Unfolding and identification of membrane proteins in situ
Abstract
Single-molecule force spectroscopy (SMFS) uses the cantilever tip of an AFM to apply a force able to unfold a single protein. The obtained force-distance curve encodes the unfolding pathway, and from its analysis it is possible to characterize the folded domains. SMFS has been mostly used to study the unfolding of purified proteins, in solution or reconstituted in a lipid bilayer. Here, we describe a pipeline for analyzing membrane proteins based on SMFS, that involves the isolation of the plasma membrane of single cells and the harvesting of force-distance curves directly from it. We characterized and identified the embedded membrane proteins combining, within a Bayesian framework, the information of the shape of the obtained curves, with the information from Mass Spectrometry and proteomic databases. The pipeline was tested with purified/reconstituted proteins and applied to five cell types where we classified the unfolding of their most abundant membrane proteins. We validated our pipeline by overexpressing 4 constructs, and this allowed us to gather structural insights of the identified proteins, revealing variable elements in the loop regions. Our results set the basis for the investigation of the unfolding of membrane proteins in situ, and for performing proteomics from a membrane fragment.
Data availability
Data not present as Supplementary data are available in https://github.com/galvanetto/NativeSMFS/releases as well as the open source software to read them.
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Data from native SMFSGithub, https://github.com/galvanetto/NativeSMFS/releases.
Article and author information
Author details
Funding
H2020 European Research Council (MSCA IF-2014-EF-655157)
- Arin Marchesi
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Patricia Bassereau, Institut Curie, France
Version history
- Preprint posted: August 13, 2019 (view preprint)
- Received: January 28, 2022
- Accepted: September 8, 2022
- Accepted Manuscript published: September 12, 2022 (version 1)
- Version of Record published: October 4, 2022 (version 2)
- Version of Record updated: October 7, 2022 (version 3)
Copyright
© 2022, Galvanetto et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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