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

Nature Methods volume 15, pages 947–954 (2018)Cite this article

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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.

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Fig. 1: The DRRAFTER framework.
Fig. 2: DRRAFTER recovers near-native models over a diverse benchmark set and two blind test cases.
Fig. 3: Estimating DRRAFTER model accuracy.
Fig. 4: DRRAFTER can accelerate manual model building into low-resolution density maps.
Fig. 5: Typical mistakes that may occur during DRRAFTER modeling and possible solutions.

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Data availability

The accession codes used in this study are as follows: E. coli L25-5S rRNA (PDB 1DFU and 1B75), sex-lethal RRM (PDB 1B7F and 3SXL), ribotoxin restrictocin sarcin-ricin loop analog (PDB 1JBS and 1AQZ), the SmpB–tmRNA complex (PDB 1P6V and 1K8H), the HutP antitermination complex (PDB 1WPU and 1WPV), the mRNA-binding domain of the SelB elongation factor (PDB 1WSU and 1LVA), the NusA transcriptional regulator (PDB 2ASB and 1K0R), the methyltransferase RumA in complex with rRNA (PDB 2BH2 and 1UWV), the PP7 coat protein and viral RNA (PDB 2QUX and 2QUD), Puf4 bound to the 3′ UTR of the target transcript (PDB 3BX2 and 3BWT), tri-snRNP (EMD-2966 and EMD-8012; PDB 4YHU and 5GAN, in addition to all PDB codes listed in Extended Data Table 1 of ref. 9), the mitochondrial ribosome (EMD-2490 and EMD-2787; PDB 4CE4, 4V19, and 4V1A), CRISPR–Cas9–sgRNA complex (EMD-3276; PDB 5F9R and 4ZT0), U1 snRNP (EMD-8622; PDB 3CW1, 3PGW, 5GMK and 5UZ5), the spliceosomal P complex (PDB 5MQ0, 5WSG, 5I8Q and 6BK8), HIV-1 RTIC (described in ref. 44), and Tetrahymena telomerase (EMD-6443; PDB 5KMZ, 2VOP, 5C9H, 2M21 and 4ERD), MS2 packaged genome (EMD-8397 and EMD-3403; PDB 5TC1). The DRRAFTER models of the U1 snRNP (from the 3.6 Å map) and the packaged MS2 genome (from the 3.6 Å map) are available in Supplementary Data 1 and 2. DRRAFTER models for all other systems are available at https://purl.stanford.edu/jj049gk5411.

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Acknowledgements

We thank members of the Das lab for useful discussions and members of the Rosetta community for discussions and code sharing. We thank J. Feigon and her lab for sharing their coordinates for the 8.9 Å Tetrahymena telomerase cryo-EM structure. Calculations were performed on the Stanford Sherlock cluster and Stanford BioX3 cluster, supported by NIH Shared Instrumentation Grant 1S10RR02664701. This work was supported by a Gabilan Stanford Graduate Fellowship (K.K.), the National Science Foundation (GRFP to K.K.), and the National Institutes of Health through awards T32 GM008294 (K.P.L. and K.K.), NIGMS R35 GM122579 (R.D.), R21 CA121487 (R.D.), R01 GM121487 (R.D. and P. Bradley), R01 GM114178 (R.Z.), R01 GM126157 (R.Z.), and R01 GM071940 (Z.H.Z.).

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Authors and Affiliations

  1. Biophysics Program, Stanford University, Stanford, CA, USA

    Kalli Kappel, Kevin P. Larsen & Rhiju Das

  2. Electron Imaging Center for Nanomachines, California NanoSystems Institute, University of California, Los Angeles (UCLA), Los Angeles, CA, USA

    Shiheng Liu & Z. Hong Zhou

  3. Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA

    Shiheng Liu & Z. Hong Zhou

  4. Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA

    Kevin P. Larsen, Georgios Skiniotis, Elisabetta Viani Puglisi & Joseph D. Puglisi

  5. Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA

    Georgios Skiniotis

  6. Department of Biochemistry and Molecular Genetics, University of Colorado Denver Anschutz Medical Campus, Aurora, CO, USA

    Rui Zhao

  7. Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA

    Rhiju Das

  8. Department of Physics, Stanford University, Stanford, CA, USA

    Rhiju Das

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Contributions

K.K. and R.D. designed the computational approach. K.K. implemented the method and performed the tests and analysis. S.L., Z.H.Z., and R.Z. provided the U1 snRNP and P complex blind test cases. K.P.L., G.S., E.V.P., and J.D.P. provided the HIV-1 RTIC test case and provided initial feedback on the method. K.K. and R.D. wrote the manuscript with input from S.L., K.P.L., G.S., E.V.P., J.D.P., Z.H.Z., and R.Z.

