Name
Abstract | ||
|---|---|---|
Motivation: Enzymatic digestion under appropriate reducing conditions followed by mass spectrometry analysis has emerged as the primary method for disulfide bond analysis. The large amount of mass spectral data collected in the mass spectrometry experiment requires effective computational approaches to automate the interpretation process. Although different approaches have been developed for such purpose, they always choose to ignore the frequently observed internal ion fragments and they lack a reasonable quality control strategy and calibrated scoring scheme for the statistical validation and ranking of the reported results. Results: In this research, we present a new computational approach, DISC (DISulfide bond Characterization), for matching an input MS/MS spectrum against the putative disulfide linkage structures hypothetically constructed from the protein database. More specifically, we consider different ion types including a variety of internal ions that frequently observed in mass spectra resulted from disulfide linked peptides, and introduce an effective two-layer scoring scheme to evaluate the significance of the matching between spectrum and structure, based on which we have also developed a useful target-decoy strategy for providing quality control and reporting false discovery rate in the final results. Systematic experiments conducted on both low-complexity and high-complexity datasets demonstrated the efficiency of our proposed method for the identification of disulfide bonds from MS/MS spectra, and showed its potential in characterizing disulfide bonds at the proteome scale instead of just a single protein. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1093/bioinformatics/btx667 | BIOINFORMATICS |
Field | DocType | Volume |
Tandem,Data mining,Crystallography,Computer science,Mass spectrum,Disulfide Linkage | Journal | 33 |
Issue | ISSN | Citations |
23 | 1367-4803 | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Liu | 1 | 28 | 3.86 |
| Weiping Sun | 2 | 37 | 6.66 |
| Baozhen Shan | 3 | 24 | 4.59 |
| Kaizhong Zhang | 4 | 2303 | 514.76 |