Literature DB >> 11881835

The use of accurate mass tags for high-throughput microbial proteomics.

Richard D Smith1, Gordon A Anderson, Mary S Lipton, Christophe Masselon, Ljiljana Pasa-Tolic, Yufeng Shen, Harold R Udseth.   

Abstract

We describe and review progress towards a global strategy that aims to extend the sensitivity, dynamic range, comprehensiveness, and throughput of proteomic measurements for microbial systems based upon the use of polypeptide accurate mass tags (AMTs) produced by global protein enzymatic digestions. The two-stage strategy exploits high accuracy mass measurements using Fourier transform ion cyclotron resonance mass spectrometry (FTICR) to validate polypeptide AMTs for a specific organism, from potential mass tags tentatively identified using tandem mass spectrometry (MS/MS), providing the basis for subsequent measurements without the need for routine MS/MS. A high-resolution capillary liquid chromatography separation combined with high sensitivity, and high-resolution accurate FTICR measurements is shown to be capable of characterizing polypeptide mixtures of more than 10(5) components, sufficient for broad protein identification using AMTs. Advantages of the approach include the high confidence of protein identification, its broad proteome coverage, and the capability for stable-isotope labeling methods for precise relative protein abundance measurements. The strategy has been initially evaluated using the microorganisms Saccharomyces cerevisiae and Deinococcus radiodurans. Additional developments, including the use of multiplexed-MS/MS capabilities and methods for dynamic range expansion of proteome measurements that promise to further extend the quality of proteomics measurements, are also described.

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Year:  2002        PMID: 11881835     DOI: 10.1089/15362310252780843

Source DB:  PubMed          Journal:  OMICS        ISSN: 1536-2310


  15 in total

1.  Proteome analyses using accurate mass and elution time peptide tags with capillary LC time-of-flight mass spectrometry.

Authors:  Eric F Strittmatter; P Lee Ferguson; Keqi Tang; Richard D Smith
Journal:  J Am Soc Mass Spectrom       Date:  2003-09       Impact factor: 3.109

2.  Microbial genomics--challenges and opportunities: the 9th International Conference on Microbial Genomes.

Authors:  Jizhong Zhou; Jeffrey H Miller
Journal:  J Bacteriol       Date:  2002-08       Impact factor: 3.490

3.  DeMix-Q: Quantification-Centered Data Processing Workflow.

Authors:  Bo Zhang; Lukas Käll; Roman A Zubarev
Journal:  Mol Cell Proteomics       Date:  2016-01-04       Impact factor: 5.911

4.  Discovery of novel secreted virulence factors from Salmonella enterica serovar Typhimurium by proteomic analysis of culture supernatants.

Authors:  George S Niemann; Roslyn N Brown; Jean K Gustin; Afke Stufkens; Afshan S Shaikh-Kidwai; Jie Li; Jason E McDermott; Heather M Brewer; Athena Schepmoes; Richard D Smith; Joshua N Adkins; Fred Heffron
Journal:  Infect Immun       Date:  2010-10-25       Impact factor: 3.441

5.  Dinitrogenase-Driven Photobiological Hydrogen Production Combats Oxidative Stress in Cyanothece sp. Strain ATCC 51142.

Authors:  Natalie C Sadler; Hans C Bernstein; Matthew R Melnicki; Moiz A Charania; Eric A Hill; Lindsey N Anderson; Matthew E Monroe; Richard D Smith; Alexander S Beliaev; Aaron T Wright
Journal:  Appl Environ Microbiol       Date:  2016-11-21       Impact factor: 4.792

6.  Correlation of mRNA expression and protein abundance affected by multiple sequence features related to translational efficiency in Desulfovibrio vulgaris: a quantitative analysis.

Authors:  Lei Nie; Gang Wu; Weiwen Zhang
Journal:  Genetics       Date:  2006-10-08       Impact factor: 4.562

7.  Label-free quantitative LC-MS proteomics of Alzheimer's disease and normally aged human brains.

Authors:  Victor P Andreev; Vladislav A Petyuk; Heather M Brewer; Yuliya V Karpievitch; Fang Xie; Jennifer Clarke; David Camp; Richard D Smith; Andrew P Lieberman; Roger L Albin; Zafar Nawaz; Jimmy El Hokayem; Amanda J Myers
Journal:  J Proteome Res       Date:  2012-05-17       Impact factor: 4.466

8.  Integrative analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: a non-linear model to predict abundance of undetected proteins.

Authors:  Wandaliz Torres-García; Weiwen Zhang; George C Runger; Roger H Johnson; Deirdre R Meldrum
Journal:  Bioinformatics       Date:  2009-05-15       Impact factor: 6.937

9.  Initial proteome analysis of model microorganism Haemophilus influenzae strain Rd KW20.

Authors:  Eugene Kolker; Samuel Purvine; Michael Y Galperin; Serg Stolyar; David R Goodlett; Alexey I Nesvizhskii; Andrew Keller; Tao Xie; Jimmy K Eng; Eugene Yi; Leroy Hood; Alex F Picone; Tim Cherny; Brian C Tjaden; Andrew F Siegel; Thomas J Reilly; Kira S Makarova; Bernhard O Palsson; Arnold L Smith
Journal:  J Bacteriol       Date:  2003-08       Impact factor: 3.490

10.  Identification of proteins in human cytomegalovirus (HCMV) particles: the HCMV proteome.

Authors:  Susan M Varnum; Daniel N Streblow; Matthew E Monroe; Patricia Smith; Kenneth J Auberry; Ljiljana Pasa-Tolic; Dai Wang; David G Camp; Karin Rodland; Steven Wiley; William Britt; Thomas Shenk; Richard D Smith; Jay A Nelson
Journal:  J Virol       Date:  2004-10       Impact factor: 5.103

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