George T. DeTitta

A deliberate approach to screening for initial crystallization conditions of biological macromolecules

A method to rationally predict crystallization conditions for a previously uncrystallized macromolecule has not yet been developed. One way around this problem is to determine initial crystallization conditions by casting a wide net, surveying a large number of chemical and physical conditions to locate crystallization leads. A facility that executes the rapid survey of crystallization lead conditions is described in detail. Results and guidelines for the initial screening of crystallization conditions, applicable to both manual and robotic setups, are discussed.

Structure solution by minimal-function phase refinement and Fourier filtering. II. Implementation and applications

The minimal function, R(psi), has been used to provide the basis for a new computer-intensive direct-methods procedure that shows potential for providing fully automatic routine solutions for structures in the 200-400 atom range. This procedure, which has been called shake-and-bake, is an iterative process in which real-space filtering is alternated with phase refinement using a technique that reduces the value of R(psi). It has been successfully tested using experimental data for a dozen known structures ranging in size from 25 to 317 atoms and crystallizing in a variety of space groups. The details of this procedure, the parameters used and the results of these applications are described.

Understanding the physical properties that control protein crystallization by analysis of large-scale experimental data.

Crystallization is the most serious bottleneck in high-throughput protein-structure determination by diffraction methods. We have used data mining of the large-scale experimental results of the Northeast Structural Genomics Consortium and experimental folding studies to characterize the biophysical properties that control protein crystallization. This analysis leads to the conclusion that crystallization propensity depends primarily on the prevalence of well-ordered surface epitopes capable of mediating interprotein interactions and is not strongly influenced by overall thermodynamic stability. We identify specific sequence features that correlate with crystallization propensity and that can be used to estimate the crystallization probability of a given construct. Analyses of entire predicted proteomes demonstrate substantial differences in the amino acid–sequence properties of human versus eubacterial proteins, which likely reflect differences in biophysical properties, including crystallization propensity. Our thermodynamic measurements do not generally support previous claims regarding correlations between sequence properties and protein stability.

Macromolecular crystallization in a high throughput laboratory—the search phase

Macromolecular crystallization efforts are frequently divided into a search phase, during which approximate conditions are sought, and an optimization phase, when the approximate conditions are optimized to yield crystals of sufficient quality for diffraction work. Faced with the possibility that, on a yearly basis, many hundreds of proteins might be generated, both in our laboratories and at the laboratories of our collaborators, we have recently designed and commissioned a high throughput robotics lab designed for the search phase. The lab is capable of setting up and photographically evaluating over 60,000 microbatch crystallization experiments per week. In the first four months of operation we have set up crystallization experiments for more than one hundred proteins.

Structure solution by minimal-function phase refinement and Fourier filtering. I. Theoretical basis

Eliminating the N atomic position vectors rj, j = 1, 2, ..., N, from the system of equations defining the normalized structure factors EH yields a system of identities that the EHs must satisfy, provided that the set of EHs is sufficiently large. Clearly, for fixed N and specified space group, this system of identities depends only on the set [H], consisting of n reciprocal-lattice vectors H, and is independent of the crystal structure, which is assumed for simplicity to consist of N identical atoms per unit cell. However, for a fixed crystal structure, the magnitudes magnitude of /EH/ are uniquely determined so that a system of identities is obtained among the corresponding phases psi H alone, which depends on the presumed known magnitudes magnitude of /EH/ and which must of necessity be satisfied. The known conditional probability distributions of triplets and quartets, given the values of certain magnitudes magnitude of /E/, lead to a function R(psi) of phases, uniquely determined by magnitudes magnitude of /E/ and having the property that RT < 1/2 < RR, where RT is the value of R(psi) when the phases are equal to their true values, no matter what the choice of origin and enantiomorph, and RR is the value of R(psi) when the phases are chosen at random. The following conjecture is therefore plausible: the global minimum of R(psi), where the phases are constrained to satisfy all identities among them that are known to exist, is attained when the phases are equal to their true values and is thus equal to RT.(ABSTRACT TRUNCATED AT 250 WORDS)

Intelligent decision support for protein crystal growth

Current structural genomics projects are likely to produce hundreds of proteins a year for structural analysis. The primary goal of our research is to speed up the process of crystal growth for proteins in order to enable the determination of protein structure using single crystal X-ray diffraction. We describe Max, a working prototype that includes a high-throughput crystallization and evaluation setup in the wet laboratory and an intelligent software system in the computer laboratory. A robotic setup for crystal growth is able to prepare and evaluate over 40 thousand crystallization experiments a day. Images of the crystallization outcomes captured with a digital camera are processed by an image-analysis component that uses the two-dimensional Fourier transform to perform automated classification of the experiment outcome. An information repository component, which stores the data obtained from crystallization experiments, was designed with an emphasis on correctness, completeness, and reproducibility. A case-based reasoning component provides support for the design of crystal growth experiments by retrieving previous similar cases, and then adapting these in order to create a solution for the problem at hand. While work on Max is still in progress, we report here on the implementation status of its components, discuss how our work relates to other research, and describe our plans for the future.

