Many enzymes can self-assemble into higher-order structures with helical symmetry. A particularly noteworthy example is that of nitrilases, enzymes in which oligomerization of dimers into spiral homo-oligomers is a requirement for their enzymatic function. Nitrilases are widespread in nature where they catalyze the hydrolysis of nitriles into the corresponding carboxylic acid and ammonia. Here, we present the Cryo-EM structure, at 3 & Aring; resolution, of a C-terminal truncate nitrilase from Rhodococcus sp. V51B that assembles in helical filaments. The model comprises a complete turn of the helical arrangement with a substrate-intermediate bound to the catalytic cysteine. The structure was solved having added the substrate to the protein. The length and stability of filaments was made more substantial in the presence of the aromatic substrate, benzonitrile, but not for aliphatic nitriles or dinitriles. The overall structure maintains the topology of the nitrilase family, and the filament is formed by the association of dimers in a chain-like mechanism that stabilizes the spiral. The active site is completely buried inside each monomer, while the substrate binding pocket was observed within the oligomerization interfaces. The present structure is in a closed configuration, judging by the position of the lid, suggesting that the intermediate is one of the covalent adducts. The proximity of the active site to the dimerization and oligomerization interfaces, allows the dimer to sense structural changes once the benzonitrile was bound, and translated to the rest of the filament, stabilizing the helical structure.

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein/nucleic-acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: E. coli beta-galactosidase with inhibitor, SARS-CoV-2 RNA-dependent RNA polymerase with covalently bound nucleotide analog, and SARS-CoV-2 ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. We found that (1) the quality of submitted ligand models and surrounding atoms varied, as judged by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics, and contact scores, and (2) a composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

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.

Biological macromolecules have complex three-dimensional shapes that are experimentally examined using X-ray crystallography and electron cryomicroscopy. Interpreting the data that these methods yield involves building 3D atomic models. With almost every data set, some portion of the time put into creating these models must be spent manually modifying the model in order to make it consistent with the data; this is difficult and time-consuming, in part because the data are 'blurry' in three dimensions. This paper describes the design and assessment of CootVR (available at http://hamishtodd1.github.io/cvr), a prototype computer program for performing this task in virtual reality, allowing structural biologists to build molecular models into cryo-EM and crystallographic data using their hands. CootVR was timed against Coot for a very specific model-building task, and was found to give an order-of-magnitude speedup for this task. A from-scratch model build using CootVR was also attempted; from this experience it is concluded that currently CootVR does not give a speedup over Coot overall.

Covalent linkages between constituent blocks of macromolecules and ligands have been subject to inconsistent treatment during the model-building, refinement and deposition process. This may stem from a number of sources, including difficulties with initially detecting the covalent linkage, identifying the correct chemistry, obtaining an appropriate restraint dictionary and ensuring its correct application. The analysis presented herein assesses the extent of problems involving covalent linkages in the Protein Data Bank (PDB). Not only will this facilitate the remediation of existing models, but also, more importantly, it will inform and thus improve the quality of future linkages. By considering linkages of known type in the CCP4 Monomer Library (CCP4-ML), failure to model a covalent linkage is identified to result in inaccurate (systematically longer) interatomic distances. Scanning the PDB for proximal atom pairs that do not have a corresponding type in the CCP4-ML reveals a large number of commonly occurring types of unannotated potential linkages; in general, these may or may not be covalently linked. Manual consideration of the most commonly occurring cases identifies a number of genuine classes of covalent linkages. The recent expansion of the CCP4-ML is discussed, which has involved the addition of over 16 000 and the replacement of over 11 000 component dictionaries using AceDRG. As part of this effort, the CCP4-ML has also been extended using AceDRG link dictionaries for the aforementioned linkage types identified in this analysis. This will facilitate the identification of such linkage types in future modelling efforts, whilst concurrently easing the process involved in their application. The need for a universal standard for maintaining link records corresponding to covalent linkages, and references to the associated dictionaries used during modelling and refinement, following deposition to the PDB is emphasized. The importance of correctly modelling covalent linkages is demonstrated using a case study, which involves the covalent linkage of an inhibitor to the main protease in various viral species, including SARS-CoV-2. This example demonstrates the importance of properly modelling covalent linkages using a comprehensive restraint dictionary, as opposed to just using a single interatomic distance restraint or failing to model the covalent linkage at all.

