Correcting Pervasive Errors in RNA Crystallography with Rosetta

使用 Rosetta 纠正 RNA 晶体学中普遍存在的错误

基本信息

批准号：
8545874
负责人：
Rhiju Das
金额：
$ 18.71万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-09-30 至 2016-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8545874
关键词：
Active Sites Algorithms Anti-Bacterial Agents Antiviral Therapy Bacteria Benchmarking Binding Binding Sites Biological Process Catalytic RNA Collaborations Computer software Crystallography Data Data Set Deposition Disease Ensure Enzymes Evaluation Exhibits Functional RNA Funding Future Genetic Goals Grant HIV Hour Image Length Letters Libraries Life Ligand Binding Ligands Manuals Maps Methods Modeling Monitor Nucleotides Organism Play Positioning Attribute Process Protein Biosynthesis Proteins Publications RNA RNA Folding Regulation Renaissance Research Resolution Retroviridae Ribonucleoproteins Ribosomes Role Small RNA Source Structure United States National Institutes of Health Validation Vertebral column Viral Visual Work X ray diffraction analysis X-Ray Diffraction base computerized tools conformer density electron density improved inorganic phosphate neoplastic cell novel success sugar three dimensional structure tool

项目摘要

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.

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 作为完全自动化的服务器可用，它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。