve missing amino acid assignments and to identify the most probable peptide by comparing the theoretical spectra of the candidate sequences that were generated from the MILP sequencing stages with the experimental tandem mass spectrum. (c) 2006 American Institute of Chemical Engineers AIChEJ, 53: 160-173, 2007.
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