Literature DB >> 36178620

Identification of Plant Protein-Metabolite Interactions by Limited Proteolysis-Coupled Mass Spectrometry (LiP-MS).

Jhon Venegas-Molina1,2, Petra Van Damme3, Alain Goossens4,5.   

Abstract

The interactions between metabolites and proteins constitute crucial events in cell signaling and metabolism. In recent years, large-scale proteomics techniques have emerged to identify and characterize protein-metabolite interactions. However, their implementation in plants is generally lagging behind, preventing a complete understanding of the regulatory mechanisms governing plant physiology. Recently, a novel approach to identify metabolite-binding proteins, namely, limited proteolysis-coupled mass spectrometry (LiP-MS), was developed originally for microbial proteomes. Here, we present an adapted and accessible version of the LiP-MS protocol for use in plants. Plant proteomes are extracted and incubated with the metabolite of interest or control treatment, followed by a limited digestion by a nonspecific/promiscuous protease. Subsequently, a conventional shotgun proteomics sample preparation is performed including a complete digestion with the sequence-specific protease trypsin. Finally, label-free proteomics analysis is applied to identify structure-dependent proteolytic patterns corresponding to protein targets of the specific metabolite and their binding sites. Given its amenability to relatively high throughput, the LiP-MS approach may open a potent avenue for the discovery of novel regulatory mechanisms in plant species.
© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Chemoproteomics; Interactomics; Metabolites; Metabolite–protein interactions; Plant proteomics; Protein binding

Mesh:

Substances:

Year:  2023        PMID: 36178620     DOI: 10.1007/978-1-0716-2624-5_5

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  16 in total

1.  MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

Authors:  Jürgen Cox; Matthias Mann
Journal:  Nat Biotechnol       Date:  2008-11-30       Impact factor: 54.908

2.  The MaxQuant computational platform for mass spectrometry-based shotgun proteomics.

Authors:  Stefka Tyanova; Tikira Temu; Juergen Cox
Journal:  Nat Protoc       Date:  2016-10-27       Impact factor: 13.491

3.  OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data.

Authors:  Hannes L Röst; George Rosenberger; Pedro Navarro; Ludovic Gillet; Saša M Miladinović; Olga T Schubert; Witold Wolski; Ben C Collins; Johan Malmström; Lars Malmström; Ruedi Aebersold
Journal:  Nat Biotechnol       Date:  2014-03       Impact factor: 54.908

4.  The Perseus computational platform for comprehensive analysis of (prote)omics data.

Authors:  Stefka Tyanova; Tikira Temu; Pavel Sinitcyn; Arthur Carlson; Marco Y Hein; Tamar Geiger; Matthias Mann; Jürgen Cox
Journal:  Nat Methods       Date:  2016-06-27       Impact factor: 28.547

5.  Measuring protein structural changes on a proteome-wide scale using limited proteolysis-coupled mass spectrometry.

Authors:  Simone Schopper; Abdullah Kahraman; Pascal Leuenberger; Yuehan Feng; Ilaria Piazza; Oliver Müller; Paul J Boersema; Paola Picotti
Journal:  Nat Protoc       Date:  2017-10-26       Impact factor: 13.491

6.  Proteome-wide identification of ubiquitin interactions using UbIA-MS.

Authors:  Xiaofei Zhang; Arne H Smits; Gabrielle Ba van Tilburg; Huib Ovaa; Wolfgang Huber; Michiel Vermeulen
Journal:  Nat Protoc       Date:  2018-02-15       Impact factor: 13.491

7.  A Map of Protein-Metabolite Interactions Reveals Principles of Chemical Communication.

Authors:  Ilaria Piazza; Karl Kochanowski; Valentina Cappelletti; Tobias Fuhrer; Elad Noor; Uwe Sauer; Paola Picotti
Journal:  Cell       Date:  2018-01-04       Impact factor: 41.582

8.  Detecting Protein-Small Molecule Interactions Using Limited Proteolysis-Mass Spectrometry (LiP-MS).

Authors:  Monika Pepelnjak; Natalie de Souza; Paola Picotti
Journal:  Trends Biochem Sci       Date:  2020-05-29       Impact factor: 13.807

9.  Dynamic 3D proteomes reveal protein functional alterations at high resolution in situ.

Authors:  Valentina Cappelletti; Thomas Hauser; Ilaria Piazza; Monika Pepelnjak; Liliana Malinovska; Tobias Fuhrer; Yaozong Li; Christian Dörig; Paul Boersema; Ludovic Gillet; Jan Grossbach; Aurelien Dugourd; Julio Saez-Rodriguez; Andreas Beyer; Nicola Zamboni; Amedeo Caflisch; Natalie de Souza; Paola Picotti
Journal:  Cell       Date:  2020-12-23       Impact factor: 41.582

Review 10.  Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial.

Authors:  Christina Ludwig; Ludovic Gillet; George Rosenberger; Sabine Amon; Ben C Collins; Ruedi Aebersold
Journal:  Mol Syst Biol       Date:  2018-08-13       Impact factor: 11.429

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