Complex-centric proteome profiling by SEC-SWATH-MS for the parallel detection of hundreds of protein complexes

[1]  R. Aebersold,et al.  Proteomic and interactomic insights into the molecular basis of cell functional diversity , 2020, Nature Reviews Molecular Cell Biology.

[2]  Gary D Bader,et al.  EPIC: software toolkit for elution profile-based inference of protein complexes , 2019, Nature Methods.

[3]  Ruedi Aebersold,et al.  Parallel accumulation – serial fragmentation combined with data-independent acquisition (diaPASEF): Bottom-up proteomics with near optimal ion usage , 2019, bioRxiv.

[4]  Ruedi Aebersold,et al.  A Global Screen for Assembly State Changes of the Mitotic Proteome by SEC-SWATH-MS , 2019, bioRxiv.

[5]  J. Yates Faculty Opinions recommendation of Global Membrane Protein Interactome Analysis using In vivo Crosslinking and Mass Spectrometry-based Protein Correlation Profiling. , 2019, Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature.

[6]  Ruedi Aebersold,et al.  Descriptor : Generation of a zebra fi sh SWATH-MS spectral library to quantify 10 , 000 proteins , 2019 .

[7]  Ruedi Aebersold,et al.  Complex‐centric proteome profiling by SEC‐SWATH‐MS , 2019, Nature Protocols.

[8]  Andreas Ruepp,et al.  CORUM: the comprehensive resource of mammalian protein complexes—2019 , 2018, Nucleic Acids Res..

[9]  Patrick Breheny,et al.  p-Value Histograms: Inference and Diagnostics , 2018, High-throughput.

[10]  Matthias Mann,et al.  A Novel LC System Embeds Analytes in Pre-formed Gradients for Rapid, Ultra-robust Proteomics* , 2018, Molecular & Cellular Proteomics.

[11]  Thomas Burger,et al.  Gentle Introduction to the Statistical Foundations of False Discovery Rate in Quantitative Proteomics. , 2018, Journal of proteome research.

[12]  Jesper V Olsen,et al.  Performance Evaluation of the Q Exactive HF-X for Shotgun Proteomics. , 2018, Journal of proteome research.

[13]  Oliver M. Bernhardt,et al.  Optimization of Experimental Parameters in Data-Independent Mass Spectrometry Significantly Increases Depth and Reproducibility of Results* , 2017, Molecular & Cellular Proteomics.

[14]  A. Emili,et al.  Protein complexes, big data, machine learning and integrative proteomics: lessons learned over a decade of systematic analysis of protein interaction networks , 2017, Expert review of proteomics.

[15]  Michael J MacCoss,et al.  Statistical control of peptide and protein error rates in large-scale targeted DIA analyses , 2017, Nature Methods.

[16]  R. Greg Stacey,et al.  A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE) , 2017, BMC Bioinformatics.

[17]  Claire D. McWhite,et al.  Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes , 2017, Molecular systems biology.

[18]  Devin K. Schweppe,et al.  Architecture of the human interactome defines protein communities and disease networks , 2017, Nature.

[19]  I. Voutsadakis Proteasome expression and activity in cancer and cancer stem cells , 2017, Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine.

[20]  Nichollas E. Scott,et al.  Interactome disassembly during apoptosis occurs independent of caspase cleavage , 2017, Molecular systems biology.

[21]  Yasset Perez-Riverol,et al.  A multi-center study benchmarks software tools for label-free proteome quantification , 2016, Nature Biotechnology.

[22]  K. Chung,et al.  Precise assembly and regulation of 26S proteasome and correlation between proteasome dysfunction and neurodegenerative diseases , 2016, BMB reports.

[23]  A. Sickmann,et al.  Global profiling of protein complexes: current approaches and their perspective in biomedical research , 2016, Expert review of proteomics.

[24]  Brett Larsen,et al.  Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry , 2016, bioRxiv.

[25]  Adele Bourmaud,et al.  Parallel reaction monitoring using quadrupole‐Orbitrap mass spectrometer: Principle and applications , 2016, Proteomics.

[26]  Lars Malmström,et al.  TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics , 2016, Nature Methods.

[27]  Yigong Shi,et al.  An atomic structure of the human 26S proteasome , 2016, Nature Structural &Molecular Biology.

[28]  L. Trinkle-Mulcahy,et al.  Recent advances in large-scale protein interactome mapping , 2016, F1000Research.

[29]  M. Tinti,et al.  Global Membrane Protein Interactome Analysis using In vivo Crosslinking and Mass Spectrometry-based Protein Correlation Profiling* , 2016, Molecular & Cellular Proteomics.

