A Test and Refinement of Folding Free Energy Nearest Neighbor Parameters for RNA Including N6-Methyladenosine

RNA folding free energy change parameters are widely used to predict RNA secondary structure and to design RNA sequences. These parameters include terms for the folding free energies of helices and loops. Although the full set of parameters has only been traditionally available for the four common bases and backbone, it is well known that covalent modifications of nucleotides are widespread in natural RNAs. Covalent modifications are also widely used in engineered sequences. We recently derived a full set of nearest neighbor terms for RNA that includes N-6-methyladenosine (m(6)A). In this work, we test the model using 98 optical melting experiments, matching duplexes with or without N-6-methylation of A. Most experiments place RRACH, the consensus site of N-6-methylation, in a variety of contexts, including helices, bulge loops, internal loops, dangling ends, and terminal mismatches. For matched sets of experiments that include either A or m6A in the same context, we find that the parameters for m(6)A are as accurate as those for A. Across all experiments, the root mean squared deviation between estimated and experimental free energy changes is 0.67 kcal/mol. We used the new experimental data to refine the set of nearest neighbor parameter terms for m(6)A. These parameters enable prediction of RNA secondary structures including m(6)A, which can be used to model how N-6-methylation of A affects RNA structure. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecom-mons.org/licenses/by/4.0/).

Automated summary

RNA folding free energy change parameters are used to predict RNA secondary structure and design RNA sequences. This study derived a full set of nearest neighbor terms for RNA including N-6-methyladenosine (m(6)A) and tested the model using optical melting experiments. The results showed that the parameters for m(6)A are as accurate as those for A, and the refined parameter set enables prediction of RNA secondary structures with m(6)A.