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Protein–RNA interactions: structural biology and computational modeling techniques

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Abstract

RNA-binding proteins are functionally diverse within cells, being involved in RNA-metabolism, translation, DNA damage repair, and gene regulation at both the transcriptional and post-transcriptional levels. Much has been learnt about their interactions with RNAs through structure determination techniques and computational modeling. This review gives an overview of the structural data currently available for protein–RNA complexes, and discusses the technical issues facing structural biologists working to solve their structures. The review focuses on three techniques used to solve the 3-dimensional structure of protein–RNA complexes at atomic resolution, namely X-ray crystallography, solution nuclear magnetic resonance (NMR) and cryo-electron microscopy (cryo-EM). The review then focuses on the main computational modeling techniques that use these atomic resolution data: discussing the prediction of RNA-binding sites on unbound proteins, docking proteins, and RNAs, and modeling the molecular dynamics of the systems. In conclusion, the review looks at the future directions this field of research might take.

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Acknowledgements

SJ was funded by the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS) research programme. SJ would like to thank the editors for the invitation to contribute to this special issue and to wish a very happy 80th birthday to Professor Don Winzor.

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  1. Information and Computational Science Group, The James Hutton Institute, Invergowrie, Dundee, DD2 5DA, UK

    Susan Jones

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  1. Susan Jones
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Correspondence to Susan Jones.

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Susan Jones declares that she has no conflicts of interest.

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This article does not contain any studies with human participants or animals performed by the authors.

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This article is part of a Special Issue on ‘Analytical Quantitative Relations in Biochemistry’ edited by Damien Hall and Stephen Harding

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Jones, S. Protein–RNA interactions: structural biology and computational modeling techniques. Biophys Rev 8, 359–367 (2016). https://doi.org/10.1007/s12551-016-0223-9

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