PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-15 (15)
 

Clipboard (0)
None

Select a Filter Below

Journals
Year of Publication
Document Types
1.  Structure determination of LpxD from the lipopolysaccharide-synthesis pathway of Acinetobacter baumannii  
Crystal structures of the protein LpxD from A. baumannii were solved in apo forms that are suitable for structure-based antibacterial drug discovery.
Acinetobacter baumannii is a Gram-negative bacterium that is resistant to many currently available antibiotics. The protein LpxD is a component of the biosynthetic pathway for lipopolysaccharides in the outer membrane of this bacterium and is a potential target for new antibacterial agents. This paper describes the structure determination of apo forms of LpxD in space groups P21 and P4322. These crystals contained six and three copies of the protein molecule in the asymmetric unit and diffracted to 2.8 and 2.7 Å resolution, respectively. A comparison of the multiple protein copies in the asymmetric units of these crystals reveals a common protein conformation and a conformation in which the relative orientation between the two major domains in the protein is altered.
doi:10.1107/S1744309112048890
PMCID: PMC3539694  PMID: 23295477
bacterial proteins; drug targets
2.  Structure determination of LpxA from the lipopolysaccharide-synthesis pathway of Acinetobacter baumannii  
Crystal structures of the LpxA protein from A. baumannii were solved in apo forms that were suitable for structure-based antibacterial drug discovery.
Acinetobacter baumannii is a Gram-negative pathogenic bacterium which is resistant to most currently available antibiotics and that poses a significant health threat to hospital patients. LpxA is a key enzyme in the biosynthetic pathway of the lipopolysaccharides that are components of the bacterial outer membrane. It is a potential target for antibacterial agents that might be used to fight A. baumannii infections. This paper describes the structure determination of the apo form of LpxA in space groups P212121 and P63. These crystal forms contained three and one protein molecules in the asymmetric unit and diffracted to 1.8 and 1.4 Å resolution, respectively. A comparison of the conformations of the independent protein monomers within and between the two crystal asymmetric units revealed very little structural variation across this set of structures. In the P63 crystal form the enzymatic site is exposed and is available for the introduction of small molecules of the type used in fragment-based drug discovery and structure-based lead optimization.
doi:10.1107/S174430911204571X
PMCID: PMC3509968  PMID: 23192027
bacterial proteins; drug targets
3.  The Phenix Software for Automated Determination of Macromolecular Structures 
Methods (San Diego, Calif.)  2011;55(1):94-106.
X-ray crystallography is a critical tool in the study of biological systems. It is able to provide information that has been a prerequisite to understanding the fundamentals of life. It is also a method that is central to the development of new therapeutics for human disease. Significant time and effort are required to determine and optimize many macromolecular structures because of the need for manual interpretation of complex numerical data, often using many different software packages, and the repeated use of interactive three-dimensional graphics. The Phenix software package has been developed to provide a comprehensive system for macromolecular crystallographic structure solution with an emphasis on automation. This has required the development of new algorithms that minimize or eliminate subjective input in favour of built-in expert-systems knowledge, the automation of procedures that are traditionally performed by hand, and the development of a computational framework that allows a tight integration between the algorithms. The application of automated methods is particularly appropriate in the field of structural proteomics, where high throughput is desired. Features in Phenix for the automation of experimental phasing with subsequent model building, molecular replacement, structure refinement and validation are described and examples given of running Phenix from both the command line and graphical user interface.
doi:10.1016/j.ymeth.2011.07.005
PMCID: PMC3193589  PMID: 21821126
Macromolecular Crystallography; Automation; Phenix; X-ray; Diffraction; Python
4.  The structure of LpxD from Pseudomonas aeruginosa at 1.3 Å resolution 
The crystal structure of the bacterial protein LpxD from P. aeruginosa was solved and refined at 1.3 Å resolution. The overall domain architecture and biological assembly are similar to those found in previously solved structures of LpxD from other species.
