We will develop the methodology to obtain electron microscope image data routinely at a resolution around 2.5 Angstroms from protein crystals, and use this ability to extend the resolution in the map of bacteriorhodopsin from 3.5 Angstroms to 2.5 Angstroms. Electron crystallography has developed to the point where atomic models can be developed for protein structures based on electron diffraction and image data. The model for bacteriorhodopsin, the first protein for which EM data was used to interpret the atomic structure, was based on a three-dimensional density map with a resolution of 3.5 Angstroms in the best direction. Although this model has been highly useful in interpreting a wealth of biophysical data on the structure and function of the protein, a fully detailed understanding of both the structural and functional principles will require extension of the density map to at least 2.5 Angstroms resolution. The use of conventional refinement procedures, based on diffraction intensity measurements, faces the problem that electron scattering at lower resolution, below about 2.5 Angstroms, is strongly dependent on the redistribution of electrons that occurs in interatomic bond formation. Calculation of diffraction intensities from the model, to compare with the experimental diffraction intensities, should thus include information about the nature and orientation of bonds, information that is not available at the early stages of structure refinement. We will solve this problem by extending the resolution of the density map to 2.5 Angstroms by direct phase determination from images that contain information at higher resolution than previously available. Advances in specimen preparation provide samples that diffract to beyond 2.5 Angstroms, and the current intermediate voltage electron microscopes provide resolution that routinely exceeds 2.5 Angstroms. Although image resolution with bR beyond 3.5 Angstroms has already been demonstrated, the quality of the images still falls well below the ideal level. We will work to identify factors that limit the signal-to-noise ratio at high resolution, and derive techniques to overcome these limitations.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Program Projects (P01)
Project #
1P01GM051487-01
Application #
3756607
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
1
Fiscal Year
1994
Total Cost
Indirect Cost
Name
Lawrence Berkeley National Laboratory
Department
Type
DUNS #
078576738
City
Berkeley
State
CA
Country
United States
Zip Code
94720
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