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pmid:27114452

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

Mark Larance · Kathryn J Kirkwood · Michele Tinti · Alejandro Brenes Murillo · Michael A J Ferguson · Angus I Lamond

Molecular & cellular proteomics : MCP · 2016 doi:10.1074/mcp.O115.055467

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Experiment

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Data

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Global Membrane Protein Interactome Analysis using In vivo Crosslinking and Mass Spectrometry-based Protein Correlation Profiling.
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Mark Larance
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Kathryn J Kirkwood
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Michele Tinti
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Alejandro Brenes Murillo
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Michael A J Ferguson
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Angus I Lamond
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Molecular & cellular proteomics : MCP
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2016
abstract
We present a methodology using in vivo crosslinking combined with HPLC-MS for the global analysis of endogenous protein complexes by protein correlation profiling. Formaldehyde crosslinked protein complexes were extracted with high yield using denaturing buffers that maintained complex solubility during chromatographic separation. We show this efficiently detects both integral membrane and membrane-associated protein complexes,in addition to soluble complexes, allowing identification and analysis of complexes not accessible in native extracts. We compare the protein complexes detected by HPLC-MS protein correlation profiling in both native and formaldehyde crosslinked U2OS cell extracts. These proteome-wide data sets of both in vivo crosslinked and native protein complexes from U2OS cells are freely available via a searchable online database (www.peptracker.com/epd). Raw data are also available via ProteomeXchange (identifier PXD003754).
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To create an elution profile for an individual protein group in each of the three biological replicates in each experiment type (either native, or PFA crosslinked), we used the MaxQuant label free quantitation (LFQ) algorithm (24).
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We present a methodology using in vivo crosslinking combined with HPLC-MS for the global analysis of endogenous protein complexes by protein correlation profiling.
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To start the identification of previously unknown protein-protein interactions, the profiles were hierarchically clustered, as previously described (6), to identify highly similar whole SEC profiles (Pearson correlation ∼0.95) between different protein groups (supplemental Table S2).
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In the next step we used a machine learning approach similar to the one described previously (9, 10) to generate a single interaction prediction score, which combined multiple protein peak attributes with information retrieved from protein interaction databases.
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This resulted in the prediction of 475 protein complexes (Fig. 7C), including 63 membrane complexes and many other previously known complexes, but also featuring multiple predictions of novel protein interactions (supplemental Table S9).
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Membrane Protein Interactome Analysis using In vivo Crosslinking and Mass Spectrometry-based Pro
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