scholarly journals High Mass Accuracy Phosphopeptide Identification Using Tandem Mass Spectra

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Rovshan G. Sadygov

Phosphoproteomics is a powerful analytical platform for identification and quantification of phosphorylated peptides and assignment of phosphorylation sites. Bioinformatics tools to identify phosphorylated peptides from their tandem mass spectra and protein sequence databases are important part of phosphoproteomics. In this work, we discuss general informatics aspects of mass-spectrometry-based phosphoproteomics. Some of the specifics of phosphopeptide identifications stem from the labile nature of phosphor groups and expanded peptide search space. Allowing for modifications of Ser, Thr, and Tyr residues exponentially increases effective database size. High mass resolution and accuracy measurements of precursor mass-to-charge ratios help to restrict the search space of candidate peptide sequences. The higher-order fragmentations of neutral loss ions enhance the fragment ion mass spectra of phosphorylated peptides. We show an example of a phosphopeptide identification where accounting for fragmentation from neutral loss species improves the identification scores in a database search algorithm by 50%.

PROTEOMICS ◽  
2009 ◽  
Vol 9 (7) ◽  
pp. 1763-1770 ◽  
Author(s):  
Hua Xu ◽  
Liwen Wang ◽  
Larry Sallans ◽  
Michael A. Freitas

2019 ◽  
Vol 35 (17) ◽  
pp. 3196-3198 ◽  
Author(s):  
Tobias Depke ◽  
Raimo Franke ◽  
Mark Brönstrup

Abstract Summary Compound identification is one of the most eminent challenges in the untargeted analysis of complex mixtures of small molecules by mass spectrometry. Similarity of tandem mass spectra can provide valuable information on putative structural similarities between known and unknown analytes and hence aids feature identification in the bioanalytical sciences. We have developed CluMSID (Clustering of MS2 spectra for metabolite identification), an R package that enables researchers to make use of tandem mass spectra and neutral loss pattern similarities as a part of their metabolite annotation workflow. CluMSID offers functions for all analysis steps from import of raw data to data mining by unsupervised multivariate methods along with respective (interactive) visualizations. A detailed tutorial with example data is provided as supplementary information. Availability and implementation CluMSID is available as R package from https://github.com/tdepke/CluMSID/and from https://bioconductor.org/packages/CluMSID/. Supplementary information Supplementary data are available at Bioinformatics online.


1999 ◽  
Vol 190-191 ◽  
pp. 281-293 ◽  
Author(s):  
Keiji G Asano ◽  
Douglas E Goeringer ◽  
David J Butcher ◽  
Scott A McLuckey

2017 ◽  
Vol 17 (1) ◽  
pp. 290-295 ◽  
Author(s):  
Sebastian Dorl ◽  
Stephan Winkler ◽  
Karl Mechtler ◽  
Viktoria Dorfer

Author(s):  
Xiaoyu Yang ◽  
Pedatsur Neta ◽  
Yuri A. Mirokhin ◽  
Dmitrii V. Tchekhovskoi ◽  
Concepcion A. Remoroza ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document