Detecting elusive biologically significant structural differences with serial crystallography

通过系列晶体学检测难以捉摸的生物学上显着的结构差异

• 批准号: 9752613
• 负责人: ALEXEI SUAREZ SOARES
• 金额: $ 19.11万
• 依托单位: BROOKHAVEN SCIENCE ASSOC-BROOKHAVEN LAB
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9752613
• 关键词: Active Sites; Algorithms; Binding; Biological; Cells; Computer software; Crystallization; Crystallography; Data; Data Set; Disease; Environment; Excision; Face; Genetic Polymorphism; Health; Human; Image; Individual; Investments; Knowledge; Ligands; Light; Measures; Methods; Modernization; Molecular Conformation; Pathway interactions; Pattern; Polymorph; Population Sizes; Protein Dynamics; Proteins; Resolution; Roentgen Rays; Sampling; Scheme; Software Framework; Source; Specimen; Structure; Synchrotrons; Techniques; Therapeutic; Time; Training; Weight; Work; base; beamline; dynamic system; improved; response; structural biology; three dimensional structure; tool; x-ray free-electron laser

Project Summary/Abstract: Issues underlying human health depend on understanding proteins in different conformational states (perturbed either by therapeutic compounds or by changes in their environment). The high brilliance of modern synchrotron and XFEL facilities can gather many samples of each conformation state of a specimen containing proteins in multiple conformational states, yielding thousands of data points that, if correctly clustered, can provide snapshots of the protein in each of its states. By gaining the cooperation of the major developers of clustering software, we will combine the strengths of existing tools with new algorithms to answer the urgent problem of re-organizing mixed data from proteins in multiple states into multiple data from proteins in single states. Working independently the software developers that are collaborating on this project have developed paradigm-changing clustering software. Each of these algorithms works well in specific cases, but none are sufficient to solve solve all the clustering problems we now face. Serial crystallography is a powerful technique in which diffraction patterns from many crystals of the same substance are studied to understand the possible 3-dimensional structure or structures of the substance. It is an essential technique that was made possible by brilliant new X-ray free electron laser (XFEL) light sources and has become an important technique at synchrotrons as well. The data may be organized either as stills (usually at XFELs) or narrow wedges (serial crystallography at synchtrotrons, SXS). In either case the stills and wedges must be carefully organized into highly homogeneous clusters of data that can be merged for processing. There are several alternative approaches to discovering appropriate clusters, based, for example, on com- parisons of crystallographic cell parameters or, alternatively, on comparisons of intensities of diffraction reflection amplitudes. In many cases, if the quality and correct clustering criteria are known in advance these existing tools are adequate, especially when their only task is to sort good images from bad ones. However, when one tries to separate polymorphs, or to follow sequential states in a dynamic system, one requires more effective clustering algorithms; no single clustering criterion is sufficient. Clustering based on cell parameters is effective at the early stages of clustering when dealing with partial data sets. One might investigate other criteria such as differences of Wilson plots to measure similarities of data. When the original data are complete (> 75% today for similar applications), or one wants to achieve higher levels of completeness, one can cluster on correlation of intensi- ties. Perhaps one must adjust weighting of criteria by resolution ranges. This project is exploring multi-stage sequential clustering, developing optimal tools that will move from one clustering criterion to another, leading to merged sets of sufficiently complete reflection-intensity data. This will provide information most sensitive to the phenomena being investigated to allow work within an integrated software framework.

项目摘要/摘要：人类健康的潜在问题取决于对不同蛋白质的理解 构象状态(被治疗性化合物或其环境变化所扰乱)。这个 现代同步加速器和XFEL设备的高亮度可以收集每种构象状态的许多样品 包含多种构象状态的蛋白质的样本，产生数千个数据点，如果 正确地聚集在一起，可以提供蛋白质在每种状态下的快照。通过获得该组织的合作 作为集群软件的主要开发商，我们将把现有工具的优势与新算法相结合，以 回答将来自多个状态的蛋白质的混合数据重新组织为来自 蛋白质处于单一状态。在这个项目上合作的软件开发人员独立工作 开发了改变范式的集群软件。这些算法中的每一个在特定fiC的情况下都工作得很好， 但是，没有一个是足够的fi来解决我们现在面临的所有集群问题。系列结晶学是一种强大的 研究同一物质的多个晶体的衍射图以了解 物质可能的一个或多个三维结构。这是一项基本的技术，它是 由辉煌的新型X射线自由电子激光(XFEL)光源成为可能并已成为一项重要技术 在同步加速器也是如此。数据可以被组织为静止图像(通常在XFEL上)或窄楔形(序列 同步加速器的结晶学)。在任何一种情况下，剧照和楔子都必须精心组织成高度 可合并以进行处理的同构数据群集。 有几种可选的方法来发现适当的集群，例如，基于COM- 晶胞参数的一部分，或者，另一种方法是比较衍射反射强度 波幅。在许多情况下，如果预先知道质量和正确的聚类标准，则这些现有工具 是足够的，特别是当他们唯一的任务是将好的图像与坏的图像区分开来的时候。然而，当一个人试图 分离多态，或者在动态系统中遵循顺序状态，需要更有效的集群 算法；没有单一的聚类标准是充分的fi。基于像元参数的聚类在早期是有效的 处理部分数据集时的聚类阶段。人们可以调查其他标准，如差异 威尔逊的图谋来衡量数据的相似性。原始数据完成时(目前类似数据为75% 应用)，或者想要达到更高级别的完备性，可以根据强度相关性进行聚类。 领带。也许人们必须根据分辨率范围调整标准的权重。该项目正在探索多阶段 顺序聚类，开发从一个聚类标准到另一个聚类标准的最佳工具，从而导致 合并后的SuFfi集合最近完整地完成了fl反射强度数据。这将提供最敏感的信息 正在对现象进行调查，以便在集成软件框架内工作。