A METHOD TO PRODUCE MICROSEED STOCK FOR USE IN THE CRYSTALLIZATION OF BIOLOGICAL MACROMOLECULES

A method is presented for producing a seed-stock mixture for macromolecular crystallization. A PTFE bead and micro-centrifuge tube act as mortar and pestle for pulverizing seed crystals of macromolecules. Energy for the beads motion is supplied by a vortex mixer or an ultrasonic bath. The crushed crystal is serially diluted to prepare a seed-stock mixture of the desired concentration for crystallization. Crystals produced using both hanging-drop vapor diffusion and a capillary microbatch method show expected dilution behavior. This technique of producing seed stock is compared with traditional means and advantages over the standard protocol are demonstrated.

Characterization of Trypanosoma brucei dihydroorotate dehydrogenase as a possible drug target; structural, kinetic and RNAi studies

Nucleotide biosynthesis pathways have been reported to be essential in some protozoan pathogens. Hence, we evaluated the essentiality of one enzyme in the pyrimidine biosynthetic pathway, dihydroorotate dehydrogenase (DHODH) from the eukaryotic parasite Trypanosoma brucei through gene knockdown studies. RNAi knockdown of DHODH expression in bloodstream form T. brucei did not inhibit growth in normal medium, but profoundly retarded growth in pyrimidine‐depleted media or in the presence of the known pyrimidine uptake antagonist 5‐fluorouracil (5‐FU). These results have significant implications for the development of therapeutics to combat T. brucei infection. Specifically, a combination therapy including a T. brucei‐specific DHODH inhibitor plus 5‐FU may prove to be an effective therapeutic strategy. We also show that this trypanosomal enzyme is inhibited by known inhibitors of bacterial Class 1A DHODH, in distinction to the sensitivity of DHODH from human and other higher eukaryotes. This selectivity is supported by the crystal structure of the T. brucei enzyme, which is reported here at a resolution of 1.95 Å. Additional research, guided by the crystal structure described herein, is needed to identify potent inhibitors of T. brucei DHODH.

Blocking S-adenosylmethionine synthesis in yeast allows selenomethionine incorporation and multiwavelength anomalous dispersion phasing

Saccharomyces cerevisiae is an ideal host from which to obtain high levels of posttranslationally modified eukaryotic proteins for x-ray crystallography. However, extensive replacement of methionine by selenomethionine for anomalous dispersion phasing has proven intractable in yeast. We report a general method to incorporate selenomethionine into proteins expressed in yeast based on manipulation of the appropriate metabolic pathways. sam1− sam2− mutants, in which the conversion of methionine to S-adenosylmethionine is blocked, exhibit reduced selenomethionine toxicity compared with wild-type yeast, increased production of protein during growth in selenomethionine, and efficient replacement of methionine by selenomethionine, based on quantitative mass spectrometry and x-ray crystallography. The structure of yeast tryptophanyl-tRNA synthetase was solved to 1.8 Å by using multiwavelength anomalous dispersion phasing with protein that was expressed and purified from the sam1− sam2− strain grown in selenomethionine. Six of eight selenium residues were located in the structure.

Automatic classification of sub‐microlitre protein‐crystallization trials in 1536‐well plates

A technique for automatically evaluating microbatch (400 nl) protein-crystallization trials is described. This method addresses analysis problems introduced at the sub-microlitre scale, including non-uniform lighting and irregular droplet boundaries. The droplet is segmented from the well using a loopy probabilistic graphical model with a two-layered grid topology. A vector of 23 features is extracted from the droplet image using the Radon transform for straight-edge features and a bank of correlation filters for microcrystalline features. Image classification is achieved by linear discriminant analysis of its feature vector. The results of the automatic method are compared with those of a human expert on 32 1536-well plates. Using the human-labeled images as ground truth, this method classifies images with 85% accuracy and a ROC score of 0.84. This result compares well with the experimental repeatability rate, assessed at 87%. Images falsely classified as crystal-positive variously contain speckled precipitate resembling microcrystals, skin effects or genuine crystals falsely labeled by the human expert. Many images falsely classified as crystal-negative variously contain very fine crystal features or dendrites lacking straight edges. Characterization of these misclassifications suggests directions for improving the method.