Automated structure refinement of macromolecular assemblies from cryo-EM maps using Rosetta

By R. Wang, Yifan Song, Benjamin A. Barad, Yifan Cheng, James S. Fraser, Frank DiMaio. 2016

444 citations recorded by the source corpus.

Cryo-EM has revealed many challenging yet exciting macromolecular assemblies at near-atomic resolution (3-4.5Å), providing biological phenomena with molecular descriptions. However, at these resolutions accurately positioning individual atoms remains challenging and may be error-prone. Manually refining thousands of amino acids – typical in a macromolecular assembly – is tedious and time-consuming. We present an automated method that can improve the atomic details in models manually built in near-atomic-resolution cryo-EM maps. Applying the method to three systems recently solved by cryo-EM, we are able to improve model geometry while maintaining or improving the fit-to-density. Backbone placement errors are automatically detected and corrected, and the refinement shows a large radius of convergence. The results demonstrate the method is amenable to structures with symmetry, of very large size, and containing RNA as well as covalently bound ligands. The method should streamline the cryo-EM structure determination process, providing accurate and unbiased atomic structure interpretation of such maps.

Subfields: Materials Chemistry, Molecular Biology, Structural Biology

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Citations in the corpus

Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms. (2020, unknown): “%) was first fit into the cryo-EM density as a rigid body in UCSF ChimeraX and then relaxed into the final densityusing RosettaFastRelaxmover in torsion space.”
Unexpected Receptor Functional Mimicry Elucidates Activation of Coronavirus Fusion (2020, unknown): “Glycan refinement relied on a dedicated Rosetta protocol, which uses physically realistic geometries based on prior knowledge of saccharide chemical properties (Frenz et al., 2019), and was aided by using both sharpened and unsharpened maps.”
ISOLDE: a physically realistic environment for model building into low-resolution electron-density maps (2018, methodology): “Arguably the state of the art in automated model rebuilding is ROSETTA (Wang et al., 2016), which uses extensive, massively parallel fragment-based search methods to optimize a structure, often achieving impressive results even in lowresolution maps.”
The human coronavirus HCoV-229E S-protein structure and receptor binding (2019, unknown): “real_space_refine (62) and Rosetta (63).”