Protein Shape Sampled by Ion Mobility Mass Spectrometry Consistently Improves Protein Structure Prediction

Authors

Micah T. Donor, George Fox UniversityFollow
SM Bargeen Alam Turzo
Justin T. Seffernick
Amber D. Rolland
Stein Heinze
James S. Prell
Vicki H. Wysocki
Steffen Lindert

Document Type

Article

Publication Date

7-28-2022

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.

Comments

Originally published in Nature Communications, Volume 13, Article Number 4377, (2022).

DOI: https://doi.org/10.1038/s41467-022-32075-9

https://www.nature.com/articles/s41467-022-32075-9

Recommended Citation

Donor, Micah T.; Turzo, SM Bargeen Alam; Seffernick, Justin T.; Rolland, Amber D.; Heinze, Stein; Prell, James S.; Wysocki, Vicki H.; and Lindert, Steffen, "Protein Shape Sampled by Ion Mobility Mass Spectrometry Consistently Improves Protein Structure Prediction" (2022). Faculty Publications - Department of Biological & Molecular Science. 157.
https://digitalcommons.georgefox.edu/bio_fac/157