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Namhee Kim, Christian Laing, Segun Jung, Shereef Elmetwaly, Jeremy Curuksu and Tamar Schlick, New York University A current challenge in RNA structure prediction is a description of global helical arrangements compatible with a given RNA secondary structures. I present a hierarchical Monte Carlo approach to describe RNA helical geometries by a coarse-grained sampling of 3D graphs guided by knowledge-based potentials derived from bend, twist, and radii of gyration measures based on known structures. The coarse-grained model using newly developed 3D RNA graphs accelerates the global sampling of candidate RNA topologies. A comparison of predicted graphs to reference graphs from both solved structures and predicted structures using other programs indicates promise for this graph-based sampling approach for characterizing 3D global helical arrangements in large RNAs from a given secondary structure. The final topologies also offer reasonable candidates for further refinement. |
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