Literature DB >> 24125593

Expanding tandem mass spectral libraries of phosphorylated peptides: advances and applications.

Yingwei Hu1, Henry Lam.   

Abstract

The identification of phosphorylated proteins remains a challenge in proteomics, partially due to the difficulty in assigning tandem mass (MS/MS) spectra to their originating peptide sequences with correct phosphosite localization. Because of its advantages in efficiency and sensitivity, spectral library searching is a promising alternative to conventional sequence database searching. Our work aims to construct the largest collision-induced dissociation (CID) MS/MS spectral libraries of phosphorylated peptides in human (Homo sapiens) and four model organisms (Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, and Mus musculus) to date, to facilitate phosphorylated peptide identification by spectral library searching. We employed state-of-the-art search methods to published data and applied two recently published phosphorylation site localization tools (PhosphoRS and PTMProphet) to ascertain the phosphorylation sites. To further increase the coverage of this library, we predicted "semi-empirical" spectra for peptides containing known phosphorylation sites from the corresponding template unphosphorylated peptide spectra. The performance of the spectral libraries built were evaluated and found to be superior to conventional database searching in terms of sensitivity. Updated spectral libraries of phosphorylated peptides are made freely available for use with the spectral search engine SpectraST. The work flow being developed will be used to continuously update the libraries when new data become available.

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Year:  2013        PMID: 24125593     DOI: 10.1021/pr4007443

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  7 in total

1.  Spectral Library Based Analysis of Arginine Phosphorylations in Staphylococcus aureus.

Authors:  Sabryna Junker; Sandra Maaβ; Andreas Otto; Stephan Michalik; Friedrich Morgenroth; Ulf Gerth; Michael Hecker; Dörte Becher
Journal:  Mol Cell Proteomics       Date:  2017-11-28       Impact factor: 5.911

Review 2.  Algorithms and design strategies towards automated glycoproteomics analysis.

Authors:  Han Hu; Kshitij Khatri; Joseph Zaia
Journal:  Mass Spectrom Rev       Date:  2016-01-04       Impact factor: 10.946

Review 3.  Computational phosphoproteomics: from identification to localization.

Authors:  Dave C H Lee; Andrew R Jones; Simon J Hubbard
Journal:  Proteomics       Date:  2015-02-17       Impact factor: 3.984

Review 4.  Cardiovascular proteomics in the era of big data: experimental and computational advances.

Authors:  Maggie P Y Lam; Edward Lau; Dominic C M Ng; Ding Wang; Peipei Ping
Journal:  Clin Proteomics       Date:  2016-12-05       Impact factor: 3.988

5.  Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets.

Authors:  Johannes Griss; Yasset Perez-Riverol; Steve Lewis; David L Tabb; José A Dianes; Noemi Del-Toro; Marc Rurik; Mathias W Walzer; Oliver Kohlbacher; Henning Hermjakob; Rui Wang; Juan Antonio Vizcaíno
Journal:  Nat Methods       Date:  2016-06-27       Impact factor: 28.547

Review 6.  Phosphoproteomics in the Age of Rapid and Deep Proteome Profiling.

Authors:  Nicholas M Riley; Joshua J Coon
Journal:  Anal Chem       Date:  2015-11-19       Impact factor: 6.986

7.  Optimization of TripleTOF spectral simulation and library searching for confident localization of phosphorylation sites.

Authors:  Ayano Takai; Tomoya Tsubosaka; Yasuhiro Hirano; Naoki Hayakawa; Fumitaka Tani; Pekka Haapaniemi; Veronika Suni; Susumu Y Imanishi
Journal:  PLoS One       Date:  2019-12-02       Impact factor: 3.240

  7 in total

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