X-ray crystallography remains the primary method for the determination of the atomic structure of biological macromolecules. At the time of writing, more than 80 000 structures form the Protein Data Bank (PDB; http://www.wwpdb.org
; Westbrook et al.
), of which roughly 87% have been solved using X-ray crystallography.
The rate at which macromolecular crystallography (MX) data sets can now be measured at synchrotron-radiation facilities (Winter & McAuley, 2011
) raises issues relating to the effective use of beamtime. Automated tools that allow synchrotron beamline users to be as efficient as possible are under continual development (Bahar et al.
; Keegan & Winn, 2007
; Panjikar et al.
; Winter & McAuley, 2011
). The tool described here, Nearest-cell
, is a useful addition to this automation armoury.
Somewhat masked by the success of MX, numerous challenges remain in protein production, purification and crystallization. This is particularly the case for complexes comprising multiple protein subunits as well as membrane proteins, where there can be an elevated risk of purifying host-system expression byproducts along with the target of interest. Given the difficulties associated with crystallizing many of these ‘high-impact’ targets, it is often the case that the ‘impurity’ protein crystallizes more readily. A ready way of determining whether a crystal might arise from an impurity, such as Nearest-cell, is particularly useful in these situations.
has been installed at the MX beamlines at the Diamond Light Source and uses output from automated data-analysis pipelines such as fast_dp
(Winter & McAuley, 2011
) to provide users with a putative list of similar unit cells (and hence, potentially, structures) in the PDB.