Protein crystallization in the structural genomics era

There are five broad areas where noteworthy advances have occurred in the field of macromolecular crystallization in the past 10 years, though some areas have seen the major part of those advances in only the last two years. This is largely a consequence of the international structural genomics initiative and its early results. The five areas are: (1) Physical studies and characterization of the protein crystallization process; (2) Development of new practical approaches and procedures; (3) The implementation of protein engineering by genetic means to enhance both purification and crystallization; (4) The creation of new screening conditions based on information and databases emerging from structural genomics; and (5) Development and implementation of automation, robotics, and mass screening of crystallization conditions using very small amounts of protein. A brief summary is provided here of the progress in the past few years and the influence of the structural genomics project.     

Laboratory scale structural genomics

At Lawrence Livermore National Laboratory, the development of the TB structural genomics consortium crystallization facility has paralleled several local proteomics research efforts that have grown out of gene expression microarray and comparative genomics studies. Collective experience gathered from TB consortium labs and other centers involved in the NIH-NIGMS protein structure initiative allows us to explore the possibilities and challenges of pursuing structural genomics on an academic laboratory scale. We discuss our procedures and protocols for genomic targeting approaches, primer design, cloning, small scale expression screening, scale-up and purification, through to automated crystallization screening and data collection. The procedures are carried out by a small group using a combination of traditional approaches, innovative molecular biochemistry approaches, software automation, and a modest investment in robotic equipment.     

Protein biophysical properties that correlate with crystallization success in Thermotoga maritima: maximum clustering strategy for structural genomics

Cost and time reduction are two of the driving forces in the development of new strategies for protein crystallization and subsequent structure determination. Here, we report the analysis of the Thermotoga maritima proteome, in which we compare the proteins that were successfully expressed, purified and crystallized versus the rest of the proteome. This set of almost 500 proteins represents one of the largest, internally consistent, protein expression and crystallization datasets available. The analysis shows that individual parameters, such as isoelectric point, sequence length, average hydropathy, low complexity regions (SEG), and combinations of these biophysical properties for crystallized proteins define a distinct subset of the T. maritima proteome. The distribution profiles of the various biophysical properties in the expression/crystallization set are then used to extract rules to improve target selection and improve the efficiency and output of structural genomics, as well as general structural biology efforts.     

Macromolecular crystallization with microfluidic free-interface diffusion

Fluidigm Corp. released the Topaz 1.96 and 4.96 crystallization chips in the fall of 2004. Topaz 1.96 and 4.96 are the latest evolution of Fluidigm's microfluidics crystallization technologies that enable ultra-low-volume rapid screening for macromolecular crystallization. Topaz 1.96 and 4.96 are similar to each other but represent a major redesign of the Topaz system and have substantially improved ease of automation and ease of use, improved efficiency and even further reduced the amount of material needed. With the release of the new Topaz system, Fluidigm continues to set the standard in low-volume crystallization screening, which is having an increasing impact in the field of structural genomics and more generally in structural biology. It is likely that further optimization and increased utility of the Topaz crystallization system will emerge. It is also probable that further innovation and the emergence of competing technologies will be seen.     

Data mining crystallization databases: knowledge-based approaches to optimize protein crystal screens

Protein crystallization is a major bottleneck in protein X-ray crystallography, the workhorse of most structural proteomics projects. Because the principles that govern protein crystallization are too poorly understood to allow them to be used in a strongly predictive sense, the most common crystallization strategy entails screening a wide variety of solution conditions to identify the small subset that will support crystal nucleation and growth. We tested the hypothesis that more efficient crystallization strategies could be formulated by extracting useful patterns and correlations from the large data sets of crystallization trials created in structural proteomics projects. A database of crystallization conditions was constructed for 755 different proteins purified and crystallized under uniform conditions. Forty-five percent of the proteins formed crystals. Data mining identified the conditions that crystallize the most proteins, revealed that many conditions are highly correlated in their behavior, and showed that the crystallization success rate is markedly dependent on the organism from which proteins derive. Of the proteins that crystallized in a 48-condition experiment, 60% could be crystallized in as few as 6 conditions and 94% in 24 conditions. Consideration of the full range of information coming from crystal screening trials allows one to design screens that are maximally productive while consuming minimal resources, and also suggests further useful conditions for extending existing screens.     

