Crystallization: Digging into the Past to Learn Lessons for the Future

Abstract

Crystals of biological macromolecules have been observed and grown for well over a century. More effort has been put into biological crystallization in the last few decades due to the importance of X-ray crystal structures, the advent of synchrotron radiation sources, improved computational speed, better software, and the availability of recombinant protein. Here we focus on two important areas of crystal growth: firstly, on techniques for stabilizing the protein sample, and secondly, on strategies and approaches for selecting the crystallization cocktails most suitable for different strategies.

Membrane Protein Crystallisation: Current Trends and Future Perspectives

“…Of note is the absence of high salt conditions in our database. This contrasts with a recent analysis of crystallization space reported for the entire PDB in 2014 25. This analysis clearly demonstrates the most successful crystallization reagents are PEG 3350 and ammonium sulfate, which only make up 4.0 and 3.5 % of our database.…”

“…This suggests that pH range is an important parameter to optimize and consider when designing membrane protein screens. We also noticed that the spread of pH values from 3.0 to 10.5 appears to be wider for membrane proteins than the recent analysis of the entire PDB, which is fairly narrow between 5 and 9 25.…”

“…Therefore MemGold II contains a different set of these chemicals, which can be essential to enable proteins to interact and pack into a crystal (PepT St ) 36. It is interesting to note that one of the most successful commercial crystallization screens is the Hampton PEG/Ion screen, which also involves screening many different polyvalent and monovalent salts against the most successful precipitant for soluble proteins PEG 3350 25. This is possibly something that should be replicated for membrane proteins.…”

“…As of August 2015 the number of crystallization examples in our database is > 500 and in this chapter we present an updated analysis from these conditions. Here we compare the results of these past analyses with each other and with those focused on soluble proteins 25. The aim of this chapter is to equip the protein crystallographer with the knowledge to design their own screens using information that is up to date and relevant to membrane protein samples.…”

Classification of crystallization outcomes using deep convolutional neural networks

“…In order to help run these trials, commercial crystallization screens have been developed; each screen generally contains 96 formulations designed to promote crystal growth. Whether these screens are equally effective or not [ 5 , 6 ] remains debated, but their overall yield is in any case paltry. Typically fewer than 5% of crystallization attempts produce useful results (with a success rate as low as 0.2% in some contexts [ 7 ]).…”