4.8 Article

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

Journal

NATURE COMMUNICATIONS
Volume 13, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41467-022-32075-9

Keywords

-

Funding

  1. NIH [P41 GM128577, R21A125804]
  2. Sloan Research Fellowship

Ask authors/readers for more resources

This study develops an algorithm to predict protein collision cross sections (CCS) and integrates experimental ion mobility data into Rosetta-based molecular modeling to predict protein structures from sequence.
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. 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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available