RNA nanotechnology seeks to create nanoscale machines by repurposing natural RNA modules. The field is slowed by the current need for human intuition during three-dimensional structural design. Here, we demonstrate that three distinct problems in RNA nanotechnology can be reduced to a pathfinding problem and automatically solved through an algorithm called RNAMake. First, RNAMake discovers highly stable single-chain solutions to the classic problem of aligning a tetraloop and its sequence-distal receptor, with experimental validation from chemical mapping, gel electrophoresis, solution X-ray scattering and crystallography with 2.55 Å resolution. Second, RNAMake automatically generates structured tethers that integrate 16S and 23S ribosomal RNAs into single-chain ribosomal RNAs that remain uncleaved by ribonucleases and assemble onto messenger RNA. Third, RNAMake enables the automated stabilization of small-molecule binding RNAs, with designed tertiary contacts that improve the binding affinity of the ATP aptamer and improve the fluorescence and stability of the Spinach RNA in cell extracts and in living Escherichia coli cells.
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RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning
Nature Communications Open Access 27 November 2019
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The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request. Furthermore, all of our chemical mapping data are available on https://rmdb.stanford.edu, and a detailed table of the accession identifications is given in the Supplementary Information.
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We thank S. Bonilla for assistance in performing the native gel assays and A. Watkins for discussions about ribosome tether design. We thank the Straight lab for graciously providing X. laevis whole cell lysate. This work was supported by the National Institutes of Health, through NIGMS MIRA R35 GM122579 (R.D.), R01 GM121487 (R.D.), New Innovator Award 1DP2GM110838 (J.B.L.), Ruth L. Kirschstein National Research Service Award Postdoctoral Fellowships GM112294 (J.D.Y.) and GM100953 (D.E.), P01 GM066275 (D.H.) and R35 GM118070 (J.S.K.), a Stanford School of Medicine Discovery Innovation Award (R.D.), Army Research Office W911NF-16-1-0372 (M.C.J.), the National Science Foundation through MCB-1716766 (M.C.J.), Career Award 1452441 (J.B.L.) and Graduate Research Fellowship DGE-1324585 (A.E.D.), the David and Lucile Packard Foundation (M.C.J.) and the Camille Dreyfus Teacher-Scholar Program (M.C.J.).
Stanford University is filing a patent on aptamer stabilization with RNAMake.
Peer review information: Nature Nanotechnology thanks Peixuan Guo, Nils (G) Walter and other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Yesselman, J.D., Eiler, D., Carlson, E.D. et al. Computational design of three-dimensional RNA structure and function. Nat. Nanotechnol. 14, 866–873 (2019). https://doi.org/10.1038/s41565-019-0517-8
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