Crystallization data mining in structural genomics: using positive and negative results to optimize protein crystallization screens

Recent efforts to collect and mine crystallization data from structural genomics (SG) consortia have led to the identification of minimal screens and novel screening strategies that can be used to streamline the crystallization process. Two groups, the Joint Center for Structural Genomics and the University of Toronto, carried out large-scale crystallization trials on different sets of bacterial targets (539, JCSG and 755, Toronto), using different sample processing and crystallization methods, and then analyzed their results to identify the smallest subset of conditions that would have crystallized the maximum number of protein targets. The JCSG Core Screen contains 67 conditions (from 480) while the Toronto Minimal Screen contains 6 (from 48). While the exact conditions included in the two screens do not overlap, the major precipitants of the conditions are similar and thus both screens can be used to determine if a protein has a natural propensity to crystallize. In addition, studies from other groups including the University of Queensland, the Mycobacterium tuberculosis SG group, the Southeast Collaboratory for SG, and the York Structural Biology Laboratory indicate that alternative crystallization strategies may be more successful at identifying initial crystallization conditions than typical sparse matrix screens. These minimal screens and alternative screening strategies are already being used to optimize the crystallization processes within large SG efforts. The differences between these results, however, demonstrate that additional studies which examine the influence of protein biophysical properties and sample preparation methods on crystal formation must also be carried out before more robust screens can be identified.     

Carboxylic acids in crystallization of macromolecules: learning from successful crystallization experiments

The production of macromolecular crystals suitable for structural analysis is one of the most important and limiting steps in the structure determination process. Often, preliminary crystallization trials are performed using hundreds of empirically selected conditions. Carboxylic acids and/or their salts are one of the most popular components of these empirically derived crystallization conditions. Our findings indicate that almost 40 % of entries deposited to the Protein Data Bank (PDB) reporting crystallization conditions contain at least one carboxylic acid. In order to analyze the role of carboxylic acids in macromolecular crystallization, a large-scale analysis of the successful crystallization experiments reported to the PDB was performed. The PDB is currently the largest source of crystallization data, however it is not easily searchable. These complications are due to a combination of a free text format, which is used to capture information on the crystallization experiments, and the inconsistent naming of chemicals used in crystallization experiments. Despite these difficulties, our approach allows for the extraction of over 47,000 crystallization conditions from the PDB. Initially, the selected conditions were investigated to determine which carboxylic acids or their salts are most often present in crystallization solutions. From this group, selected sets of crystallization conditions were analyzed in detail, assessing parameters such as concentration, pH, and precipitant used. Our findings will lead to the design of new crystallization screens focused around carboxylic acids.     

Crystal structure of the N-terminal domain of E. coli Lon protease

We report here the first crystal structure of the N-terminal domain of an A-type Lon protease. Lon proteases are ubiquitous, multidomain, ATP-dependent enzymes with both highly specific and non-specific protein binding, unfolding, and degrading activities. We expressed and purified a stable, monomeric 119-amino acid N-terminal subdomain of the Escherichia coli A-type Lon protease and determined its crystal structure at 2.03 A (Protein Data Bank [PDB] code 2ANE). The structure was solved in two crystal forms, yielding 14 independent views. The domain exhibits a unique fold consisting primarily of three twisted beta-sheets and a single long alpha-helix. Analysis of recent PDB depositions identified a similar fold in BPP1347 (PDB code 1ZBO), a 203-amino acid protein of unknown function from Bordetella parapertussis, crystallized as part of a structural genomics effort. BPP1347 shares sequence homology with Lon N-domains and with a family of other independently expressed proteins of unknown functions. We postulate that, as is the case in Lon proteases, this structural domain represents a general protein and polypeptide interaction domain.     

