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：人类健康的基础问题取决于了解不同的蛋白质 构象状态（由热化合物或环境变化扰动）。 现代同步加速器和XFEL设施的高光彩可以收集每个构象状态的许多样本 在多种构象状态中含有蛋白质的标本的，产生数千个数据点，如果 正确聚类，可以在其每个状态中提供蛋白质的快照。通过获得合作 聚类软件的主要开发人员，我们将将现有工具的优势与新算法相结合 回答从多个状态中重新组织蛋白质混合数据的紧急问题 单个状态中的蛋白质。独立工作的软件开发人员在该项目上合作 已经开发了改变范式的聚类软件。这些算法中的每一个在特定情况下都很好地效果很好， 但是，没有一个能够解决我们现在面临的所有聚类问题。连续晶体学是一种强大的 从同一物质的许多晶体中衍射模式的技术都可以理解 物质的可能的3维结构或结构。这是一种必不可少的技术 出色的新X射线免费电子激光器（XFEL）光源可能可能成为重要的技术 在同步基因上也是如此。数据可以作为剧照（通常在Xfels）或狭窄的楔子（串行）组织 SXS同步基因的晶体学。无论哪种情况，蒸馏器和楔子都必须小心地组织到高度 可以合并用于处理的数据群。 有几种替代方法来发现适当的簇，例如， 晶体学细胞参数的parison，或者，在衍射反射强度的比较上 放大器。在许多情况下，如果事先知道这些现有工具的质量和正确的聚类标准 足够了，尤其是当他们的唯一任务是从不良图像中排序好图像时。但是，当一个人试图 单独的多晶型物，或遵循动态系统中的顺序状态，需要更有效的聚类 算法；没有单个聚类标准是足够的。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。一个人可能会调查其他标准，例如差异 威尔逊图以衡量数据的相似性。当原始数据完成时（今天> 75％ 应用程序），或者想要达到更高水平的完整性，可以集中在强化的相关性上 关系。也许必须通过分辨率范围调整标准的加权。这个项目正在探索多阶段 顺序聚类，开发最佳工具，这些工具将从一个聚类标准转移到另一个聚类标准，导致 合并的一组成熟的完整反射强度数据。这将为您提供最敏感的信息 正在调查现象以允许在集成的软件框架内工作。