ABSTRACT
Elucidating the structures of membrane proteins is essential to our understanding of disease states and a critical component in the rational design of drugs. Structural characterization of a membrane protein begins with its detergent solubilization from the lipid bilayer and its purification within a functionally stable protein-detergent complex (PDC). Crystallization of the PDC typically occurs by changing the solution environment to decrease solubility and promote interactions between exposed hydrophilic surface residues. As membrane proteins have been observed to form crystals close to the phase separation boundaries of the detergent used to form the PDC, knowledge of these boundaries under different chemical conditions provides a foundation to rationally design crystallization screens. We have carried out dye-based detergent phase partitioning studies using different combinations of 10 polyethylene glycols (PEG), 11 salts, and 11 detergents to generate a significant amount of chemically diverse phase boundary data. The resulting curves were used to guide the formulation of a 1536-cocktail crystallization screen for membrane proteins. We are making both the experimentally derived phase boundary data and the 1536 membrane screen available through the high-throughput crystallization facility located at the Hauptman-Woodward Institute. The phase boundary data have been packaged into an interactive Excel spreadsheet that allows investigators to formulate grid screens near a given phase boundary for a particular detergent. The 1536 membrane screen has been applied to 12 membrane proteins of unknown structures supplied by the structural genomics and structural biology communities, with crystallization leads for 10/12 samples and verification of one crystal using X-ray diffraction.
Subject(s)
Detergents/chemistry , Membrane Proteins/chemistry , Animals , Crystallization , Polyethylene Glycols/chemistryABSTRACT
PURPOSE: Treatment of glioblastoma multiforme (GBM) continues to pose a significant therapeutic challenge, with most tumors recurring within the previously irradiated tumor bed. To improve outcomes, we must be able to identify and treat resistant cell populations. Rad51, an enzyme involved in homologous recombinational repair, leads to increased resistance of tumor cells to cytotoxic treatments such as radiotherapy. We hypothesized that Rad51 might contribute to GBM's apparent radioresistance and consequently influence survival. METHODS AND MATERIALS: A total of 68 patients with an initial diagnosis of GBM were retrospectively evaluated; for 10 of these patients, recurrent tumor specimens were used to construct a tissue microarray. Rad51 protein expression was then correlated with the actual and predicted survival using recursive partitioning analysis. RESULTS: Rad51 protein was elevated in 53% of the GBM specimens at surgery. The Rad51 levels correlated directly with survival, with a median survival of 15 months for patients with elevated Rad51 compared with 9 months for patients with low or absent levels of Rad51 (p = .05). At disease recurrence, 70% of patients had additional increases in Rad51 protein. Increased Rad51 levels at disease recurrence similarly predicted for improved overall survival, with a mean survival of 16 months from the second craniotomy compared with only 4 months for patients with low Rad51 levels (p = .13). CONCLUSION: Elevated levels of the double-stranded DNA repair protein Rad51 predicted for an increase survival duration in patients with GBM, at both initial tumor presentation and disease recurrence.
Subject(s)
Brain Neoplasms/mortality , Brain Neoplasms/radiotherapy , Glioblastoma/mortality , Glioblastoma/radiotherapy , Neoplasm Proteins/metabolism , Rad51 Recombinase/metabolism , Adult , Aged , Aged, 80 and over , Brain Neoplasms/metabolism , Female , Glioblastoma/metabolism , Humans , Ki-67 Antigen/metabolism , Male , Middle Aged , Neoplasm Recurrence, Local/mortality , Radiation Tolerance , Retrospective Studies , Young AdultABSTRACT
A program, AutoSherlock, has been developed to present crystallization screening results in terms of chemical space. This facilitates identification of lead conditions, rational interpretation of results and directions for the optimization of crystallization conditions.
ABSTRACT
An efficient optimization method for the crystallization of biological macromolecules has been developed and tested. This builds on a successful high-throughput technique for the determination of initial crystallization conditions. The optimization method takes an initial condition identified through screening and then varies the concentration of the macromolecule, precipitant, and the growth temperature in a systematic manner. The amount of sample and number of steps is minimized and no biochemical reformulation is required. In the current application a robotic liquid handling system enables high-throughput use, but the technique can easily be adapted in a nonautomated setting. This method has been applied successfully for the rapid optimization of crystallization conditions in nine representative cases.