[30]  Ruedi Aebersold,et al.  SWATH2stats: An R/Bioconductor Package to Process and Convert Quantitative SWATH-MS Proteomics Data for Downstream Analysis Tools , 2016, PloS one.

[31]  W. I. Mohamed,et al.  Cullin–RING ubiquitin E3 ligase regulation by the COP9 signalosome , 2016, Nature.

[32]  Marco Y. Hein,et al.  A Human Interactome in Three Quantitative Dimensions Organized by Stoichiometries and Abundances , 2015, Cell.

[33]  M. Naumann,et al.  Diversity of COP9 signalosome structures and functional consequences , 2015, FEBS letters.

[34]  Greg W. Clark,et al.  Panorama of ancient metazoan macromolecular complexes , 2015, Nature.

[35]  Samuel H Payne,et al.  Peptide-Centric Proteome Analysis: An Alternative Strategy for the Analysis of Tandem Mass Spectrometry Data* , 2015, Molecular & Cellular Proteomics.

[36]  Edward L. Huttlin,et al.  The BioPlex Network: A Systematic Exploration of the Human Interactome , 2015, Cell.

[37]  A. Goldberg,et al.  Blocking Cancer Growth with Less POMP or Proteasomes. , 2015, Molecular cell.

[38]  Nichollas E. Scott,et al.  Development of a computational framework for the analysis of protein correlation profiling and spatial proteomics experiments. , 2015, Journal of proteomics.

[39]  Lars Malmström,et al.  DIANA - algorithmic improvements for analysis of data-independent acquisition MS data , 2015, Bioinform..

[40]  Brendan MacLean,et al.  Building high-quality assay libraries for targeted analysis of SWATH MS data , 2015, Nature Protocols.

[41]  Lorenz Blum,et al.  Improving the Swiss Grid Proteomics Portal: Requirements and new Features based on Experience and Usability Considerations , 2013, IWSG.

[42]  Bridget E. Begg,et al.  A Proteome-Scale Map of the Human Interactome Network , 2014, Cell.

[43]  Davide Heller,et al.  STRING v10: protein–protein interaction networks, integrated over the tree of life , 2014, Nucleic Acids Res..

[44]  E. Wang,et al.  CSN6 drives carcinogenesis by positively regulating Myc stability , 2014, Nature Communications.

[45]  Eric W. Deutsch,et al.  A repository of assays to quantify 10,000 human proteins by SWATH-MS , 2014, Scientific Data.

[46]  U. Hassiepen,et al.  Crystal structure of the human COP9 signalosome , 2014, Nature.

[47]  H. Lähdesmäki,et al.  Quantitative proteomics analysis of signalosome dynamics in primary T cells identifies the surface receptor CD6 as a Lat adaptor–independent TCR signaling hub , 2014, Nature Immunology.

[48]  Ben C. Collins,et al.  A tool for the automated, targeted analysis of data-independent acquisition MS-data: OpenSWATH , 2014 .

[49]  Ludovic C. Gillet,et al.  Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system , 2013, Nature Methods.

[50]  R. Milo What is the total number of protein molecules per cell volume? A call to rethink some published values , 2013, BioEssays : news and reviews in molecular, cellular and developmental biology.

[51]  A. Lamond,et al.  Characterization of Native Protein Complexes and Protein Isoform Variation Using Size-fractionation-based Quantitative Proteomics* , 2013, Molecular & Cellular Proteomics.

[52]  Peter Willett,et al.  What is a tutorial , 2013 .

[53]  K. Jung,et al.  Deregulation of the COP9 signalosome-cullin-RING ubiquitin-ligase pathway: mechanisms and roles in urological cancers. , 2013, The international journal of biochemistry & cell biology.

[54]  R. Aebersold,et al.  Genotype-phenotype relationships in light of a modular protein interaction landscape. , 2013, Molecular bioSystems.

[55]  Eric W. Deutsch,et al.  A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis , 2013, Nature.

[56]  Damian Szklarczyk,et al.  STRING v9.1: protein-protein interaction networks, with increased coverage and integration , 2012, Nucleic Acids Res..

[57]  Johannes Griss,et al.  The Proteomics Identifications (PRIDE) database and associated tools: status in 2013 , 2012, Nucleic Acids Res..

[58]  Andrei L. Turinsky,et al.  A Census of Human Soluble Protein Complexes , 2012, Cell.

[59]  L. Foster,et al.  A high-throughput approach for measuring temporal changes in the interactome , 2012, Nature Methods.

[60]  R. Aebersold,et al.  Selected reaction monitoring–based proteomics: workflows, potential, pitfalls and future directions , 2012, Nature Methods.

[61]  Brian Burke,et al.  A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells , 2012, The Journal of cell biology.

