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Rendering RNA in 3D

Nature Methods volume 9, pages 552–553 (2012)Cite this article

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Integrating biochemical footprinting data with molecular dynamics yields more accurate RNA three-dimensional structure predictions.

Noncoding RNAs fine-tune how genes are expressed, controlling processes crucial for healthy cell growth such as chromosome packaging and protein synthesis. In many cases, noncoding RNAs must fold into specific three-dimensional shapes in order to act. Yet although high-throughput sequencing experiments are resulting in the discovery of thousands of noncoding RNAs, predicting RNA structure from its sequence remains a huge challenge. In this issue, Ding et al.1 show how readily obtainable biochemical footprinting data can be harnessed to improve computational predictions of RNA structures. Inventive methods for translating biochemical data into useful modeling parameters and mitigating noisy data point the way to a better integration of empirical data with sequence-based prediction strategies.

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Figure 1: Prediction of RNA three-dimensional structure with footprinting and molecular dynamics.

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Authors and Affiliations

  1. Reza Behrouzi and Sarah A. Woodson are at the T.C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland, USA.,

    Reza Behrouzi & Sarah A Woodson

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  1. Reza Behrouzi
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  2. Sarah A Woodson
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Correspondence to Sarah A Woodson.

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The authors declare no competing financial interests.

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Behrouzi, R., Woodson, S. Rendering RNA in 3D. Nat Methods 9, 552–553 (2012). https://doi.org/10.1038/nmeth.2045

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