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Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction

Author

Listed:
  • SM Bargeen Alam Turzo

    (Ohio State University)

  • Justin T. Seffernick

    (Ohio State University)

  • Amber D. Rolland

    (University of Oregon)

  • Micah T. Donor

    (University of Oregon)

  • Sten Heinze

    (Ohio State University)

  • James S. Prell

    (University of Oregon)

  • Vicki H. Wysocki

    (Ohio State University)

  • Steffen Lindert

    (Ohio State University)

Abstract

Ion mobility (IM) mass spectrometry provides structural information about protein shape and size in the form of an orientationally-averaged collision cross-section (CCSIM). While IM data have been used with various computational methods, they have not yet been utilized to predict monomeric protein structure from sequence. Here, we show that IM data can significantly improve protein structure determination using the modelling suite Rosetta. We develop the Rosetta Projection Approximation using Rough Circular Shapes (PARCS) algorithm that allows for fast and accurate prediction of CCSIM from structure. Following successful testing of the PARCS algorithm, we use an integrative modelling approach to utilize IM data for protein structure prediction. Additionally, we propose a confidence metric that identifies near native models in the absence of a known structure. The results of this study demonstrate the ability of IM data to consistently improve protein structure prediction.

Suggested Citation

  • SM Bargeen Alam Turzo & Justin T. Seffernick & Amber D. Rolland & Micah T. Donor & Sten Heinze & James S. Prell & Vicki H. Wysocki & Steffen Lindert, 2022. "Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32075-9
    DOI: 10.1038/s41467-022-32075-9
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    References listed on IDEAS

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