Review of serial femtosecond crystallography including the COVID-19 pandemic impact and future outlook

The Pixel Anomaly Detection Tool: a user-friendly GUI for classifying detector frames using machine-learning approaches

Data collection at X-ray free electron lasers has particular experimental challenges, such as continuous sample delivery or the use of novel ultrafast high-dynamic-range gain-switching X-ray detectors. This can result in a multitude of data artefacts, which can be detrimental to accurately determining structure-factor amplitudes for serial crystallography or single-particle imaging experiments. Here, a new data-classification tool is reported that offers a variety of machine-learning algorithms to sort data trained either on manual data sorting by the user or by profile fitting the intensity distribution on the detector based on the experiment. This is integrated into an easy-to-use graphical user interface, specifically designed to support the detectors, file formats and software available at most X-ray free electron laser facilities. The highly modular design makes the tool easily expandable to comply with other X-ray sources and detectors, and the supervised learning approach enables even the novice user to sort data containing unwanted artefacts or perform routine data-analysis tasks such as hit finding during an experiment, without needing to write code.

X-Ray Crystallography of Viruses

Since the 1970s and for about 40 years, X-ray crystallography has been by far the most powerful approach for determining virus structures at close to atomic resolutions. Information provided by these studies has deeply and extensively enriched and shaped our vision of the virus world. In turn, the ever-increasing complexity and size of the virus structures being investigated have constituted a major driving force for methodological and conceptual developments in X-ray macromolecular crystallography (MX). Landmarks of the structure determination of viral particles, such as the ones from the first animal viruses or from the first membrane-containing viruses, have often been associated with methodological breakthroughs in X-ray crystallography.In recent years, the advent of new detectors with fast frame rate, high sensitivity, and low-noise background has changed the way MX data is collected, enabling new types of studies at X-ray free-electron laser and synchrotron facilities. In parallel, a very high degree of automation has been established at most MX synchrotron beamlines, allowing the screening of large number of crystals with very high throughputs. This has proved crucial for fragment-based drug design projects, of special relevance for the identification of new antiviral drugs.This change in the usage of X-ray crystallography is also mirrored in the recent advances in cryo-electron microscopy (cryo-EM), which can nowadays produce macromolecule structures at resolutions comparable to those obtained by MX. Since this technique is especially amenable for large protein assemblies, cryo-EM has progressively turned into the favored technique to study the structure of large viral particles at high resolution.In this chapter, we present the common ground of proteins and virus crystallography with an emphasis in the peculiarities of virus-related studies.