X-ray data analysis in the presence of structural variability

存在结构变异时的 X 射线数据分析

• 批准号: 9552204
• 负责人: WLADEK MINOR
• 金额: $ 33.52万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-22 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9552204
• 关键词: Address; Affect; Automation; Behavior; Biological; Biological Process; Cell physiology; Characteristics; Communities; Complex; Complication; Computer software; Crystallization; Data; Data Analyses; Data Collection; Data Quality; Data Set; Descriptor; Dimensions; Disease; Drug Design; Foundations; Health; Individual; Internet; Investigation; Ligand Binding; Ligands; Maps; Measures; Methodology; Methods; Mission; Modeling; Molecular; Morphologic artifacts; Noise; Online Systems; Outcome; Pharmaceutical Preparations; Phase; Procedures; Property; Radiation-Induced Change; Research Design; Research Personnel; Residual state; Roentgen Rays; Sampling; Signal Transduction; Software Tools; Source; Specimen; Speed; Structural Models; Structure; System; Techniques; Temperature; Uncertainty; United States National Institutes of Health; Validation; Weight; Work; X ray diffraction analysis; X-Ray Crystallography; base; combinatorial; computerized data processing; data space; data structure; design; detector; electron density; experience; experimental study; innovation; interest; method development; novel; novel strategies; public health relevance; structural biology; tool

 DESCRIPTION (provided by applicant): The proposal "X-ray data analysis in the presence of structural variability" aims to advance diffraction data analysis methods so that the variability between crystals and within crystals is optimally modeled during data processing in reciprocal space and during structural analysis in real space. The significance of the proposed work results from the importance of the technique, which generates uniquely-detailed information. X-ray structures are used to understand cellular processes at the atomic level directly, to explain and validate results obtain by other techniques, to generate hypotheses for detailed studies of cellular process, and to guide drug design studies - all of which are highly relevant to the NIH mission. Macromolecular crystals are frequently of limited size and crystal lattice order. Both may result in the need for combining data from multiple crystals for successful structure solution, with the limited order generating diffraction artifacts and correlating with non-isomorphism between different specimens. Non-isomorphism hinders the averaging of data sets from multiple crystals, because for successful averaging, data need to be very similar. The problems with averaging are compounded by incompleteness of the data in a single data set, radiation-induced changes in the crystal under investigation, and lack of statistical measures that would inform experimenters regarding whether or not the data analysis is progressing in the right direction. There are also technical challenges associated with averaging multiple data sets that result from the combinatorial complexity of data analysis when a large number of data sets need to be analyzed. Final difficulty appears when the analysis of the structural results obtained from multi-crystal experiments must separate the desired biological signals, e.g. the presence of a ligand or a specific dynamic behavior of the molecules, from the noise. Our proposal addresses these problems by developing and implementing innovative approaches. In Aim 1, new approaches will be developed and implemented for averaging multiple, potentially incomplete data sets resulting from one or more crystals. Owing to our innovative approach to modeling the components of non-isomorphism, we expect that even quite non-isomorphous data sets can be used together to solve challenging structures. In Aim 2, methods that will analyze the results of averaging data sets from multiple crystals in real space will be developed. The descriptors of averaging will be correlated with the outcomes of the structural analysis, so that the contributors to variability in real space can be quantified and interpreted. Finally, in Am 3, a web-based server will be developed in order to provide these methods to the structural biology community.

 描述（由申请人提供）：“存在结构变异性时的X射线数据分析”提案旨在推进衍射数据分析方法，以便在倒易空间中的数据处理期间和在真实的空间中的结构分析期间对晶体之间和晶体内的变异性进行最佳建模。所提出的工作的重要性源于该技术的重要性，该技术产生了非常详细的信息。X射线结构用于直接在原子水平上理解细胞过程，解释和验证通过其他技术获得的结果，为细胞过程的详细研究生成假设，并指导药物设计研究-所有这些都与NIH的使命高度相关。大分子晶体通常具有有限的尺寸和晶格顺序。两者都可能导致需要结合来自多个晶体的数据以获得成功的结构解，其中有限阶产生衍射伪影并与不同样品之间的非同构相关。非同构性阻碍了对来自多个晶体的数据集进行平均，因为为了成功地进行平均，数据需要非常相似。由于单一数据集的数据不完整，所研究的晶体中的辐射诱导变化，以及缺乏统计措施来告知实验者数据分析是否朝着正确的方向发展，因此平均化的问题更加复杂。当需要分析大量数据集时，由于数据分析的组合复杂性，还存在与平均多个数据集相关的技术挑战。当分析从多晶体实验获得的结构结果时，最后的困难出现了，必须将所需的生物信号，例如配体的存在或分子的特定动力学行为与噪声分离。我们的建议通过制定和实施创新方法来解决这些问题。在目标1中，将开发和实施新的方法，用于平均由一个或多个晶体产生的多个可能不完整的数据集。由于我们对非同构组件建模的创新方法，我们希望即使是非常非同构的数据集也可以一起使用来解决具有挑战性的结构。在目标2中，将开发分析在真实的空间中对多个晶体的数据集进行平均的结果的方法。平均化的描述符将与结构分析的结果相关联，以便可以量化和解释真实的空间中的可变性的贡献者。最后，在上午3，一个基于网络的服务器将被开发，以提供这些方法的结构生物学社区。