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.

Crystallographic resolution is a key characteristic of diffraction data and represents one of the first decisions an experimenter has to make in data evaluation. Conservative approaches to the high-resolution cutoff determination are based on a number of criteria applied to the processed X-ray diffraction data only. However, high-resolution data that are weaker than arbitrary cutoffs can still result in the improvement of electron-density maps and refined structure models. Therefore, the impact of reflections from resolution shells higher than those previously used in conservative structure refinement should be analysed by the paired refinement protocol. For this purpose, a tool calledPAIREFwas developed to provide automation of this protocol. As a new feature, a complete cross-validation procedure has also been implemented. Here, the design, usage and control of the program are described, and its application is demonstrated on six data sets. The results prove that the inclusion of high-resolution data beyond the conventional criteria can lead to more accurate structure models.

Interleukin 24 (IL‐24) is a cytokine with the potential to be an effective treatment for autoimmune diseases and cancer. However, its instability and difficulties in its production have hampered detailed biological and biophysical studies. We approached the challenges of IL‐24 production by using the PROSS algorithm to design more stable variants of IL‐24. We used homology models built from the sequences and known structures of IL‐20 and IL‐19 and predicted and produced several extensively mutated IL‐24 variants that were highly stable and produced in large yields; one of them was crystallized (IL‐24B, PDB ID 6GG1; 3D Interactive at http://proteopedia.org/w/Journal: FEBS_Journal:1). The mutated variants, however, lost most of their binding capacity to the extracellular parts of cognate receptors. While the affinity to the receptor 2 (IL‐20R2) was preserved, the variants lost affinity to IL‐20R1 and IL‐22R1 (shared receptors 1). Back engineering of the variants revealed that reintroduction of a single IL‐24 wild‐type residue (T198) to the patch interacting with receptors 1 restored 80% of the binding affinity and signaling capacity, accompanied by an acceptable drop in the protein stability by 9 °C. Multiple sequence alignment explains the stabilizing effect of the mutated residues in the IL‐24 variants by their presence in the related and more stable cytokines IL‐20 and IL‐19. Our homology‐based approach can enhance existing methods for protein engineering and represents a viable alternative to study and produce difficult proteins for which only in silico structural information is available, estimated as >40% of all important drug targets.