Native-like RNA Tertiary Structures Using a Sequence-Encoded Cleavage Agent and Refinement by Discrete Molecular Dynamics

Abstract

The difficulty of analyzing higher order RNA structure, especially for folding intermediates and for RNAs whose functions require domains that are conformationally flexible, emphasizes the need for new approaches for modeling RNA tertiary structure accurately. Here, we report a concise approach that makes use of facile RNA structure probing experiments that are then interpreted using a computational algorithm, carefully tailored to optimize both the resolution and refinement speed for the resulting structures, without requiring user intervention. The RNA secondary structure is first established using SHAPE chemistry. We then use a sequence-directed cleavage agent, that can be placed arbitrarily in many helical motifs, to obtain high quality inter-residue distances. We interpret this in-solution chemical information using a fast, coarse grained, discrete molecular dynamics engine in which each RNA nucleotide is represented by pseudoatoms for the phosphate, ribose and nucleobase groups. By this approach, we refine base paired positions in yeast tRNA Asp to 4 Å RMSD without any preexisting information or assumptions about secondary or tertiary structures. This blended experimental and computational approach has the potential to yield native-like models for the diverse universe of functionally important RNAs whose structures cannot be characterized by conventional structural methods.

Single-molecule correlated chemical probing of RNA

“…Because RING analysis identifies dense arrays of nucleotide interdependencies reflective of RNA tertiary structure, we explored whether these interactions could be used as restraints to model 3D RNA folds. A small number of constraints, reflective of through-space RNA structure, are often sufficient to yield high-quality structure models (15,16). We used a two-step interaction potential (Fig.…”

“…We used a two-step interaction potential (Fig. S6 A-C) to introduce free-energy bonuses when constituent nucleotides come into proximity during discrete molecular dynamics simulation (15)(16)(17). Introduction of RING constraints caused each RNA to preferentially sample collapsed states during the simulation (Fig.…”

RNA Junction Motifs as Scaffolds for Construction of Multifunctional RNA Nanoparticles

“…In the bottom-up approaches of RNA nanotechnology, RNA building blocks are engineered to self-assemble into nanoscale materials with applications in nanomedicine and nanodevices (Guo, 2005). Computational modeling of RNA 3D structures, which accounts for noncanonical base-base pairs (Das et al, 2010), long-range tertiary interactions (Gherghe et al, 2009;Lavender et al, 2010), and iondependent folding (Draper et al, 2005), can help design the building blocks, predict the final structure, and characterize the assembly kinetics. The major challenges of computational RNA 3D structure modeling come from the vast conformational space of RNA and inaccuracy in the force field describing RNA folding.…”

“…For example, the selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) chemistry developed by Weeks and colleagues (Deigan et al, 2009;Weeks, 2010) characterizes the probability of base pairing for each nucleotide. Other experiments such as fluorescence resonance energy transfer (FRET) (Rueda et al, 2004), cross-linking (Harris et al, 1994;Pinard et al, 2001;Yu et al, 2008), and tethered hydroxyl radical probing (t-HRP) (Das et al, 2008;Gherghe et al, 2009) can probe internucleotide distances. The solvent accessibility of individual nucleotides can also be explored by solution hydroxyl radical probing (HRP) experiments (Cate et al, 1996;Pastor et al, 2000;Tullius and Greenbaum, 2005).…”

“…The solvent accessibility of individual nucleotides can also be explored by solution hydroxyl radical probing (HRP) experiments (Cate et al, 1996;Pastor et al, 2000;Tullius and Greenbaum, 2005). Incorporation of experimentally derived structural information with computational modeling can markedly reduce the allowed conformational space and thereby facilitate the computational prediction of native RNA ensembles (Das et al, 2008;Gherghe et al, 2009;Jonikas et al, 2009;Lavender et al, 2010;Yang et al, 2010;Yu et al, 2008).…”