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.

Coot is a tool widely used for model building, refinement, and validation of macromolecular structures. It has been extensively used for crystallography and, more recently, improvements have been introduced to aid in cryo-EM model building and refinement, as cryo-EM structures with resolution ranging 2.5-4 A are now routinely available. Model building into these maps can be time-consuming and requires experience in both biochemistry and building into low-resolution maps. To simplify and expedite the model building task, and minimize the needed expertise, new tools are being added in Coot. Some examples include morphing, Geman-McClure restraints, full-chain refinement, and Fourier-model based residue-type-specific Ramachandran restraints. Here, we present the current state-of-the-art in Coot usage.

Regularisers stabilise macromolecular refinement, ensuring consistency between derived models and prior knowledge. Restraints representing chemical information help local structure adopt chemically reasonable conformations. At lower resolutions, supplementary restraints are used: encouraging consistency with models of homologous structures, formation of hydrogen bonding networks, nucleotide base pairing and stacking. Jelly-body restraints stabilise model refinement without injecting externally derived information. An anharmonic penalty function is used in order to control robustness to outliers due to inconsistencies between data and prior. Additional restraints and robust estimation are also useful for model building, increasing real space refinement convergence radius and stability.

N-Glycosylation is one of the most common post-translational modifications and is implicated in, for example, protein folding and interaction with ligands and receptors. N-Glycosylation trees are complex structures of linked carbohydrate residues attached to asparagine residues. While carbohydrates are typically modeled in protein structures, they are often incomplete or have the wrong chemistry. Here, new tools are presented to automatically rebuild existing glycosylation trees, to extend them where possible, and to add new glycosylation trees if they are missing from the model. The method has been incorporated in the PDB-REDO pipeline and has been applied to build or rebuild 16 452 carbohydrate residues in 11 651 glycosylation trees in 4498 structure models, and is also available from the PDB-REDO web server. With better modeling of N-glycosylation, the biological function of this important modification can be better and more easily understood.

Coot is a graphics application that is used to build or manipulate macromolecular models; its particular forte is manipulation of the model at the residue level. The model-building tools of Coot have been combined and extended to assist or automate the building of N-linked glycans. The model is built by the addition of monosaccharides, placed by variation of internal coordinates. The subsequent model is refined by real-space refinement, which is stabilized with modified and additional restraints. It is hoped that these enhanced building tools will help to reduce building errors of N-linked glycans and improve our knowledge of the structures of glycoproteins.

Newly transcribed eukaryotic precursor messenger RNAs (pre-mRNAs) are processed at their 3' ends by the ~1-megadalton multiprotein cleavage and polyadenylation factor (CPF). CPF cleaves pre-mRNAs, adds a polyadenylate tail, and triggers transcription termination, but it is unclear how its various enzymes are coordinated and assembled. Here, we show that the nuclease, polymerase, and phosphatase activities of yeast CPF are organized into three modules. Using electron cryomicroscopy, we determined a 3.5-angstrom-resolution structure of the ~200-kilodalton polymerase module. This revealed four β propellers, in an assembly markedly similar to those of other protein complexes that bind nucleic acid. Combined with in vitro reconstitution experiments, our data show that the polymerase module brings together factors required for specific and efficient polyadenylation, to help coordinate mRNA 3'-end processing.

Coot is a molecular-graphics program primarily aimed at model building using X-ray data. Recently, tools for the manipulation and representation of ligands have been introduced. Here, these new tools for ligand validation and comparison are described. Ligands in the wwPDB have been scored by density-fit, distortion and atom-clash metrics. The distributions of these scores can be used to assess the relative merits of the particular ligand in the proteinligand complex of interest by means of `sliders' akin to those now available for each accession code on the wwPDB websites.

