Graph-based sampling for approximating global helical topologies of RNA

Significance RNA molecules are important components of the cellular machinery and perform many essential roles, including catalysis, transcription, and regulation. Because the structural features are intimately connected to their biological functions, there is great interest in predicting RNA structure from sequence. Present RNA 3D folding algorithms are limited to small RNA structures due to inefficient sampling of RNA structure space. We report a computational approach to predict RNA 3D topologies based on hierarchical sampling of RNA 3D candidate topologies represented as 3D graphs guided by geometrical measures based on known structures. The combination of tools shows great promise for assembling global features of RNA architecture. Applications to RNA design can be envisioned. A current challenge in RNA structure prediction is the description of global helical arrangements compatible with a given secondary structure. Here we address this problem by developing a hierarchical graph sampling/data mining approach to reduce conformational space and accelerate global sampling of candidate topologies. Starting from a 2D structure, we construct an initial graph from size measures deduced from solved RNAs and junction topologies predicted by our data-mining algorithm RNAJAG trained on known RNAs. We sample these graphs in 3D space guided by knowledge-based statistical potentials derived from bending and torsion measures of internal loops as well as radii of gyration for known RNAs. Graph sampling results for 30 representative RNAs are analyzed and compared with reference graphs from both solved structures and predicted structures by available programs. This comparison indicates promise for our graph-based sampling approach for characterizing global helical arrangements in large RNAs: graph rmsds range from 2.52 to 28.24 Å for RNAs of size 25–158 nucleotides, and more than half of our graph predictions improve upon other programs. The efficiency in graph sampling, however, implies an additional step of translating candidate graphs into atomic models. Such models can be built with the same idea of graph partitioning and build-up procedures we used for RNA design.

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