The CCP4 suite: integrative software for macromolecular crystallography.

The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world.

xia2.multiplex: a multi-crystal data-analysis pipeline.

In macromolecular crystallography, radiation damage limits the amount of data that can be collected from a single crystal. It is often necessary to merge data sets from multiple crystals; for example, small-wedge data collections from micro-crystals, in situ room-temperature data collections and data collection from membrane proteins in lipidic mesophases. Whilst the indexing and integration of individual data sets may be relatively straightforward with existing software, merging multiple data sets from small wedges presents new challenges. The identification of a consensus symmetry can be problematic, particularly in the presence of a potential indexing ambiguity. Furthermore, the presence of non-isomorphous or poor-quality data sets may reduce the overall quality of the final merged data set. To facilitate and help to optimize the scaling and merging of multiple data sets, a new program, xia2.multiplex, has been developed which takes data sets individually integrated with DIALS and performs symmetry analysis, scaling and merging of multi-crystal data sets. xia2.multiplex also performs analysis of various pathologies that typically affect multi-crystal data sets, including non-isomorphism, radiation damage and preferential orientation. After the description of a number of use cases, the benefit of xia2.multiplex is demonstrated within a wider autoprocessing framework in facilitating a multi-crystal experiment collected as part of in situ room-temperature fragment-screening experiments on the SARS-CoV-2 main protease.

DIALS as a toolkit.

The DIALS software for the processing of X-ray diffraction data is presented, with an emphasis on how the suite may be used as a toolkit for data processing. The description starts with an overview of the history and intent of the toolkit, usage as an automated system, command-line use, and ultimately how new tools can be written using the API to perform bespoke analysis. Consideration is also made to the application of DIALS to techniques outside of macromolecular X-ray crystallography.

Scanning electron microscopy as a method for sample visualization in protein X-ray crystallography.

Developing methods to determine high-resolution structures from micrometre- or even submicrometre-sized protein crystals has become increasingly important in recent years. This applies to both large protein complexes and membrane proteins, where protein production and the subsequent growth of large homogeneous crystals is often challenging, and to samples which yield only micro- or nanocrystals such as amyloid or viral polyhedrin proteins. The versatile macromolecular crystallography microfocus (VMXm) beamline at Diamond Light Source specializes in X-ray diffraction measurements from micro- and nanocrystals. Because of the possibility of measuring data from crystalline samples that approach the resolution limit of visible-light microscopy, the beamline design includes a scanning electron microscope (SEM) to visualize, locate and accurately centre crystals for X-ray diffraction experiments. To ensure that scanning electron microscopy is an appropriate method for sample visualization, tests were carried out to assess the effect of SEM radiation on diffraction quality. Cytoplasmic polyhedrosis virus polyhedrin protein crystals cryocooled on electron-microscopy grids were exposed to SEM radiation before X-ray diffraction data were collected. After processing the data with DIALS, no statistically significant difference in data quality was found between datasets collected from crystals exposed and not exposed to SEM radiation. This study supports the use of an SEM as a tool for the visualization of protein crystals and as an integrated visualization tool on the VMXm beamline.

Scaling diffraction data in the DIALS software package: algorithms and new approaches for multi-crystal scaling.

In processing X-ray diffraction data, the intensities obtained from integration of the diffraction images must be corrected for experimental effects in order to place all intensities on a common scale both within and between data collections. Scaling corrects for effects such as changes in sample illumination, absorption and, to some extent, global radiation damage that cause the measured intensities of symmetry-equivalent observations to differ throughout a data set. This necessarily requires a prior evaluation of the point-group symmetry of the crystal. This paper describes and evaluates the scaling algorithms implemented within the DIALS data-processing package and demonstrates the effectiveness and key features of the implementation on example macromolecular crystallographic rotation data. In particular, the scaling algorithms enable new workflows for the scaling of multi-crystal or multi-sweep data sets, providing the analysis required to support current trends towards collecting data from ever-smaller samples. In addition, the implementation of a free-set validation method is discussed, which allows the quantification of the suitability of scaling-model and algorithm choices.