Jain Swati, Schlick Tamar
Department of Chemistry, New York University, 1001 Silver, 100 Washington Square East, New York, NY 10003, USA.
Department of Chemistry, New York University, 1001 Silver, 100 Washington Square East, New York, NY 10003, USA; Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA; New York University-East China Normal University Center for Computational Chemistry at New York University Shanghai, Room 340, Geography Building, North Zhongshan Road, 3663 Shanghai, China.
J Mol Biol. 2017 Nov 24;429(23):3587-3605. doi: 10.1016/j.jmb.2017.09.017. Epub 2017 Oct 5.
Coarse-grained models represent attractive approaches to analyze and simulate ribonucleic acid (RNA) molecules, for example, for structure prediction and design, as they simplify the RNA structure to reduce the conformational search space. Our structure prediction protocol RAGTOP (RNA-As-Graphs Topology Prediction) represents RNA structures as tree graphs and samples graph topologies to produce candidate graphs. However, for a more detailed study and analysis, construction of atomic from coarse-grained models is required. Here we present our graph-based fragment assembly algorithm (F-RAG) to convert candidate three-dimensional (3D) tree graph models, produced by RAGTOP into atomic structures. We use our related RAG-3D utilities to partition graphs into subgraphs and search for structurally similar atomic fragments in a data set of RNA 3D structures. The fragments are edited and superimposed using common residues, full atomic models are scored using RAGTOP's knowledge-based potential, and geometries of top scoring models is optimized. To evaluate our models, we assess all-atom RMSDs and Interaction Network Fidelity (a measure of residue interactions) with respect to experimentally solved structures and compare our results to other fragment assembly programs. For a set of 50 RNA structures, we obtain atomic models with reasonable geometries and interactions, particularly good for RNAs containing junctions. Additional improvements to our protocol and databases are outlined. These results provide a good foundation for further work on RNA structure prediction and design applications.
粗粒度模型是分析和模拟核糖核酸(RNA)分子的一种有吸引力的方法,例如用于结构预测和设计,因为它们简化了RNA结构以减少构象搜索空间。我们的结构预测协议RAGTOP(RNA-As-Graphs Topology Prediction)将RNA结构表示为树形图,并对图拓扑进行采样以生成候选图。然而,为了进行更详细的研究和分析,需要从粗粒度模型构建原子模型。在这里,我们提出了基于图的片段组装算法(F-RAG),将RAGTOP生成的候选三维(3D)树形图模型转换为原子结构。我们使用相关的RAG-3D实用程序将图划分为子图,并在RNA 3D结构数据集中搜索结构相似的原子片段。使用公共残基对片段进行编辑和叠加,使用RAGTOP基于知识的势对完整的原子模型进行评分,并对得分最高的模型的几何结构进行优化。为了评估我们的模型,我们评估了相对于实验解析结构的全原子均方根偏差(RMSD)和相互作用网络保真度(一种残基相互作用的度量),并将我们的结果与其他片段组装程序进行比较。对于一组50个RNA结构,我们获得了具有合理几何结构和相互作用的原子模型,对于含有连接的RNA尤其适用。概述了我们的协议和数据库的进一步改进。这些结果为RNA结构预测和设计应用的进一步工作提供了良好的基础。