The Crystallography of Macromolecules

描述（由申请人提供）：“The Crystallography of Macromolecules”提案解决了衍射数据分析方法在x射线晶体学领域的局限性。这项工作的重要性是由这项技术的重要性决定的，这项技术可以在原子水平上产生关于细胞过程的独特详细信息。晶体学获得的结构结果用于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设，并指导药物设计研究——所有这些都与NIH的使命高度相关。该提案侧重于解决晶体尺寸和顺序不足以从单晶中获得结构的常见情况的方法开发。这在大型真核复合体和膜蛋白的情况下尤其常见，其中结构信息对NIH的使命最有价值。单晶的衍射能力直接关系到样品的微观顺序和尺寸。它也是结构解决成功的主要相关因素之一。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单位细胞尺寸，其特征仍然是有些不同的顺序。这种非同构性通常足够高，使得它们的解决方案不可能使用平均数据。此外，使用多个数据集使决策复杂化，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。提出的解决方案依赖于层次分析。首先，衍射斑轮廓的形状将使用一种新的方法（目标1）进行建模。这将为下一步奠定基础，其中将实现来自多个晶格的重叠布拉格斑剖面的反卷积（目标2）。Aim 1中开发的算法的另一个好处是，它们将自动派生集成参数并识别工件，使整个过程更加健壮。这对于高通量和多晶分析尤其重要。在Aim 3中，将对来自多个晶体的数据进行比较，以确定应该合并以产生最佳结果的数据子集。这种分析的关键方面将是识别和评估数据集之间的非同构性。实验决策策略是Aim 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是简化数据缩减的导航，并将先前目标的结果与硬件和计算方面的其他改进相结合。

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.