Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters
Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters
复制标题
DOI：
10.1261/rna.1270809
复制
发表时间：
2009-02-01
期刊：
影响因子：
4.5
通讯作者：
Altman, Russ B.
中科院分区：
文献类型：
--
作者：
Jonikas, Magdalena A.;Radmer, Randall J.;Altman, Russ B.
理解复杂RNA分子的功能关键在于理解它们的结构。然而，创建RNA的三维（3D）结构模型仍然是一个重大的挑战。我们提出了一种用于RNA建模的协议（核酸模拟工具[NAST]），该协议在粗粒度分子动力学引擎中使用基于RNA特定知识的潜力来生成合理的3D结构。我们证明NAST的能力，仅使用二级结构和三级接触预测生成，集群，和排名结构。对于酵母苯丙氨酸tRNA和嗜热四膜虫I组内含子的P4-P6结构域，最佳排序簇中的代表性结构分别平均为8.0 +/-0.3 A和16.3 +/-1.0 ARMSD。粗粒度的分辨率使我们能够模拟大分子，如158个残基的P4-P6或388个残基的T。嗜热菌I组内含子。NAST的一个优点是能够根据它们与实验数据的兼容性对结构相似的诱饵集群进行排名。我们成功地使用理想的小角度X射线散射数据和理想的和实验的溶剂可及性数据来选择tRNA和P4-P6的最佳结构簇。最后，我们使用NAST在Azoarcus和Twort核酶的晶体结构中构建缺失的环，并将晶体学数据纳入T.嗜热菌组I内含子，创建整个分子的集成模型。我们的软件包可在https://simtk.org/home/nast上免费获得。
Understanding the function of complex RNA molecules depends critically on understanding their structure. However, creating three-dimensional (3D) structural models of RNA remains a significant challenge. We present a protocol (the nucleic acid simulation tool [NAST]) for RNA modeling that uses an RNA-specific knowledge-based potential in a coarse-grained molecular dynamics engine to generate plausible 3D structures. We demonstrate NAST's capabilities by using only secondary structure and tertiary contact predictions to generate, cluster, and rank structures. Representative structures in the best ranking clusters averaged 8.0 +/- 0.3 A and 16.3 +/- 1.0 angstrom RMSD for the yeast phenylalanine tRNA and the P4-P6 domain of the Tetrahymena thermophila group I intron, respectively. The coarse-grained resolution allows us to model large molecules such as the 158-residue P4-P6 or the 388-residue T. thermophila group I intron. One advantage of NAST is the ability to rank clusters of structurally similar decoys based on their compatibility with experimental data. We successfully used ideal small-angle X-ray scattering data and both ideal and experimental solvent accessibility data to select the best cluster of structures for both tRNA and P4-P6. Finally, we used NAST to build in missing loops in the crystal structures of the Azoarcus and Twort ribozymes, and to incorporate crystallographic data into the Michel-Westhof model of the T. thermophila group I intron, creating an integrated model of the entire molecule. Our software package is freely available at https://simtk.org/home/nast.