  Crystallization is a key step in macromolecular structure determination by
crystallography. While a robust theoretical treatment of the process is
available, due to the complexity of the system, the experimental process is
still largely one of trial and error. In this article, efforts in the field are
discussed together with a theoretical underpinning using a solubility phase
diagram. Prior knowledge has been used to develop tools that computationally
predict the crystallization outcome and define mutational approaches that
enhance the likelihood of crystallization. For the most part these tools are
based on binary outcomes (crystal or no crystal), and the full information
contained in an assembly of crystallization screening experiments is lost. The
potential of this additional information is illustrated by examples where new
biological knowledge can be obtained and where a target can be sub-categorized
to predict which class of reagents provides the crystallization driving force.
Computational analysis of crystallization requires complete and correctly
formatted data. While massive crystallization screening efforts are under way,
the data available from many of these studies are sparse. The potential for
this data and the steps needed to realize this potential are discussed.
