Atomic accuracy models from 4.5 Å cryo-electron microscopy data with density-guided iterative local refinement
By Frank DiMaio, Yifan Song, Xueming Li, Matthias J. Brunner, Chunfu Xu, V. Conticello, Edward H. Egelman, T. Marlovits, Yifan Cheng, David Baker. 2015
317 citations recorded by the source corpus.
We describe a general approach for refining protein structure models on the basis of cryo-electron microscopy maps with near-atomic resolution. The method integrates Monte Carlo sampling with local density-guided optimization, Rosetta all-atom refinement and real-space B-factor fitting. In tests on experimental maps of three different systems with 4.5-Å resolution or better, the method consistently produced models with atomic-level accuracy largely independently of starting-model quality, and it outperformed the molecular dynamics–based MDFF method. Cross-validated model quality statistics correlated with model accuracy over the three test systems.
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
- David Baker
- Yang Zhang
- Jeffrey J. Gray
- Frank DiMaio
- Tanja Kortemme
- Hahnbeom Park
- Robin Pearce
- David E. Kim
In Structural Biology
- Yang Zhang
- Robin Pearce
- Yifan Cheng
- Pavel V. Afonine
- D. Veesler
- Tristan Cragnolini
- A. Walls
- Frank DiMaio
In Surfaces, Coatings and Films
Citations in the corpus
- The Rosetta all-atom energy function for macromolecular modeling and design (2017, background): “…of freedom during gradient-based minimization has been shown to improve Rosetta’s ability to refine low-resolution structures determined by X-ray crystallography and cryo-electron microscopy, 52 as well as its ability to discriminate near-native conformations in the absence of experimental data.”
- Real-space refinement in PHENIX for cryo-EM and crystallography (2018, methodology): “This involved some rebuilding and refinement using algorithms implemented in the Rosetta suite (DiMaio et al., 2015).”
- Protein homology model refinement by large-scale energy optimization (2018, methodology): “In previous studies this concept — exploring the energy landscape with iterative multiscale modeling — was limited either by strongly restraining input structures (4 – 6), carrying out only a few iterations (33), or using experimental data to guide the search (34).”
- Glycan shield and epitope masking of a coronavirus spike protein observed by cryo-electron microscopy (2016, methodology): “The final model, which we built and refined with Coot 22 and Rosetta [23] [24] [25] , includes residues 23 to 1224, with internal breaks between residues 110-121, 882-890 and 992-1001 (Supplementary Fig.”
- Integrating model simulation tools and cryo‐electron microscopy (2022, methodology): “148,161 However, only (cid:1) 50% of submitted models for the 2019 cryo-EM Model Challenge included refined B-factors, 221 and refined B-factors are largely not present in older models from cryo-EM maps.”
- I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction (2022, methodology): “For high-resolution cryo-EM density maps, atomic structures can be constructed by programs traditionally used for X-ray crystallography 90 , but these programs perform relatively poorly for medium-to-low-resolution density maps 91 .”
- Automatically fixing errors in glycoprotein structures with Rosetta (2018, methodology): “…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 using Coot (Emsley and Cowtan, 2004).”