Structure Prediction of RNA Loops with a Probabilistic Approach

The knowledge of the tertiary structure of RNA loops is important for understanding their functions. In this work we develop an efficient approach named RNApps, specifically designed for predicting the tertiary structure of RNA loops, including hairpin loops, internal loops, and multi-way junction loops. It includes a probabilistic coarse-grained RNA model, an all-atom statistical energy function, a sequential Monte Carlo growth algorithm, and a simulated annealing procedure. The approach is tested with a dataset including nine RNA loops, a 23S ribosomal RNA, and a large dataset containing 876 RNAs. The performance is evaluated and compared with a homology modeling based predictor and an ab initio predictor. It is found that RNApps has comparable performance with the former one and outdoes the latter in terms of structure predictions. The approach holds great promise for accurate and efficient RNA tertiary structure prediction.Author Summary: RNA is an important and versatile macromolecule participating in a variety of biological processes. In addition to experimental approaches, computational prediction of 3D structure of RNAs and loops is an alternative and important source of gaining structure information and insights into their functions. The prediction of RNA loop structures is of particular interest since RNA functions often reside in the loop regions and about 46% of nucleotides in an RNA chain remain unpaired. For this purpose, we develop an approach RNApps based on a probabilistic coarse-grained RNA model. The probabilistic nature of the model, together with a sequential Monte Carlo (SMC) growth algorithm, allows a natural and continuous sampling of structures in 3D space, making the approach unique. The coarse-graining nature of the model further increases the efficiency. Here we test this new approach with various types of loops, including hairpin loops, internal loops, and multi-way junction loops, and make comparisons with other structure predictors.