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High-throughput biochemistry in RNA sequence space: predicting structure and function

Abstract

RNAs are central to fundamental biological processes in all known organisms. The set of possible intramolecular interactions of RNA nucleotides defines the range of alternative structural conformations of a specific RNA that can coexist, and these structures enable functional catalytic properties of RNAs and/or their productive intermolecular interactions with other RNAs or proteins. However, the immense combinatorial space of potential RNA sequences has precluded predictive mapping between RNA sequence and molecular structure and function. Recent advances in high-throughput approaches in vitro have enabled quantitative thermodynamic and kinetic measurements of RNA–RNA and RNA–protein interactions, across hundreds of thousands of sequence variations. In this Review, we explore these techniques, how they can be used to understand RNA function and how they might form the foundations of an accurate model to predict the structure and function of an RNA directly from its nucleotide sequence. The experimental techniques and modelling frameworks discussed here are also highly relevant for the sampling of sequence–structure–function space of DNAs and proteins.

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Fig. 1: Diversity of RNA secondary and tertiary structures.
Fig. 2: Using next-generation sequencing chips for high-throughput biochemical measurements.
Fig. 3: Predicting experimental parameters from RNA sequence information.
Fig. 4: Structural modelling to predict RNA–RNA binding energies verified with RNA array binding data.
Fig. 5: Thermodynamic and kinetic models for RNA–protein interaction and RNA-guided protein binding.
Fig. 6: Single-molecule experiments carried out across a large sequence space.
Fig. 7: Feedback between experimental data, mechanistic modelling and deep learning methods.

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Acknowledgements

The authors thank E. Sharma for discussions. This work was supported in part by NIH grants R01GM111990, P50HG007735, R01HG009909, P01GM066275, UM1HG009436 and R01GM121487 to W.J.G. W.J.G. acknowledges support as a Chan Zuckerberg Investigator. E.M. was supported by the Swedish Research Council grant 2020-06459.

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Marklund, E., Ke, Y. & Greenleaf, W.J. High-throughput biochemistry in RNA sequence space: predicting structure and function. Nat Rev Genet (2023). https://doi.org/10.1038/s41576-022-00567-5

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