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

Bargeen Alam Turzo Sm, Justin T. Seffernick, Amber D. Rolland, Micah T. Donor, Sten Heinze, James S. Prell, Vicki H. Wysocki and Steffen Lindert ()
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Bargeen Alam Turzo Sm: 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

Nature Communications, 2022, vol. 13, issue 1, 1-15

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-32075-9

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