Literature DB >> 23063720

ComplexQuant: high-throughput computational pipeline for the global quantitative analysis of endogenous soluble protein complexes using high resolution protein HPLC and precision label-free LC/MS/MS.

Cuihong Wan1, Jian Liu, Vincent Fong, Andrew Lugowski, Snejana Stoilova, Dylan Bethune-Waddell, Blake Borgeson, Pierre C Havugimana, Edward M Marcotte, Andrew Emili.   

Abstract

The experimental isolation and characterization of stable multi-protein complexes are essential to understanding the molecular systems biology of a cell. To this end, we have developed a high-throughput proteomic platform for the systematic identification of native protein complexes based on extensive fractionation of soluble protein extracts by multi-bed ion exchange high performance liquid chromatography (IEX-HPLC) combined with exhaustive label-free LC/MS/MS shotgun profiling. To support these studies, we have built a companion data analysis software pipeline, termed ComplexQuant. Proteins present in the hundreds of fractions typically collected per experiment are first identified by exhaustively interrogating MS/MS spectra using multiple database search engines within an integrative probabilistic framework, while accounting for possible post-translation modifications. Protein abundance is then measured across the fractions based on normalized total spectral counts and precursor ion intensities using a dedicated tool, PepQuant. This analysis allows co-complex membership to be inferred based on the similarity of extracted protein co-elution profiles. Each computational step has been optimized for processing large-scale biochemical fractionation datasets, and the reliability of the integrated pipeline has been benchmarked extensively. This article is part of a Special Issue entitled: From protein structures to clinical applications.
Copyright © 2012 Elsevier B.V. All rights reserved.

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Year:  2012        PMID: 23063720     DOI: 10.1016/j.jprot.2012.10.001

Source DB:  PubMed          Journal:  J Proteomics        ISSN: 1874-3919            Impact factor:   4.044


  8 in total

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Authors:  Mamiko Yajima; Gary M Wessel
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Review 2.  Multidimensional proteomics for cell biology.

Authors:  Mark Larance; Angus I Lamond
Journal:  Nat Rev Mol Cell Biol       Date:  2015-04-10       Impact factor: 94.444

Review 3.  Next-generation Interactomics: Considerations for the Use of Co-elution to Measure Protein Interaction Networks.

Authors:  Daniela Salas; R Greg Stacey; Mopelola Akinlaja; Leonard J Foster
Journal:  Mol Cell Proteomics       Date:  2019-12-02       Impact factor: 5.911

4.  SECAT: Quantifying Protein Complex Dynamics across Cell States by Network-Centric Analysis of SEC-SWATH-MS Profiles.

Authors:  George Rosenberger; Moritz Heusel; Isabell Bludau; Ben C Collins; Claudia Martelli; Evan G Williams; Peng Xue; Yansheng Liu; Ruedi Aebersold; Andrea Califano
Journal:  Cell Syst       Date:  2020-12-16       Impact factor: 10.304

5.  Proteome-wide dataset supporting the study of ancient metazoan macromolecular complexes.

Authors:  Sadhna Phanse; Cuihong Wan; Blake Borgeson; Fan Tu; Kevin Drew; Greg Clark; Xuejian Xiong; Olga Kagan; Julian Kwan; Alexandr Bezginov; Kyle Chessman; Swati Pal; Graham Cromar; Ophelia Papoulas; Zuyao Ni; Daniel R Boutz; Snejana Stoilova; Pierre C Havugimana; Xinghua Guo; Ramy H Malty; Mihail Sarov; Jack Greenblatt; Mohan Babu; W Brent Derry; Elisabeth R Tillier; John B Wallingford; John Parkinson; Edward M Marcotte; Andrew Emili
Journal:  Data Brief       Date:  2015-12-12

6.  Open questions - in brief: beyond -omics, missing motor proteins, and getting from molecules to organisms.

Authors:  Stephen J Benkovic; Julie Theriot; Dagmar Ringe
Journal:  BMC Biol       Date:  2013-01-31       Impact factor: 7.431

7.  A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE).

Authors:  R Greg Stacey; Michael A Skinnider; Nichollas E Scott; Leonard J Foster
Journal:  BMC Bioinformatics       Date:  2017-10-23       Impact factor: 3.169

8.  Global Analysis of Membrane-associated Protein Oligomerization Using Protein Correlation Profiling.

Authors:  Zachary McBride; Donglai Chen; Christy Reick; Jun Xie; Daniel B Szymanski
Journal:  Mol Cell Proteomics       Date:  2017-09-08       Impact factor: 5.911

  8 in total

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