Macro-to-micro structural proteomics: native source proteins for high-throughput crystallization

Structural biology and structural genomics projects routinely rely on recombinantly expressed proteins, but many proteins and complexes are difficult to obtain by this approach. We investigated native source proteins for high-throughput protein crystallography applications. The Escherichia coli proteome was fractionated, purified, crystallized, and structurally characterized. Macro-scale fermentation and fractionation were used to subdivide the soluble proteome into 408 unique fractions of which 295 fractions yielded crystals in microfluidic crystallization chips. Of the 295 crystals, 152 were selected for optimization, diffraction screening, and data collection. Twenty-three structures were determined, four of which were novel. This study demonstrates the utility of native source proteins for high-throughput crystallography.     

Crystal structures of MBP fusion proteins

Although chaperone‐assisted protein crystallization remains a comparatively rare undertaking, the number of crystal structures of polypeptides fused to maltose‐binding protein (MBP) that have been deposited in the Protein Data Bank (PDB) has grown dramatically during the past decade. Altogether, 102 fusion protein structures were detected by Basic Local Alignment Search Tool (BLAST) analysis. Collectively, these structures comprise a range of sizes, space groups, and resolutions that are typical of the PDB as a whole. While most of these MBP fusion proteins were equipped with short inter‐domain linkers to increase their rigidity, fusion proteins with long linkers have also been crystallized. In some cases, surface entropy reduction mutations in MBP appear to have facilitated the formation of crystals. A comparison of the structures of fused and unfused proteins, where both are available, reveals that MBP‐mediated structural distortions are very rare.     

The challenge of protein structure determination--lessons from structural genomics

The process of experimental determination of protein structure is marred with a high ratio of failures at many stages. With availability of large quantities of data from high-throughput structure determination in structural genomics centers, we can now learn to recognize protein features correlated with failures; thus, we can recognize proteins more likely to succeed and eventually learn how to modify those that are less likely to succeed. Here, we identify several protein features that correlate strongly with successful protein production and crystallization and combine them into a single score that assesses "crystallization feasibility." The formula derived here was tested with a jackknife procedure and validated on independent benchmark sets. The "crystallization feasibility" score described here is being applied to target selection in the Joint Center for Structural Genomics, and is now contributing to increasing the success rate, lowering the costs, and shortening the time for protein structure determination. Analyses of PDB depositions suggest that very similar features also play a role in non-high-throughput structure determination, suggesting that this crystallization feasibility score would also be of significant interest to structural biology, as well as to molecular and biochemistry laboratories.     

Evaluating protein structures determined by structural genomics consortia

Structural genomics projects are providing large quantities of new 3D structural data for proteins. To monitor the quality of these data, we have developed the protein structure validation software suite (PSVS), for assessment of protein structures generated by NMR or X-ray crystallographic methods. PSVS is broadly applicable for structure quality assessment in structural biology projects. The software integrates under a single interface analyses from several widely-used structure quality evaluation tools, including PROCHECK (Laskowski et al., J Appl Crystallog 1993;26:283-291), MolProbity (Lovell et al., Proteins 2003;50:437-450), Verify3D (Luthy et al., Nature 1992;356:83-85), ProsaII (Sippl, Proteins 1993;17: 355-362), the PDB validation software, and various structure-validation tools developed in our own laboratory. PSVS provides standard constraint analyses, statistics on goodness-of-fit between structures and experimental data, and knowledge-based structure quality scores in standardized format suitable for database integration. The analysis provides both global and site-specific measures of protein structure quality. Global quality measures are reported as Z scores, based on calibration with a set of high-resolution X-ray crystal structures. PSVS is particularly useful in assessing protein structures determined by NMR methods, but is also valuable for assessing X-ray crystal structures or homology models. Using these tools, we assessed protein structures generated by the Northeast Structural Genomics Consortium and other international structural genomics projects, over a 5-year period. Protein structures produced from structural genomics projects exhibit quality score distributions similar to those of structures produced in traditional structural biology projects during the same time period. However, while some NMR structures have structure quality scores similar to those seen in higher-resolution X-ray crystal structures, the majority of NMR structures have lower scores. Potential reasons for this "structure quality score gap" between NMR and X-ray crystal structures are discussed.     