LpxD is a bacterial protein that is part of the biosynthesis pathway of lipid A and is responsible for transferring 3-hydroxymyristic acid from the R-3-hydroxymyristoyl-acyl carrier protein to the 2-OH group of UDP-3-O-(3-hydroxymyristoyl) glucosamine. The crystal structure of LpxD from Pseudomonas aeruginosa has been determined at high resolution (1.3 Å). The crystal belonged to space group H3, with unit-cell parameters a = b = 106.19, c = 93.38 Å, and contained one molecule in the asymmetric unit. The structure was solved by molecular replacement using the known structure of LpxD from Escherichia coli (PDB entry 3eh0) as a search model and was refined to R work = 16.4% (R free = 18.5%) using 91 655 reflections. The final protein model includes 355 amino-acid residues (including 16 amino acids from a 20 amino-acid N-terminal His tag), one chloride ion and two ethylene glycol molecules.
doi:10.1107/S1744309111018811
PMCID: PMC3144788  PMID: 21795786
bacterial proteins; biosynthesis pathways; LpxD; Pseudomonas aeruginosa
5.  Towards automated crystallographic structure refinement with phenix.refine  
phenix.refine is a program within the PHENIX package that supports crystallographic structure refinement against experimental data with a wide range of upper resolution limits using a large repertoire of model parameterizations. This paper presents an overview of the major phenix.refine features, with extensive literature references for readers interested in more detailed discussions of the methods.
phenix.refine is a program within the PHENIX package that supports crystallographic structure refinement against experimental data with a wide range of upper resolution limits using a large repertoire of model parameterizations. It has several automation features and is also highly flexible. Several hundred parameters enable extensive customizations for complex use cases. Multiple user-defined refinement strategies can be applied to specific parts of the model in a single refinement run. An intuitive graphical user interface is available to guide novice users and to assist advanced users in managing refinement projects. X-ray or neutron diffraction data can be used separately or jointly in refinement. phenix.refine is tightly integrated into the PHENIX suite, where it serves as a critical component in automated model building, final structure refinement, structure validation and deposition to the wwPDB. This paper presents an overview of the major phenix.refine features, with extensive literature references for readers interested in more detailed discussions of the methods.
doi:10.1107/S0907444912001308
PMCID: PMC3322595  PMID: 22505256
structure refinement; PHENIX; joint X-ray/neutron refinement; maximum likelihood; TLS; simulated annealing; subatomic resolution; real-space refinement; twinning; NCS
6.  phenix.model_vs_data: a high-level tool for the calculation of crystallographic model and data statistics 
Journal of Applied Crystallography  2010;43(Pt 4):669-676.
Application of phenix.model_vs_data to the contents of the Protein Data Bank shows that the vast majority of deposited structures can be automatically analyzed to reproduce the reported quality statistics. However, the small fraction of structures that elude automated re-analysis highlight areas where new software developments can help retain valuable information for future analysis.
phenix.model_vs_data is a high-level command-line tool for the computation of crystallographic model and data statistics, and the evaluation of the fit of the model to data. Analysis of all Protein Data Bank structures that have experimental data available shows that in most cases the reported statistics, in particular R factors, can be reproduced within a few percentage points. However, there are a number of outliers where the recomputed R values are significantly different from those originally reported. The reasons for these discrepancies are discussed.
doi:10.1107/S0021889810015608
PMCID: PMC2906258  PMID: 20648263
PHENIX; Protein Data Bank; data quality; model quality; structure validation; R factors
7.  Detection and correction of underassigned rotational symmetry prior to structure deposition 
An X-ray structural model can be reassigned to a higher symmetry space group using the presented framework if its noncrystallographic symmetry operators are close to being exact crystallographic relationships. About 2% of structures in the Protein Data Bank can be reclassified in this way.
Up to 2% of X-ray structures in the Protein Data Bank (PDB) potentially fit into a higher symmetry space group. Redundant protein chains in these structures can be made compatible with exact crystallographic symmetry with minimal atomic movements that are smaller than the expected range of coordinate uncertainty. The incidence of problem cases is somewhat difficult to define precisely, as there is no clear line between underassigned symmetry, in which the subunit differences are unsupported by the data, and pseudosymmetry, in which the subunit differences rest on small but significant intensity differences in the diffraction pattern. To help catch symmetry-assignment problems in the future, it is useful to add a validation step that operates on the refined coordinates just prior to structure deposition. If redundant symmetry-related chains can be removed at this stage, the resulting model (in a higher symmetry space group) can readily serve as an isomorphous replacement starting point for re-refinement using re-indexed and re-integrated raw data. These ideas are implemented in new software tools available at http://cci.lbl.gov/labelit.
doi:10.1107/S0907444910001502
PMCID: PMC2865365  PMID: 20445225
underassigned rotational symmetry; LABELIT; validation
8.  Decision-making in structure solution using Bayesian estimates of map quality: the PHENIX AutoSol wizard 
Ten measures of experimental electron-density-map quality are examined and the skewness of electron density is found to be the best indicator of actual map quality. A Bayesian approach to estimating map quality is developed and used in the PHENIX AutoSol wizard to make decisions during automated structure solution.
