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.

Our understanding of the structure–function relationships of biomolecules and thereby applying it to drug discovery programs are substantially dependent on the availability of the structural information of ligand–protein complexes. However, the correct interpretation of the electron density of a small molecule bound to a crystal structure of a macromolecule is not trivial. Our analysis involving quality assessment of ~0.28 million small molecule–protein binding site pairs derived from crystal structures corresponding to ~66,000 PDB entries indicates that the majority (65%) of the pairs might need little (54%) or no (11%) attention. Out of the remaining 35% of pairs that need attention, 11% of the pairs (including structures with high/moderate resolution) pose serious concerns. Unfortunately, most users of crystal structures lack the training to evaluate the quality of a crystal structure against its experimental data and, in general, rely on the resolution as a ‘gold standard’ quality metric. Our work aims to sensitize the non-crystallographers that resolution, which is a global quality metric, need not be an accurate indicator of local structural quality. In this article, we demonstrate the use of several freely available tools that quantify local structural quality and are easy to use from a non-crystallographer’s perspective. We further propose a few solutions for consideration by the scientific community to promote quality research in structural biology and applied areas.

SAD phasing can be challenging when the signal-to-noise ratio is low. In such cases, having an accurate estimate of the substructure content can determine whether or not the substructure of anomalous scatterer positions can successfully be determined. Here, a likelihood-based target function is proposed to accurately estimate the strength of the anomalous scattering contribution directly from the measured intensities, determining a complex correlation parameter relating the Bijvoet mates as a function of resolution. This gives a novel measure of the intrinsic anomalous signal. The SAD likelihood target function also accounts for correlated errors in the measurement of intensities from Bijvoet mates, which can arise from the effects of radiation damage. When the anomalous signal is assumed to come primarily from a substructure comprising one anomalous scatterer with a known value of f'' and when the protein composition of the crystal is estimated correctly, the refined complex correlation parameters can be interpreted in terms of the atomic content of the primary anomalous scatterer before the substructure is known. The maximum-likelihood estimation of substructure content was tested on a curated database of 357 SAD cases with useful anomalous signal. The prior estimates of substructure content are highly correlated to the content determined by phasing calculations, with a correlation coefficient (on a log-log basis) of 0.72.

Crystallographic phasing strategies increasingly require the exploration and ranking of many hypotheses about the number, types and positions of atoms, molecules and/or molecular fragments in the unit cell, each with only a small chance of being correct. Accelerating this move has been improvements in phasing methods, which are now able to extract phase information from the placement of very small fragments of structure, from weak experimental phasing signal or from combinations of molecular replacement and experimental phasing information. Describing phasing in terms of a directed acyclic graph allows graph-management software to track and manage the path to structure solution. The crystallographic software supporting the graph data structure must be strictly modular so that nodes in the graph are efficiently generated by the encapsulated functionality. To this end, the development of new software, Phasertng, which uses directed acyclic graphs natively for input/output, has been initiated. In Phasertng, the codebase of Phaser has been rebuilt, with an emphasis on modularity, on scripting, on speed and on continuing algorithm development. As a first application of phasertng, its advantages are demonstrated in the context of phasertng.xtricorder, a tool to analyse and triage merged data in preparation for molecular replacement or experimental phasing. The description of the phasing strategy with directed acyclic graphs is a generalization that extends beyond the functionality of Phasertng, as it can incorporate results from bioinformatics and other crystallographic tools, and will facilitate multifaceted search strategies, dynamic ranking of alternative search pathways and the exploitation of machine learning to further improve phasing strategies.

Good prior estimates of the effective root-mean-square deviation (r.m.s.d.) between the atomic coordinates of the model and the target optimize the signal in molecular replacement, thereby increasing the success rate in difficult cases. Previous studies using protein structures solved by X-ray crystallography as models showed that optimal error estimates (refined after structure solution) were correlated with the sequence identity between the model and target, and with the number of residues in the model. Here, this work has been extended to find additional correlations between parameters of the model and the target and hence improved prior estimates of the coordinate error. Using a graph database, a curated set of 6030 molecular-replacement calculations using models that had been solved by X-ray crystallography was analysed to consider about 120 model and target parameters. Improved estimates were achieved by replacing the sequence identity with the Gonnet score for sequence similarity, as well as by considering the resolution of the target structure and the MolProbity score of the model. This approach was extended by analysing 12 610 additional molecular-replacement calculations where the model was determined by NMR. The median r.m.s.d. between pairs of models in an ensemble was found to be correlated with the estimated r.m.s.d. to the target. For models solved by NMR, the overall coordinate error estimates were larger than for structures determined by X-ray crystallography, and were more highly correlated with the number of residues.

