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Model evaluation is a necessary step for better prediction and design of three-dimensional RNA structures.For proteins, this has been widely studied and the knowledge-based statistical potential has been proved to be one of effective ways to solve this problem.Currently, a few knowledge-based statistical potentials have also been proposed to evaluate predicted models of RNA tertiary structures.The benchmark tests showed that they can identify the native structures effectively but further improvements are needed to identify near-native structures and those with non-canonical base pairs.Here we present a novel knowledge-based potential, 3dRNAscore, which combines distance-based and dihedral-based energies.The benchmarks on different testing datasets all show that 3dRNAscore are more efficient than other existing scoring methods (mainly knowledge-based statistical potential) in recognizing native state from a pool of near-native states of RNA as well as in ranking near-native states of RNA models generated by different ways.