Development of an Expert Crystallization Knowledge System

专家结晶知识系统的开发

• 批准号: 8210845
• 负责人: Edward H Snell
• 金额: $ 33.46万
• 依托单位: HAUPTMAN-WOODWARD MEDICAL RESEARCH INST
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8210845
• 关键词: Archives; Automobile Driving; Behavior; Biochemical; Biochemical Process; Biochemistry; Biological; Categories; Cell Nucleus; Chemicals; Chemistry; Classification; Communities; Computer software; Crystallization; Custom; Data; Data Analyses; Data Set; Databases; Deposition; Development; Ensure; Feedback; Fingerprint; Future; Growth; Housing; Human; Image; Image Analysis; Imagery; Individual; Knowledge; Laboratories; Lead; Libraries; Life; Liquid substance; Medical Research; Metadata; Methods; Mining; Molecular Structure; Online Systems; Outcome; Output; Pattern; Phase; Precipitation; Preparation; Process; Proteins; Recording of previous events; Research Institute; Resources; Roentgen Rays; Sampling; Screening Result; Screening procedure; Services; Skin; Sodium Chloride; Solid; Solubility; Solutions; Structure; System; Targeted Research; Techniques; Testing; Time; Update; Validation; Visual; Work; X ray diffraction analysis; X-Ray Diffraction; base; chemical fingerprinting; computer infrastructure; computing resources; coping; data exchange; data mining; design; flexibility; graphical user interface; high throughput screening; image archival system; improved; interdisciplinary approach; interoperability; knowledge base; macromolecule; novel; platform-independent; protein expression; public health relevance; research study; software development; structural biology; structural genomics; success; technology/technique; time use; tool; trend

DESCRIPTION (provided by applicant): The majority of our knowledge of biological structure comes from crystals. With the structure of a macromolecule one can visualize how it works, and how it interacts with other macromolecules - one can see life on an atomic scale. Among methods employed to reveal the details of molecular structure, none rivals single crystal X-ray diffraction for its generality of application (85% of the contents of the protein data bank), clarity of view, and lack of ambiguity in the interpretation. Crystallization is a significant 'bottleneck' in structural biology. The Hauptman-Woodward Medical Research Institute (HWI) operates a mature High- Throughput crystallization-Screening (HTS) laboratory for the general biomedical community and Structural Genomics groups. Macromolecular samples are screened against 1,536 different chemical cocktails that encompass both an incomplete factorial sampling of chemical space and examples of commercially available screens. Images of all the crystallization experiments are recorded for six weeks at weekly intervals. These images are archived. To date we have built up a library of over 90 million time-resolved images of almost 16 million crystallization experiments comprising over 10,000 biological macromolecules, each combined with the different chemical cocktails. We hypothesize that by analyzing the outcomes in terms of chemical space and dynamics, a temporal phase diagram of solubility can be constructed. From this phase diagram, optimized crystallization conditions and factors that drive that optimization can be predicted. In a multidisciplinary approach, we will use this data to develop an expert crystallization knowledge system. Our aims are to accomplish this by (1) making the data archive readily and rapidly accessible; (2) to continuously acquire and update data as it becomes available; (3) to use the data to establish trends and guide crystallization; and (4) to develop new crystallization knowledge from the data. The cocktails used for crystallization screening chemically decrease the solubility of the macromolecules, driving the system to a state of supersaturation that can lead to crystallization. We will focus on structural genomics samples (~40% of our data) where complete information about the sample is available. We will incorporate an X-ray feedback mechanism to supplement the visual data for characterization of both crystal and precipitate. Initial studies show that analyzing the outcomes in terms of chemical space and dynamics does produce an empirical phase diagram of solubility over time. From these preliminary studies with a limited amount of this data, we have defined trajectories to traverse this space effectively, rationally guiding successful crystallization. Using screening data and historical trends, we will generate specific chemical advice, based upon statistical and probabilistic analysis of the whole dataset, describing how to crystallize and optimize individual samples. We will also identify trends in crystallization behavior as a function of the biochemistry. This approach will greatly improve the transfer of information from the crystallization- screening laboratory to immediately benefit the almost 900 different laboratories that are currently making use of the service. By incorporating commercially available screens, we can relate in-house data to screening results from other laboratories, expanding our analysis to develop crystallization and optimization strategies for samples beyond those we set up in the High-throughput crystallization-screening laboratory. Our data analysis will improve the success rate of crystallization in general, and enable structural studies of a wider range of biologically and medically relevant macromolecules. PUBLIC HEALTH RELEVANCE: The majority of our knowledge of biological structure comes from crystals (85% of the structures deposited in the protein databank). Crystal growth has repeatedly been identified as the rate-limiting step in macromolecular structure determination. By the analysis and use of a unique data set of over 16 million crystallization experiments and 90 million images, we can improve the crystallization process by providing specific advice on optimization, and establish general predictive information for the biomedical structural biology community in general.