Estimates of the quality of experimental maps are important in many stages of structure determination of macromolecules. Map quality is defined here as the correlation between a map and the corresponding map obtained using phases from the final refined model. Here, ten different measures of experimental map quality were examined using a set of 1359 maps calculated by re-analysis of 246 solved MAD, SAD and MIR data sets. A simple Bayesian approach to estimation of map quality from one or more measures is presented. It was found that a Bayesian estimator based on the skewness of the density values in an electron-density map is the most accurate of the ten individual Bayesian estimators of map quality examined, with a correlation between estimated and actual map quality of 0.90. A combination of the skewness of electron density with the local correlation of r.m.s. density gives a further improvement in estimating map quality, with an overall correlation coefficient of 0.92. The PHENIX AutoSol wizard carries out automated structure solution based on any combination of SAD, MAD, SIR or MIR data sets. The wizard is based on tools from the PHENIX package and uses the Bayesian estimates of map quality described here to choose the highest quality solutions after experimental phasing.
doi:10.1107/S0907444909012098
PMCID: PMC2685735  PMID: 19465773
structure solution; scoring; Protein Data Bank; phasing; decision-making; PHENIX; experimental electron-density maps
9.  Autoindexing the diffraction patterns from crystals with a pseudotranslation 
Lattice patterns containing alternating strong and weak reflections can be identified by a targeted search for the weak signals, permitting a wider range of diffraction patterns to be indexed automatically.
Rotation photographs can be readily indexed if enough candidate Bragg spots are identified to properly sample the reciprocal lattice. However, while automatic indexing algorithms are widely used for macromolecular data processing, they can produce incorrect results in special situations where a subset of Bragg spots is systematically overlooked. This is a potential outcome in cases where a noncrystallographic translational symmetry operator closely mimics an exact crystallo­graphic translation. In these cases, a visual inspection of the diffraction image will reveal alternating strong and weak reflections. However, reliable detection of the weak-intensity reflections by software requires a systematic search for a diffraction signal targeted at specific reciprocal-space locations calculated a priori by considering all possible pseudotranslations. Care must be exercised to distinguish between true lattice diffraction and spurious signals contributed by neighboring overlapping Bragg spots, non-Bragg diffraction and noise. Such procedures have been implemented within the autoindexing program LABELIT and applied to known cases from publicly available data sets. Routine use of this type of signal search adds only a few seconds to the typical run time for autoindexing. The program can be downloaded from http://cci.lbl.gov/labelit.
doi:10.1107/S0907444909010725
PMCID: PMC2685732  PMID: 19465769
subgroups; sublattices; cosets; noncrystallographic symmetry
10.  Iterative-build OMIT maps: map improvement by iterative model building and refinement without model bias 
A procedure for carrying out iterative model building, density modification and refinement is presented in which the density in an OMITregion is essentially unbiased by an atomic model. Density from a set of overlapping OMIT regions can be combined to create a composite ‘iterative-build’ OMIT map that is everywhere unbiased by an atomic model but also everywhere benefiting from the model-based information present elsewhere in the unit cell. The procedure may have applications in the validation of specific features in atomic models as well as in overall model validation. The procedure is demonstrated with a molecular-replacement structure and with an experimentally phased structure and a variation on the method is demonstrated by removing model bias from a structure from the Protein Data Bank.
doi:10.1107/S0907444908004319
PMCID: PMC2424225  PMID: 18453687
11.  Iterative-build OMIT maps: map improvement by iterative model building and refinement without model bias 
An OMIT procedure is presented that has the benefits of iterative model building density modification and refinement yet is essentially unbiased by the atomic model that is built.
