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Predicting the shapes of protein complexes through collision cross section measurements and database searches.

Landreh, Michael and Sahin, Cagla and Gault, Joseph and Sadeghi, Samira and Drum, Chester Lee and Uzdavinys, Povilas and Drew, David and Allison, Timothy M and Degiacomi, Matteo T. and Marklund, Erik G. (2020) 'Predicting the shapes of protein complexes through collision cross section measurements and database searches.', Analytical chemistry., 92 (18). pp. 12297-12303.


In structural biology, collision cross sections (CCSs) from ion mobility mass spectrometry (IM-MS) measurements are routinely compared to computationally or experimentally derived protein structures. Here, we investigate whether CCS data can inform about the shape of a protein in the absence of specific reference structures. Analysis of the proteins in the CCS database shows that protein complexes with low apparent densities are structurally more diverse than those with a high apparent density. Although assigning protein shapes purely on CCS data is not possible, we find that we can distinguish oblate- and prolate-shaped protein complexesby using the CCS, molecular weight, and oligomeric states to mine the Protein Data Bank (PDB) for potentially similar protein structures. Furthermore, comparing the CCS of a ferritin cage to the solution structures in the PDB reveals significant deviations caused by structural collapse on the gas phase. We then apply the strategy to an integral membrane protein by comparing the shapes of a prokaryotic and a eukaryotic sodium/proton antiporter homologue. We conclude that mining the PDB with IM-MS data is a time-effective way to derive low-resolution structural models.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Publisher statement:This document is the Accepted Manuscript version of a Published Work that appeared in final form in Analytical chemistry, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see
Date accepted:14 July 2020
Date deposited:14 July 2020
Date of first online publication:14 July 2020
Date first made open access:14 July 2021

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