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MEPSi: A tool for simulating tomograms of membrane-embedded proteins

Borja Rodríguez de Francisco, View ORCID ProfileArmel Bezault, Xiao-Ping Xu, View ORCID ProfileDorit Hanein, View ORCID ProfileNiels Volkmann
doi: https://doi.org/10.1101/2022.07.27.501771
Borja Rodríguez de Francisco
1Institut Pasteur, Université Paris Cité, CNRS UMR3528, Structural Studies of Macromolecular Machines in Cellulo Unit, Paris, France
2Institut Pasteur, Université Paris Cité, CNRS UMR3528, Structural Image Analysis Unit, Paris, France
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Armel Bezault
1Institut Pasteur, Université Paris Cité, CNRS UMR3528, Structural Studies of Macromolecular Machines in Cellulo Unit, Paris, France
2Institut Pasteur, Université Paris Cité, CNRS UMR3528, Structural Image Analysis Unit, Paris, France
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Xiao-Ping Xu
3Scintillon Institute, San Diego, California 92121 USA
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Dorit Hanein
1Institut Pasteur, Université Paris Cité, CNRS UMR3528, Structural Studies of Macromolecular Machines in Cellulo Unit, Paris, France
3Scintillon Institute, San Diego, California 92121 USA
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Niels Volkmann
2Institut Pasteur, Université Paris Cité, CNRS UMR3528, Structural Image Analysis Unit, Paris, France
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  • ORCID record for Niels Volkmann
  • For correspondence: niels.volkmann@pasteur.fr
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ABSTRACT

The throughput and fidelity of cryogenic cellular electron tomography (cryo-ET) is constantly increasing through advances in cryogenic electron microscope hardware, direct electron detection devices, and powerful image processing algorithms. However, the need for careful optimization of sample preparations and for access to expensive, high-end equipment, make cryo-ET a costly and time-consuming technique. Generally, only after the last step of the cryo-ET workflow, when reconstructed tomograms are available, it becomes clear whether the chosen imaging parameters were suitable for a specific type of sample in order to answer a specific biological question. Tools for a-priory assessment of the feasibility of samples to answer biological questions and how to optimize imaging parameters to do so would be a major advantage. Here we describe MEPSi (Membrane Embedded Protein Simulator), a simulation tool aimed at rapid and convenient evaluation and optimization of cryo-ET data acquisition parameters for studies of transmembrane proteins in their native environment. We demonstrate the utility of MEPSi by showing how to detangle the influence of different data collection parameters and different orientations in respect to tilt axis and electron beam for two examples: (1) simulated plasma membranes with embedded single-pass transmembrane αIIbβ3 integrin receptors and (2) simulated virus membranes with embedded SARS-CoV-2 spike proteins.

HIGHLIGHTS

  • Tool to simulate tomograms of membrane-embedded proteins

  • Detangles influence of data acquisition parameters from sample quality issues

  • Rapid evaluation and optimization of cryo-ET data acquisition parameters

  • Proof-of-concept provided with integrins and SARS-CoV-2 spike simulations

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Competing Interest Statement

The authors have declared no competing interest.

  • ABBREVIATIONS

    Cryo-EM
    Cryogenic Electron Microscopy
    Cryo-ET
    Cryogenic Electron Tomography
    CTF
    Contrast Transfer Function
    MEPSi
    Membrane Embedded Protein Simulator
    MSE
    Mean-Square Error
    MSSIM
    Mean Structural Similarity Index Method
    PSNR
    Peak Signal-to-Noise Ratio
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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    Posted July 29, 2022.
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    MEPSi: A tool for simulating tomograms of membrane-embedded proteins
    Borja Rodríguez de Francisco, Armel Bezault, Xiao-Ping Xu, Dorit Hanein, Niels Volkmann
    bioRxiv 2022.07.27.501771; doi: https://doi.org/10.1101/2022.07.27.501771
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    MEPSi: A tool for simulating tomograms of membrane-embedded proteins
    Borja Rodríguez de Francisco, Armel Bezault, Xiao-Ping Xu, Dorit Hanein, Niels Volkmann
    bioRxiv 2022.07.27.501771; doi: https://doi.org/10.1101/2022.07.27.501771

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