Electron cryomicroscopy (cryoEM), particularly single particle cryoEM, has experienced tremendous successes in term of achievable resolution. It is now possible to determine near atomic structures of large protein assemblies with high symmetry, such as icosahedral virus. However, for large protein complex with low symmetry, achieving the similar resolution is still very difficult. This is partly because of the very tedious process of collecting very large datasets of images, and the tremendous demands of computational power to process such large dataset. We have set up a unique easy-to-operate electron microscope system specifically to facilitate this process (automated acquisition software and a very large format, 8K x 8K pixel digital camera). In this application, we seek funds to set up a GPU computing enabled computer cluster for fast data processing. Together with the electron microscope system, we aim to establish an integrated cryoEM system that will overcome the current resolution limitation of cryoEM in studying large protein complexes without high symmetry. Our goal is to routinely collect and process 105 - 106 particle images. The proposed GPU computer cluster will be a medium sized cluster of GPU-CPU units with each server class headnode connected with the just available Nvidia S1070 GPU box containing four Tesla GPU processors. It will be dedicated for fast processing of cryoEM datasets, particularly very large datasets of ~ 1 million particle images. Establish such a GPU computer cluster is a critical step towards routinely determine near atomic resolution structure of macromolecular complex by single particle cryoEM. Many biomedical research projects funded by NIH, particularly those require structural determination of large complexes;will be benefited from such technological advancement. All developed software will be freely provided.

National Institute of Health (NIH)
National Center for Research Resources (NCRR)
Biomedical Research Support Shared Instrumentation Grants (S10)
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Special Emphasis Panel (ZRG1-CB-Q (30))
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Birken, Steven
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University of California San Francisco
Schools of Medicine
San Francisco
United States
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