Molecular Dynamics Simulations of RNA Stem-Loop Folding Using an Atomistic Force Field and a Generalized Born
Tadashi Ando1,2
1Department of Applied Electronics, Tokyo University of Science, 6-3-1 Niijuku, Katsushika-ku, Tokyo 125-8585, Japan.
Abstract:
Accurate modeling of the structural dynamics of ribonucleic acid (RNA) molecules, including common stem-loop motifs, remains challenging. This study presents de novo folding simulations of a diverse set of 26 RNA stem-loops, ranging from 10 to 36 residues, with and without bulges or internal loops, starting from their extended conformations. These simulations employed conventional molecular dynamics using an atomistic force field extensively refined by the Shaw group (TanD., Proc. Natl. Acad. Sci. U.S.A.2018, 115, E1346-E1355, 10.1073/pnas.1713027115) and an implicit solvent model developed by the Simmerling group (NguyenH., J. Chem. Theory Comput.2015, 11, 3714-3728, 10.1021/acs.jctc.5b00271). The 18 stem-loops without bulges or internal loops were folded into their respective structures, retaining all native base pairs in the stem regions. For most of these models, root mean square deviation (RMSD) values relative to experimentally determined structures were <2 Å for stem regions and <5 Å for the molecules. Furthermore, five of the eight stem-loops containing bulges or internal loops were successfully folded into structures with all respective native base pairs in the stem regions. The models initially formed stems directly connected to hairpin loops, followed by the remaining duplex stems between the bulge or internal loop and the terminal. The RMSD values for these structures were 0.9-4.5 Å for the stem regions and 2.8-8.3 Å for the molecules. The RMSD values for the loop regions were approximately 4 Å for all models. Accurate modeling of loop structures remains challenging in simulations using the implicit solvent model. However, our success in recapitulating the RNA stem folding of fundamental stem-loop motifs represents a pivotal step toward enhancing reliable and accurate modeling of RNA structural dynamics.
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