RosettaES: A greedy sampling strategy enabling automated interpretation of difficult cryoEM maps

By Brandon Frenz, A. Walls, Edward H. Egelman, D. Veesler, Frank DiMaio. 2017

120 citations recorded by the source corpus.

Accurate atomic modeling of macromolecular structures into cryo-electron microscopy (cryo-EM) maps is a major challenge, as the moderate resolution makes accurate placement of atoms difficult. We present Rosetta enumerative sampling (RosettaES), an automated tool that uses a fragment-based sampling strategy for de novo model completion of macromolecular structures from cryo-EM density maps at 3–5-Å resolution. On a benchmark set of nine proteins, RosettaES was able to identify near-native conformations in 85% of segments. RosettaES was also used to determine models for three challenging macromolecular structures.

Subfields: Materials Chemistry, Structural Biology, Surfaces, Coatings and Films

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Jared Adolf-Bryfogle
2. William R. Schief
3. D. Veesler
4. A. Walls
5. Brandon Frenz
6. A. J. Borst
7. Frank DiMaio
8. David Baker

In Structural Biology

1. D. Veesler
2. A. Walls
3. Frank DiMaio
4. Brandon Frenz

In Surfaces, Coatings and Films

1. Frank DiMaio
2. A. Walls
3. D. Veesler
4. Brandon Frenz

Citations in the corpus

• Automatically fixing errors in glycoprotein structures with Rosetta (2018, methodology): “…done using a homology model of the mouse hepatitis virus (Walls et al., 2016a) in combination with Rosetta de novo (Wang et al., 2015), RosettaES (Frenz et al., 2017), Rosetta den- 136 Structure 27, 134–139, January 2, 2019 sity-guided iterative refinement (DiMaio et al., 2015), and handtracing…”