Maximum-likelihood crystallization

The crystallization facility of the TB Structural Genomics Consortium, one of nine NIH-sponsored structural genomics pilot projects, employs a combinatorial random sampling technique in high-throughput crystallization screening. Although data are still sparse and a comprehensive analysis cannot be performed at this stage, preliminary results appear to validate the random-screening concept. A discussion of statistical crystallization data analysis aims to draw attention to the need for comprehensive and valid sampling protocols. In view of limited overlap in techniques and sampling parameters between the publicly funded high-throughput crystallography initiatives, exchange of information should be encouraged, aiming to effectively integrate data mining efforts into a comprehensive predictive framework for protein crystallization.     

Progress of structural genomics initiatives: an analysis of solved target structures

The explosion in gene sequence data and technological breakthroughs in protein structure determination inspired the launch of structural genomics (SG) initiatives. An often stated goal of structural genomics is the high-throughput structural characterisation of all protein sequence families, with the long-term hope of significantly impacting on the life sciences, biotechnology and drug discovery. Here, we present a comprehensive analysis of solved SG targets to assess progress of these initiatives. Eleven consortia have contributed 316 non-redundant entries and 323 protein chains to the Protein Data Bank (PDB), and 459 and 393 domains to the CATH and SCOP structure classifications, respectively. The quality and size of these proteins are comparable to those solved in traditional structural biology and, despite huge scope for duplicated efforts, only 14% of targets have a close homologue (>/=30% sequence identity) solved by another consortium. Analysis of CATH and SCOP revealed the significant contribution that structural genomics is making to the coverage of superfamilies and folds. A total of 67% of SG domains in CATH are unique, lacking an already characterised close homologue in the PDB, whereas only 21% of non-SG domains are unique. For 29% of domains, structure determination revealed a remote evolutionary relationship not apparent from sequence, and 19% and 11% contributed new superfamilies and folds. The secondary structure class, fold and superfamily distributions of this dataset reflect those of the genomes. The domains fall into 172 different folds and 259 superfamilies in CATH but the distribution is highly skewed. The most populous of these are those that recur most frequently in the genomes. Whilst 11% of superfamilies are bacteria-specific, most are common to all three superkingdoms of life and together the 316 PDB entries have provided new and reliable homology models for 9287 non-redundant gene sequences in 206 completely sequenced genomes. From the perspective of this analysis, it appears that structural genomics is on track to be a success, and it is hoped that this work will inform future directions of the field.     

Effect of low-complexity regions on protein structure determination

It has been previously shown that protein sequences containing a quasi-repetitive assortment of amino acids are common in genomes and databases such as Swiss-Prot but are under-represented in the structure-based Protein Data Bank (PDB). Structural genomics groups have been using the absence of these “low-complexity” sequences for several years as a way to select proteins that have a good chance of successful structure determination. In this study, we examine the data deposited in the PDB as well as the available data from structural genomics groups in TargetDB and PepcDB to reveal interesting trends that could be taken into consideration when using low-complexity sequences as part of the target selection process.     

