Statistical methods for the objective design of screening procedures for macromolecular crystallization

The crystallization of a new macromolecule is still very much a trial-and-e rror process. As is well known, it requires the search of a large parameter space of experimental settings to rnd the relatively few idiosyncratic con ditions that lead to diffraction-quality crystals. Crystallographers have d eveloped a variety of screens to help identify initial crystallization cond itions, including those based on systematic grids, incomplete factorial and sparse-matrix approaches. These are somewhat subjectively formulated based on accumulated data from past crystallization experiments. Ideally, one wo uld prefer as objective a procedure as possible; however, that requires obj ective methods that incorporate a broad source of crystallization data. The Biological Macromolecular Crystallization Database (BMCD), a repository of all published crystallization conditions, is an obvious source of this dat a. This database has been augmented with a hierarchical classification of t he macromolecules contained in the BMCD as well as extensive data on the ad ditives used with them. A statistical analysis of the augmented BMCD shows the existence of significant correlations between families of macromolecule s and the experimental conditions under which they crystallize. This in tur n leads to a Bayesian technique for determining the probability of success of a set of experimental conditions based on the data in the BMCD as well a s facts about a macromolecule known prior to crystallization. This has been incorporated into software that enables users to rank experimental conditi ons for new macromolecules generated by a dense partial factorial design. F inally, an additional advantage of the software described here is that it a lso facilitates the accumulation of the data required for improving the acc uracy of estimation of the probabilities of success - knowledge of the cond itions which lead to failure of crystallization.