Protein crystallization: virtual screening and optimization.

Advances in genomics have yielded entire genetic sequences for a variety of prokaryotic and eukaryotic organisms. This accumulating information has escalated the demands for three-dimensional protein structure determinations. As a result, high-throughput structural genomics has become a major international research focus. This effort has already led to several significant improvements in X-ray crystallographic and nuclear magnetic resonance methodologies. Crystallography is currently the major contributor to three-dimensional protein structure information. However, the production of soluble, purified protein and diffraction-quality crystals are clearly the major roadblocks preventing the realization of high-throughput structure determination. This paper discusses a novel approach that may improve the efficiency and success rate for protein crystallization. An automated nanodispensing system is used to rapidly prepare crystallization conditions using minimal sample. Proteins are subjected to an incomplete factorial screen (balanced parameter screen), thereby efficiently searching the entire "crystallization space" for suitable conditions. The screen conditions and scored experimental results are subsequently analyzed using a neural network algorithm to predict new conditions likely to yield improved crystals. Results based on a small number of proteins suggest that the combination of a balanced incomplete factorial screen and neural network analysis may provide an efficient method for producing diffraction-quality protein crystals.

Preliminary X-ray crystallographic studies of yeast mitochondrial protein Tom70p.

Protein translocations across mitochondrial membranes play critical roles in mitochondrion biogenesis. Protein transport from the cell cytosol to the mitochondrial matrix is carried out by the translocase of the outer membrane (TOM) complex and the translocase of the inner membrane (TIM) complexes. Tom70p is an important TOM-complex member and a major surface receptor of the protein-translocation machinery in the outer mitochondrial membrane. To investigate the mechanism by which Tom70p functions to deliver the mitochondrial protein precursors, the cytosolic fragment of yeast Tom70p (cTom70p) was crystallized. The crystals diffract to 3.2 A using a synchrotron X-ray source and belong to space group P2(1), with unit-cell parameters a = 44.89, b = 168.78, c = 83.41 A, alpha = 90.00, beta = 102.74, gamma = 90.00 degrees. There are two Tom70p molecules in one asymmetric unit, which corresponds to a solvent content of approximately 51%. Structure determination by MAD methods is under way.

Efficient protein crystallization.

High-throughput molecular biology and crystallography advances have placed an increasing demand on crystallization, the one remaining bottleneck in macromolecular crystallography. This paper describes three experimental approaches, an incomplete factorial crystallization screen, a high-throughput nanoliter crystallization system, and the use of a neural net to predict crystallization conditions via a small sample (approximately 0.1%) of screening results. The use of these technologies has the potential to reduce time and sample requirements. Initial experimental results indicate that the incomplete factorial design detects initial crystallization conditions not previously discovered using commercial screens. This may be due to the ability of the incomplete factorial screen to sample a broader portion of "crystallization space," using a multidimensional set of components, concentrations, and physical conditions. The incomplete factorial screen is complemented by a neural network program used to model crystallization. This capability is used to help predict new crystallization conditions. An automated, nanoliter crystallization system, with a throughput of up to 400 conditions/h in 40-nl droplets (total volume), accommodates microbatch or traditional "sitting-drop" vapor diffusion experiments. The goal of this research is to develop a fully-automated high-throughput crystallization system that integrates incomplete factorial screen and neural net capabilities.