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Biomedical subjects

Katherine A Kantardjieff

Publications and source records attributed to Katherine A Kantardjieff.

8 recordsLinked to original sources
PubMed2005

Structure of pyrR (Rv1379) from Mycobacterium tuberculosis: a persistence gene and protein drug target.

The Mycobacterium tuberculosis pyrR gene (Rv1379) encodes a protein that regulates the expression of pyrimidine-nucleotide biosynthesis (pyr) genes in a UMP-dependent manner. Because pyrimidine biosynthesis is an essential step in the progression of TB, the gene product pyrR is an attractive antitubercular drug target. The 1.9 A native structure of Mtb pyrR determined by the TB Structural Genomics Consortium facilities in trigonal space group P3(1)21 is reported, with unit-cell parameters a = 66.64, c = 154.72 A at 120 K and two molecules in the asymmetric unit. The three-dimensional structure and residual uracil phosphoribosyltransferase activity point to a common PRTase ancestor for pyrR. However, while PRPP- and UMP-binding sites have been retained in Mtb pyrR, a distinct dimer interaction among subunits creates a deep positively charged cleft capable of binding pyr mRNA. In silico screening of pyrimidine-nucleoside analogs has revealed a number of potential lead compounds that, if bound to Mtb pyrR, could facilitate transcriptional attenuation, particularly cyclopentenyl nucleosides.

Amino Acid Sequence↗
PubMed2004

Mycobacterium tuberculosis RmlC epimerase (Rv3465): a promising drug-target structure in the rhamnose pathway.

The Mycobacterium tuberculosis rmlC gene encodes dTDP-4-keto-6-deoxyglucose epimerase, the third enzyme in the M. tuberculosis dTDP-L-rhamnose pathway which is essential for mycobacterial cell-wall synthesis. Because it is structurally unique, highly substrate-specific and does not require a cofactor, RmlC is considered to be the most promising drug target in the pathway, and the M. tuberculosis rmlC gene was selected in the initial round of TB Structural Genomics Consortium targets for structure determination. The 1.7 A native structure determined by the consortium facilities is reported and implications for in silico screening of ligands for structure-guided drug design are discussed.

Amino Acid Sequence↗
PubMed2004

Protein isoelectric point as a predictor for increased crystallization screening efficiency.

MOTIVATION: Increased efficiency in initial crystallization screening reduces cost and material requirements in structural genomics. Because pH is one of the few consistently reported parameters in the Protein Data Bank (PDB), the isoelectric point (pI) of a protein has been explored as a useful indirect predictor for the optimal choice of range and distribution of the pH sampling in crystallization trials. RESULTS: We have analyzed 9596 unique protein crystal forms from the August 2003 PDB and have found a significant relationship between the calculated pI of successfully crystallized proteins and the difference between pI and reported pH at which they were crystallized. These preferences provide strong prior information for the design of crystallization screening experiments with significantly increased efficiency and corresponding reduction in material requirements, leading to potential cost savings of millions of US$ for structural genomics projects involving high-throughput crystallographic structure determination. AVAILABILITY: A prototype example of a screen design and efficiency estimator program, CrysPred, is available at http://www-structure.llnl.gov/cryspred/

Computer Simulation↗
PubMed2004

Laboratory scale structural genomics.

At Lawrence Livermore National Laboratory, the development of the TB structural genomics consortium crystallization facility has paralleled several local proteomics research efforts that have grown out of gene expression microarray and comparative genomics studies. Collective experience gathered from TB consortium labs and other centers involved in the NIH-NIGMS protein structure initiative allows us to explore the possibilities and challenges of pursuing structural genomics on an academic laboratory scale. We discuss our procedures and protocols for genomic targeting approaches, primer design, cloning, small scale expression screening, scale-up and purification, through to automated crystallization screening and data collection. The procedures are carried out by a small group using a combination of traditional approaches, innovative molecular biochemistry approaches, software automation, and a modest investment in robotic equipment.

Cloning, Molecular↗
PubMed2003

Effective electron-density map improvement and structure validation on a Linux multi-CPU web cluster: The TB Structural Genomics Consortium Bias Removal Web Service.

Anticipating a continuing increase in the number of structures solved by molecular replacement in high-throughput crystallography and drug-discovery programs, a user-friendly web service for automated molecular replacement, map improvement, bias removal and real-space correlation structure validation has been implemented. The service is based on an efficient bias-removal protocol, Shake&wARP, and implemented using EPMR and the CCP4 suite of programs, combined with various shell scripts and Fortran90 routines. The service returns improved maps, converted data files and real-space correlation and B-factor plots. User data are uploaded through a web interface and the CPU-intensive iteration cycles are executed on a low-cost Linux multi-CPU cluster using the Condor job-queuing package. Examples of map improvement at various resolutions are provided and include model completion and reconstruction of absent parts, sequence correction, and ligand validation in drug-target structures.

Apolipoproteins E↗
PubMed2003

Matthews coefficient probabilities: Improved estimates for unit cell contents of proteins, DNA, and protein-nucleic acid complex crystals.

Estimating the number of molecules in the crystallographic asymmetric unit is one of the first steps in a macromolecular structure determination. Based on a survey of 15641 crystallographic Protein Data Bank (PDB) entries the distribution of V(M), the crystal volume per unit of protein molecular weight, known as Matthews coefficient, has been reanalyzed. The range of values and frequencies has changed in the 30 years since Matthews first analysis of protein crystal solvent content. In the statistical analysis, complexes of proteins and nucleic acids have been treated as a separate group. In addition, the V(M) distribution for nucleic acid crystals has been examined for the first time. Observing that resolution is a significant discriminator of V(M), an improved estimator for the probabilities of the number of molecules in the crystallographic asymmetric unit has been implemented, using resolution as additional information.

Crystallization↗
PubMed2002

Concanavalin A in a dimeric crystal form: revisiting structural accuracy and molecular flexibility.

A structure of native concanavalin A (ConA), a hardy perennial of structural biology, has been determined in a dimeric crystal form at a resolution of 1.56 A (space group C222(1); unit-cell parameters a = 118.70, b = 101.38, c = 111.97 A; two molecules in the asymmetric unit). The structure has been refined to an R(free) of 0.206 (R = 0.178) after iterative model building and phase-bias removal using Shake&wARP. Correspondence between calculated water-tyrosine interactions and experimentally observed structures near the saccharide-binding site suggests that the observed interactions between Tyr12 and water in various crystal forms are to be expected and are not unique to the presence of an active site. The present structure differs from previously reported atomic resolution structures of ConA in several regions and extends insight into the conformational flexibility of this molecule. Furthermore, this third, low-temperature, structure of ConA in a different crystal form, independently refined using powerful model-bias removal techniques, affords the opportunity to revisit assessment of accuracy and precision in high- or atomic resolution protein structures. It is illustrated that several precise structures of the same molecule can differ substantially in local detail and users of crystallographic models are reminded to consider the potential impact when interpreting structures. Suggestions on how to effectively represent ensembles of crystallographic models of a given molecule are provided.

Binding Sites↗