The 3D-Reflection data viewer in Phenix [1] is based on the OpenGL library which is deprecated on MacOS.A replacement based on supported libraries, was urgently required.The NGL-HKL-viewer has been developed in a way that leverages the codebase of the original viewer while extending the functionality.Protein crystallography involves processing raw X-ray images to indexed intensities.Subsequent steps may associate additional data with these indices.As part of the changes to the 3D-Reflection data viewer the types of reflection data that can be displayed were expanded.Real valued data are displayed as spheres scaled according to their magnitudes.Colour coding through both hue and saturation can be applied as well.The viewer allows associating one data parameter with the sizes of the spheres but colour them according to another data parameter.For example, phased structure factors can be displayed as spheres scaled by amplitudes, coloured according to phases and colour saturation used to represent figures of merit.Data can be sorted into bins and be shown or hidden.NGL-HKL-viewer is scriptable from Python and part of CCTBX [2].It is based on NGL [3] and therefore portable to all computing platforms with a modern web browser.The viewer can be embedded in graphical user interfaces such as Qt5 or PySide.It is part of Phaser.Voyager [4].

Sammito, Massimo D (University of Cambridge, Cambridge, GBR); McCoy, Airlie J (University of Cambridge, Cambridge, GBR); Hatti, Kaushik (University of Cambridge, Cambridge, GBR); Oeffner, Robert D (University of Cambridge, Cambridge, GBR); Stockwell, Duncan H (University of Cambridge, Cambridge, GBR); Croll, Tristan I (University of Cambridge, Cambridge, GBR); Read, Randy (Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, GBR)

The determination of the crystal structure of a mutant protein using phases based on a previously determined crystal structure of the wild-type protein is often a straightforward molecular-replacement protocol. Such a structure determination may be difficult if there are large-scale structural differences between the wild-type and mutant proteins. In this manuscript, an interesting case is presented of the unintentional crystallization of a contaminant protein which shared some structural features with the presumed target protein, leading to difficulties in obtaining a completely satisfactory molecular-replacement structure solution. It was not immediately evident that the initial structure solution was incorrect owing to the poor quality of the X-ray diffraction data and low resolution. The structure was subsequently determined by improving the quality of the data and following a sequence-independent MarathonMR protocol. The structure corresponded to that of glycerol dehydrogenase, which crystallized as a contaminant, instead of the presumed mutant of a survival protein encoded by Salmonella typhimurium. The reasons why a solution that appeared to be reasonable was obtained with an incorrect protein model are discussed. The results presented here show that a degree of caution is warranted when handling large-scale structure-determination projects.

Indian Neem tree is known for its pesticidal and medicinal properties for centuries. Structure elucidation of large number of secondary metabolites responsible for its diverse properties has been achieved. However, this data is spread over various books, scientific reports and publications and difficult to access. We have compiled and stored structural details of neem metabolites in NeeMDB, a database which can be easily accessed, queried and downloaded. NeeMDB would be central in dissipating structural information of neem secondary metabolites world over.

Nearly 90% of the structures deposited in the Protein Data Bank (PDB) have been determined by X-ray crystallographic methods. Of these structures, 78% of the entries deposited in the last five years have been determined using the Molecular Replacement (MR) technique. MR has several advantages over other crystallographic techniques as it is based on the phase information that could be obtained from the structure of a related protein (phasing model) as a valid approximation to the unknown structure and hence, eliminates the need for more diffraction data to carry out experimental phasing. Since 2010, PDB has grown tremendously by over 50,000 depositions while the growth in the number of unique folds is negligible in comparison [1]. For a given target protein, it is likely that more than one structure is available in the PDB that could be used as a suitable phasing model. In such cases, selecting the best phasing model from among the available pool of structures requires careful examination of the parameters that determine the reliability of the phasing model for MR structure determination. Hence, it is necessary to understand the relationship between properties of phasing model and quality of the structure determined (MR model) to arrive at most reasonable MR model. In this study, we provide quantitative measures of intuitive ideas on the strategies that might be useful in choosing the best phasing model. Towards this goal, redetermination of selected structures from the PDB was carried out using the X-ray intensity data of the selected protein deposited in the PDB and several homologous structures as phasing models. A total of 716 phasing models were considered for MR structure determination of three proteins from Triosephosphate isomerase fold and Lysozyme-like fold. The RMSD of corresponding Cα positions between MR model and the structure of the selected protein re-determined by an identical MR protocol using the deposited coordinates of the protein (positive control) was calculated. A ‘Q score’ based on the polypeptide length normalized RMSD was considered as a measure of the accuracy of the MR model (MR accuracy). Resolution of the target protein was found to be the most important factor for the success of MR. The success of MR increased with the increase in sequence identity between target protein and phasing model. However, CART modeling [2] indicated that after a defined sequence identity threshold, quality of phasing model (measured by Resolution, Real-space correlation coefficient, Rwork/Rfree) seem to have a greater influence on the MR accuracy than sequence identity. Correlation of phasing model properties and MR accuracy scores obtained by using phasing models with sequence identity above the threshold also supports this observation in both the folds studied. Further, a similar trend is observed in proteins from other folds as well. However, the sequence identity threshold above which the quality of the phasing model assumes importance varies for different folds. [1] Hatti, K., et. al., (2016). Acta Crystallogr. Sect. D. 72, 1081–1089. [2] Breiman, L., et. al., (1984). Classification and Regression Trees.

