W0449

The Molecular Machinery of the Cell: Challenges for Automated Molecular Microscopy. B. Carragher, D. Fellmann, F. Guerra, R. A. Milligan, F. Mouche, J. Pulokas, J. Quispe, B. Sheehan, C. Suloway, Y. Zhu, and C. S. Potter, Dept. of Cell Biology, The Scripps Research Institute, 10550 North Torrey Pines Rd., La Jolla, CA, 92037.

Elucidating the structure and mechanism of action of large protein complexes – the molecular machines of the cell - represents an emerging frontier in understanding how the information in the genome is transformed into cellular activity. These objects are sometimes quite challenging for study by x-ray crystallography and NMR methods as they are usually quite large, may be conformationally and compositionally dynamic, and are often present in comparatively low copy numbers in the cell. They are however ideal for study by electron microscopy (EM) and this approach has great promise for routinely and efficiently providing structural information on macromolecules at a resolution sufficient to resolve the secondary structure in proteins. This information could then be used in conjunction with the high resolution x-ray structures of individual proteins to interpret very large complexes to near atomic resolution. The techniques of macromolecular microscopy are however both time consuming and labor intensive. This includes almost every aspect of the process; the preparation of specimens, the acquisition of the required very large numbers of electron micrographs, and the supervision of the sometimes complex software needed for analysis and reconstruction of three dimensional electron density maps. The challenge then is to transform EM structure determination into a high throughput methodology. To this end we are focused on the development of four core technologies to address automation for specimen handling, image acquisition, data processing and data information integration. We will report on the current performance of our existing systems and the future challenges involved in substantially improving both the sustained throughput and the yield of automated data collection and analysis.