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De novo tertiary structure prediction using RNA123—benchmarking and application to Macugen

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Abstract

The present benchmarking study utilizes the RNA123 program for de novo prediction of tertiary structures of a set of 50 RNA molecules for which X-ray/NMR structures are available, based on the nucleic acid sequence only. All molecules contain a hairpin loop motif and a helical structure of canonical and non-canonical base pairs, interrupted by bulges and internal loops to various degrees. RNA molecules with double helices made up purely by canonical base pairing, and molecules containing symmetric internal loops of non-canonical base pairing are, overall, very well predicted. Structures containing bulges and asymmetric internal loops, and more complex structures containing multiple bulges and internal loops in the same molecule, result in larger deviations from their X-ray/NMR predicted structures due to higher degree of flexibility of the nucleotide bases in these regions. In a majority of the molecules included herein, the RNA123 program was, however, able to predict the tertiary structure with a heavy atom RMSD of less than 5 Å to the X-ray/NMR structure, and the models were in most cases structurally closer to the X-ray/NMR structures than models predicted by MC-Fold and MC-Sym. A set of RNA molecules containing pseudoknot tertiary structure motifs were included, but neither of the programs was able to predict the folding of the single-stranded stem onto the helix without additional structural input. The RNA123 program was then applied to predict the tertiary structure of the RNA segment of Macugen®, the first RNA aptamer approved for clinical use, and for which no tertiary structure has yet been solved. Four possible tertiary structures were predicted for this 27-nucleic-acid-long RNA molecule, which will be used in constructing a full model of the PEGylated aptamer and its interaction with the vascular endothelial growth factor target.

RNA123-predicted secondary and tertiary structures of RNA molecule containing a short helix with a hairpin loop. The predicted model is superposed with a structure determined by NMR.

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Acknowledgments

The Faculty of Science at the University of Gothenburg and the Swedish research council (VR) are gratefully acknowledged for financial support.

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Authors and Affiliations

  1. Department of Chemistry and Molecular Biology, University of Gothenburg, 405 30, Göteborg, Sweden

    Emma S. E. Eriksson, Martin Billeter & Leif A. Eriksson

  2. National Center for Biomedical Engineering Science, National University of Ireland, Galway, Ireland

    Lokesh Joshi

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  1. Emma S. E. Eriksson
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Correspondence to Emma S. E. Eriksson.

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This paper belongs to Topical Collection 9th European Conference on Computational Chemistry (EuCo-CC9)

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Eriksson, E.S.E., Joshi, L., Billeter, M. et al. De novo tertiary structure prediction using RNA123—benchmarking and application to Macugen. J Mol Model 20, 2389 (2014). https://doi.org/10.1007/s00894-014-2389-z

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