Flexible Macromolecular Crystallography

项目摘要/摘要-核心3-柔性高分子结晶学 此第三方技术运营核心(TOC3)通过使支持ALS的产品多样化来补充TOC1 在这个结构生物学正在变革的时代，最大限度地提高灵活性的技术基础。人工智能 (AI)革命性地解决了相位问题，我们不仅将做出这些新的结构预测 我们的用户社区可访问的工具，以及其他使我们的工作流受益的人工智能，例如 用于模拟蛋白质的样品环和晶体、衍射图像解译器或变分自动编码器 域运动。一旦证明有效，这些措施将投入使用。例如，我们希望启用 通过培训直接从结晶塔板进行高效但无人值守的现场串行数据采集现已成熟和 现成的人工智能技术在其生长液滴中定位衍射质量的晶体。如果成功，即使是一个 命中率的适度提高将使使用我们的就地测角仪的系列数据收集发生革命性变化。这是原址 该功能还可以完成样品制备过程的一系列诊断测试，使我们的用户能够 了解不良衍射率的来源，并适当地集中精力。该诊断链利用 TOC1微聚焦、TOC2溶液稳定性和TOC4映射分子界面的能力。 我们独一无二的兼容宽销的机器人解决方案将进行容量升级，以帮助 简化我们的用户社区必须从同步加速器到APS的同步加速器的过渡，然后 为了进行重大升级，ALS经历了长时间的停机。我们将升级我们的X射线光学设备，以匹配 肌萎缩侧索硬化症的来源。我们还将升级机器人，以在非低温环境下提供远程访问数据收集 从零下20摄氏度到50摄氏度不等的温度，使这些宝贵的多温度工具 地理位置不同的用户社区。在这种温度下的功能研究将通过推出 最先进的差异数据分析软件，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，我们的用户将可以使用最先进的可视化技术 弱但关键的差异特征，如低占有率配体和功能相关的构象 轮班。由于片段筛选是生物科学界快速响应 对于正在出现的健康危机，我们将支持并记录最佳实践，如DMSO耐量测试 在我们启用ALS的协议中，并促进具有高级访问权限的用户组之间的协作 但可共享的样品制备工具，如片段文库和声学液滴处理机。宁可 为了让用户自己组织和分析他们的数据，我们将部署ISPyB/SynchWeb、 世界上使用最多的LIMS，用于结构生物学数据。用于合并多晶体数据的工具，以改进 在我们的全球挑战数据集竞赛中表现出色的数据质量将部署在此 框架。这将不仅使跨同步加速器数据分析在一个地方可用，而且保持一定的水平 熟悉，使我们的用户在与其他同步加速器之间的转换变得轻松。我们把我们的目标按 它们所涉及的数学运算：将数据相加以改善信号(Aim1)、对样本进行调制 诱导改变(AIM2)，并减去数据以揭示结果(AIM3)。

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).