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（受到治疗化合物或其环境变化的干扰）。 现代同步加速器和 XFEL 设施的高亮度可以收集每种构象状态的许多样本 包含处于多种构象状态的蛋白质的样本，产生数千个数据点，如果 正确聚类，可以通过获得蛋白质的合作来提供其每种状态的快照。 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结合起来 解决将多种状态蛋白质的混合数据重新组织为多种数据的紧迫问题 参与该项目的软件开发人员独立工作。 开发了改变范式的聚类软件，这些算法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。串行晶体学是一个强大的工具。 研究同一物质的许多晶体的衍射图案以了解 物质可能的 3 维结构是一项重要的技术。 明亮的新型 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 在同步加速器上，数据也可以组织为静止图像（通常在 XFEL）或窄楔形（串行）。 在任何一种情况下，都必须将蒸馏器和楔形物仔细组织成高度。 可以合并进行处理的同质数据集群。 有几种替代方法可以发现适当的集群，例如基于com- 晶胞参数的型坯，或者衍射反射强度的比较 在许多情况下，如果提前知道这些现有工具的质量和正确的聚类标准。 足够了，特别是当他们唯一的任务是将好图像与坏图像进行分类时。 分离多晶型物，或者为了遵循动态系统中的顺序状态，需要更有效的聚类 算法；基于细胞参数的聚类标准在早期是不够的。 处理部分数据集时的聚类阶段可能会调查其他标准，例如差异。 威尔逊图来衡量数据的相似性当原始数据完整时（今天相似度> 75%）。 应用程序），或者想要达到更高水平的完整性，可以根据强度的相关性进行聚类 - 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，开发将从一种聚类标准转移到另一种聚类标准的最佳工具，从而导致 合并足够完整的反射强度数据集，这将提供对最敏感的信息。 正在研究的现象允许在集成软件框架内工作。