We present FunCLAN, a novel method for finding, categorising, and measuring conformational relationships between protein complexes, as well as for finding corresponding chain pairs between them. Its purpose is to provide a general tool, applicable to a broad range of complexes. As such, it only utilises sequence similarity and superposition transformations to compare rigid bodies (chains), and how they transform with relation to each other. This lets us clusterise transformations, which lets us identify sets of similar and dissimilar transformations across the dataset. We use this information to classify conformations according to how the transformations relate to each other at chain and complex level. We have also included two data, and scale agnostic methods that can make informed predictions as to the number of clusters (conformers) present in the dataset. We also present a way to superpose entire complexes using the alignment and chain correspondence data for visualisation purposes. The entire process can be generalised to domains and even single chains, but this requires bespoke/specialised preprocessing of the data as well as prior knowledge of the structures to be analysed. This is out of the scope of this paper, but we outline a method to do so in the supplementary information. ### Competing Interest Statement The authors have declared no competing interest. Biotechnology and Biological Sciences Research Council, BB/V015591/1

Crystallography provides structural evidence of macromolecules in atomic detail. However, the atomic structure is not the direct outcome of the experiment. Diffraction data need to be processed and the phase problem must be solved to visualize the map from which an atomic model of the macromolecule is interpreted and iteratively improved. Despite this complex process from sample to scientific answer, crystallography is widely accessible in biochemical and biological research. Easy access to the experimental set-ups, free software for academic use and complimentary analytical computing, supported by automation and expert assistance, makes crystallography available to non-crystallographers. This Primer offers a practical and rational introduction to macromolecular crystallography, whether to engage directly or to critically assess results, with a focus on understanding the diffraction data, solving the phase problem, building and refining the atomic model, and interpreting the resulting atomic structure. We provide an overview of what crystallography can achieve, the key decisions and trade-offs involved, and how to evaluate outcomes effectively. This Primer offers a practical and rational introduction to macromolecular crystallography, whether to engage directly with or to critically assess results, with a focus on understanding the diffraction data, solving the phase problem, building and refining the atomic model, and interpreting the resulting atomic structure.

With the advent of next-generation modelling methods, such as AlphaFold2, structural biologists are increasingly using predicted structures to obtain structure solutions via molecular replacement (MR) or model fitting in single-particle cryogenic sample electron microscopy (cryoEM). Differences between the domain–domain orientations represented in a predicted model and a crystal structure are often a key limitation when using predicted models. Slice'N'Dice is a software package designed to address this issue by first slicing models into distinct structural units and then automatically placing the slices using either Phaser, MOLREP or PowerFit. The slicing step can use the AlphaFold predicted aligned error (PAE) or can operate via a variety of Cα-atom-based clustering algorithms, extending the applicability to structures of any origin. The number of splits can either be selected by the user or determined automatically. Slice'N'Dice is available for both MR and automated map fitting in the CCP4 and CCP-EM software suites.

The rapid advancement of automatic structure solution methods, driven by the availability of high-quality predicted structures from AlphaFold and the growing adoption of multi-crystal and serial experiments, has created a pressing need for streamlining routine operations, automating structure solution projects, and efficiently handling large volumes of data. Modern software solutions must be both robust and user-friendly, supporting manual workflows while enabling high-throughput operations to keep pace with the high data collection rates of modern beamlines. Here, we present new developments in CCP4 Cloud that address these challenges by providing predefined and customizable automatic workflows, which can be seamlessly integrated with experimental facilities, offering a powerful solution for modern macromolecular crystallography. CCP4 Cloud is available as a public service at https://cloud.ccp4.ac.uk.

With the advent of next generation modelling methods, such as AlphaFold2, structural biologists are increasingly using predicted structures as search models for Molecular Replacement (MR) when experimental structures of homologues are unavailable. Inaccuracy in domain-domain orientations is often a key limitation when using predicted models for MR. Slice’N’Dice is a software package designed to address this issue by first slicing models into distinct structural units and then automatically placing the slices using Phaser. The slicing step can use AlphaFold2’s predicted aligned error (PAE), or can operate via a variety of Cα atom clustering algorithms, extending applicability to structures of any origin. The number of splits can be selected by the user. Slice’N’Dice is available in CCP4 8.0 and is currently being adapted for cryo-EM use cases.

