Flexible Macromolecular Crystallography

柔性高分子晶体学

• 批准号: 10708036
• 负责人: James M Holton
• 金额: $ 39.48万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10708036
• 关键词: Acoustics; Active Sites; Age; Artificial Intelligence; Attention; Binding; Biological; Biological Assay; Biological Sciences; Blood capillaries; Chemicals; Collaborations; Collection; Communities; Complement; Computer software; Cryoelectron Microscopy; Crystallization; Crystallography; Data; Data Analyses; Data Collection; Data Set; Decision Making; Devices; Diagnostic; Diagnostic Procedure; Diagnostic tests; Dimethyl Sulfoxide; Disease; Drops; Elements; Epiphysial cartilage; Familiarity; Feedback; Fostering; Geography; Goals; Growth; Harvest; Health; Image; In Situ; Individual; Libraries; Ligands; Liquid substance; Location; Machine Learning; Maps; Mathematics; Measurement; Measures; Metals; Methods; Modeling; Molecular; Molecular Conformation; Motion; Noise; Optics; Phase; Preparation; Procedures; Process; Property; Proteins; Protocols documentation; Research Project Grants; Resistance; Resolution; Resources; Robot; Robotics; Roentgen Rays; Sampling; Services; Shapes; Signal Transduction; Source; Structure; Synchrotrons; System; Techniques; Technology; Temperature; Tertiary Protein Structure; Testing; Training; Visualization; base; beamline; coronavirus disease; data access; data quality; design; experimental study; flexibility; improved; insight; multiple datasets; operation; predictive tools; screening; small molecule; structural biology; success; tool

Project Summary/Abstract - Core 3 – Flexible Macromolecular Crystallography This 3rd Technology Operations Core (TOC3) complements TOC1 by diversifying the ALS-ENABLE technology base to maximize flexibility in this now transformative era for structural biology. Artificial intelligence (AI) has revolutionized solving the phase problem, and we will not only make these new structure prediction tools accessible to our User community, but also other AIs that benefit our workflows, such as object location of sample loops and crystals, diffraction image interpreters or variational auto encoders for modelling protein domain motions. These will be put to use once they are proven effective. For example, we expect to enable efficient yet unattended in-situ serial data collection direct from crystallization trays by training now mature and off-the-shelf AI technology to locate diffraction-quality crystals in their growth drops. If successful, even a modest improvement in hit rate will revolutionize serial data collection using our in-situ goniometer. This in-situ capability also completes a chain of diagnostic tests of the sample preparation process, allowing our Users to understand the origins of poor diffraction and focus their efforts appropriately. This diagnostics chain leverages the capabilities of TOC1 micro-focus, TOC2 solution stability, and TOC4 mapping molecular interfaces. Our uniquely accommodating robotics solution with broad pin compatibility will get a capacity upgrade to help ease the transitions our User community will have to make from synchrotron to synchrotron as APS and then ALS undergo long shutdowns for major upgrades. We will upgrade our X-ray optics to match the properties of the ALS-U source. We will also upgrade robotics to provide remote access data collection at non-cryo temperatures, ranging from -20C to +50C, making these valuable multi-temperature tools accessible to a geographically diverse User community. Functional studies at these temperatures will be assisted by rolling out state-of-the-art difference-data analysis software, such as PanDDA, as part of beamline workflows. By explicitly supporting difference data analysis our users will have access to state-of-the-art technology for visualizing weak yet critical difference features, such as low-occupancy ligands and functionally-relevant conformational shifts. And because fragment screening is a critical tool for the bioscience community to quickly respond to an emerging health crisis, we will support as well as document best practices such as DMSO tolerance testing in our ALS-ENABLE protocols as well as foster collaborations between user groups with access to advanced yet shareable sample preparation tools such as fragment libraries and acoustic drop liquid handlers. Rather than leave users to their own devices to organize and analyze their data, we will deploy ISPyB/SynchWeb, the world’s most heavily used LIMS for structural biology data. Tools for merging multi-crystal data for improved data quality that performed well in our global challenge data set competition will be deployed under this framework. This will not only make cross-synchrotron data analysis available in one place, but maintain a level of familiarity to ease the transition of our Users to and from other synchrotrons. We group our aims by the mathematical operations they entail: adding data together to improve signal (Aim1), modulation of the sample to induce a change (Aim2), and subtraction of data to reveal the result (Aim3).

项目概要/摘要-核心3 -柔性高分子晶体学 第三个技术运营核心（TOC 3）通过使ALS-ENABLE多样化，对TOC 1进行了补充 技术基础，以最大限度地提高灵活性，在这个变革时代的结构生物学。人工智能 (AI)彻底改变了相位问题的解决方法，我们不仅可以预测这些新的结构， 我们的用户社区可以访问的工具，以及其他有利于我们工作流程的AI，例如 用于蛋白质建模的样品环和晶体、衍射图像解释器或变分自动编码器 域运动一旦证明有效，将予以使用。例如，我们希望能够 通过培训直接从结晶塔盘进行有效但无人值守的现场串行数据收集现已成熟， 现成的人工智能技术，以定位衍射质量的晶体在他们的生长下降。如果成功，即使 命中率的适度提高将彻底改变使用我们的原位测角仪进行的串行数据收集。该原位 该功能还完成了样品制备过程的诊断测试链，使我们的用户能够 了解衍射不良的原因，并适当地集中精力。此诊断链利用 TOC 1微聚焦、TOC 2溶液稳定性和TOC 4映射分子界面的能力。 我们具有广泛引脚兼容性的独特适应性机器人解决方案将获得容量升级， 缓解我们的用户社区将不得不从同步加速器到同步加速器作为APS的过渡， ALS经历长时间的停机进行重大升级。我们将升级我们的X射线光学系统， ALS-U源。我们还将升级机器人技术，在非低温环境下提供远程访问数据收集。 温度范围从-20 ℃到+50 ℃，使这些宝贵的多温度工具可以被 不同地域的用户群。在这些温度下的功能研究将有助于推出 最先进的差分数据分析软件，如PanDDA，作为光束线工作流程的一部分。通过显式 支持差异数据分析，我们的用户将有机会获得最先进的技术， 弱但关键的差异特征，如低占有率配体和功能相关的构象 运动一样的而且由于片段筛选是生物科学界快速应对 新出现的健康危机，我们将支持并记录最佳做法，如DMSO耐受性测试 在我们的ALS-ENABLE协议中，以及促进用户组之间的合作， 还可共享的样品制备工具，例如片段库和声学滴液处理器。而 而不是让用户自己组织和分析他们的数据，我们将部署ISPyB/SynchWeb， 世界上最常用的LIMS结构生物学数据。用于合并多晶体数据的工具， 在我们的全球挑战数据集竞赛中表现良好的数据质量将在此 框架.这将不仅使交叉同步加速器数据分析在一个地方可用，但保持一个水平 熟悉的，以方便我们的用户从其他同步加速器的过渡。我们的目标是 它们需要的数学运算：将数据相加以改善信号（Aim 1）， 以引起变化（Aim 2），并减去数据以揭示结果（Aim 3）。