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Correspondence to Rhiju Das.

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Supplementary Figure 1 DRRAFTER models for ten small RNA–protein systems.

a–j, The best RMSD DRRAFTER models of the top ten scoring (RNA colored red, protein colored gray) overlaid with the deposited crystallographic coordinates (RNA colored cyan, protein colored gray) shown for a, 3 Å (left), 5 Å (middle), and 7 Å (right) simulated density maps for E. coli L25-5S rRNA (1dfu), b, sex-lethal RRM (1b7f), c, ribotoxin restrictocin sarcin-ricin loop analog (1jbs), d, SmpB–tmRNA complex (1p6v), e, HutP antitermination complex (1wpu), f, the mRNA-binding domain of the SelB elongation factor (1wsu), g, the NusA transcriptional regulator (2asb), h, the methyltransferase RumA in complex with rRNA (2bh2), i, the PP7 coat protein and viral RNA (2qux), and j, Puf4 bound to the 3′ UTR of target transcript (3bx2).

Supplementary Figure 2 Convergence of DRRAFTER models.

a–l, High-resolution RNA coordinates (cyan, left) and the top ten scoring DRRAFTER models (red, right) for the a, spliceosomal tri-snRNP U4/U6 three-way junction, b, U5 three-way junction, and c, U5 internal loop II; d, the CRISPR–Cas9–sgRNA complex sgRNA residues 11–30 and 57–68 and e, sgRNA residues 69–99; f, mitoribosome loop 1 and g, loop 2; h, yeast U1 snRNP (blind) core four-way junction, i, yeast three-way junction, j, SL2-2, and k, yeast four-way junction (DRRAFTER models of SL3-2, SL3-3, and SL3–5 colored red; DRRAFTER models of SL3–4 colored white); and l, yeast spliceosomal P complex (blind) ligated exon.

Supplementary Figure 3 Assessing the agreement between DRRAFTER models and the lower-resolution density maps versus the agreement of high-resolution coordinates to the same lower-resolution density maps.

Real-space correlation coefficients for the best RMSD DRRAFTER models out of the top ten scoring for all systems described in Supplementary Table 1 to lower-resolution density maps are plotted against correlation coefficient values for the corresponding high-resolution coordinates to the same lower-resolution density maps (see Methods for details).

Supplementary Figure 4 Comparing the accuracy of DRRAFTER models to manual models built into the lower-resolution mitoribosome map.

DRRAFTER models were built for all regions in the mitoribosome for which manual models were previously deposited. The accuracy of the manual (previously deposited) and DRRAFTER models was determined by comparing to the higher-resolution mitoribosome coordinates.

Supplementary Figure 5 Differences between the 3.6 Å and 10.5 Å maps of the packaged MS2 genome.

a, The model of region S9 (S9-1 and S9-2) (blue) built into the 3.6 Å map (blue, transparent). b, The model of region S9 (gray) built into the 10.5 Å map (gray, transparent). Each of the models fits well in the map in which it was built. There are, however, differences between the two models. c, Models built into the 3.6 Å and 10.5 Å maps overlaid. These differences are largely due to the underlying differences in the maps. d, The 3.6 Å (blue) and 10.5 Å (gray) maps overlaid.

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Supplementary Text and Figures

Supplementary Figures 1–5 and Supplementary Tables 1–4

Reporting Summary

Supplementary Data 1

U1 snRNP model

Supplementary Data 2

MS2 packaged genome model

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Kappel, K., Liu, S., Larsen, K.P. et al. De novo computational RNA modeling into cryo-EM maps of large ribonucleoprotein complexes. Nat Methods 15, 947–954 (2018). https://doi.org/10.1038/s41592-018-0172-2

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