In the recent decades, essential steps of protein structure determination such as phasing by multiple isomorphous replacement and multi wave length anomalous dispersion, molecular replacement, refinement of the structure determined and its validation have been fully automated. Several computer program suites that execute all these steps as a pipeline operation have been made available. In spite of these great advances, determination of a protein structure may turn out to be a challenging task for a variety of reasons. It might be difficult to obtain multiple isomorphous replacement or multi wave length anomalous dispersion data or the crystal may have defects such as twinning or pseudo translation. Apart from these usual difficulties, more frequent difficulties have been encountered in recent years because of the large number of projects handled by structural biologists. These new difficulties usually result from contamination of the protein of interest by other proteins or presence of proteins from pathogenic organisms that could withstand the antibiotics used to prevent bacterial contamination. It could also be a result of poor book keeping. Recently, we have developed a procedure called MarathonMR that has the power to resolve some of these problems automatically. In this communication, we describe how the MarathonMR was used to determine four different protein structures that had remained elusive for several years. We describe the plausible reasons for the difficulties encountered in determining these structures and point out that the method presented here could be a validation tool for protein structures deposited in the protein data bank.

During the past decade, the authors have collected a few X-ray diffraction data sets from protein crystals that appeared to be easy cases of molecular replacement but failed to yield structures even after extensive trials. Here, the use of a large-scale molecular replacement method that explores all structurally characterized domains as phasing models to determine the structure corresponding to two data sets collected at 1.9 and 2.3 Å resolution is reported. These two structures were of the same protein independently crystallized in 2007 and 2011. The structures derived are virtually identical and were found to consist of two compact globular domains connected by a hinge. The high resolution of one of these data sets enabled inference of the amino-acid sequence from the electron-density map. The deduced sequence is nearly identical to that of a protein from the multidrug-resistant bacterium Stenotrophomonas maltophilia. Although the structure of this protein has not been determined previously, it is homologous to the well studied DING proteins which mediate the cellular uptake of phosphate ions. The final electron-density maps from both of the data sets revealed a large density at the interface of the two globular domains that is likely to represent a phosphate ion. Thus, the structure is likely to be that of a phosphate-binding protein encoded by the S. maltophilia genome (SmPBP; PDB entry 5j1d). The nature of the phosphate-binding site of SmPBP closely resembles that of Pseudomonas fluorescens DING (PfluDING), which displays remarkable discrimination between the closely similar phosphate and arsenate ions. The results presented here illustrate that routine crystallization trials may occasionally lead to the serendipitous crystallization of a protein of unknown identity and brute-force molecular replacement through `fold space' might allow the identification of the unknown protein.

The Computational Analysis of Novel Drug Opportunities (CANDO) platform (http://protinfo.org/cando) uses similarity of compound-proteome interaction signatures to infer homology of compound/drug behavior. We constructed interaction signatures for 3733 human ingestible compounds covering 48,278 protein structures mapping to 2030 indications based on basic science methodologies to predict and analyze protein structure, function, and interactions developed by us and others. Our signature comparison and ranking approach yielded benchmarking accuracies of 12-25% for 1439 indications with at least two approved compounds. We prospectively validated 49/82 'high value' predictions from nine studies covering seven indications, with comparable or better activity to existing drugs, which serve as novel repurposed therapeutics. Our approach may be generalized to compounds beyond those approved by the FDA, and can also consider mutations in protein structures to enable personalization. Our platform provides a holistic multiscale modeling framework of complex atomic, molecular, and physiological systems with broader applications in medicine and engineering.

