Accurate RNA 3D modeling with backbone k-tree model
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Given the importance of non-coding Ribonucleic acids (RNAs) to cellular regulatory functions, it would be highly desirable to have accurate computational prediction of RNA 3D structure, a task which remains challenging. Even for a short RNA sequence, the space of tertiary conformations is immense; existing methods to identify native-like conformations mostly resort to random sampling of conformations to achieve computational feasibility. However native conformations may not be examined and prediction accuracy may be compromised due to sampling. State-of-the-art methods have yet to deliver satisfactory predictions for RNAs of length beyond 50 nucleotides. This dissertation presents a novel 3D modeling method for RNA 3D prediction from predicted nucleotide interactions. The research is based on a novel graph model, called a backbone k-tree, to tightly constrain the nucleotides conformation considered for RNA 3D structures with the objective function defined over cliques of the k-tree. It is shown that the model can efficiently predict the optimal and suboptimal structures in atomic detail from the query interactions along with the k-tree. The results indicate that in most cases the new model can predict the 3D structure with a high accuracy. It thus provides a useful tool for the accurate prediction of RNA 3D structure.