The successful identification of a protein structure is reliant on the production of high quality crystals. This requires the use of extremely pure protein, which is often in limited supply as protein production and purification can be costly and labor-intensive. Additionally, every protein provides its own crystallization challenge and specific protein groups, such as membrane proteins, are notoriously difficult to crystallize.
Furthermore, for structural determination using X-ray diffraction, not all crystals produced will diffract sufficiently. Thus, the crystallization process may have to be revisited; surface residues may have to be modified or crystallization conditions changed.
Crystal growth will occur when molecules are brought into a thermodynamically unstable state called supersaturation, achieved through the gradual removal of solvent. Nucleation, growth, and growth cessation are the three steps of crystal growth and each may require a change in experimental conditions to produce the best quality crystal.
Every protein crystallization process is hence designed and set up under a variety of conditions, adjusting experimental parameters to identify optimal crystallization conditions. These can include temperature, pH, precipitants, additives, protein concentration, and expression and purification conditions. Essentially each parameter variable becomes a crystallization experiment.
Frequently performed in 96- or 24-well plates, each sealed well will contain at least one individual experiment. This process is of paramount importance, since some proteins may only crystallize under a specific set of conditions.
In the past, screening and optimization of experimental conditions have relied on what is commonly regarded as the black art of protein crystallography, often incorporating more complex methods that were difficult to automate for high-throughput experimentation. Scientists now have the ability to automate most of these methods.
Automation, however, has resulted in a considerable amount of experimental data that must be logged. Providing a flexible and easy to use alternative to manual logs or isolated databases is necessary to fully exploit the data produced. All experimental conditions and results should be recorded, closely monitored, and easily tracked to ensure repeatability for future crystallizations. Additionally, many optimizations are often required before acceptable crystals are even visible, thus having the ability to data mine from previous experiments or initial screens is invaluable.