On the need for an international effort to capture, share and use crystallization screening data

The copper(II)-binding tripeptide GHK, a valuable crystallization and phasing tag for macromolecular crystallography

The growth of diffraction-quality crystals and experimental phasing remain two of the main bottlenecks in protein crystallography. Here, the high-affinity copper(II)-binding tripeptide GHK was fused to the N-terminus of a GFP variant and an MBP-FG peptide fusion. The GHK tag promoted crystallization, with various residues (His, Asp, His/Pro) from symmetry molecules completing the copper(II) square-pyramidal coordination sphere. Rapid structure determination by copper SAD phasing could be achieved, even at a very low Bijvoet ratio or after significant radiation damage. When collecting highly redundant data at a wavelength close to the copper absorption edge, residual S-atom positions could also be located in log-likelihood-gradient maps and used to improve the phases. The GHK copper SAD method provides a convenient way of both crystallizing and phasing macromolecular structures, and will complement the current trend towards native sulfur SAD and MR-SAD phasing.

High-Throughput Crystallization Pipeline at the Crystallography Core Facility of the Institut Pasteur

The availability of whole-genome sequence data, made possible by significant advances in DNA sequencing technology, led to the emergence of structural genomics projects in the late 1990s. These projects not only significantly increased the number of 3D structures deposited in the Protein Data Bank in the last two decades, but also influenced present crystallographic strategies by introducing automation and high-throughput approaches in the structure-determination pipeline. Today, dedicated crystallization facilities, many of which are open to the general user community, routinely set up and track thousands of crystallization screening trials per day. Here, we review the current methods for high-throughput crystallization and procedures to obtain crystals suitable for X-ray diffraction studies, and we describe the crystallization pipeline implemented in the medium-scale crystallography platform at the Institut Pasteur (Paris) as an example.

A Novel Rheological Method to Assess Drug-Polymer Interactions Regarding Miscibility and Crystallization of Drug in Amorphous Solid Dispersions for Oral Drug Delivery

Solid dispersions provide a key technology to formulate poorly water-soluble drugs, and a main task of early development is appropriate selection of polymer. This study investigates the use of a novel rheology-based approach to evaluate miscibility and interactions of drugs with polymers regarding amorphous solid drug dispersions for oral administration. Tacrolimus was used as model drug and hydroxypropyl cellulose, ethylcellulose, Soluplus^®, polyethyleneglycol 6000, Poloxamer-188 (Koliphor-188), and Eudragit^® S100 were used as excipients. Solvent-based evaporation methods were used to prepare binary solid dispersions of drug and polymer. Data of the dilute solution viscosimetry were compared with in silico calculations of the Hansen solubility parameter (HSP), as well as phase separation/crystallization data obtained from X-ray diffraction and differential scanning calorimetry. HSP calculations in some cases led to false positive predictions of tacrolimus miscibility with the tested polymers. The novel rheology-based method provided valuable insights into drug-polymer interactions and likely miscibility with polymer. It is a rather fast, inexpensive, and robust analytical approach, which could be used complementary to in silico-based evaluation of polymers in early formulation development, especially in cases of rather large active pharmaceutical ingredients.

A comparative anatomy of protein crystals: lessons from the automatic processing of 56 000 samples

The fully automatic processing of crystals of macromolecules has presented a unique opportunity to gather information on the samples that is not usually recorded. This has proved invaluable in improving sample-location, characterization and data-collection algorithms. After operating for four years, MASSIF-1 has now processed over 56 000 samples, gathering information at each stage, from the volume of the crystal to the unit-cell dimensions, the space group, the quality of the data collected and the reasoning behind the decisions made in data collection. This provides an unprecedented opportunity to analyse these data together, providing a detailed landscape of macromolecular crystals, intimate details of their contents and, importantly, how the two are related. The data show that mosaic spread is unrelated to the size or shape of crystals and demonstrate experimentally that diffraction intensities scale in proportion to crystal volume and molecular weight. It is also shown that crystal volume scales inversely with molecular weight. The results set the scene for the development of X-ray crystallography in a changing environment for structural biology.

