Crystallography. As technologies improve,
the standards get higher.

Since the determination of the first protein structure, Myoglobin, by X-ray crystallography in 1958(1), the community has witnessed many exciting changes in this field. Through the advancement of liquid handling robotics and imaging technologies, the amount of purified protein required for crystallization experiments has decreased significantly(2). Additionally, the requirement of large crystals for diffraction studies is diminishing due to brighter synchrotron sources, smaller beam sizes, advanced detectors, cryocooling, and new methods like serial femtosecond crystallography, which relies on the use of nanocrystals(2,3).  These advancements have led to the determination of many exciting classes of proteins, such as: protein:nucleic acid complexes, multi-protein complexes, membrane proteins, antibodies, ribosomes, and viruses. However, despite all of the incredible advancements in technology, the main bottleneck of protein structure determination still exists, which is obtaining diffraction quality crystals(4)

Initial Screening
The first step of protein crystallography requires finding the unique set of chemical conditions that promote the crystallization of the sample(5). Unfortunately, no universal crystallization conditions exist, and these chemical conditions have to be experimentally determined for each protein sample. In practice, this first step is typically performed by mixing a concentrated sample of purified protein against a sparse matrix screen of chemical conditions in a vapor diffusion experiment(6). Many different sparse matrix screens exist, and a few available from Anatrace include:

A critical step in crystal screening is identifying initial crystallization hits and ensuring the crystals are comprised of protein. In addition to standard brightfield microscopy, advanced techniques such as SONICC(10), trace fluorescent labeling(11), and UV Microscopy(12) exist to identify protein crystals. Furthermore, identification of nanocrystals for serial femtosecond crystallography has been performed using transmission electron microscopy(13)

Crystal Optimization
Typically, the initial crystallization hits identified through sparse matrix screening are not suitable for diffraction studies and often require a series of optimization steps to improve the crystal quality. Many strategies exist for crystal optimization, including but not limited to: grid screening, seeding, use of additives, protein modification, and orthogonal crystallization methods. To facilitate crystal optimization experiments, Anatrace offers individual condition refills and optimization reagents for all of our crystallization screens.

 
Grid Screening.  Perhaps the simplest method for crystal optimization, grid screening involves systematically varying individual components of a crystallization solution to fine tune the condition needed to grow diffraction quality crystals(14). A typical crystallization solution will contain a buffer at a certain pH, a salt, and a precipitant. Additionally, the temperature of the crystallization experiment can also be adjusted, and usually has a big effect on crystallization. Basic two-dimensional grid screening experiments will vary two of the components (i.e. salt concentration vs. % precipitant) while keeping the other variables constant. This process is repeated until the optimal crystallization condition is obtained. To simplify the setup of grid screens, there are many robotic systems, such as the dragonfly® from TTP Labtech, that allow for the fast, accurate, and highly repeatable dispensing of even the most viscous liquids.  The dragonfly® also includes software for the intuitive design of optimization screens, streamlining the entire crystallization workflow.

Seeding.  A requirement of protein crystallization is the spontaneous nucleation of protein molecules in order to provide a lattice for crystal growth. However, the conditions required for nucleation are not always the same conditions necessary for crystal growth. The seeding method was originally developed as an optimization technique where crystals not suitable for diffraction studies were crushed to form a seed stock and added to conditions that were similar to the initial crystallization condition(15). A newer application of seeding, called random microseed matrix-screening (rMMS), involves using a seed stock and rescreening through sparse matrix screens(16). The results from rMMS have been shown to produce more initial hits and better diffracting crystals.

Additives. A number of additives have been shown to improve protein crystallization(17, 18). A small selection of additives that have been successful include various detergents(19), non-detergent sulfobetaines(20), and physiologically relevant ligands.
 
Protein Modification.  There are a number of changes that can be made to the protein construct to improve crystallizability(21). A few of these include removal of post-translational modifications, addition of fusion proteins (e.g. MBP (22)), and surface entropy reduction(23). Additionally, disordered regions in proteins may prevent the formation of well-ordered crystals, and removal of these regions may improve crystallization outcomes(24). Computational prediction and removal of disordered regions has been incorporated into a number of structural genomics pipelines(25).

Orthogonal Methods. Vapor diffusion is not the only method for protein crystallization. Other methods include microbatch, dialysis, and free-interface diffusion. These methods all vary the crystallization kinetics, and how the protein traverses the phase diagram. The Microlytic Crystal Former is a high-throughput plate for free-interface diffusion crystallization.  Comprising 96 individual microchannels, the Microlytic Crystal Former is a fully SBS compliant crystallization plate, which is compatible with the most common crystallization and imaging robots. The Crystal Former was utilized to improve the diffraction of crystals of the enzyme EndoS(26,  27). Prior efforts to crystallize this protein with vapor diffusion led to crystals that diffracted poorly to 7.5 Å; however, rescreening with the Crystal Former led to the identification of new crystal forms which diffracted to 1.9 Å.  

 

References:
1)      Kendrew, J.C., et al. (1958) Nature, 181, 662-666.
2)      Giege, R. (2013) FEBS Journal, 280(24), 6457-6497.
3)      Schlichting, I. (2015) IUCrJ, 2(2), 246-255.
4)      Lin, S. X. et al. (2007) Crystal Growth and Design, 7(11), 2124-2125. 
5)      McPherson, A. (2004) Methods, 34(3), 254-265.
6)      Chayen, N. E. and Saridakis, E. (2008) Nature Methods, 5(2), 147-153.
7)      Fazio, V. J., Peat, T. S., and Newman, J. (2014) Acta Cryst F, 70, 1303-1311.
8)      Newman, J. et al. (2005) Acta Cryst D, 61, 1426-1431.
9)      Chaikuad, A., Knapp S., and von Delft, F. (2015) Acta Cryst D, 711627-1639.
10)   Haupert, L. M. and Simpson, G. J. (2011) Methods, 55(4), 379-386.
11)   Pusey, M., et al. (2015) Acta Cryst F, 71, 806-814.
12)   Gill, H. S., (2010) Acta Cryst F, 66, 354-372.
13)   Stevenson, H. P., et al (2014) PNAS, 111(23), 8470-8475.
14)   Weber, P. C. (1990) Methods, 1(1), 31-37.
15)   Stura, E. A. and Wilson, I. A. (1991) J. Cryst. Growth, 110, 270-282.
16)   Shaw Stewart, P. D. et al. (2011) Crystal Growth and Design, 11, 3432-3441.
17)   McPherson, A. et al. (2011) Crystal Growth and Design, 11, 1469-1474.
18)   Sauter, C. et al. (1999) J. Cryst. Growth, 196, 365-376.
19)   McPherson, A. et al. (1986) J. Cryst. Growth, 76, 547-553.
20)   Vuillard, L. (1996) J. Cryst. Growth, 168, 150-154.
21)   Derewenda, Z. (2010) Acta Cryst D, 66604-615.
22)   Clifton, M. C. et al (2015) PLoS One 10(4):e0125010.