Unfolding and identification of membrane proteins in situ

  1. Nicola Galvanetto  Is a corresponding author
  2. Zhongjie Ye
  3. Arin Marchesi
  4. Simone Mortal
  5. Sourav Maity
  6. Alessandro Laio
  7. Vincent Aldo Torre
  1. International School for Advanced Studies, Italy
  2. Kanazawa Medical University, Japan
  3. University of Groningen, Netherlands

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.

The following data sets were generated
    1. Galvanetto N
    2. Ye Z
    (2022) Data from native SMFS
    Github, https://github.com/galvanetto/NativeSMFS/releases.

Article and author information

Author details

  1. Nicola Galvanetto

    International School for Advanced Studies, Trieste, Italy
    For correspondence
    n.galvanetto@bioc.uzh.ch
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0408-1747
  2. Zhongjie Ye

    International School for Advanced Studies, Trieste, Italy
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0306-5267
  3. Arin Marchesi

    Nano Life Science Institute, Kanazawa Medical University, Kanazawa, Japan
    Competing interests
    The authors declare that no competing interests exist.
  4. Simone Mortal

    International School for Advanced Studies, Trieste, Italy
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6534-9324
  5. Sourav Maity

    Moleculaire Biofysica, University of Groningen, Groningen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  6. Alessandro Laio

    International School for Advanced Studies, Trieste, Italy
    Competing interests
    The authors declare that no competing interests exist.
  7. Vincent Aldo Torre

    International School for Advanced Studies, Trieste, Italy
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8133-3584

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

  1. Patricia Bassereau, Institut Curie, France

Publication history

  1. Preprint posted: August 13, 2019 (view preprint)
  2. Received: January 28, 2022
  3. Accepted: September 8, 2022
  4. Accepted Manuscript published: September 12, 2022 (version 1)
  5. Version of Record published: October 4, 2022 (version 2)
  6. 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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  1. Nicola Galvanetto
  2. Zhongjie Ye
  3. Arin Marchesi
  4. Simone Mortal
  5. Sourav Maity
  6. Alessandro Laio
  7. Vincent Aldo Torre
(2022)
Unfolding and identification of membrane proteins in situ
eLife 11:e77427.
https://doi.org/10.7554/eLife.77427

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