Li Jun, Zhang Jian, Wang Jun, Li Wenfei, Wang Wei
National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, China.
PLoS Comput Biol. 2016 Aug 5;12(8):e1005032. doi: 10.1371/journal.pcbi.1005032. eCollection 2016 Aug.
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.
了解RNA环的三级结构对于理解其功能至关重要。在这项工作中，我们开发了一种名为RNApps的高效方法，专门用于预测RNA环的三级结构，包括发夹环、内环和多分支环。它包括一个概率性粗粒度RNA模型、一个全原子统计能量函数、一个序贯蒙特卡罗生长算法和一个模拟退火过程。该方法用一个包含九个RNA环、一个23S核糖体RNA的数据集以及一个包含876个RNA的大型数据集进行了测试。对其性能进行了评估，并与基于同源建模的预测器和从头预测器进行了比较。结果发现，RNApps与前者具有相当的性能，并且在结构预测方面优于后者。该方法在准确高效的RNA三级结构预测方面具有很大的前景。
