Literature DB >> 21954881

SILACtor: software to enable dynamic SILAC studies.

Michael R Hoopmann1, Juan D Chavez, James E Bruce.   

Abstract

Stable isotope labeling by amino acids in cell culture (SILAC) is a versatile tool in proteomics that has been used to explore protein turnover on a large scale. However, these studies pose a significant undertaking that can be greatly simplified through the use of computational tools that automate the data analysis. While SILAC technology has enjoyed rapid adoption through the availability of several software tools, algorithms do not exist for the automated analysis of protein turnover data generated using SILAC technology. Presented here is a software tool, SILACtor, designed to trace and compare SILAC-labeled peptides across multiple time points. SILACtor is used to profile protein turnover rates for more than 500 HeLa cell proteins using a SILAC label-chase approach. Additionally, SILACtor contains a method for the automated generation of accurate mass and retention time inclusion lists that target peptides of interest showing fast or slow turnover rates relative to the other peptides observed in the samples. SILACtor enables improved protein turnover studies using SILAC technology and also provides a framework for features extensible to comparative SILAC analyses and targeted methods.

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Year:  2011        PMID: 21954881      PMCID: PMC3255570          DOI: 10.1021/ac2017053

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  32 in total

1.  Probability-based protein identification by searching sequence databases using mass spectrometry data.

Authors:  D N Perkins; D J Pappin; D M Creasy; J S Cottrell
Journal:  Electrophoresis       Date:  1999-12       Impact factor: 3.535

2.  Utility of accurate mass tags for proteome-wide protein identification.

Authors:  T P Conrads; G A Anderson; T D Veenstra; L Pasa-Tolić; R D Smith
Journal:  Anal Chem       Date:  2000-07-15       Impact factor: 6.986

3.  Towards defining the urinary proteome using liquid chromatography-tandem mass spectrometry. I. Profiling an unfractionated tryptic digest.

Authors:  C S Spahr; M T Davis; M D McGinley; J H Robinson; E J Bures; J Beierle; J Mort; P L Courchesne; K Chen; R C Wahl; W Yu; R Luethy; S D Patterson
Journal:  Proteomics       Date:  2001-01       Impact factor: 3.984

4.  Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry.

Authors:  D K Han; J Eng; H Zhou; R Aebersold
Journal:  Nat Biotechnol       Date:  2001-10       Impact factor: 54.908

Review 5.  Mass spectrometric-based approaches in quantitative proteomics.

Authors:  Shao-En Ong; Leonard J Foster; Matthias Mann
Journal:  Methods       Date:  2003-02       Impact factor: 3.608

6.  Dynamics of protein turnover, a missing dimension in proteomics.

Authors:  Julie M Pratt; June Petty; Isabel Riba-Garcia; Duncan H L Robertson; Simon J Gaskell; Stephen G Oliver; Robert J Beynon
Journal:  Mol Cell Proteomics       Date:  2002-08       Impact factor: 5.911

7.  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

8.  Exploring the precursor ion exclusion feature of liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry for improving protein identification in shotgun proteome analysis.

Authors:  Nan Wang; Liang Li
Journal:  Anal Chem       Date:  2008-05-15       Impact factor: 6.986

9.  Large-scale analysis of the yeast proteome by multidimensional protein identification technology.

Authors:  M P Washburn; D Wolters; J R Yates
Journal:  Nat Biotechnol       Date:  2001-03       Impact factor: 54.908

10.  Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.

Authors:  Shao-En Ong; Blagoy Blagoev; Irina Kratchmarova; Dan Bach Kristensen; Hanno Steen; Akhilesh Pandey; Matthias Mann
Journal:  Mol Cell Proteomics       Date:  2002-05       Impact factor: 5.911

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  5 in total

1.  Topograph, a software platform for precursor enrichment corrected global protein turnover measurements.

Authors:  Edward J Hsieh; Nicholas J Shulman; Dao-Fu Dai; Evelyn S Vincow; Pabalu P Karunadharma; Leo Pallanck; Peter S Rabinovitch; Michael J MacCoss
Journal:  Mol Cell Proteomics       Date:  2012-08-03       Impact factor: 5.911

2.  Find pairs: the module for protein quantification of the PeakQuant software suite.

Authors:  Martin Eisenacher; Michael Kohl; Sebastian Wiese; Romano Hebeler; Helmut E Meyer; Bettina Warscheid; Christian Stephan
Journal:  OMICS       Date:  2012-08-21

3.  SILAC peptide ratio calculator: a tool for SILAC quantitation of peptides and post-translational modifications.

Authors:  Xiaoyan Guan; Neha Rastogi; Mark R Parthun; Michael A Freitas
Journal:  J Proteome Res       Date:  2014-01-09       Impact factor: 4.466

Review 4.  Accumulation of "Old Proteins" and the Critical Need for MS-based Protein Turnover Measurements in Aging and Longevity.

Authors:  Nathan Basisty; Anja Holtz; Birgit Schilling
Journal:  Proteomics       Date:  2019-09-10       Impact factor: 3.984

Review 5.  Protein Turnover in Aging and Longevity.

Authors:  Nathan Basisty; Jesse G Meyer; Birgit Schilling
Journal:  Proteomics       Date:  2018-03       Impact factor: 3.984

  5 in total

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