Accurate Modeling in Structural Genomics

结构基因组学的精确建模

• 批准号: 8578932
• 负责人: MICHAEL LEVITT
• 金额: $ 33.53万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8578932
• 关键词: Amino Acid Sequence; Amino Acids; Biological; Cells; Complex; Computers; Cryoelectron Microscopy; Data; Dependency; Development; Drops; Drug Targeting; Electron Microscopy; Ensure; Funding; Generations; Genes; Goals; Heart; Homology Modeling; Life; Macromolecular Complexes; Mass Spectrum Analysis; Measures; Medical; Methods; Modeling; Molecular; Nucleic Acids; Peptide Sequence Determination; Phase; Positioning Attribute; Procedures; Protein Structure Initiative; Protocols documentation; Relative (related person); Reliance; Resolution; Resources; Roentgen Rays; Role; Science; Side; Speed; Structural Biologist; Structure; System; Systems Biology; Technology; Testing; Time; Vertebral column; Work; X-Ray Crystallography; base; chaperonin CCT; combinatorial; computing resources; cost; crosslink; electron density; innovation; particle; protein complex; public health relevance; restraint; sound; structural biology; structural genomics

DESCRIPTION (provided by applicant): Complexes of proteins and nucleic acids are the macromolecular machines at the heart of modern structural biology. We believe that structures of large macromolecular complexes can be solved with less experimental data and at higher throughput. Structures are still solved using methods invented decades ago and model- dependency causes severe problems for large systems solved at low-resolution. Preliminary studies done during the previous funding period show that a way forward is to build a very large number of different models and then test these models directly against the experimental data. This approach has allowed us to assign sequence to a known backbone using much less data than is the norm. Preliminary results show that with suitable built-in statistical controls, this unbiased approach works well for both low-resolution X-ray data as well as mass spectrometry with a small number of experimental cross-links. Our approach is innovative and it determined the detailed atomic structure of chaperonin CCT/TRiC, a 950 kilodalton, 8-gene quasi- degenerate system that could not be solved by conventional methods of cryo-EM or X-ray crystallography. Driven by the central hypothesis that "unbiased methods solve structures with less information and at higher throughput", we have 3 specific aims: 1. Facilitate structure determination by cross-linking and mass spectrometry (XL+MS). With optimized protocols, XL+MS will be applied to the PIC, RIG-I and RdRp systems studied by colleagues at Stanford. 2. Determine and refine spatial-arrangement of macromolecular domains and subunits with cryo- electron microscopy (cryo-EM). After calibrating methods on open form chaperonin CCT, they will be applied to the systems above to simultaneously fit both mass spec and cryo-EM data. 3. Position side chains with R-value exploration of low-resolution X-ray data. All-atom combinatorial homology models will be generated using best practices consistent with the need to generate millions of models. The fit of calculated model X-ray data and that observed (the R-value) will be optimized in an attempt to assign amino acids not seen in low-resolution structures to backbone C-alpha positions. Given the central role of structural biology in medical science, our work if successful, could produce useful structures at higher throughput. With its strong reliance on computational resources, which continue to drop exponentially in cost, these results would be obtained with fewer resources and in less time. Our work would also advance detailed functional and biological studies that are hampered by lack of confidence in side chain positions. Positive impact could be broader in that other problems in structural and systems biology could benefit from the key principles of our approach, namely: eliminate bias by examining millions of possible models that are all equivalent and built to the same consistent specifications. This set of structures then provides a statistical sanity check, showing how much better the best model is than the next best one.

描述（由申请人提供）：蛋白质和核酸的复合物是现代结构生物学核心的大分子机器。我们相信大分子复合物的结构可以用更少的实验数据和更高的通量来解决。结构仍然使用几十年前发明的方法来解决，并且模型依赖性导致在低分辨率下解决的大型系统的严重问题。在上一个供资期间进行的初步研究表明，一个前进的方向是建立大量不同的模型，然后直接根据实验数据对这些模型进行测试。这种方法使我们能够使用比规范少得多的数据将序列分配给已知的主链。初步结果表明，与适当的内置的统计控制，这种无偏的方法工作良好的低分辨率X射线数据以及质谱与少量的实验交联。我们的方法是创新的，它确定了伴侣蛋白CCT/TRiC的详细原子结构，这是一个950千道尔顿的8基因准简并系统，不能通过常规的冷冻EM或X射线晶体学方法解决。在中心假设“无偏方法以更少的信息和更高的吞吐量解决结构”的驱动下，我们有3个具体目标：1。通过交联和质谱法（XL+MS）促进结构测定。通过优化方案，XL+MS将应用于斯坦福大学同事研究的PIC、RIG-I和RdRp系统。2.用低温电子显微镜（cryo-EM）确定和细化大分子结构域和亚基的空间排列。在对开放形式伴侣蛋白CCT校准方法后，将其应用于上述系统以同时拟合质谱和冷冻-EM数据。3.利用低分辨率X射线数据的R值探索定位侧链。全原子组合同源性模型将使用与生成数百万模型的需要一致的最佳实践来生成。将优化计算的模型X射线数据和观察到的数据（R值）的拟合，以尝试将低分辨率结构中未观察到的氨基酸分配到骨架C-α位置。鉴于结构生物学在医学科学中的核心作用，我们的工作如果成功，可以以更高的通量产生有用的结构。由于其对计算资源的强烈依赖，其成本继续呈指数级下降，这些结果将以更少的资源和更少的时间获得。我们的工作还将推进详细的功能和生物学研究，这些研究受到对侧链位置缺乏信心的阻碍。积极的影响可能会更广泛，因为结构和系统生物学中的其他问题可以从我们方法的关键原则中受益，即：通过检查数百万个可能的模型来消除偏见，这些模型都是等效的，并且是按照相同的一致规范构建的。然后，这组结构提供了一个统计上的健全性检查，显示出最好的模型比下一个最好的模型好多少。