Accurate Modeling in Structural Genomics

描述(申请人提供)：蛋白质和核酸的复合体是现代结构生物学的核心大分子机器。我们相信，大分子络合物的结构可以用更少的实验数据和更高的吞吐量来解决。结构仍然是用几十年前发明的方法来解决的，模型依赖会给以低分辨率解决的大系统带来严重的问题。在前一次筹资期间所做的初步研究表明，今后的一个办法是建立大量不同的模型，然后直接根据实验数据测试这些模型。这种方法使我们能够使用比标准少得多的数据将序列分配给已知的主干。初步结果表明，在适当的内置统计控制下，这种无偏见的方法既适用于低分辨率X射线数据，也适用于具有少量实验交叉连接的质谱学。我们的方法是创新的，它确定了伴侣CCT/TIC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，不能用传统的冷冻-EM或X射线结晶学方法解决。在“无偏方法以更少的信息量和更高的吞吐量解决结构”这一中心假设的驱动下，我们有三个具体的目标：1.通过交联和质谱学(XL+MS)促进结构的确定。随着协议的优化，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)测定和优化大分子结构域和亚基的空间排列。在CCT中对开放形式伴侣的方法进行校准后，它们将被应用于上述系统，以同时拟合质量谱和低温电子显微镜数据。3.利用低分辨率X射线数据的R值探测定位侧链。将使用与生成数百万个模型的需要相一致的最佳做法来生成全原子组合同源模型。计算模型X射线数据和观测数据(R值)的拟合将得到优化，以尝试将低分辨率结构中未见的氨基酸分配给主干C-α位置。鉴于结构生物学在医学中的核心作用，我们的工作如果成功，可以以更高的产量生产有用的结构。由于其对计算资源的强烈依赖，而计算资源的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，这些研究因对侧链位置缺乏信心而受阻。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受益于我们方法的关键原则，即：通过审查数百万个所有等价并建立在相同一致规范上的可能模型来消除偏见。然后，这组结构提供了一种统计的健全性检查，显示最佳模型比次佳模型好多少。

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.