Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan.
J Chem Inf Model. 2012 Feb 27;52(2):557-67. doi: 10.1021/ci2003413. Epub 2012 Jan 26.
We developed a method, called RNA Assembler using Secondary Structure Information Effectively (RASSIE), for predicting RNA tertiary structures using known secondary structure information. We attempted a fragment assembly-based method that uses a secondary structure-based fragment library. For several typical target structures such as stem-loops, bulge-loops, and 2-way junctions, our method provided numerous good quality candidate structures in less computational time than previously proposed methods. By using a high-resolution potential energy function, we were able to select good predicted structures from candidate structures. This method of efficient conformational search and detailed structure evaluation using high-resolution potential is potentially useful for the tertiary structure prediction of RNA.
我们开发了一种名为 RNA Assembler using Secondary Structure Information Effectively (RASSIE) 的方法，用于利用已知的二级结构信息预测 RNA 的三级结构。我们尝试了一种基于片段组装的方法，该方法使用基于二级结构的片段库。对于一些典型的靶结构，如茎环、突环和 2 向连接，我们的方法在比以前提出的方法更少的计算时间内提供了许多高质量的候选结构。通过使用高分辨率的势能函数，我们能够从候选结构中选择出良好的预测结构。这种使用高分辨率势能进行高效构象搜索和详细结构评估的方法，对于 RNA 的三级结构预测可能是有用的。
