Utilizing anomalous signals for element identification in macromolecular crystallography

This article examines the application of anomalous scattering for the identification of elements within crystal structures of macromolecules.

Robust and automatic beamstop shadow outlier rejection: combining crystallographic statistics with modern clustering under a semi-supervised learning strategy

Beamstops and beamstop shadows can lead to erroneous reflection measurements in crystallographic diffraction experiments, which is a common problem in data processing. Here, a method is presented that combines crystallographic statistics with machine learning to robustly detect such reflections.

Structural studies of β-glucosidase from the thermophilic bacterium Caldicellulosiruptor saccharolyticus

Crystal structures of a β-glucosidase from the thermophilic bacterium C. saccharolyticus (Bgl1) and its complex with glucose were determined at 1.47 and 1.95 Å resolution, respectively. Comparison of Bgl1 with sequence or structural homologues showed that Bgl1 is quite similar except for two regions, one of which seems to be a unique insertion of residues that is only present in Bgl1.

CHiMP: deep-learning tools trained on protein crystallization micrographs to enable automation of experiments

A description is given of new deep-learning tools that analyse experimental micrographs of crystallization experiments to enable the automation of outcome classification, crystal detection and determination of locations to dispense compounds for fragment-based drug discovery at Diamond Light Source.