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.

Likelihood-based estimation of substructure content from single-wavelength anomalous diffraction (SAD) intensity data.

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.

'All That Glitters Is Not Gold': High-Resolution Crystal Structures of Ligand-Protein Complexes Need Not Always Represent Confident Binding Poses.

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.

Phasertng: directed acyclic graphs for crystallographic phasing.

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.

Factors influencing estimates of coordinate error for molecular replacement.

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.

Seeing but not believing: the structure of glycerol dehydrogenase initially assumed to be the structure of a survival protein from Salmonella typhimurium.

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.

Structure determination of contaminant proteins using the MarathonMR procedure.

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.

Determination of crystal structures of proteins of unknown identity using a marathon molecular replacement procedure: structure of Stenotrophomonas maltophilia phosphate-binding protein.

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 Šresolution 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.

CANDO and the infinite drug discovery frontier.

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.

NeeMDB: Convenient Database for Neem Secondary Metabolites.

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.

Synthesis, anticancer and antioxidant activities of 2,4,5-trimethoxy chalcones and analogues from asaronaldehyde: structure-activity relationship.

2,4,5-Trimethoxy chalcones and analogues were synthesized from asaronaldehyde derived from ß-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.

Abyssinones and related flavonoids as potential steroidogenesis modulators.

Abyssinones and related flavonoids were screened against 3 enzymes (3betaHSD, 17betaHSD 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.