Flexible Macromolecular Crystallography

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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).