Journal article
An algorithmic approach to automated high-throughput identification of disulfide connectivity in proteins using tandem mass spectrometry
Computational systems bioinformatics. Computational Systems Bioinformatics Conference, Vol.6, pp.41-51
01/01/2007
DOI: 10.1142/9781860948732_0009
PMID: 17951811
Abstract
Knowledge of the pattern of disulfide linkages in a protein leads to a better understanding of its tertiary structure and biological function. At the state-of-the-art, liquid chromatography/electrospray ionization-tandem mass spectrometry (LC/ESI-MS/MS) can produce spectra of the peptides in a protein that are putatively joined by a disulfide bond. In this setting, efficient algorithms are required for matching the theoretical mass spaces of all possible bonded peptide fragments to the experimentally derived spectra to determine the number and location of the disulfide bonds. The algorithmic solution must also account for issues associated with interpreting experimental data from mass spectrometry, such as noise, isotopic variation, neutral loss, and charge state uncertainty. In this paper, we propose a algorithmic approach to high-throughput disulfide bond identification using data from mass spectrometry, that addresses all the aforementioned issues in a unified framework. The complexity of the proposed solution is of the order of the input spectra. The efficacy and efficiency of the method was validated using experimental data derived from proteins with with diverse disulfide linkage patterns.
Details
- Title: Subtitle
- An algorithmic approach to automated high-throughput identification of disulfide connectivity in proteins using tandem mass spectrometry
- Creators
- Timothy Lee - San Francisco State UniversityRahul SinghTen-Yang YenBruce Macher
- Resource Type
- Journal article
- Publication Details
- Computational systems bioinformatics. Computational Systems Bioinformatics Conference, Vol.6, pp.41-51
- DOI
- 10.1142/9781860948732_0009
- PMID
- 17951811
- ISSN
- 1752-7791
- Language
- English
- Date published
- 01/01/2007
- Academic Unit
- Computer Science
- Record Identifier
- 9984446413102771
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