2,4,5-Trimethoxy chalcones and analogues were synthesized from asaronaldehyde derived from β-asarone. These novel compounds when tested against three human tumour cell lines (MCF-7, SW-982 and HeLa) using MTT assay, revealed that chalcones possessing electron donor groups in para position to carbonyl moiety of phenyl ring A, showed better inhibitory activity (2, 3, 4, 6, 7, 10, 17). When evaluated for antioxidant activities, compound 15 exhibited better free radical scavenging property in DPPH assay while compounds 2, 3, 5, 7, 9, 10, 11, 16, and 18 showed significant NO scavenging activity. All compounds exhibited very good phenyl hydrazine induced haemolysis of erythrocytes in phenylhydrazine assay. Structure–activity relationship (SAR) study using in-silico analysis matched well with in-vitro tumour cell inhibitory activity.

The distinguishing structural and functional domains of plant acyl-acyl carrier protein (ACP) thioesterases and their complex interaction with the ACP-linked fatty acid substrate complex have remained elusive. E. coli based heterologous expression and characterisation of many plant thioesterases reported so far have not been extended and linked to in silico modelling studies to explain the diversity in plant thioesterase substrate specificities. In this study, a thioesterase cDNA isolated from immature seed tissues of Jatropha curcas was found to be type B and specific to stearoyl acyl ACP when expressed in E. coli K27fadD88, a lipid utilisation mutant. Homology modelling and molecular docking of a selected region of the isolated JcFatB protein predicted that it had high affinity towards both stearate (18:0) and palmitate (16:0). Structural analysis of the sequence confirmed the presence of a transit peptide that is processed in multiple steps. The enzyme is localised in the chloroplasts and has an N-terminal inner chloroplast transmembrane domain characteristic of type B plant thioesterases. Docking of ligands with JcFatB and its comparison with a modelled Jatropha thioesterase type A provided further evidence for native substrate preferences of Jatropha thioesterases. This study provides essential clues to develop future methods for large-scale bacterial production of free fatty acids and for design of strategies to modulate the seed oil composition in this important non-edible, seed oil plant.

Epidermal growth factor receptor (EGFR) family has gained importance as a target for cancer therapy. However, somatic mutations in the tyrosine kinase domain of EGFR alter its sensitivity to anti-EGFR tyrosine kinase (TK) drugs like gefitinib (Iressa TM ). Docking studies of a few newly synthesized 6, 7-dialkoxy-4-anilinoquinazoline derivatives which showed EGFR-TK inhibitory activity were conducted. It has been found that the docking energies of these novel quniazolines are comparable with the IC50 values against A431 and MCF-7 tumor cell lines. Though the compounds with benzoxazole (1 & 2) and imidazole side chain (4) exhibited low binding energy to wild-type, but compound 3 had the lowest binding energy to its mutants as well (T790M, L858R and double-mutant). Compounds 1, 2, 4 and gefitinib showed affinity only for selective EGFR variants.

Epidermal growth factor receptor (EGFR) is a cell membrane receptor serving as a molecular target for Non Small Cell Lung cancer (NSCLC).The aim of the study was to analyze the weak ion interactions taking place in the tyrosine kinase domain of Human and Drosophila EGFR.This study throws light on the environment preferability, stabilizing residues taking part in weak and strong interactions of EGFR TK domain.Analysis of short, medium and long range contacts showed that the cation-interactions are mainly formed by long-range contacts, whereas CH…OC interactions are formed by both long and short range interactions.Except Pro, other non-polar hydrophobic amino acids in Drosophila EGFR TK domain were present in buried environment.However, in humans other than Pro and Ala, which were involved in SS [CH…OC] and SM [NH…OC] interactions, other non-polar hydrophobic amino acid preferred to be in buried environment.The results observed in this study will be useful for understanding the contribution of weak and strong interactions to the stability of EGFR TK domain.Further, the weak interactions have distinct roles in the stability of EGFR TK domain in addition to other conventional strong interactions.The results can lead to the identification of novel targets for NSCLC drugs which in turn will serve as a major break through in the treatment of NSCLC.

Abyssinones and related flavonoids were screened against 3 enzymes (3 beta HSD, 17 beta HSD and Aromatase) of steroidogenesis pathway. The virtual screening experiment shows high affinity for flavonones than their respective chalcones. A 4' -OH blocked prenylated flavonone 2b (2-(2', 2'-dimethyl chroman-6'-yl)-7-hydroxy chroman-4-one) had consistent binding affinity to all the three enzymes used in this study showing higher binding affinity to aromatase. A good correlation was observed between cytotoxic data (MCF-7, breast cancer cell line) and docking results indicating flavonone as a better steroidogenesis modulator in hormone dependent cancer.