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1.  Using surface-bound rubidium ions for protein phasing 
Rubidium is a monovalent metal that can be used as a counterion in protein solutions. X-ray anomalous scattering from rubidium ions bound to the protein surface was used for phasing of the crystal structure of the hsp60 apical domain from Thermus thermophilus. Multiple-wavelength anomalous dispersion (MAD) data were collected from a crystal obtained from a solution containing 0.2 M rubidium salt. One molecule of protein (147 amino acids) binds one well ordered and one poorly ordered Rb atom. Phases calculated with the program SHARP were sufficient for automatic tracing and side-chain assignment using the program ARP/wARP. The data show that bound rubidium ions can be used to determine protein structures and to study the interaction of monovalent metal ions with proteins and other macromolecules.
PMCID: PMC3691023  PMID: 11418770
2.  Streptococcus pneumonia YlxR at 1.35 Å shows a putative new fold 
The structure of the YlxR protein of unknown function from Streptococcus pneumonia was determined to 1.35 Å. YlxR is expressed from the nusA/infB operon in bacteria and belongs to a small protein family (COG2740) that shares a conserved sequence motif GRGA(Y/W). The family shows no significant amino-acid sequence similarity with other proteins. Three-wavelength diffraction MAD data were collected to 1.7 Å from orthorhombic crystals using synchrotron radiation and the structure was determined using a semi-automated approach. The YlxR structure resembles a two-layer α/β sandwich with the overall shape of a cylinder and shows no structural homology to proteins of known structure. Structural analysis revealed that the YlxR structure represents a new protein fold that belongs to the α–β plait superfamily. The distribution of the electrostatic surface potential shows a large positively charged patch on one side of the protein, a feature often found in nucleic acid-binding proteins. Three sulfate ions bind to this positively charged surface. Analysis of potential binding sites uncovered several substantial clefts, with the largest spanning 3/4 of the protein. A similar distribution of binding sites and a large sharply bent cleft are observed in RNA-binding proteins that are unrelated in sequence and structure. It is proposed that YlxR is an RNA-binding protein.
PMCID: PMC2792016  PMID: 11679764
3.  Map-likelihood phasing 
A map-likelihood function is described that can yield phase probabilities with very low model bias.
The recently developed technique of maximum-likelihood density modification [Terwilliger (2000 ▶), Acta Cryst. D56, 965–972] allows a calculation of phase probabilities based on the likelihood of the electron-density map to be carried out separately from the calculation of any prior phase probabilities. Here, it is shown that phase-probability distributions calculated from the map-likelihood function alone can be highly accurate and that they show minimal bias towards the phases used to initiate the calculation. Map-likelihood phase probabilities depend upon expected characteristics of the electron-density map, such as a defined solvent region and expected electron-density distributions within the solvent region and the region occupied by a macromolecule. In the simplest case, map-likelihood phase-probability distributions are largely based on the flatness of the solvent region. Though map-likelihood phases can be calculated without prior phase information, they are greatly enhanced by high-quality starting phases. This leads to the technique of prime-and-switch phasing for removing model bias. In prime-and-switch phasing, biased phases such as those from a model are used to prime or initiate map-likelihood phasing, then final phases are obtained from map-likelihood phasing alone. Map-likelihood phasing can be applied in cases with solvent content as low as 30%. Potential applications of map-likelihood phasing include unbiased phase calculation from molecular-replacement models, iterative model building, unbiased electron-density maps for cases where 2Fo − Fc or σA-weighted maps would currently be used, structure validation and ab initio phase determination from solvent masks, non-crystallographic symmetry or other knowledge about expected electron density.
doi:10.1107/S0907444901013749
PMCID: PMC2745887  PMID: 11717488
map-likelihood phasing
4.  Maximum-likelihood density modification using pattern recognition of structural motifs 
A likelihood-based density-modification method is extended to include pattern recognition of structural motifs.
The likelihood-based approach to density modification [Terwilliger (2000 ▶), Acta Cryst. D56, 965–972] is extended to include the recognition of patterns of electron density. Once a region of electron density in a map is recognized as corresponding to a known structural element, the likelihood of the map is reformulated to include a term that reflects how closely the map agrees with the expected density for that structural element. This likelihood is combined with other aspects of the likelihood of the map, including the presence of a flat solvent region and the electron-density distribution in the protein region. This likelihood-based pattern-recognition approach was tested using the recognition of helical segments in a largely helical protein. The pattern-recognition method yields a substantial phase improvement over both conventional and likelihood-based solvent-flattening and histogram-matching methods. The method can potentially be used to recognize any common structural motif and incorporate prior knowledge about that motif into density modification.
doi:10.1107/S0907444901013737
PMCID: PMC2745886  PMID: 11717487
density modification; pattern recognition

Results 1-4 (4)