Literature DB >> 28580222

Prediction of Hopeless Peptides Unlikely to be Selected for Targeted Proteome Analysis.

Fumio Matsuda1,2, Atsumi Tomita1, Hiroshi Shimizu1.   

Abstract

In targeted proteomics using liquid chromatography-tandem triple quadrupole mass spectrometry (LC/MS/MS) in the selected reaction monitoring (SRM) mode, selecting the best observable or visible peptides is a key step in the development of SRM assay methods of target proteins. A direct comparison of signal intensities among all candidate peptides by brute-force LC/MS/MS analysis is a concrete approach for peptide selection. However, the analysis requires an SRM method with hundreds of transitions. This study reports on the development of a method for predicting and identifying hopeless peptides to reduce the number of candidate peptides needed for brute-force experiments. Hopeless peptides are proteotypic peptides that are unlikely to be selected for targets in SRM analysis owing to their poor ionization characteristics. Targeted proteomics data from Escherichia coli demonstrated that the relative ionization efficiency between two peptides could be predicted from sequences of two peptides, when a multivariate regression model is used. Validation of the method showed that >20% of the candidate peptides could be successfully eliminated as hopeless peptides with a false positive rate of less than 2%.

Entities:  

Keywords:  hopeless peptide; in silico prediction; multivariate regression analysis; selected reaction monitoring; targeted proteomics

Year:  2017        PMID: 28580222      PMCID: PMC5451515          DOI: 10.5702/massspectrometry.A0056

Source DB:  PubMed          Journal:  Mass Spectrom (Tokyo)        ISSN: 2186-5116


  27 in total

1.  AAindex: amino acid index database.

Authors:  S Kawashima; M Kanehisa
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

Review 2.  Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions.

Authors:  Paola Picotti; Ruedi Aebersold
Journal:  Nat Methods       Date:  2012-05-30       Impact factor: 28.547

3.  Computational prediction of proteotypic peptides for quantitative proteomics.

Authors:  Parag Mallick; Markus Schirle; Sharon S Chen; Mark R Flory; Hookeun Lee; Daniel Martin; Jeffrey Ranish; Brian Raught; Robert Schmitt; Thilo Werner; Bernhard Kuster; Ruedi Aebersold
Journal:  Nat Biotechnol       Date:  2006-12-31       Impact factor: 54.908

4.  Nano-scale liquid chromatography coupled to tandem mass spectrometry using the multiple reaction monitoring mode based quantitative platform for analyzing multiple enzymes associated with central metabolic pathways of Saccharomyces cerevisiae using ultra fast mass spectrometry.

Authors:  Fumio Matsuda; Tairo Ogura; Atsumi Tomita; Ichiro Hirano; Hiroshi Shimizu
Journal:  J Biosci Bioeng       Date:  2014-07-21       Impact factor: 2.894

5.  A large-scale targeted proteomics assay resource based on an in vitro human proteome.

Authors:  Masaki Matsumoto; Fumiko Matsuzaki; Kiyotaka Oshikawa; Naoki Goshima; Masatoshi Mori; Yoshifumi Kawamura; Koji Ogawa; Eriko Fukuda; Hirokazu Nakatsumi; Tohru Natsume; Kazuhiko Fukui; Katsuhisa Horimoto; Takeshi Nagashima; Ryo Funayama; Keiko Nakayama; Keiichi I Nakayama
Journal:  Nat Methods       Date:  2016-12-26       Impact factor: 28.547

6.  Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics.

Authors:  Paola Picotti; Bernd Bodenmiller; Lukas N Mueller; Bruno Domon; Ruedi Aebersold
Journal:  Cell       Date:  2009-08-06       Impact factor: 41.582

7.  Complete set of ORF clones of Escherichia coli ASKA library (a complete set of E. coli K-12 ORF archive): unique resources for biological research.

Authors:  Masanari Kitagawa; Takeshi Ara; Mohammad Arifuzzaman; Tomoko Ioka-Nakamichi; Eiji Inamoto; Hiromi Toyonaga; Hirotada Mori
Journal:  DNA Res       Date:  2006-01-09       Impact factor: 4.458

Review 8.  Review of software tools for design and analysis of large scale MRM proteomic datasets.

Authors:  Christopher M Colangelo; Lisa Chung; Can Bruce; Kei-Hoi Cheung
Journal:  Methods       Date:  2013-05-21       Impact factor: 3.608

9.  A database of mass spectrometric assays for the yeast proteome.

Authors:  Paola Picotti; Henry Lam; David Campbell; Eric W Deutsch; Hamid Mirzaei; Jeff Ranish; Bruno Domon; Ruedi Aebersold
Journal:  Nat Methods       Date:  2008-11       Impact factor: 28.547

10.  Quantitative targeted absolute proteomic analysis of transporters, receptors and junction proteins for validation of human cerebral microvascular endothelial cell line hCMEC/D3 as a human blood-brain barrier model.

Authors:  Sumio Ohtsuki; Chiemi Ikeda; Yasuo Uchida; Yumi Sakamoto; Florence Miller; Fabienne Glacial; Xavier Decleves; Jean-Michel Scherrmann; Pierre-Olivier Couraud; Yoshiyuki Kubo; Masanori Tachikawa; Tetsuya Terasaki
Journal:  Mol Pharm       Date:  2012-12-11       Impact factor: 4.939

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  3 in total

1.  Increased carvone production in Escherichia coli by balancing limonene conversion enzyme expression via targeted quantification concatamer proteome analysis.

Authors:  Erika Yoshida; Motoki Kojima; Munenori Suzuki; Fumio Matsuda; Kazutaka Shimbo; Akiko Onuki; Yousuke Nishio; Yoshihiro Usuda; Akihiko Kondo; Jun Ishii
Journal:  Sci Rep       Date:  2021-11-11       Impact factor: 4.379

2.  Comparative Targeted Proteomics of the Central Metabolism and Photosystems in SigE Mutant Strains of Synechocystis sp. PCC 6803.

Authors:  Yuma Tokumaru; Kiyoka Uebayashi; Masakazu Toyoshima; Takashi Osanai; Fumio Matsuda; Hiroshi Shimizu
Journal:  Molecules       Date:  2018-05-01       Impact factor: 4.411

3.  Assessment of Protein Content and Phosphorylation Level in Synechocystis sp. PCC 6803 under Various Growth Conditions Using Quantitative Phosphoproteomic Analysis.

Authors:  Masakazu Toyoshima; Yuma Tokumaru; Fumio Matsuda; Hiroshi Shimizu
Journal:  Molecules       Date:  2020-08-06       Impact factor: 4.411

  3 in total

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