BLASTing away preconceptions in crystallization trials

Crystallization is in many cases a critical step for solving the three-dimensional structure of a protein molecule. Determining which set of chemicals to use in the initial screen is typically agnostic of the protein under investigation; however, crystallization efficiency could potentially be improved if this were not the case. Previous work has assumed that sequence similarity may provide useful information about appropriate crystallization cocktails; however, the authors are not aware of any quantitative verification of this assumption. This research investigates whether, given current information, one can detect any correlation between sequence similarity and crystallization cocktails. BLAST was used to quantitate the similarity between protein sequences in the Protein Data Bank, and this was compared with three estimations of the chemical similarities of the respective crystallization cocktails. No correlation was detected between proteins of similar (but not identical) sequence and their crystallization cocktails, suggesting that methods of determining screens based on this assumption are unlikely to result in screens that are better than those currently in use.

Cinder: keeping crystallographers app-y

The process of producing suitable crystals for X-ray diffraction analysis most often involves the setting up of hundreds (or thousands) of individual crystallization trials, each of which must be repeatedly examined for crystals or hints of crystallinity. Currently, the only real way to address this bottleneck is to use an automated imager to capture images of the trials. However, the images still need to be assessed for crystals or other outcomes. Ideally, there would exist some rapid and reliable machine-analysis tool to translate the images into a quantitative result. However, as yet no such tool exists in wide usage, despite this being a well recognized problem. One of the issues in creating robust automatic image-analysis software is the lack of reliable data for training machine-learning algorithms. Here, a mobile application, Cinder, has been developed which allows crystallization images to be scored quickly on a smartphone or tablet. The Cinder scores are inserted into the appropriate table in a crystallization database and are immediately available to the user through a more sophisticated web interface, allowing more detailed analyses. A sharp increase in the number of scored images was observed after Cinder was released, which in turn provides more data for training machine-learning tools.

Classification of crystallization outcomes using deep convolutional neural networks

The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algorithms are trained and tested on different parts of this data set. We find that more than 94% of the test images can be correctly labeled, irrespective of their experimental origin. Because crystal recognition is key to high-density screening and the systematic analysis of crystallization experiments, this approach opens the door to both industrial and fundamental research applications.

Development of the first model of a phosphorylated, ATP/Mg2+-containing B-Raf monomer by molecular dynamics simulations: a tool for structure-based design

A model of phosphorylated and ATP-containing B-Raf protein kinase is needed as a tool for the structure-based design of new allosteric inhibitors, since no crystal structure of such a system has been resolved. Here, we present the development of such a model as well as a thorough analysis of its structural features. This model was prepared using a systematic molecular dynamics approach considering the presence or absence of both the phosphate group at the Thr599 site and the ATP molecule. Then, different structural features (i.e. DFG motif, Mg²⁺ binding loop, activation loop, phosphorylation site and αC-helix region) were analysed for each trajectory to validate the aimed 2pBRAF_ATP model. Moreover, the structure and activating interactions of this 2pBRAF_ATP model were found to be in agreement with previously reported information. Finally, the model was further validated by means of a molecular docking study with our previously developed lead compound I confirming that this ATP-containing, phosphorylated protein model is suitable for further structure-based design studies.

Computational crystallization

Crystallization is a key step in macromolecular structure determination by crystallography. While a robust theoretical treatment of the process is available, due to the complexity of the system, the experimental process is still largely one of trial and error. In this article, efforts in the field are discussed together with a theoretical underpinning using a solubility phase diagram. Prior knowledge has been used to develop tools that computationally predict the crystallization outcome and define mutational approaches that enhance the likelihood of crystallization. For the most part these tools are based on binary outcomes (crystal or no crystal), and the full information contained in an assembly of crystallization screening experiments is lost. The potential of this additional information is illustrated by examples where new biological knowledge can be obtained and where a target can be sub-categorized to predict which class of reagents provides the crystallization driving force. Computational analysis of crystallization requires complete and correctly formatted data. While massive crystallization screening efforts are under way, the data available from many of these studies are sparse. The potential for this data and the steps needed to realize this potential are discussed.

Analysis of crystallization data in the Protein Data Bank

The Protein Data Bank (PDB) is the largest available repository of solved protein structures and contains a wealth of information on successful crystallization. Many centres have used their own experimental data to draw conclusions about proteins and the conditions in which they crystallize. Here, data from the PDB were used to reanalyse some of these results. The most successful crystallization reagents were identified, the link between solution pH and the isoelectric point of the protein was investigated and the possibility of predicting whether a protein will crystallize was explored.

