Flexible Macromolecular Crystallography

柔性高分子晶体学

• 批准号: 10506287
• 负责人: James M Holton
• 金额: $ 46.54万
• 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/10506287
• 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; Logistics; Machine Learning; 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; Variant; 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).

项目总结/摘要 - 核心 3 - 柔性高分子晶体学 第三个技术运营核心 (TOC3) 通过使 ALS-ENABLE 多样化来补充 TOC1 在这个结构生物学变革时代最大限度地提高灵活性的技术基础。人工智能 （AI）彻底改变了相位问题的解决，我们不仅会做出这些新的结构预测 我们的用户社区可以使用的工具，还有其他有利于我们工作流程的人工智能，例如对象位置 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，这些将投入使用。例如，我们期望启用 通过现已成熟且成熟的培训，直接从结晶托盘高效且无人值守的原位串行数据收集 现成的人工智能技术可在生长滴中定位衍射质量的晶体。如果成功的话，即使是 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。这个现场 能力还完成了样品制备过程的一系列诊断测试，使我们的用户能够 了解不良衍射的根源并适当地集中精力。该诊断链利用 TOC1 微焦点、TOC2 溶液稳定性和 TOC4 分子界面映射功能。 我们独特的机器人解决方案具有广泛的引脚兼容性，将获得容量升级以帮助 简化我们的用户社区必须从同步加速器到同步加速器作为 APS 的过渡，然后 ALS 因重大升级而经历长时间停机。我们将升级我们的 X 射线光学器件以匹配 ALS-U 源。我们还将升级机器人技术，以提供非低温远程访问数据收集 温度范围为 -20C 至 +50C，使这些有价值的多温度工具可供普通用户使用 地理上多样化的用户社区。这些温度下的功能研究将通过推出来协助 最先进的差异数据分析软件，例如 PanDDA，作为光束线工作流程的一部分。通过明确地 支持差异数据分析，我们的用户将能够使用最先进的可视化技术 弱但关键的差异特征，例如低占用配体和功能相关的构象 轮班。而且因为片段筛选是生物科学界快速响应的关键工具 新兴的健康危机，我们将支持并记录最佳实践，例如 DMSO 耐受性测试 在我们的 ALS-ENABLE 协议中，以及促进有权访问高级功能的用户组之间的协作 还有可共享的样品制备工具，例如片段库和声学滴液处理器。相当 与让用户在自己的设备上组织和分析数据相比，我们将部署 ISPyB/SynchWeb， 世界上使用最广泛的 LIMS 来获取结构生物学数据。用于合并多晶数据以改进的工具 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部署 框架。这不仅可以使跨同步加速器数据分析在一个地方可用，而且可以保持一个水平 的熟悉程度，以方便我们的用户与其他同步加速器之间的转换。我们将我们的目标分组为 它们涉及的数学运算：将数据相加以改善信号（目标 1）、样本调制 引发变化（目标 2），并减去数据以揭示结果（目标 3）。