Data- and diversity-driven development of a Shotgun crystallization screen using the Protein Data Bank

Abstract

Protein crystallization has for decades been a critical and restrictive step in macromolecular structure determination via X-ray diffraction. Crystallization typically involves a multi-stage exploration of the available chemical space, beginning with an initial sampling (screening) followed by iterative refinement (optimization). Effective screening is important for reducing the number of optimization rounds required, reducing the cost and time required to determine a structure. Here, an initial screen (Shotgun II) derived from analysis of the up-to-date Protein Data Bank (PDB) is proposed and compared with the previously derived (2014) Shotgun I screen. In an update to that analysis, it is clarified that the Shotgun approach entails finding the crystallization conditions that cover the most diverse space of proteins by sequence found in the PDB, which can be mapped to the well known maximum coverage problem in computer science. With this realization, it was possible to apply a more effective algorithm for selecting conditions. In-house data demonstrate that compared with alternatives, the Shotgun I screen has been remarkably successful over the seven years that it has been in use, indicating that Shotgun II is also likely to be a highly effective screen.

Keywords: Protein Data Bank; REMARK280; Shotgun; crystallization screens; data mining.