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Nat Methods. 2018 Nov;15(11):947-954. doi: 10.1038/s41592-018-0172-2. Epub 2018 Oct 30.

De novo computational RNA modeling into cryo-EM maps of large ribonucleoprotein complexes.

Author information

1
Biophysics Program, Stanford University, Stanford, CA, USA.
2
Electron Imaging Center for Nanomachines, California NanoSystems Institute, University of California, Los Angeles (UCLA), Los Angeles, CA, USA.
3
Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA.
4
Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA.
5
Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.
6
Department of Biochemistry and Molecular Genetics, University of Colorado Denver Anschutz Medical Campus, Aurora, CO, USA.
7
Biophysics Program, Stanford University, Stanford, CA, USA. rhiju@stanford.edu.
8
Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA. rhiju@stanford.edu.
9
Department of Physics, Stanford University, Stanford, CA, USA. rhiju@stanford.edu.

Abstract

Increasingly, cryo-electron microscopy (cryo-EM) is used to determine the structures of RNA-protein assemblies, but nearly all maps determined with this method have biologically important regions where the local resolution does not permit RNA coordinate tracing. To address these omissions, we present de novo ribonucleoprotein modeling in real space through assembly of fragments together with experimental density in Rosetta (DRRAFTER). We show that DRRAFTER recovers near-native models for a diverse benchmark set of RNA-protein complexes including the spliceosome, mitochondrial ribosome, and CRISPR-Cas9-sgRNA complexes; rigorous blind tests include yeast U1 snRNP and spliceosomal P complex maps. Additionally, to aid in model interpretation, we present a method for reliable in situ estimation of DRRAFTER model accuracy. Finally, we apply DRRAFTER to recently determined maps of telomerase, the HIV-1 reverse transcriptase initiation complex, and the packaged MS2 genome, demonstrating the acceleration of accurate model building in challenging cases.

PMID:
30377372
PMCID:
PMC6636682
DOI:
10.1038/s41592-018-0172-2
[Indexed for MEDLINE]
Free PMC Article

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