Protein Crystallography in Vaccine Research and Development

The use of protein X-ray crystallography for structure-based design of small-molecule drugs is well-documented and includes several notable success stories. However, it is less well-known that structural biology has emerged as a major tool for the design of novel vaccine antigens. Here, we review the important contributions that protein crystallography has made so far to vaccine research and development. We discuss several examples of the crystallographic characterization of vaccine antigen structures, alone or in complexes with ligands or receptors. We cover the critical role of high-resolution epitope mapping by reviewing structures of complexes between antigens and their cognate neutralizing, or protective, antibody fragments. Most importantly, we provide recent examples where structural insights obtained via protein crystallography have been used to design novel optimized vaccine antigens. This review aims to illustrate the value of protein crystallography in the emerging discipline of structural vaccinology and its impact on the rational design of vaccines.

Using textons to rank crystallization droplets by the likely presence of crystals

The visual inspection of crystallization experiments is an important yet time-consuming and subjective step in X-ray crystallography. Previously published studies have focused on automatically classifying crystallization droplets into distinct but ultimately arbitrary experiment outcomes; here, a method is described that instead ranks droplets by their likelihood of containing crystals or microcrystals, thereby prioritizing for visual inspection those images that are most likely to contain useful information. The use of textons is introduced to describe crystallization droplets objectively, allowing them to be scored with the posterior probability of a random forest classifier trained against droplets manually annotated for the presence or absence of crystals or microcrystals. Unlike multi-class classification, this two-class system lends itself naturally to unidirectional ranking, which is most useful for assisting sequential viewing because images can be arranged simply by using these scores: this places droplets with probable crystalline behaviour early in the viewing order. Using this approach, the top ten wells included at least one human-annotated crystal or microcrystal for 94% of the plates in a data set of 196 plates imaged with a Minstrel HT system. The algorithm is robustly transferable to at least one other imaging system: when the parameters trained from Minstrel HT images are applied to a data set imaged by the Rock Imager system, human-annotated crystals ranked in the top ten wells for 90% of the plates. Because rearranging images is fundamental to the approach, a custom viewer was written to seamlessly support such ranked viewing, along with another important output of the algorithm, namely the shape of the curve of scores, which is itself a useful overview of the behaviour of the plate; additional features with known usefulness were adopted from existing viewers. Evidence is presented that such ranked viewing of images allows faster but more accurate evaluation of drops, in particular for the identification of microcrystals.

A drunken search in crystallization space

The REMARK280 field of the Protein Data Bank is the richest open source of successful crystallization information. The REMARK280 field is optional and currently uncurated, so significant effort needs to be applied to extract reliable data. There are well over 15 000 crystallization conditions available commercially from 12 different vendors. After putting the PDB crystallization information and the commercial cocktail data into a consistent format, these data are used to extract information about the overlap between the two sets of crystallization conditions. An estimation is made as to which commercially available conditions are most appropriate for producing well diffracting crystals by looking at which commercial conditions are found unchanged (or almost unchanged) in the PDB. Further analyses include which commercial kits are the most appropriate for shotgun or more traditional approaches to crystallization screening. This analysis suggests that almost 40% of the crystallization conditions found currently in the PDB are identical or very similar to a commercial condition.

Statistical analysis of crystallization database links protein physico-chemical features with crystallization mechanisms

X-ray crystallography is the predominant method for obtaining atomic-scale information about biological macromolecules. Despite the success of the technique, obtaining well diffracting crystals still critically limits going from protein to structure. In practice, the crystallization process proceeds through knowledge-informed empiricism. Better physico-chemical understanding remains elusive because of the large number of variables involved, hence little guidance is available to systematically identify solution conditions that promote crystallization. To help determine relationships between macromolecular properties and their crystallization propensity, we have trained statistical models on samples for 182 proteins supplied by the Northeast Structural Genomics consortium. Gaussian processes, which capture trends beyond the reach of linear statistical models, distinguish between two main physico-chemical mechanisms driving crystallization. One is characterized by low levels of side chain entropy and has been extensively reported in the literature. The other identifies specific electrostatic interactions not previously described in the crystallization context. Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from solution conditions leading to successful crystallization, the model offers future avenues for optimizing crystallization screens based on partial structural information. The availability of crystallization data coupled with structural outcomes analyzed through state-of-the-art statistical models may thus guide macromolecular crystallization toward a more rational basis.

Crystallization screening: the influence of history on current practice

While crystallization historically predates crystallography, it is a critical step for the crystallographic process. The rich history of crystallization and how that history influences current practices is described. The tremendous impact of crystallization screens on the field is discussed.

