Ab initio Prediction of RNA Nucleotide Interactions with Backbone k-Tree Model

Abstract: Given the importance of non-coding RNAs to cellular regulatory functions and rapid growth of RNA transcripts, computational prediction of RNA tertiary structure remains highly demanded yet significantly 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 gain computational feasibility. However native conformations may not be examined and prediction accuracy may be compromised due to sampling. In particular, the state-of-the-art methods have yet to deliver the desired prediction performance for RNAs of length beyond 50. This paper presents the work to tackle a key step in the RNA tertiary structure prediction problem, the prediction of the nucleotide interactions that constitute the desired tertiary structure. The research is established upon a novel graph model, called backbone k-tree, to markably constrain nucleotide interaction relationships in RNA tertiary structure. It is shown that the new model makes it possible to efficiently predict the optimal set of nucleotide interactions from the query sequence, including the interactions in all recently revealed families. Evident by the preliminary results, the new method can predict with a high accuracy the nucleotide interactions that constitute the tertiary structure of the query sequence, thus providing a viable solution towards ab initio prediction of RNA tertiary structure.