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RosettaES: a sampling strategy enabling automated interpretation of difficult cryo-EM maps
Brandon Frenz1, Alexandra C Walls1, Edward H Egelman2
1Department of Biochemistry, University of Washington, Seattle, Washington, USA.
Nature Methods
|June 20, 2017
Summary
RosettaES is a new tool for atomic modeling in cryo-electron microscopy (cryo-EM) maps. It accurately completes macromolecular structures from moderate-resolution maps, aiding in structural biology research.
Area of Science:
- Structural Biology
- Computational Biology
- Biophysics
Background:
- Accurate atomic modeling into cryo-electron microscopy (cryo-EM) maps is challenging due to moderate resolution.
- Precise placement of atoms in cryo-EM density maps remains a significant hurdle in structural determination.
Purpose of the Study:
- To present Rosetta enumerative sampling (RosettaES), an automated tool for de novo model completion of macromolecular structures.
- To address the challenge of atomic modeling in cryo-EM maps at 3-5 Å resolution.
Main Methods:
- RosettaES employs a fragment-based sampling strategy for de novo model completion.
- The tool automates the process of fitting atomic models into cryo-EM density maps.
Main Results:
- RosettaES identified near-native conformations in 85% of segments on a benchmark set of nine proteins.
- The tool successfully determined models for three challenging macromolecular structures.
Conclusions:
- RosettaES is an effective automated tool for macromolecular model completion in cryo-EM.
- The method improves the accuracy of atomic placement in moderate-resolution cryo-EM maps.
