Flexible Macromolecular Crystallography

柔性高分子晶体学

基本信息

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
项目类别：
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

项目摘要

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 技术基础，以在当今的结构生物学变革时代最大限度地提高灵活性。（人工智能）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例如，一旦证明它们有效，就会投入使用。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以在晶体生长过程中定位衍射质量的晶体，如果成功的话，甚至可以产生一个晶体。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。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利用该诊断链。 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 APS 的过渡，然后 ALS 经历长期停机以进行重大升级，我们将升级我们的 X 射线光学器件以匹配其性能。我们还将升级机器人技术，以提供非低温的远程访问数据收集。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不同的用户社区将通过推出来协助这些温度下的功能研究。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。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但可共享的样品制备工具，例如片段库和声学滴液处理程序。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 结构生物学数据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。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。我们按以下目标进行分组。它们需要的操作：将数据数学相加以改善信号（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）。