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1.  Crystallization of the GMPPCP complex of the NG domains of Thermus aquaticus Ffh and FtsY 
The GTPases Ffh and FtsY are components of the prokaryotic signal recognition particle protein-targeting pathway. The two proteins interact in a GTP-dependent manner, forming a complex that can be stabilized by use of the non-hydrolyzable GTP analog GMPPCP. Crystals of the complex of the NG GTPase domains of the two proteins have been obtained from ammonium sulfate solutions. Crystals grow with several different morphologies, predominately as poorly diffracting plates and needle clusters, but occasionally as well diffracting rods. It has been demonstrated that all forms of the crystals observed contain an intact complex. Diffraction data to 2.0 Å resolution have been measured.
PMCID: PMC3543697  PMID: 14501130
2.  Statistical density modification using local pattern matching 
Statistical density modification can make use of local patterns of density found in protein structures to improve crystallographic phases.
A method for improving crystallographic phases is presented that is based on the preferential occurrence of certain local patterns of electron density in macromolecular electron-density maps. The method focuses on the relationship between the value of electron density at a point in the map and the pattern of density surrounding this point. Patterns of density that can be superimposed by rotation about the central point are considered equivalent. Standard templates are created from experimental or model electron-density maps by clustering and averaging local patterns of electron density. The clustering is based on correlation coefficients after rotation to maximize the correlation. Experimental or model maps are also used to create histograms relating the value of electron density at the central point to the correlation coefficient of the density surrounding this point with each member of the set of standard patterns. These histograms are then used to estimate the electron density at each point in a new experimental electron-density map using the pattern of electron density at points surrounding that point and the correlation coefficient of this density to each of the set of standard templates, again after rotation to maximize the correlation. The method is strengthened by excluding any information from the point in question from both the templates and the local pattern of density in the calculation. A function based on the origin of the Patterson function is used to remove information about the electron density at the point in question from nearby electron density. This allows an estimation of the electron density at each point in a map, using only information from other points in the process. The resulting estimates of electron density are shown to have errors that are nearly independent of the errors in the original map using model data and templates calculated at a resolution of 2.6 Å. Owing to this independence of errors, information from the new map can be combined in a simple fashion with information from the original map to create an improved map. An iterative phase-improvement process using this approach and other applications of the image-reconstruction method are described and applied to experimental data at resolutions ranging from 2.4 to 2.8 Å.
doi:10.1107/S0907444903015142
PMCID: PMC2745877  PMID: 14501107
density modification; pattern matching
3.  Improving macromolecular atomic models at moderate resolution by automated iterative model building, statistical density modification and refinement 
A procedure for iterative model-building, statistical density modification and refinement at moderate resolution (up to about 2.8 Å) is described.
An iterative process for improving the completeness and quality of atomic models automatically built at moderate resolution (up to about 2.8 Å) is described. The process consists of cycles of model building interspersed with cycles of refinement and combining phase information from the model with experimental phase information (if any) using statistical density modification. The process can lead to substantial improvements in both the accuracy and completeness of the model compared with a single cycle of model building. For eight test cases solved by MAD or SAD at resolutions ranging from 2.0 to 2.8 Å, the fraction of models built and assigned to sequence was 46–91% (mean of 65%) after the first cycle of building and refinement, and 78–­95% (mean of 87%) after 20 cycles. In an additional test case, an incorrect model of gene 5 protein (PDB code 2gn5; r.m.s.d. of main-chain atoms from the more recent refined structure 1vqb at 1.56 Å) was rebuilt using only structure-factor amplitude information at varying resolutions from 2.0 to 3.0 Å. Rebuilding was effective at resolutions up to about 2.5 Å. The resulting models had 60–­80% of the residues built and an r.m.s.d. of main-chain atoms from the refined structure of 0.20 to 0.62 Å. The algorithm is useful for building preliminary models of macromolecules suitable for an experienced crystallographer to extend, correct and fully refine.
doi:10.1107/S0907444903009922
PMCID: PMC2745880  PMID: 12832760
density modification; model building; refinement

Results 1-3 (3)