Correcting Pervasive Errors in RNA Crystallography with Rosetta

描述（由申请人提供）：非编码rna及其在细胞和病毒机制中的关键作用的不断发现激发了基于破坏或操纵相关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三维结构的形成以及与配体和蛋白质的结合。不幸的是，晶体学模型，我们最丰富的RNA结构信息来源，由于人工拟合RNA主干到实验密度图的模糊性，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衍射的细化和MolProbity验证结合在一起，在Rosetta下创建了电子密度辅助的枚举实空间细化。ERRASER方法纠正了基准RNA数据集（包括核糖体亚基）中大多数可识别的糖皱错误、立体冲突、可疑的骨干旋转和不正确的键长/角度。此外，平均而言，该方法提高了Rfree因子以严格保留数据。在这项探索性资助中，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酶活性位点，配体结合位点和核糖核蛋白机器所必需的。其次，我们的目标是使ERRASER作为一个全自动服务器可用，这将改进所有现有的pdb沉积RNA和核糖核蛋白模型，并使晶体学家能够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模型拟合的歧义，ERRASER将使RNA晶体学具有显着更少的错误。

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.