描述（由申请人提供）：我们对生物结构的大部分知识来自晶体。有了大分子的结构，人们可以想象它是如何工作的，以及它如何与其他大分子相互作用-人们可以看到原子尺度上的生命。在揭示分子结构细节的方法中，没有一种方法能与单晶X射线衍射相媲美，因为它具有应用的普遍性（占蛋白质数据库的85%）、视图的清晰度和解释的明确性。结晶是结构生物学中的一个重要“瓶颈”。Hauptman-Woodward医学研究所（HWI）为一般生物医学界和结构基因组学团体运营着一个成熟的高通量结晶筛选（HTS）实验室。针对1，536种不同的化学混合物筛选大分子样品，所述化学混合物包括化学空间的不完全析因取样和市售筛选的实例。所有结晶实验的图像每周记录一次，持续六周。这些图像已存档。到目前为止，我们已经建立了一个包含近1600万个结晶实验的9000多万个时间分辨率图像的库，其中包括10,000多个生物大分子，每个生物大分子都与不同的化学鸡尾酒相结合。我们假设，通过分析化学空间和动力学方面的结果，可以构建溶解度的时间相图。从该相图中，可以预测优化的结晶条件和驱动该优化的因素。在一个多学科的方法，我们将使用这些数据来开发一个专家结晶知识系统。我们的目标是通过以下方式实现这一目标：（1）使数据档案易于快速访问;（2）不断获取和更新可用的数据;（3）使用数据建立趋势并指导结晶;（4）从数据中开发新的结晶知识。用于结晶筛选的鸡尾酒化学地降低大分子的溶解度，将系统驱动到可导致结晶的过饱和状态。我们将专注于结构基因组学样本（约占我们数据的40%），其中可以获得有关样本的完整信息。我们将采用X射线反馈机制来补充晶体和沉淀物表征的视觉数据。初步研究表明，从化学空间和动力学的角度分析结果确实会产生溶解度随时间变化的经验相图。从这些有限数据的初步研究中，我们已经定义了有效穿越该空间的轨迹，合理地指导成功的结晶。使用筛选数据和历史趋势，我们将根据整个数据集的统计和概率分析生成特定的化学建议，描述如何结晶和优化单个样品。我们还将确定结晶行为的趋势作为生物化学的函数。这种方法将大大改善结晶筛选实验室的信息传输，使目前正在使用该服务的近900个不同的实验室立即受益。通过整合商用筛选器，我们可以将内部数据与其他实验室的筛选结果相关联，从而扩展我们的分析，为我们在高通量结晶筛选实验室中设置的样品开发结晶和优化策略。我们的数据分析将提高结晶的成功率，并使更广泛的生物和医学相关的大分子的结构研究。 公共卫生相关性：我们对生物结构的大部分知识来自晶体（85%的结构存储在蛋白质数据库中）。晶体生长已被反复确定为大分子结构测定的限速步骤。通过分析和使用超过1600万个结晶实验和9000万张图像的独特数据集，我们可以通过提供具体的优化建议来改进结晶过程，并为生物医学结构生物学社区建立一般的预测信息。