Literature DB >> 20077412

Free computational resources for designing selected reaction monitoring transitions.

Jennifer A Cham Mead1, Luca Bianco, Conrad Bessant.   

Abstract

Selected reaction monitoring (SRM) is a technique for quantifying specific proteins using triple quadrupole MS. Proteins are digested into peptides and fed into MS following HPLC separation. The stream of ionized peptides is filtered by m/z ratio so only specific peptide targets enter the collision cell, where they are fragmented into product ions. A specific product ion is then filtered from the cell and its intensity measured. By spiking an isotopically labeled version of each target peptide into a sample, both native and surrogate peptides enter MS, pass the filters and transition into product ions in tandem; thus the quantity of the native peptide may be calculated by examining the relative intensities of the native and surrogate signals. The choice of precursor-to-product ion transitions is critical for SRM, but predicting the best candidates is challenging and time-consuming. To alleviate this problem, software tools for designing and optimizing transitions have recently emerged, predominantly driven by data from public proteomics repositories, such as the Global Proteome Machine and PeptideAtlas. In this review, we provide an overview of the state-of-the-art in automated SRM transition design tools in the public domain, explaining how the systems work and how to use them.

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Year:  2010        PMID: 20077412     DOI: 10.1002/pmic.200900396

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  20 in total

1.  A computational tool to detect and avoid redundancy in selected reaction monitoring.

Authors:  Hannes Röst; Lars Malmström; Ruedi Aebersold
Journal:  Mol Cell Proteomics       Date:  2012-04-24       Impact factor: 5.911

2.  Mass spectrometry-based detection and quantification of plasma glycoproteins using selective reaction monitoring.

Authors:  Yeoun Jin Kim; Zaya Zaidi-Ainouch; Sebastien Gallien; Bruno Domon
Journal:  Nat Protoc       Date:  2012-04-12       Impact factor: 13.491

3.  Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics.

Authors:  Amelia C Peterson; Jason D Russell; Derek J Bailey; Michael S Westphall; Joshua J Coon
Journal:  Mol Cell Proteomics       Date:  2012-08-03       Impact factor: 5.911

Review 4.  Toward sensitive and accurate analysis of antibody biotherapeutics by liquid chromatography coupled with mass spectrometry.

Authors:  Bo An; Ming Zhang; Jun Qu
Journal:  Drug Metab Dispos       Date:  2014-09-02       Impact factor: 3.922

Review 5.  The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics.

Authors:  Lindsay K Pino; Brian C Searle; James G Bollinger; Brook Nunn; Brendan MacLean; Michael J MacCoss
Journal:  Mass Spectrom Rev       Date:  2017-07-09       Impact factor: 10.946

6.  Development of a pharmaceutical hepatotoxicity biomarker panel using a discovery to targeted proteomics approach.

Authors:  Ben C Collins; Christine A Miller; Alexandra Sposny; Phillip Hewitt; Martin Wells; William M Gallagher; Stephen R Pennington
Journal:  Mol Cell Proteomics       Date:  2012-04-23       Impact factor: 5.911

Review 7.  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

8.  Bioinformatics Tools for Mass Spectrometry-Based High-Throughput Quantitative Proteomics Platforms.

Authors:  Alexey V Nefedov; Miroslaw J Gilski; Rovshan G Sadygov
Journal:  Curr Proteomics       Date:  2011-07       Impact factor: 0.837

Review 9.  Application of targeted mass spectrometry in bottom-up proteomics for systems biology research.

Authors:  Nathan P Manes; Aleksandra Nita-Lazar
Journal:  J Proteomics       Date:  2018-02-13       Impact factor: 4.044

Review 10.  An assessment of current bioinformatic solutions for analyzing LC-MS data acquired by selected reaction monitoring technology.

Authors:  Mi-Youn K Brusniak; Caroline S Chu; Ulrike Kusebauch; Mark J Sartain; Julian D Watts; Robert L Moritz
Journal:  Proteomics       Date:  2012-04       Impact factor: 3.984

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