The Crystallography of Macromolecules

描述(申请人提供)：《大分子结晶学》提案解决了X射线结晶学领域中衍射数据分析方法的局限性。这项工作的重要性取决于这项技术的重要性，该技术在原子水平上产生关于细胞过程的独特而详细的信息。结晶学获得的结构结果被用来解释和验证其他生物物理、生化和细胞生物学技术获得的结果，为细胞过程的详细研究产生假说，并指导药物设计研究--所有这些都与NIH的任务高度相关。该提案的重点是开发方法，以解决晶体尺寸和顺序不足以从单晶体获得结构的常见情况。这在大型真核复合体和膜蛋白的情况下尤其常见，在这些情况下，结构信息对NIH任务最有价值。单晶的衍射率与样品的微观有序度和尺寸直接相关。它也是结构解决方案成功的主要相关因素之一。用来解决单晶情况下数据不足问题的方法是使用多个晶体并在它们之间平均数据，这允许检索甚至非常低的信号。然而，同一蛋白质的不同晶体，即使它们非常相似，即具有相同的晶格对称性和非常相似的晶胞尺寸，仍然具有稍微不同的顺序。这种非同构通常高到足以使他们不可能用平均数据求解。此外，多个数据集的使用使决策变得复杂，因为每个数据集包含不同的信息，并且不清楚何时以及如何组合它们。建议的解决方案依赖于层次分析。首先，将使用一种新的方法(目标1)来模拟衍射点轮廓的形状。这将为下一步奠定基础，在该步骤中，将实现来自多个晶格的重叠布拉格光斑轮廓的反卷积(目标2)。在Aim 1中开发的算法的另一个好处是，它们将自动派生积分参数并识别伪像，从而使整个过程更加健壮。这对于高通量和多晶体分析尤其重要。在目标3中，将对来自多个晶体的数据进行比较，以确定应合并以产生最佳结果的数据子集。这一分析的关键方面将是识别和评估数据集之间的非同构。实验决策策略是目标4的主题。支持向量机(SVM)方法将用于评估可用数据集对可能的结构求解方法的适用性。在数据不足的情况下，它将确定需要改进的最重要因素。目标5是简化数据减少的导航，并将先前目标的结果与硬件和计算方面的其他改进结合起来。

DESCRIPTION (provided by applicant): The proposal "The Crystallography of Macromolecules" addresses the limitations of diffraction data analysis methods in the field of X-ray crystallography. The significance of this work is determined by the importance of the technique, which generates uniquely-detailed information about cellular processes at the atomic level. The structural results obtained with crystallography are used to explain and validate results obtain by other biophysical, biochemical and cell biology techniques, to generate hypotheses for detailed studies of cellular process and to guide drug design studies - all of which are highly relevant to NIH mission. The proposal focuses on method development to address a frequent situation, where the crystal size and order is insufficient to obtain a structure from a single crystal. This is particularly frequent in cases of large eukaryotic complexes and membrane proteins, where the structural information is the most valuable to the NIH mission. The diffraction power of a single crystal is directly related to the microscopic order and size of that specimen. It is also one of the main correlates of structure solution success. The method used to solve the problem of data insufficiency in the case of a single crystal is to use multiple crystals and to average data between them, which allows to retrieve even very low signals. However, different crystals of the same protein, even if they are very similar i.e. have the same crystal lattice symmetry and very similar unit cell dimensions, still are characterized by a somewhat different order. This non-isomorphism is often high enough to make their solution with averaged data impossible. Moreover, the use of multiple data sets complicates decision making as each of the datasets contains different information and it is not clear when and how to combine them. The proposed solution relies on hierarchical analysis. First, the shape of the diffraction spot profiles will be modeled using a novel approach (Aim 1). This will form the ground for the next step, in which deconvolution of overlapping Bragg spot profiles from multiple lattices will be achieved (Aim 2). An additional benefit of algorithms developed in Aim 1 is that they will automatically derive the integration parameters and identify artifacts, making the whole process more robust. This is particularly significant for high-throughput and multiple crystal analysis. In Aim 3, comparison of data from multiple crystals will be performed to identify subsets of data that should be merged to produce optimal results. The critical aspect of this analysis will be the identification and assessment of non- isomorphism between datasets. The experimental decision-making strategy is the subject of Aim 4. The Support Vector Machine (SVM) method will be used to evaluate the suitability of available datasets for possible methods of structure solution. In cases of insufficient data it will identify the most significant factor that needs to be improved. Aim 5 is to simplify navigation of data reduction and to integrate the results of previous aims with other improvements in hardware and computing.