[62]  Ludovic C. Gillet,et al.  Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis* , 2012, Molecular & Cellular Proteomics.

[63]  Julian Mintseris,et al.  A Protein Complex Network of Drosophila melanogaster , 2011, Cell.

[64]  R. Zhao,et al.  Roles of COP9 signalosome in cancer , 2011, Cell cycle.

[65]  R. Aebersold,et al.  mProphet: automated data processing and statistical validation for large-scale SRM experiments , 2011, Nature Methods.

[66]  Yang Song,et al.  Development of FRET Assay into Quantitative and High-throughput Screening Technology Platforms for Protein–Protein Interactions , 2010, Annals of Biomedical Engineering.

[67]  R. Aebersold,et al.  Generating and navigating proteome maps using mass spectrometry , 2010, Nature Reviews Molecular Cell Biology.

[68]  Rutger O. Vogel,et al.  LC‐MS/MS as an alternative for SDS‐PAGE in blue native analysis of protein complexes , 2009, Proteomics.

[69]  R. Aebersold,et al.  Evolution of organelle-associated protein profiling. , 2009, Journal of proteomics.

[70]  R. Aebersold,et al.  An integrated workflow for charting the human interaction proteome: insights into the PP2A system , 2009, Molecular systems biology.

[71]  R. Aebersold,et al.  Selected reaction monitoring for quantitative proteomics: a tutorial , 2008, Molecular systems biology.

[72]  Kenta Okamoto,et al.  Dissecting β‐ring assembly pathway of the mammalian 20S proteasome , 2008, The EMBO journal.

[73]  Ming Dong,et al.  A "tagless" strategy for identification of stable protein complexes genome-wide by multidimensional orthogonal chromatographic separation and iTRAQ reagent tracking. , 2008, Journal of proteome research.

[74]  Qiang Gao,et al.  Toward chromatographic analysis of interacting protein networks. , 2008, Journal of chromatography. A.

[75]  Hans-Werner Mewes,et al.  CORUM: the comprehensive resource of mammalian protein complexes , 2007, Nucleic Acids Res..

[76]  Octave Noubibou Doudieu,et al.  CORUM: the comprehensive resource of mammalian protein complexes , 2007, Nucleic Acids Res..

[77]  A. Emili,et al.  β‐Subunit appendages promote 20S proteasome assembly by overcoming an Ump1‐dependent checkpoint , 2007, The EMBO journal.

[78]  Rod B. Watson,et al.  Mapping the Arabidopsis organelle proteome. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[79]  Xiaohui S. Xie,et al.  A Mammalian Organelle Map by Protein Correlation Profiling , 2006, Cell.

[80]  P. Bork,et al.  Proteome survey reveals modularity of the yeast cell machinery , 2006, Nature.

[81]  Sean R. Collins,et al.  Global landscape of protein complexes in the yeast Saccharomyces cerevisiae , 2006, Nature.

[82]  Tohru Natsume,et al.  A heterodimeric complex that promotes the assembly of mammalian 20S proteasomes , 2005, Nature.

[83]  M. Mann,et al.  Proteomic characterization of the human centrosome by protein correlation profiling , 2003, Nature.

[84]  John D. Storey,et al.  Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[85]  Ruedi Aebersold,et al.  The study of macromolecular complexes by quantitative proteomics , 2003, Nature Genetics.

[86]  M. Mann,et al.  Stable Isotope Labeling by Amino Acids in Cell Culture, SILAC, as a Simple and Accurate Approach to Expression Proteomics* , 2002, Molecular & Cellular Proteomics.

[87]  J. Hopfield,et al.  From molecular to modular cell biology , 1999, Nature.

[88]  L. Pauling,et al.  Sickle cell anemia a molecular disease. , 1949, Science.

[89]  Soon Gang Choi,et al.  Protein Interactomics by Two-Hybrid Methods. , 2018, Methods in molecular biology.

[90]  Ravali Adusumilli,et al.  Data Conversion with ProteoWizard msConvert. , 2017, Methods in molecular biology.

[91]  Moritz Heusel Complex-centric Proteome Profiling by SEC-SWATH Mass Spectrometry , 2017 .

[92]  R. Aebersold,et al.  Crosslinking and Mass Spectrometry: An Integrated Technology to Understand the Structure and Function of Molecular Machines. , 2016, Trends in biochemical sciences.

[93]  Leonard J Foster,et al.  Protein correlation profiling-SILAC to study protein-protein interactions. , 2014, Methods in molecular biology.

[94]  B. Warscheid Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) , 2014, Methods in Molecular Biology.

[95]  Valerica Raicu,et al.  Structure and Function of Molecular Machines , 2008 .