Correcting Pervasive Errors in RNA Crystallography with Rosetta

描述(申请人提供)：不断发现的非编码RNA及其在细胞和病毒机制中的关键作用正在激发基于干扰或操纵所涉及的RNA的新型抗菌、抗肿瘤和抗病毒治疗。RNA的大部分生物学功能依赖于复杂的3D结构的形成以及与配体和蛋白质的结合。不幸的是，结晶学模型，我们最丰富的RNA结构信息来源，由于在手动将RNA骨架拟合到实验密度图中时存在模糊性，因此包含普遍的错误。我们最近将Rosetta高分辨率RNA结构预测与基于PHENIX衍射的精化和MolProbity验证相结合，在Rosetta下创建了电子密度辅助的枚举实空间精化。ERRASER方法纠正了包括核糖体亚基在内的RNA数据集基准中的大多数可识别的糖折叠错误、空间冲突、可疑的主干旋转体和错误的键长度/角度。此外，平均而言，该方法改进了RFree因子，以严格搁置数据。在这项探索性拨款中，我们首先旨在扩展ERRASER，以解决RNA/配体、RNA/蛋白质和RNA晶体接触的歧义，这将是纠正RNA酶活性部位、配体结合部位和核糖核蛋白机器所必需的。其次，我们的目标是使ERRASER成为一个全自动化的服务器，既可以完善所有现有的PDB沉积的RNA和核糖核蛋白模型，又能够使结晶学家快速纠正他们未来数据集中的错误。通过快速和系统地消除RNA模型的歧义，ERRASER将使RNA结晶学的误差大大减少。

DESCRIPTION (provided by applicant): The continuing discoveries of non-coding RNAs and their critical roles in cellular and viral machinery are inspiring novel antibacterial, antitumor, nd antiviral therapies based on disrupting or manipulating the RNAs involved. Most of RNA's biological functions depend on the formation of intricate 3D structure and binding to ligands and proteins. Unfortunately, crystallographic models, our richest sources of RNA structural information, contain pervasive errors due to ambiguities in manually fitting RNA backbones into experimental density maps. We have recently brought Rosetta high- resolution RNA structure prediction together with PHENIX diffraction-based refinement and MolProbity validation, to create Enumerative Real-space Refinement ASsisted by Electron density under Rosetta. The ERRASER method corrects the majority of identifiable sugar pucker errors, steric clashes, suspicious backbone rotamers, and incorrect bond lengths/angles in a benchmark of RNA data sets, including a ribosomal subunit. Furthermore, the method, on average, improves Rfree factors to rigorously set- aside data. In this exploratory grant, we first aim to expand ERRASER to resolve ambiguities at RNA/ligand, RNA/protein, and RNA crystal contacts, as will be necessary for correcting RNA enzyme active sites, ligand binding sites, and ribonucleoprotein machines. Second, we aim to make ERRASER available as a fully automated server that will both refine all extant PDB-deposited RNA and ribonucleoprotein models and enable crystallographers to rapidly correct errors in their future data sets. By rapidly and systematicall disambiguating RNA model fitting, ERRASER will enable RNA crystallography with significantly fewer errors.