Literature DB >> 21548781

Quantitative, high-resolution proteomics for data-driven systems biology.

Jürgen Cox1, Matthias Mann.   

Abstract

Systems biology requires comprehensive data at all molecular levels. Mass spectrometry (MS)-based proteomics has emerged as a powerful and universal method for the global measurement of proteins. In the most widespread format, it uses liquid chromatography (LC) coupled to high-resolution tandem mass spectrometry (MS/MS) to identify and quantify peptides at a large scale. This peptide intensity information is the basic quantitative proteomic data type. It is used to quantify proteins between different proteome states, including the temporal variation of the proteome, to determine the complete primary structure of proteins including posttranslational modifications, to localize proteins to organelles, and to determine protein interactions. Here, we describe the principles of analysis and the areas of biology where proteomics can make unique contributions. The large-scale nature of proteomics data and its high accuracy pose special opportunities as well as challenges in systems biology that have been largely untapped so far.

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Year:  2011        PMID: 21548781     DOI: 10.1146/annurev-biochem-061308-093216

Source DB:  PubMed          Journal:  Annu Rev Biochem        ISSN: 0066-4154            Impact factor:   23.643


  238 in total

1.  The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study.

Authors:  Roberto Romero; Offer Erez; Eli Maymon; Piya Chaemsaithong; Zhonghui Xu; Percy Pacora; Tinnakorn Chaiworapongsa; Bogdan Done; Sonia S Hassan; Adi L Tarca
Journal:  Am J Obstet Gynecol       Date:  2017-03-03       Impact factor: 8.661

2.  Database independent proteomics analysis of the ostrich and human proteome.

Authors:  A F Maarten Altelaar; Danny Navarro; Jos Boekhorst; Bas van Breukelen; Berend Snel; Shabaz Mohammed; Albert J R Heck
Journal:  Proc Natl Acad Sci U S A       Date:  2011-12-22       Impact factor: 11.205

3.  Unbiased selective isolation of protein N-terminal peptides from complex proteome samples using phospho tagging (PTAG) and TiO(2)-based depletion.

Authors:  Geert P M Mommen; Bas van de Waterbeemd; Hugo D Meiring; Gideon Kersten; Albert J R Heck; Ad P J M de Jong
Journal:  Mol Cell Proteomics       Date:  2012-06-22       Impact factor: 5.911

4.  Posttranslational Protein Modifications in Plant Metabolism.

Authors:  Giulia Friso; Klaas J van Wijk
Journal:  Plant Physiol       Date:  2015-09-03       Impact factor: 8.340

Review 5.  Circadian mRNA expression: insights from modeling and transcriptomics.

Authors:  Sarah Lück; Pål O Westermark
Journal:  Cell Mol Life Sci       Date:  2015-10-26       Impact factor: 9.261

6.  QuantFusion: Novel Unified Methodology for Enhanced Coverage and Precision in Quantifying Global Proteomic Changes in Whole Tissues.

Authors:  Harsha P Gunawardena; Jonathon O'Brien; John A Wrobel; Ling Xie; Sherri R Davies; Shunqiang Li; Matthew J Ellis; Bahjat F Qaqish; Xian Chen
Journal:  Mol Cell Proteomics       Date:  2015-11-23       Impact factor: 5.911

7.  MASH Suite Pro: A Comprehensive Software Tool for Top-Down Proteomics.

Authors:  Wenxuan Cai; Huseyin Guner; Zachery R Gregorich; Albert J Chen; Serife Ayaz-Guner; Ying Peng; Santosh G Valeja; Xiaowen Liu; Ying Ge
Journal:  Mol Cell Proteomics       Date:  2015-11-23       Impact factor: 5.911

8.  Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification.

Authors:  Nathan P Manes; Jessica M Mann; Aleksandra Nita-Lazar
Journal:  J Vis Exp       Date:  2015-08-17       Impact factor: 1.355

9.  Comparison of the membrane proteome of virulent Mycobacterium tuberculosis and the attenuated Mycobacterium bovis BCG vaccine strain by label-free quantitative proteomics.

Authors:  Harsha P Gunawardena; Meghan E Feltcher; John A Wrobel; Sheng Gu; Miriam Braunstein; Xian Chen
Journal:  J Proteome Res       Date:  2013-10-28       Impact factor: 4.466

Review 10.  Quantification of histone modifications using ¹⁵N metabolic labeling.

Authors:  Chunchao Zhang; Yifan Liu; Philip C Andrews
Journal:  Methods       Date:  2013-02-27       Impact factor: 3.608

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