In this paper, AUSPEX, a new software tool for experimental X-ray data analysis, is presented. Exploring the behaviour of diffraction intensities and the associated estimated uncertainties facilitates the discovery of underlying problems and can help users to improve their data acquisition and processing in order to obtain better structural models. The program enables users to inspect the distribution of observed intensities (or amplitudes) against resolution as well as the associated estimated uncertainties (sigmas). It is demonstrated how AUSPEX can be used to visually and automatically detect ice-ring artefacts in integrated X-ray diffraction data. Such artefacts can hamper structure determination, but may be difficult to identify from the raw diffraction images produced by modern pixel detectors. The analysis suggests that a significant portion of the data sets deposited in the PDB contain ice-ring artefacts. Furthermore, it is demonstrated how other problems in experimental X-ray data caused, for example, by scaling and data-conversion procedures can be detected by AUSPEX.

Using Coot to Model N-linked CarbohydratesCoot [1] is molecular graphics application initially designed for model-building and validation of protein models using X-ray data.Since its release, the focus of development has shifted to include building and validation tools for ligands and protein-ligand complexes.A particular specialization included recently is the task of building models of glycosylation.Recently the start of the art for glycosylation structures and model-building has been reviewed [2] in which Coot's building has been analysed.This presentation will discuss monomer, link, torsion and reference distance restraints and demonstrate how Coot has been updated and can be used for rapid modelbuilding and refinement of N-linked glycosylation.

A freely available small-molecule structure database, the Crystallography Open Database (COD), is used for the extraction of molecular-geometry information on small-molecule compounds. The results are used for the generation of new ligand descriptions, which are subsequently used by macromolecular model-building and structure-refinement software. To increase the reliability of the derived data, and therefore the new ligand descriptions, the entries from this database were subjected to very strict validation. The selection criteria made sure that the crystal structures used to derive atom types, bond and angle classes are of sufficiently high quality. Any suspicious entries at a crystal or molecular level were removed from further consideration. The selection criteria included (i) the resolution of the data used for refinement (entries solved at 0.84 Å resolution or higher) and (ii) the structure-solution method (structures must be from a single-crystal experiment and all atoms of generated molecules must have full occupancies), as well as basic sanity checks such as (iii) consistency between the valences and the number of connections between atoms, (iv) acceptable bond-length deviations from the expected values and (v) detection of atomic collisions. The derived atom types and bond classes were then validated using high-order moment-based statistical techniques. The results of the statistical analyses were fed back to fine-tune the atom typing. The developed procedure was repeated four times, resulting in fine-grained atom typing, bond and angle classes. The procedure will be repeated in the future as and when new entries are deposited in the COD. The whole procedure can also be applied to any source of small-molecule structures, including the Cambridge Structural Database and the ZINC database.

[1] Berman, HM, Henrick, K. & Nakamura, H.(2003). Nat Struct Biol 10, 980.[2] Westbrook, JD, Shao, C., Feng, Z., Zhuravleva, M., Velankar, S. & Young, J.(2015). Bioinformatics 31, 1274-1278.[3] Adams, PD, Aertgeerts, K., Bauer, C., Bell, JA, Berman, HM, Bhat, TN, Blaney, JM, Bolton, E., Bricogne, G., Brown, D., Burley, SK, Case, DA, Clark, KL, Darden, T., Emsley, P., Feher, VA, Feng, Z., Groom, CR, Harris, SF, Hendle, J., Holder, T., Joachimiak, A., Kleywegt, GJ, Krojer, T., Marcotrigiano, J., Mark, AE, Markley, JL, Miller, M., Minor, W., Montelione, GT, Murshudov, G., Nakagawa, A., Nakamura, H., Nicholls, A., Nicklaus, M., Nolte, RT, Padyana, AK, Peishoff, CE, Pieniazek, S., Read, RJ, Shao, C., Sheriff, S., Smart, O., Soisson, S., Spurlino, J., Stouch, T., Svobodova, R., Tempel, W., Terwilliger, TC, Tronrud, D., Velankar, S., Ward, SC, Warren, GL, Westbrook, JD, Williams, P., Yang, H. & Young, J.(2016). Structure 24, 502-508.

An introduction to the Proceedings of the 2016 CCP4 Study Weekend on protein-ligand complex structures.