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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Schopper S, Kahraman A, Leuenberger P, Feng Y, Piazza I, Müller O, Boersema PJ, Picotti P (2017) Measuring protein structural changes on a proteome-wide scale using limited proteolysis-coupled mass spectrometry. Nat Protoc 12(11):2391–2410. https://doi.org/10.1038/nprot.2017.100
Pepelnjak M, de Souza N, Picotti P (2020) Detecting protein-small molecule interactions using limited proteolysis-mass spectrometry (LiP-MS). Trends Biochem Sci 45(10):919–920. https://doi.org/10.1016/j.tibs.2020.05.006
Piazza I, Kochanowski K, Cappelletti V, Fuhrer T, Noor E, Sauer U, Picotti P (2018) A map of protein-metabolite interactions reveals principles of chemical communication. Cell 172(1–2):358–372. https://doi.org/10.1016/j.cell.2017.12.006
Cappelletti V, Hauser T, Piazza I, Pepelnjak M, Malinovska L, Fuhrer T, Li Y, Dörig C, Boersema P, Gillet L, Grossbach J, Dugourd A, Saez-Rodriguez J, Beyer A, Zamboni N, Caflisch A, de Souza N, Picotti P (2021) Dynamic 3D proteomes reveal protein functional alterations at high resolution in situ. Cell 184(2):545–559. https://doi.org/10.1016/j.cell.2020.12.021
Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26(12):1367–1372. https://doi.org/10.1038/nbt.1511
Tyanova S, Temu T, Cox J (2016) The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat Protoc 11(12):2301–2319. https://doi.org/10.1038/nprot.2016.136
Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein MY, Geiger T, Mann M, Cox J (2016) The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods 13(9):731–740. https://doi.org/10.1038/nmeth.3901
Ludwig C, Gillet L, Rosenberger G, Amon S, Collins BC, Aebersold R (2018) Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol Syst Biol 14(8):e8126. https://doi.org/10.15252/msb.20178126
Röst HL, Rosenberger G, Navarro P, Gillet L, Miladinović SM, Schubert OT, Wolski W, Collins BC, Malmström J, Malmström L, Aebersold R (2014) OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat Biotechnol 32(3):219–223. https://doi.org/10.1038/nbt.2841
Zhang X, Smits AH, van Tilburg GBA, Ovaa H, Huber W, Vermeulen M (2018) Proteome-wide identification of ubiquitin interactions using UbIA-MS. Nat Protoc 13(3):530–550. https://doi.org/10.1038/nprot.2017.147
Batut B, Hiltemann S, Bagnacani A, Baker D, Bhardwaj V, Blank C, Bretaudeau A, Brillet-Guéguen L, Čech M, Chilton J, Clements D, Doppelt-Azeroual O, Erxleben A, Freeberg MA, Gladman S, Hoogstrate Y, Hotz H-R, Houwaart T, Jagtap P, Larivière D, Le Corguillé G, Manke T, Mareuil F, Ramirez F, Ryan D, Sigloch FC, Soranzo N, Wolff J, Videm P, Wolfien M, Wubuli A, Yusuf D, Galaxy Training Network, Taylor J, Backofen R, Nekrutenko A, Grüning B (2018) Community-driven data analysis training for biology. Cell Syst 6(6):752–758. https://doi.org/10.1016/j.cels.2018.05.012
Willems P, Fels U, Staes A, Gevaert K, Van Damme P (2021) Use of hybrid data-dependent and-independent acquisition spectral libraries empowers dual-proteome profiling. J Proteome Res 20(2):1165–1177. https://doi.org/10.1021/acs.jproteome.0c00350
Tyanova S, Mann M, Cox J (2014) MaxQuant for in-depth analysis of large SILAC datasets. Methods Mol Biol 1188:351–364. https://doi.org/10.1007/978-1-4939-1142-4_24
Sinitcyn P, Hamzeiy H, Salinas Soto F, Itzhak D, McCarthy F, Wichmann C, Steger M, Ohmayer U, Distler U, Kaspar-Schoenefeld S, Prianichnikov N, Yilmaz S, Rudolph JD, Tenzer S, Perez-Riverol Y, Nagaraj N, Humphrey SJ, Cox J (2021) MaxDIA enables library-based and library-free data-independent acquisition proteomics. Nat Biotechnol. in press. https://doi.org/10.1038/s41587-021-00968-7
Fahrner M, Föll M (2021) Library generation for DIA analysis (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/proteomics/tutorials/DIA_lib_OSW/tutorial.html. Online. Accessed 29 July 2021
Fahrner M, Föll M (2021) DIA analysis using OpenSwathWorkflow (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/proteomics/tutorials/DIA_Analysis_OSW/tutorial.html. Online. Accessed 29 July 2021
Jonckheere V, Fijalkowska D, Van Damme P (2018) Omics assisted N-terminal proteoform and protein expression profiling on methionine aminopeptidase 1 (MetAP1) deletion. Mol Cell Proteomics 17(4):694–708. https://doi.org/10.1074/mcp.RA117.000360
Chiva C, Olivella R, Borràs E, Espadas G, Pastor O, Solé A, Sabido E (2018) QCloud: a cloud-based quality control system for mass spectrometry-based proteomics laboratories. PLoS One 13(1):e0189209. https://doi.org/10.1371/journal.pone.0189209
Mellacheruvu D, Wright Z, Couzens AL, Lambert J-P, St-Denis NA, Li T, Miteva YV, Hauri S, Sardiu ME, Low TY, Halim VA, Bagshaw RD, Hubner NC, al-Hakim A, Bouchard A, Faubert D, Fermin D, Dunham WH, Goudreault M, Lin Z-Y, Badillo BG, Pawson T, Durocher D, Coulombe B, Aebersold R, Superti-Furga G, Colinge J, Heck AJR, Choi H, Gstaiger M, Mohammed S, Cristea IM, Bennett KL, Washburn MP, Raught B, Ewing RM, Gingras AC, Nesvizhskii AI (2013) The CRAPome: a contaminant repository for affinity purification-mass spectrometry data. Nat Methods 10(8):730–736. https://doi.org/10.1038/nmeth.2557
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Venegas-Molina, J., Van Damme, P., Goossens, A. (2023). Identification of Plant Protein–Metabolite Interactions by Limited Proteolysis-Coupled Mass Spectrometry (LiP-MS). In: Skirycz, A., Luzarowski, M., Ewald, J.C. (eds) Cell-Wide Identification of Metabolite-Protein Interactions. Methods in Molecular Biology, vol 2554. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2624-5_5
Download citation
DOI: https://doi.org/10.1007/978-1-0716-2624-5_5
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-2623-8
Online ISBN: 978-1-0716-2624-5
eBook Packages: Springer Protocols