Literature DB >> 32817346

Analytical Guidelines for co-fractionation Mass Spectrometry Obtained through Global Profiling of Gold Standard Saccharomyces cerevisiae Protein Complexes.

Chi Nam Ignatius Pang1, Sara Ballouz2, Daniel Weissberger3, Loïc M Thibaut4, Joshua J Hamey1, Jesse Gillis5, Marc R Wilkins1, Gene Hart-Smith6.   

Abstract

Co-fractionation MS (CF-MS) is a technique with potential to characterize endogenous and unmanipulated protein complexes on an unprecedented scale. However this potential has been offset by a lack of guidelines for best-practice CF-MS data collection and analysis. To obtain such guidelines, this study thoroughly evaluates novel and published Saccharomyces cerevisiae CF-MS data sets using very high proteome coverage libraries of yeast gold standard complexes. A new method for identifying gold standard complexes in CF-MS data, Reference Complex Profiling, and the Extending 'Guilt-by-Association' by Degree (EGAD) R package are used for these evaluations, which are verified with concurrent analyses of published human data. By evaluating data collection designs, which involve fractionation of cell lysates, it is found that near-maximum recall of complexes can be achieved with fewer samples than published studies. Distributing sample collection across orthogonal fractionation methods, rather than a single high resolution data set, leads to particularly efficient recall. By evaluating 17 different similarity scoring metrics, which are central to CF-MS data analysis, it is found that two metrics rarely used in past CF-MS studies - Spearman and Kendall correlations - and the recently introduced Co-apex metric frequently maximize recall, whereas a popular metric-Euclidean distance-delivers poor recall. The common practice of integrating external genomic data into CF-MS data analysis is also evaluated, revealing that this practice may improve the precision and recall of known complexes but is generally unsuitable for predicting novel complexes in model organisms. If studying nonmodel organisms using orthologous genomic data, it is found that particular subsets of fractionation profiles (e.g. the lowest abundance quartile) should be excluded to minimize false discovery. These assessments are summarized in a series of universally applicable guidelines for precise, sensitive and efficient CF-MS studies of known complexes, and effective predictions of novel complexes for orthogonal experimental validation.
© 2020 Pang et al.

Entities:  

Keywords:  Bioinformatics; Chromatography; Protein complex analysis; Saccharomyces cerevisiae; Yeast*; co-fractionation mass spectrometry; protein complexes; protein correlation profiling; protein-protein interactions

Mesh:

Substances:

Year:  2020        PMID: 32817346      PMCID: PMC7664123          DOI: 10.1074/mcp.RA120.002154

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  50 in total

1.  A consensus of core protein complex compositions for Saccharomyces cerevisiae.

Authors:  Joris J Benschop; Nathalie Brabers; Dik van Leenen; Linda V Bakker; Hanneke W M van Deutekom; Nynke L van Berkum; Eva Apweiler; Philip Lijnzaad; Frank C P Holstege; Patrick Kemmeren
Journal:  Mol Cell       Date:  2010-06-25       Impact factor: 17.970

2.  Profiling the Escherichia coli membrane protein interactome captured in Peptidisc libraries.

Authors:  Michael Luke Carlson; R Greg Stacey; John William Young; Irvinder Singh Wason; Zhiyu Zhao; David G Rattray; Nichollas Scott; Craig H Kerr; Mohan Babu; Leonard J Foster; Franck Duong Van Hoa
Journal:  Elife       Date:  2019-07-31       Impact factor: 8.140

3.  Interaction landscape of membrane-protein complexes in Saccharomyces cerevisiae.

Authors:  Mohan Babu; James Vlasblom; Shuye Pu; Xinghua Guo; Chris Graham; Björn D M Bean; Helen E Burston; Franco J Vizeacoumar; Jamie Snider; Sadhna Phanse; Vincent Fong; Yuen Yi C Tam; Michael Davey; Olha Hnatshak; Navgeet Bajaj; Shamanta Chandran; Thanuja Punna; Constantine Christopolous; Victoria Wong; Analyn Yu; Gouqing Zhong; Joyce Li; Igor Stagljar; Elizabeth Conibear; Shoshana J Wodak; Andrew Emili; Jack F Greenblatt
Journal:  Nature       Date:  2012-09-02       Impact factor: 49.962

4.  Investigating the Network Basis of Negative Genetic Interactions in Saccharomyces cerevisiae with Integrated Biological Networks and Triplet Motif Analysis.

Authors:  Chi Nam Ignatius Pang; Apurv Goel; Marc R Wilkins
Journal:  J Proteome Res       Date:  2018-02-08       Impact factor: 4.466

5.  Detection and characterization of low abundance glycopeptides via higher-energy C-trap dissociation and orbitrap mass analysis.

Authors:  Gene Hart-Smith; Mark J Raftery
Journal:  J Am Soc Mass Spectrom       Date:  2011-11-15       Impact factor: 3.109

6.  A Pan-plant Protein Complex Map Reveals Deep Conservation and Novel Assemblies.

Authors:  Claire D McWhite; Ophelia Papoulas; Kevin Drew; Rachael M Cox; Viviana June; Oliver Xiaoou Dong; Taejoon Kwon; Cuihong Wan; Mari L Salmi; Stanley J Roux; Karen S Browning; Z Jeffrey Chen; Pamela C Ronald; Edward M Marcotte
Journal:  Cell       Date:  2020-03-18       Impact factor: 41.582

7.  BioGRID: a general repository for interaction datasets.

Authors:  Chris Stark; Bobby-Joe Breitkreutz; Teresa Reguly; Lorrie Boucher; Ashton Breitkreutz; Mike Tyers
Journal:  Nucleic Acids Res       Date:  2006-01-01       Impact factor: 16.971

8.  Global Membrane Protein Interactome Analysis using In vivo Crosslinking and Mass Spectrometry-based Protein Correlation Profiling.

Authors:  Mark Larance; Kathryn J Kirkwood; Michele Tinti; Alejandro Brenes Murillo; Michael A J Ferguson; Angus I Lamond
Journal:  Mol Cell Proteomics       Date:  2016-04-25       Impact factor: 5.911

9.  Architecture of the human interactome defines protein communities and disease networks.

Authors:  Edward L Huttlin; Raphael J Bruckner; Joao A Paulo; Joe R Cannon; Lily Ting; Kurt Baltier; Greg Colby; Fana Gebreab; Melanie P Gygi; Hannah Parzen; John Szpyt; Stanley Tam; Gabriela Zarraga; Laura Pontano-Vaites; Sharan Swarup; Anne E White; Devin K Schweppe; Ramin Rad; Brian K Erickson; Robert A Obar; K G Guruharsha; Kejie Li; Spyros Artavanis-Tsakonas; Steven P Gygi; J Wade Harper
Journal:  Nature       Date:  2017-05-17       Impact factor: 49.962

10.  Genomic data integration systematically biases interactome mapping.

Authors:  Michael A Skinnider; R Greg Stacey; Leonard J Foster
Journal:  PLoS Comput Biol       Date:  2018-10-17       Impact factor: 4.475

View more
  1 in total

1.  Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments.

Authors:  Michael A Skinnider; Leonard J Foster
Journal:  Nat Methods       Date:  2021-07-01       Impact factor: 28.547

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.