Summary
Major technological advances have increased the throughput of macromolecular crystallography, however, in many cases the identification of favorable crystallization conditions remains the rate-limiting step. Here, we present an automated method for the optimization of crystallization screens that iteratively adjusts precipitant concentrations based on the results of previous crystallization trials. In this Iterative Screen Optimization (ISO) method, the outcome of each crystallization experiment is visualized and manually scored using the program RockMaker. The scores are then used to generate a new crystallization screen by adjusting the precipitant concentrations of each reservoir solution to achieve a metastable macromolecular solution that favors crystal nucleation and growth. To facil
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