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.

In late 2020, the results of CASP14, the 14th event in a series of competitions to assess the latest developments in computational protein structure-prediction methodology, revealed the giant leap forward that had been made by Google's Deepmind in tackling the prediction problem. The level of accuracy in their predictions was the first instance of a competitor achieving a global distance test score of better than 90 across all categories of difficulty. This achievement represents both a challenge and an opportunity for the field of experimental structural biology. For structure determination by macromolecular X-ray crystallography, access to highly accurate structure predictions is of great benefit, particularly when it comes to solving the phase problem. Here, details of new utilities and enhanced applications in the CCP4 suite, designed to allow users to exploit predicted models in determining macromolecular structures from X-ray diffraction data, are presented. The focus is mainly on applications that can be used to solve the phase problem through molecular replacement.

Because of the strong electron-atom interaction, the kinematic theory of diffraction cannot be used to describe the scattering of electrons by an assembly of atoms due to the strong dynamical diffraction that needs to be taken into account. In this paper, the scattering of high-energy electrons by a regular array of light atoms is solved exactly by applying the T-matrix formalism to the corresponding Schrödinger's equation in spherical coordinates. The independent atom model is used, where each atom is represented by a sphere with an effective constant potential. The validity of the forward scattering approximation and the phase grating approximation, assumed by the popular multislice method, is discussed, and an alternative interpretation of multiple scattering is proposed and compared with existing interpretations.

Nowadays, progress in the determination of three-dimensional macromolecular structures from diffraction images is achieved partly at the cost of increasing data volumes. This is due to the deployment of modern high-speed, high-resolution detectors, the increased complexity and variety of crystallographic software, the use of extensive databases and high-performance computing. This limits what can be accomplished with personal, offline, computing equipment in terms of both productivity and maintainability. There is also an issue of long-term data maintenance and availability of structure-solution projects as the links between experimental observations and the final results deposited in the PDB. In this article, CCP4 Cloud, a new front-end of the CCP4 software suite, is presented which mitigates these effects by providing an online, cloud-based environment for crystallographic computation. CCP4 Cloud was developed for the efficient delivery of computing power, database services and seamless integration with web resources. It provides a rich graphical user interface that allows project sharing and long-term storage for structure-solution projects, and can be linked to data-producing facilities. The system is distributed with the CCP4 software suite version 7.1 and higher, and an online publicly available instance of CCP4 Cloud is provided by CCP4.

In this contribution, the current protocols for modelling covalent linkages within the CCP4 suite are considered. The mechanism used for modelling covalent linkages is reviewed: the use of dictionaries for describing changes to stereochemistry as a result of the covalent linkage and the application of link-annotation records to structural models to ensure the correct treatment of individual instances of covalent linkages. Previously, linkage descriptions were lacking in quality compared with those of contemporary component dictionaries. Consequently, AceDRG has been adapted for the generation of link dictionaries of the same quality as for individual components. The approach adopted by AceDRG for the generation of link dictionaries is outlined, which includes associated modifications to the linked components. A number of tools to facilitate the practical modelling of covalent linkages available within the CCP4 suite are described, including a new restraint-dictionary accumulator, the Make Covalent Link tool and AceDRG interface in Coot, the 3D graphical editor JLigand and the mechanisms for dealing with covalent linkages in the CCP4i2 and CCP4 Cloud environments. These integrated solutions streamline and ease the covalent-linkage modelling workflow, seamlessly transferring relevant information between programs. Current recommended practice is elucidated by means of instructive practical examples. By summarizing the different approaches to modelling linkages that are available within the CCP4 suite, limitations and potential pitfalls that may be encountered are highlighted in order to raise awareness, with the intention of improving the quality of future modelled covalent linkages in macromolecular complexes.

