Macromolecular Crystallography Core

Macromolecular Crystallography (MX) Technology Core - Abstract The Macromolecular Crystallography (MX) Core oversees operation of the two state-of-the-art NIH-funded MX beamlines at the National Synchrotron Light Source-II (NSLS-II). The Highly Automated MX (AMX) beamline (17-ID-1), and the Frontier MX (FMX) beamline (17-ID-2) began user operation in January of 2017, and support projects at the forefront of bio-medical science that require regular access to the most advanced instrumentation and beam properties. At the same time, the beamlines and software enable routine experiments that are impossible elsewhere in the United States, and automated data-collection workflows for the higher throughput required for many NIH-funded projects tackling drug development. General user operation of AMX and FMX is funded by NIH and the Department of Energy (DOE) Biological and Environmental Research (BER); incremental improvements to existing capabilities and user driven experimental needs described in this proposed research are supported by the NIH while the DOE-BER companion grant funds development of new capabilities or upgrades. The ongoing Advanced Photon Source (APS) upgrade and the upcoming Advanced Light Source (ALS) upgrade shutdowns have a lasting impact on user demand for state-of-the-art capabilities already in high demand at NSLS-II but also for higher throughput and automation. Our ambitious plans to deliver user driven optimal resources described in this technological core are managed by a team with well-matched expertise. We provide access to world leading MX facilities and expertly train research groups to best use our resources. We ensure our resources are kept at the state of the art with regular updates, upgrades and when necessary, we develop new capabilities. Users access our two beamlines with the most advanced remote tools and our secure, high performance computing facilities for automated advanced data analysis, with aim to deliver beyond the status quo of data reduction; at completion of beamtime research groups access results from data analysis including structural models. To remain at the forefront of structural biology, our staff collaborates with international leading groups to implement and develop new technologies necessary to eventually optimize data collection and analysis from all possible samples. With experts in the field, we are exploring artificial intelligence, machine learning and large language models to further develop new technologies, when current methods and software demonstrate limitations. We are disseminating training materials and applications using standard processes.