A procedure for carrying out iterative model building, density modification and refinement is presented in which the density in an OMIT region is essentially unbiased by an atomic model. Density from a set of overlapping OMIT regions can be combined to create a composite ‘iterative-build’ OMIT map that is everywhere unbiased by an atomic model but also everywhere benefiting from the model-based information present elsewhere in the unit cell. The procedure may have applications in the validation of specific features in atomic models as well as in overall model validation. The procedure is demonstrated with a molecular-replacement structure and with an experimentally phased structure and a variation on the method is demonstrated by removing model bias from a structure from the Protein Data Bank.
doi:10.1107/S0907444908004319
PMCID: PMC2424225  PMID: 18453687
model building; model validation; macromolecular models; Protein Data Bank; refinement; OMIT maps; bias; structure refinement; PHENIX
12.  Iterative model building, structure refinement and density modification with the PHENIX AutoBuild wizard 
The highly automated PHENIX AutoBuild wizard is described. The procedure can be applied equally well to phases derived from isomorphous/anomalous and molecular-replacement methods.
The PHENIX AutoBuild wizard is a highly automated tool for iterative model building, structure refinement and density modification using RESOLVE model building, RESOLVE statistical density modification and phenix.refine structure refinement. Recent advances in the AutoBuild wizard and phenix.refine include automated detection and application of NCS from models as they are built, extensive model-completion algorithms and automated solvent-molecule picking. Model-completion algorithms in the AutoBuild wizard include loop building, crossovers between chains in different models of a structure and side-chain optimization. The AutoBuild wizard has been applied to a set of 48 structures at resolutions ranging from 1.1 to 3.2 Å, resulting in a mean R factor of 0.24 and a mean free R factor of 0.29. The R factor of the final model is dependent on the quality of the starting electron density and is relatively independent of resolution.
doi:10.1107/S090744490705024X
PMCID: PMC2394820  PMID: 18094468
model building; model completion; macromolecular models; Protein Data Bank; structure refinement; PHENIX
13.  Surprises and pitfalls arising from (pseudo)symmetry 
The presence of pseudosymmetry can cause problems in structure determination and refinement. The relevant background and representative examples are presented.
It is not uncommon for protein crystals to crystallize with more than a single molecule per asymmetric unit. When more than a single molecule is present in the asymmetric unit, various pathological situations such as twinning, modulated crystals and pseudo translational or rotational symmetry can arise. The presence of pseudosymmetry can lead to uncertainties about the correct space group, especially in the presence of twinning. The background to certain common pathologies is presented and a new notation for space groups in unusual settings is introduced. The main concepts are illustrated with several examples from the literature and the Protein Data Bank.
doi:10.1107/S090744490705531X
PMCID: PMC2394827  PMID: 18094473
pathology; twinning; pseudosymmetry
14.  A New Generation of Crystallographic Validation Tools for the Protein Data Bank 
Structure(London, England:1993)  2011;19(10):1395-1412.
Summary
This report presents the conclusions of the X-ray Validation Task Force of the worldwide Protein Data Bank (PDB). The PDB has expanded massively since current criteria for validation of deposited structures were adopted, allowing a much more sophisticated understanding of all the components of macromolecular crystals. The size of the PDB creates new opportunities to validate structures by comparison with the existing database, and the now-mandatory deposition of structure factors creates new opportunities to validate the underlying diffraction data. These developments highlighted the need for a new assessment of validation criteria. The Task Force recommends that a small set of validation data be presented in an easily understood format, relative to both the full PDB and the applicable resolution class, with greater detail available to interested users. Most importantly, we recommend that referees and editors judging the quality of structural experiments have access to a concise summary of well-established quality indicators.
Highlights
► Validation criteria used by the PDB for X-ray crystal structures have been reassessed ► Key scores should be presented prominently in an easily understood format ► A concise validation report should be available to referees of papers on crystal structures
doi:10.1016/j.str.2011.08.006
PMCID: PMC3195755  PMID: 22000512
15.  PHENIX: a comprehensive Python-based system for macromolecular structure solution 
The PHENIX software for macromolecular structure determination is described.
Macromolecular X-ray crystallography is routinely applied to understand biological processes at a molecular level. How­ever, significant time and effort are still required to solve and complete many of these structures because of the need for manual interpretation of complex numerical data using many software packages and the repeated use of interactive three-dimensional graphics. PHENIX has been developed to provide a comprehensive system for macromolecular crystallo­graphic structure solution with an emphasis on the automation of all procedures. This has relied on the development of algorithms that minimize or eliminate subjective input, the development of algorithms that automate procedures that are traditionally performed by hand and, finally, the development of a framework that allows a tight integration between the algorithms.
doi:10.1107/S0907444909052925
PMCID: PMC2815670  PMID: 20124702
PHENIX; Python; macromolecular crystallography; algorithms

Results 1-15 (15)