Secondary structure prediction for RNA sequences including N6-methyladenosine

Elzbieta Kierzek (), Xiaoju Zhang, Richard M. Watson, Scott D. Kennedy, Marta Szabat, Ryszard Kierzek and David H. Mathews ()
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Elzbieta Kierzek: Institute of Bioorganic Chemistry Polish Academy of Sciences
Xiaoju Zhang: University of Rochester
Richard M. Watson: University of Rochester
Scott D. Kennedy: University of Rochester
Marta Szabat: Institute of Bioorganic Chemistry Polish Academy of Sciences
Ryszard Kierzek: Institute of Bioorganic Chemistry Polish Academy of Sciences
David H. Mathews: University of Rochester

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: Abstract There is increasing interest in the roles of covalently modified nucleotides in RNA. There has been, however, an inability to account for modifications in secondary structure prediction because of a lack of software and thermodynamic parameters. We report the solution for these issues for N6-methyladenosine (m6A), allowing secondary structure prediction for an alphabet of A, C, G, U, and m6A. The RNAstructure software now works with user-defined nucleotide alphabets of any size. We also report a set of nearest neighbor parameters for helices and loops containing m6A, using experiments. Interestingly, N6-methylation decreases folding stability for adenosines in the middle of a helix, has little effect on folding stability for adenosines at the ends of helices, and increases folding stability for unpaired adenosines stacked on a helix. We demonstrate predictions for an N6-methylation-activated protein recognition site from MALAT1 and human transcriptome-wide effects of N6-methylation on the probability of adenosine being buried in a helix.

Date: 2022
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DOI: 10.1038/s41467-022-28817-4

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