Correcting Pervasive Errors in RNA Crystallography with Rosetta

描述（由申请人提供）：非编码 RNA 的不断发现及其在细胞和病毒机制中的关键作用正在激发基于破坏或操纵相关 RNA 的新型抗菌、抗肿瘤和抗病毒疗法。 RNA 的大部分生物学功能取决于复杂 3D 结构的形成以及与配体和蛋白质的结合。不幸的是，晶体学模型是我们最丰富的 RNA 结构信息来源，但由于手动将 RNA 主链拟合到实验密度图中的模糊性，存在普遍的错误。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 下创建由电子密度辅助的枚举实空间细化。 ERRASER 方法纠正了 RNA 数据集（包括核糖体亚基）基准中大多数可识别的糖褶皱错误、空间冲突、可疑的主链旋转异构体以及不正确的键长/角度。此外，该方法平均将 Rfree 因子改进为严格预留的数据。在这项探索性资助中，我们的首要目标是扩展 ERRASER，以解决 RNA/配体、RNA/蛋白质和 RNA 晶体接触方面的歧义，这对于纠正 RNA 酶活性位点、配体结合位点和核糖核蛋白机器是必要的。其次，我们的目标是使 ERRASER 作为全自动服务器使用，既可以完善所有现有的 PDB 沉积的 RNA 和核糖核蛋白模型，又使晶体学家能够快速纠正未来数据集中的错误。通过快速、系统地消除 RNA 模型拟合的歧义，ERRASER 将使 RNA 晶体学的错误显着减少。 公共健康相关性：RNA 分子在所有生命系统（包括致病细菌、HIV 等逆转录病毒和肿瘤细胞）中传递和调节遗传信息方面发挥着重要作用。由于我们对 RNA 如何折叠成复杂的 3D 结构的理解不完善，针对这些 RNA 的新的潜在挽救生命的疗法受到阻碍。我们的工作目标是开发一种新工具来纠正 RNA 晶体学模型中普遍存在的错误，RNA 晶体学模型是我们最丰富的 3D 信息来源。

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. PUBLIC HEALTH RELEVANCE: RNA molecules play fundamental roles in transmitting and regulating genetic information in all living systems, including disease-causing bacteria, retroviruses like HIV, and tumor cells. New potentially life-saving therapies that target these RNAs are being hindered by our imperfect understanding of how RNAs fold into intricate 3D structures. Our work aims to develop a new tool that corrects pervasive mistakes in RNA crystallographic models, which are our richest sources of 3D information.