Structural genomics of GPCRs

G protein-coupled receptors (GPCRs) are targets for 60-70% of drugs in development today. Traditionally, the drug discovery process has relied on screening of chemical compounds to identify novel and more-efficient drug molecules. Structure-based drug design, however, provides a targeted approach but has been severely hampered by limited knowledge of high-resolution structures of GPCRs owing to the difficulties encountered in their expression, purification and crystallization. In addition to individual laboratories studying specific GPCRs, structural genomics initiatives have been established as large networks with a wide range of expertise in protein expression, purification and crystallography. Several of these national and international consortia have included GPCRs in their programs. Milligram quantities of GPCRs can now be expressed in several expression systems and purified to high homogeneity. However, success in crystallization still requires major technological improvement.     

Reliable scale-up of membrane protein over-expression by bacterial auto-induction: From microwell plates to pilot scale fermentations

The production of well-ordered crystals of membrane proteins for structural investigation by X-ray diffraction typically requires extensive crystallization trials and may involve the screening of multiple detergents, lipids and other additives. Purification of sufficient amounts of protein for such trials is hampered by the fact that even when over-expressed, membrane proteins represent only a small percentage of the total protein content of bacteria. Fermentation-scale cultures of cells are therefore usually required. To maximize the efficiency and reduce the cost of such cultures, in the UK Membrane Protein Structure Initiative we have systematically investigated the use of auto-induction as an alternative to induction of expression with isopropyl-β-D-thiogalactoside. We report here the benefits of first optimizing expression on a multiwell plate scale by systematically varying the concentrations of glucose, glycerol, lactose and succinate present in the auto-induction medium. For subsequent scale-up, comparison of isopropyl-β-D-thiogalactoside induction in shake-flasks with auto-induction in shake-flasks and in 1L fermenters without and with control of pH and aeration revealed that highest yields of target protein were obtained using the latter culture conditions. However, analysis of the time-course of expression highlighted the importance of choosing the correct time for harvest. The high yields of target protein that can be obtained in a single batch by auto-induction, performed on a 30 l scale in a fermenter, obviate batch-to-batch variations that can add an unwanted variable to crystallization screening experiments. The approach described should therefore be of great utility for membrane protein production for structural studies.     

Refolding of a membrane protein in a microfluidics reactor

Membrane protein production for structural studies is often hindered by the formation of non-specific aggregates from which the protein has to be denatured and then refolded to a functional state. We developed a new approach, which uses microfluidics channels, to refold protein correctly in quantities sufficient for structural studies. Green fluorescent protein (GFP), a soluble protein, and bacteriorhodopsin (BR), a transmembrane protein, were used to demonstrate the efficiency of the process. Urea-denatured GFP refolded as the urea diffused away from the protein, forming in the channel a uniform fluorescent band when observed by confocal microscopy. Sodium dodecyl sulphate-denatured BR refolded within the channel on mixing with detergent–lipid mixed micelles. The refolding, monitored by absorbance spectroscopy, was found to be flow rate dependent. This potential of microfluidic reactors for screening protein-folding conditions and producing protein would be particularly amenable for high-throughput applications required in structural genomics. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Structural characterization of proteins using residue environments

A primary challenge for structural genomics is the automated functional characterization of protein structures. We have developed a sequence-independent method called S-BLEST (Structure-Based Local Environment Search Tool) for the annotation of previously uncharacterized protein structures. S-BLEST encodes the local environment of an amino acid as a vector of structural property values. It has been applied to all amino acids in a nonredundant database of protein structures to generate a searchable structural resource. Given a query amino acid from an experimentally determined or modeled structure, S-BLEST quickly identifies similar amino acid environments using a K-nearest neighbor search. In addition, the method gives an estimation of the statistical significance of each result. We validated S-BLEST on X-ray crystal structures from the ASTRAL 40 nonredundant dataset. We then applied it to 86 crystallographically determined proteins in the protein data bank (PDB) with unknown function and with no significant sequence neighbors in the PDB. S-BLEST was able to associate 20 proteins with at least one local structural neighbor and identify the amino acid environments that are most similar between those neighbors.