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（由适用提供）：建议“在存在结构变异性的情况下X射线数据分析”旨在推进衍射数据分析方法，以便在互惠空间和实际空间中的结构分析期间在数据处理过程中为晶体和晶体之间的变异性最佳地建模。拟议工作的重要性是由于该技术的重要性而产生了独特的信息。 X射线结构用于直接理解原子水平的细胞过程，以解释和验证其他技术获得的结果，以产生假设，以详细研究细胞过程，并指导药物设计研究 - 所有这些都与NIH任务高度相关。大分子晶体通常具有有限的尺寸和晶体晶格顺序。两者都可能导致需要将来自多个晶体的数据结合起来成功结构解决方案，而有限的顺序产生了衍射工件，并在不同样本之间使用非同态校正。非同态性阻碍了多个晶体的数据集的平均值，因为为了成功平均，数据必须非常相似。平均问题的问题是，单个数据集中的数据不完整，辐射引起的调查晶体变化以及缺乏统计措施，这些统计措施会告知专家有关数据分析是否朝着正确的方向发展。当需要分析大量数据集时，与数据集合的组合复杂性相关的多个数据集也存在技术挑战。当对从多晶体实验获得的结构结果的分析必须分开所需的生物学信号，例如来自噪声的配体或分子的特定动态行为的存在。我们的建议通过开发和实施创新方法来解决这些问题。在AIM 1中，将开发和实施新方法，以平均一个或多个晶体导致的多个可能是不完整的数据集。由于我们对非同态组成部分进行建模的创新方法，我们期望即使是非常非同构的数据集也可以一起使用来解决挑战结构。在AIM 2中，将开发出分析来自真实空间中多个晶体的数据集结果的方法。平均描述符将与结构分析的结果相关，以便可以量化和解释真实空间可变性的贡献者。最后，在AM 3中，将开发基于Web的服务器，以便为结构生物学社区提供这些方法。