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：人类健康的根本问题取决于对不同蛋白质的理解。 构象状态（由治疗化合物或由其环境的变化扰动）。的 现代同步加速器和XFEL设备的高亮度可以收集每个构象状态的许多样品 包含多种构象状态蛋白质的样本，产生数千个数据点，如果 正确聚类，可以提供蛋白质在其每个状态下的快照。通过获得合作， 作为集群软件的主要开发人员，我们将联合收割机结合现有工具的优势和新的算法， 解决了将来自多种状态的蛋白质的混合数据重新组织成来自 蛋白质处于单一状态。独立工作的软件开发人员正在这个项目上合作 已经开发出了改变模式的集群软件。这些算法中的每一个在特定情况下都能很好地工作， 但没有一个足以解决我们现在面临的所有聚类问题。连续结晶学是一种强大的 一种研究同一物质的许多晶体的衍射图案以了解 物质可能的三维结构。这是一个基本的技术， X射线自由电子激光（XFEL）光源的出现使其成为可能，并已成为一项重要的技术 在同步加速器上也是如此。数据可以组织为静止图像（通常在XFEL上）或窄楔形图像（连续 同步加速器上的晶体学，SXS）。在任何一种情况下，剧照和楔形必须仔细组织成高度 可以合并用于处理的数据的同质集群。 有几种替代方法来发现适当的集群，例如，基于com， 晶体学晶胞参数的型坯，或者衍射反射强度的比较 振幅在许多情况下，如果预先知道质量和正确的聚类标准， 是足够的，特别是当他们的唯一任务是从坏的图像中选出好的图像时。然而，当一个人试图 分离的多态性，或遵循动态系统中的顺序状态，需要更有效的聚类 算法;没有单一的聚类标准是足够的。基于细胞参数的聚类在早期有效 处理部分数据集时的聚类阶段。人们可能会研究其他标准，如差异 威尔逊图来衡量数据的相似性。当原始数据完整时（对于类似情况， 应用程序），或者想要实现更高级别的完整性，可以根据强度的相关性进行聚类， 关系的也许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， 充分完整的反射强度数据的合并集。这将提供最敏感的信息， 正在研究的现象，以允许在集成软件框架内工作。