Crystallization data mining in structural genomics: using positive and negative results to optimize protein crystallization screens.

Abstract

Recent efforts to collect and mine crystallization data from structural genomics (SG) consortia have led to the identification of minimal screens and novel screening strategies that can be used to streamline the crystallization process. Two groups, the Joint Center for Structural Genomics and the University of Toronto, carried out large-scale crystallization trials on different sets of bacterial targets (539, JCSG and 755, Toronto), using different sample processing and crystallization methods, and then analyzed their results to identify the smallest subset of conditions that would have crystallized the maximum number of protein targets. The JCSG Core Screen contains 67 conditions (from 480) while the Toronto Minimal Screen contains 6 (from 48). While the exact conditions included in the two screens do not overlap, the major precipitants of the conditions are similar and thus both screens can be used to determine if a protein has a natural propensity to crystallize. In addition, studies from other groups including the University of Queensland, the Mycobacterium tuberculosis SG group, the Southeast Collaboratory for SG, and the York Structural Biology Laboratory indicate that alternative crystallization strategies may be more successful at identifying initial crystallization conditions than typical sparse matrix screens. These minimal screens and alternative screening strategies are already being used to optimize the crystallization processes within large SG efforts. The differences between these results, however, demonstrate that additional studies which examine the influence of protein biophysical properties and sample preparation methods on crystal formation must also be carried out before more robust screens can be identified.