W0351

Automated CRYSTOOL Crystallization Screening at the TB Structural Genomics Crystallization Facility. Brent Segelke, Timothy Lekin, Dominque Toppani, Johana Schafer, Bernhard Rupp. Macromolecular Crystallography, Biology and Biotechnology Program, Lawrence Livermore National Laboratory, L448, POB 808, Livermore, CA 94551.

The M. tuberculosis Structural Genomics Consortium Crystallization facility is being developed at Lawrence Livermore National Lab. Our efforts focus on adapting automated design of crystallization screens using CRYSTOOL to automated setup, tracking and analysis. By considering crystal screening as a sampling problem, we have previously demonstrated, by probability theory, the inherent efficiency of CRYSTOOL screening. Using CRYSTOOL we are able to generate any number of random combinations of crystallization conditions from a large set of starting stock solutions and have interfaced CRYSTOOL to an automated liquid-handling system (Packard, MPII-HT). 1 ml each of three 96 condition CRYSTOOL-Screens can be mixed in under 3h. Sitting-drop experiments are set up in 96-well Intelli-Plates using a Hydra Plus One (Apogent inc.) in ~2 min per plate. Intelli-Plates, have been designed for robotic handling and ease of crystal harvesting. Plates are imaged with a robotic imaging system, VersaScan developed with Velocity11, and images are processed with prototype crystal detection software. Control software and a LIM system integrate the VersaS-can with a plate mover, the liquid-handling robot with a sealer yet to be added for complete automation. Our current throughput is estimated at 12 proteins (288 conditions each) per day. To date, we have processed >100 protein samples from consortium facilities or member labs and ~35% of proteins provided yield lead conditions from initial screens. Optimization remains a significant bottleneck. Mining the database of crystallization experiments amassed thus far provides quantitative success rate comparisons for reagents used in crystallization. With continued application of automated CRYSTOOL we will be able to narrow initial screens to “hot spots” in crystallization parameter space and increase our success rate and throughput while reducing cost.

This work was performed under U.S. DOE auspices by the University of California, Lawrence Livermore National Laboratory, Contract W-7405-Eng-48.