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.

RNA环的三级结构知识对于理解其功能具有重要意义。本研究开发了一种名为RNApps的高效方法，专门用于预测RNA环的三级结构，涵盖发夹环、内部环及多分支环。该方法包含概率性粗粒度RNA模型、全原子统计能量函数、序贯蒙特卡洛生长算法以及模拟退火流程。我们采用包含9个RNA环、1条23S核糖体RNA的数据集，以及包含876条RNA的大型数据集对该方法进行测试。通过与基于同源建模的预测器和从头算预测器进行对比评估，结果表明RNApps的性能与前者不相上下，且在结构预测任务中优于后者。该方法在精准高效的RNA三级结构预测领域展现出巨大应用潜力。