For over 40 years, the Collaborative Computational Project Number 4 in Protein Crystallography (CCP4) has maintained, developed, and provided an integrated Suite [1] of world-class software that allows researchers to determine macromolecular structures by X-ray crystallography and other biophysical techniques.Traditionally, the Suite is operated via CCP4i(2) graphical user interface, available for all major desktop platforms.More recent developments include interfaces that offer users the convenience of crystallographic computing on mobile devices and access to cloud-based resources.There are several good reasons for exploiting the distributed computing paradigm in crystallography.First, cloud-based solutions have become particularly appealing given recent advances in automated structure solution methods.Such methods are demanding for both computing power and various databases, making them less convenient for offline setups.Second, the cloud model of operations relieves researchers from the burden of maintaining software locally, providing 24/7 access to always ready, tested, and updated software setup.Third, cloud computing streamlines data management and logistics.Collected data may be put in cloud-based projects directly from synchrotrons, bypassing offload to user devices.Cloud projects can be shared in real-time between a team of researchers working from various geographic locations.This aspect has been particularly helpful at the virtual CCP4 workshops during the pandemic.CCP4 currently provides two interfaces for online work [2].CCP4 Online, started from automatic Molecular Replacement service "BALBES" in 2008, is a web portal allowing users to run in the cloud the molecular replacement and experimental phasing pipelines in the CCP4 suite.In 2020, CCP4 released an advanced online platform, CCP4 Cloud, featuring a full desktop experience online.CCP4 Cloud includes an HTML5 interface for most crystallographic tasks and allows to develop and maintain structure solution projects completely online using common web browsers on any modern platform, including mobile devices.We will discuss the latest developments, achieved results, and future directions.Providing a global computing infrastructure for protein crystallography is now a feasible task; are we ready to accept it in practice?

The Collaborative Computational Project Number 4 in Protein Crystallography (CCP4) exists to maintain, develop and provide world-class software that allows researchers to determine macromolecular structures by X-ray crystallography and other biophysical techniques.Over 40 years of existence, CCP4 Software was assembled and distributed as an integrated Suite of programs, traditionally operated via CCP4i (2) GUI in Linux, OSX and Windows platforms.Modern trends in computing suggest a fast-growing interest to mobile platforms and cloud solutions for data management and operations in practically all areas.Answering to these trends, CCP4 releases beta version of CCP4 Cloud, developed for essentially remote and distributed deployment of CCP4 Software.CCP4 Cloud allows a user to keep all necessary data and projects in the cloud and perform all scope of crystallographic computations, from image processing to final refinement, ligand fitting and deposition, remotely via a common web-browser, optionally complemented with CCP4 Cloud Client for interactive model building with Coot.The talk will present architectural solutions and key features of CCP4 Cloud such as ability to seamlessly import data from 3rd party sites (e.g., synchrotrons), high scalability of computational background, convenient (big) data management for multiple users, rich graphical interface with built-in molecular graphics, enhanced data and structure solution pathway provenance.CCP4 Cloud may be used from CCP4 Web portal with any device running a modern web-browser (including tablets and smartphones).All the source code is open and freely available for installation elsewhere to serve local researchers in a lab, or institution, or pharma, or a synchrotron.

Collaborative Computational Project 4 (CCP4) was founded in 1979 with the aim of supporting computational macromolecular crystallography (MX) by promoting communication among developers, sharing algorithms as they were implemented, and promoting interoperability of software and data.From modest beginnings, CCP4 has contributed hugely to the growth and maturation of MX, enabling this discipline to obtain a central role in the investigation of mechanistic cell biology and in the exploitation of scientific insights in biotechnological and pharmaceutical applications.This talk will review some of the history of CCP4, and describe the current status and ambitions of the project as it continues to address important challenges during its fifth decade of activity.

This letter announces that PDBx/mmCIF format files will become mandatory for crystallographic depositions to the Protein Data Bank (PDB).

The CCP4 (Collaborative Computational Project, Number 4) software suite for macromolecular structure determination by X-ray crystallography groups brings together many programs and libraries that, by means of well established conventions, interoperate effectively without adhering to strict design guidelines. Because of this inherent flexibility, users are often presented with diverse, even divergent, choices for solving every type of problem. Recently, CCP4 introduced CCP4i2, a modern graphical interface designed to help structural biologists to navigate the process of structure determination, with an emphasis on pipelining and the streamlined presentation of results. In addition, CCP4i2 provides a framework for writing structure-solution scripts that can be built up incrementally to create increasingly automatic procedures.