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1.  Autotracing of Escherichia coli acetate CoA-transferase α-subunit structure using 3.4 Å MAD and 1.9 Å native data 
The automation of protein structure determination is an essential component for high-throughput structural analysis in protein X-ray crystallography and is a key element in structural genomics. This highly challenging undertaking relies at present on the availability of high-quality native and derivatized protein crystals diffracting to high or moderate resolution, respectively. Obtaining such crystals often requires significant effort. The present study demonstrates that phases obtained at low resolution (>3.0 Å) from crystals of SeMet-labeled protein can be successfully used for automated structure determination. The crystal structure of acetate CoA-transferase α-subunit was solved using 3.4 Å multiwavelength anomalous dispersion data collected from a crystal containing SeMet-substituted protein and 1.9 Å data collected from a native protein crystal.
PMCID: PMC2792021  PMID: 12454473
2.  Automated side-chain model building and sequence assignment by template matching 
A method for automated macromolecular side-chain model building and for aligning the sequence to the map is described.
An algorithm is described for automated building of side chains in an electron-density map once a main-chain model is built and for alignment of the protein sequence to the map. The procedure is based on a comparison of electron density at the expected side-chain positions with electron-density templates. The templates are constructed from average amino-acid side-chain densities in 574 refined protein structures. For each contiguous segment of main chain, a matrix with entries corresponding to an estimate of the probability that each of the 20 amino acids is located at each position of the main-chain model is obtained. The probability that this segment corresponds to each possible alignment with the sequence of the protein is estimated using a Bayesian approach and high-confidence matches are kept. Once side-chain identities are determined, the most probable rotamer for each side chain is built into the model. The automated procedure has been implemented in the RESOLVE software. Combined with automated main-chain model building, the procedure produces a preliminary model suitable for refinement and extension by an experienced crystallographer.
PMCID: PMC2745879  PMID: 12499538
model building; template matching
3.  Automated main-chain model building by template matching and iterative fragment extension 
A method for automated macromolecular main-chain model building is described.
An algorithm for the automated macromolecular model building of polypeptide backbones is described. The procedure is hierarchical. In the initial stages, many overlapping polypeptide fragments are built. In subsequent stages, the fragments are extended and then connected. Identification of the locations of helical and β-strand regions is carried out by FFT-based template matching. Fragment libraries of helices and β-strands from refined protein structures are then positioned at the potential locations of helices and strands and the longest segments that fit the electron-density map are chosen. The helices and strands are then extended using fragment libraries consisting of sequences three amino acids long derived from refined protein structures. The resulting segments of polypeptide chain are then connected by choosing those which overlap at two or more Cα positions. The fully automated procedure has been implemented in RESOLVE and is capable of model building at resolutions as low as 3.5 Å. The algorithm is useful for building a preliminary main-chain model that can serve as a basis for refinement and side-chain addition.
PMCID: PMC2745878  PMID: 12499537
model building; template matching; fragment extension
4.  Rapid automatic NCS identification using heavy-atom substructures 
A rapid algorithm for identifying NCS in heavy-atom sites is described.
An important component of a fully automated system for structure solution and phase improvement through density modification is a capability for identification of non-crystallographic symmetry as early in the process as possible. Algorithms exist for finding NCS in heavy-atom sites, but currently require of the order of N 5 comparisons to be made, where N is the number of sites to be examined, including crystallographically related locations. A method described here based on considering only sets of sites that have common interatomic distances reduces the computational time by several orders of magnitude. Additionally, searches for proper symmetry allow the identification of NCS in cases where only one heavy atom is present per NCS copy.
PMCID: PMC2745885  PMID: 12454504
non-crystallographic symmetry; heavy-atom substructures
5.  Statistical density modification with non-crystallographic symmetry 
Statistical density modification can make use of NCS in a crystal and can include estimates of the deviations from perfect NCS.
Statistical density modification is a technique for phase improvement through a calculation of the posterior probability of the phases, given experimental phase information and expectations about features of the electron-density map. The technique can take advantage of both estimates of electron density in the map and uncertainties or probability distributions for those estimates. For crystals with non-crystallographic symmetry (NCS), this allows the use of the expected similarity of electron density at NCS-related points without requiring an implicit assumption that these regions are identical.
PMCID: PMC2745884  PMID: 12454468
density modification; non-crystallographic symmetry

Results 1-5 (5)