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

• Published in: Nature communications Vol. 13; no. 1; pp. 4377 - 15
• Main Authors: Turzo, SM Bargeen Alam, Seffernick, Justin T., Rolland, Amber D., Donor, Micah T., Heinze, Sten, Prell, James S., Wysocki, Vicki H., Lindert, Steffen
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28-07-2022; Nature Publishing Group; Nature Portfolio
• Subjects: 119/118; 631/114/2411; 631/1647/296; 631/45/535/1267; 631/57/2266; Algorithms; Amino acid sequence; Computer applications; Cross-sections; Humanities and Social Sciences; Ion Mobility Spectrometry - methods; Ionic mobility; Ions; Mass spectrometry; Mass Spectrometry - methods; Mass spectroscopy; Mobility; Modelling; Molecular modelling; multidisciplinary; Predictions; Protein structure; Proteins; Proteins - chemistry; Science; Science (multidisciplinary); Scientific imaging; Spectroscopy
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-32075-9

Ion mobility (IM) mass spectrometry provides structural information about protein shape and size in the form of an orientationally-averaged collision cross-section (CCS
IM
). 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 CCS
IM
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.
Collision cross sections (CCS) from ion mobility mass spectrometry provide information about protein shape and size. Here, the authors develop an algorithm to predict CCS and integrate experimental ion mobility data into Rosetta-based molecular modelling to predict protein structures from sequence.