Comparing chemistry to outcome: the development of a chemical distance metric, coupled with clustering and hierarchal visualization applied to macromolecular crystallography

Many bioscience fields employ high-throughput methods to screen multiple biochemical conditions. The analysis of these becomes tedious without a degree of automation. Crystallization, a rate limiting step in biological X-ray crystallography, is one of these fields. Screening of multiple potential crystallization conditions (cocktails) is the most effective method of probing a proteins phase diagram and guiding crystallization but the interpretation of results can be time-consuming. To aid this empirical approach a cocktail distance coefficient was developed to quantitatively compare macromolecule crystallization conditions and outcome. These coefficients were evaluated against an existing similarity metric developed for crystallization, the C6 metric, using both virtual crystallization screens and by comparison of two related 1,536-cocktail high-throughput crystallization screens. Hierarchical clustering was employed to visualize one of these screens and the crystallization results from an exopolyphosphatase-related protein from Bacteroides fragilis, (BfR192) overlaid on this clustering. This demonstrated a strong correlation between certain chemically related clusters and crystal lead conditions. While this analysis was not used to guide the initial crystallization optimization, it led to the re-evaluation of unexplained peaks in the electron density map of the protein and to the insertion and correct placement of sodium, potassium and phosphate atoms in the structure. With these in place, the resulting structure of the putative active site demonstrated features consistent with active sites of other phosphatases which are involved in binding the phosphoryl moieties of nucleotide triphosphates. The new distance coefficient, CD_coeff, appears to be robust in this application, and coupled with hierarchical clustering and the overlay of crystallization outcome, reveals information of biological relevance. While tested with a single example the potential applications related to crystallography appear promising and the distance coefficient, clustering, and hierarchal visualization of results undoubtedly have applications in wider fields.

Crystallization reports are the backbone of Acta Cryst. F, but do they have any spine?

Crystallization of macromolecules is famously difficult. By knowing what has worked for others, researchers can ease the process, both in the case where the protein has already been crystallized and in the situation where more general guidelines are needed. The 264 crystallization communications published in Acta Crystallographica Section F in 2012 have been reviewed, and from this analysis some information about trends in crystallization has been gleaned. More importantly, it was found that there are several ways in which the utility of these communications could be increased: to make each individual paper a more complete crystallization record; and to provide a means for taking a snapshot of what the current `best practices' are in the field.

An overview of biological macromolecule crystallization

The elucidation of the three dimensional structure of biological macromolecules has provided an important contribution to our current understanding of many basic mechanisms involved in life processes. This enormous impact largely results from the ability of X-ray crystallography to provide accurate structural details at atomic resolution that are a prerequisite for a deeper insight on the way in which bio-macromolecules interact with each other to build up supramolecular nano-machines capable of performing specialized biological functions. With the advent of high-energy synchrotron sources and the development of sophisticated software to solve X-ray and neutron crystal structures of large molecules, the crystallization step has become even more the bottleneck of a successful structure determination. This review introduces the general aspects of protein crystallization, summarizes conventional and innovative crystallization methods and focuses on the new strategies utilized to improve the success rate of experiments and increase crystal diffraction quality.

The role of structural bioinformatics resources in the era of integrative structural biology

The history and the current state of the PDB and EMDB archives is briefly described, as well as some of the challenges that they face. It seems natural that the role of structural biology archives will change from being a pure repository of historic data into becoming an indispensable resource for the wider biomedical community. As part of this transformation, it will be necessary to validate the biomacromolecular structure data and ensure the highest possible quality for the archive holdings, to combine structural data from different spatial scales into a unified resource and to integrate structural data with functional, genetic and taxonomic data as well as other information available in bioinformatics resources. Some recent developments and plans to address these challenges at PDBe are presented.

Some practical guidelines for UV imaging in the protein crystallization laboratory

High-throughput imaging of protein crystallization experiments with ultraviolet (UV) light has recently become commercially available and can enable crystallographers to differentiate between crystals of protein and those of salt, as the visualization of protein crystals is based on intrinsic tryptophan fluorescence. Unfortunately, UV imaging is not a panacea, as some protein crystals will not fluoresce under UV excitation and some salt crystals are UV-fluorescently active. As a new technology, there is little experience within the general community on how to use this technology effectively and what caveats to look out for. Here, an attempt is made to identify some of the common problems that may arise using UV-imaging technology by examining test proteins, common crystallization reagents and a range of proteins by assessing their UV-Vis absorbance spectra. Some pointers are offered as to which systems may not be appropriate for this methodology.

Call for a crystallization ontology

Editorial.