Abstract

Structured noncoding RNAs underlie fundamental cellular processes, but determining their three-dimensional structures remains challenging. We demonstrate that integrating 1H NMR chemical shift data with Rosetta de novo modeling can be used to consistently determine high-resolution RNA structures. On a benchmark set of 23 noncanonical RNA motifs, including 11 'blind' targets, chemical-shift Rosetta for RNA (CS-Rosetta-RNA) recovered experimental structures with high accuracy (0.6–2.0 Å all-heavy-atom r.m.s. deviation) in 18 cases.

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Acknowledgements

We thank H. Al-Hashimi for suggesting the problem and the Rosetta community for sharing code, and G. Varani and J.D. Puglisi for providing experimental 1H chemical shift data (for HIV-1 TAR apical loop and hepatitis C virus IRES subdomain IIa, respectively). Calculations were carried out on the BioX2 cluster (US National Science Foundation award CNS-0619926) and Extreme Science and Engineering Discovery Environment (XSEDE) (allocation MCB090153). We acknowledge financial support from a Burroughs Wellcome Career Award at the Scientific Interface (to R.D.), US National Institutes of Health (NIH) grant R21 GM102716 (to R.D.), a C.V. Starr Asia/Pacific Stanford Graduate Fellowship (to P.S.), NIH grant GM73969 (to E.P.N.), NIH grant GM22939 (to D.H.T.), the Swiss National Science Foundation and the European Commission (BIO-NMR, ERC-Starting Grant, Marie Curie Fellowship; to M.C.E. and R.K.O.S.), and the Deutsche Forschungsgemeinschaft (Cluster of Excellence) and the European Commission (Bio-NMR, WeNMR, Marie Curie Fellowship; to M.C., M.Z. and H.S.).

Author information

Affiliations

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

    • Parin Sripakdeevong
    •  & Rhiju Das
  2. Center for Biomolecular Magnetic Resonance, Institute for Organic Chemistry and Chemical Biology, Johann Wolfgang Goethe University Frankfurt, Frankfurt, Germany.

    • Mirko Cevec
    • , Melanie Ziegeler
    •  & Harald Schwalbe
  3. Department of Biochemistry and Cell Biology, Rice University, Houston, Texas, USA.

    • Andrew T Chang
    •  & Edward P Nikonowicz
  4. Department of Biochemistry, University of Oxford, Oxford, UK.

    • Michèle C Erat
  5. Institute of Inorganic Chemistry, University of Zurich, Zurich, Switzerland.

    • Michèle C Erat
    •  & Roland K O Sigel
  6. Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.

    • Qin Zhao
  7. Department of Biology and Biochemistry, University of Houston, Houston, Texas, USA.

    • George E Fox
    •  & Xiaolian Gao
  8. Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA.

    • Scott D Kennedy
  9. Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland.

    • Ryszard Kierzek
  10. Department of Chemistry, University of Rochester, Rochester, New York, USA.

    • Douglas H Turner
  11. Department of Biochemistry, Stanford University, Stanford, California, USA.

    • Rhiju Das
  12. Department of Physics, Stanford University, Stanford, California, USA.

    • Rhiju Das

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Contributions

P.S. and R.D. designed the research. P.S. implemented the method, generated the data, analyzed the results, and wrote the paper. R.D. assisted in analyzing the data and writing the paper. M.C., A.T.C., M.C.E., M.Z., Q.Z., G.E.F., X.G., S.D.K., R.K., E.P.N., H.S., R.K.O.S. and D.H.T. provided NMR spectroscopy data for the 11 blind targets and participated in evaluating the blinded trials. All authors discussed the results and commented on the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Rhiju Das.

Supplementary information

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  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–11, Supplementary Tables 1–3, Supplementary Results and Supplementary Note

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    Supplementary Data

    Data files for the top five CS-Rosetta-RNA models, the reference experimental structures, and the experimental 1H chemical shift data for each of the 23 RNA motifs in the benchmark (included readme file provides details about the contents).

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DOI

https://doi.org/10.1038/nmeth.2876

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